M06007054i'i' EFFECT OF CLIMATE SMART AGRICULTURAL PRACTICES ON POVERTY AMONG FARMERS IN NORTH WEST NIGERIA /~, ;:,rc ~~ DEKPA l~~ ~ E) ORCID.ORG/0000-0001-6427-7576 Thesis submitted for the degree Doctor of Philosophy in Agriculture Economics at the North-West University Promoter: Prof. A. S. Oyekale Co-Promoter: Prof. 0. I. Oladele Graduation May 2018 Student number: 266267 64 !8B NW NO!mi·Wl'ST UNIVERSITY NOOROWES-UNIVERSITTIT UNIBBITI YA BOKONE•BOPtilRIMA ABSTRACT The current level of poverty in Nigeria is disturbing and climate change impedes food security and increases poverty indirectly and directly on individuals or households. Farming households are changing agricultural practices as a result of global observation of climatic and environmental changes. This research work recognised a link which exists between climate change, climate smart agricultural practices (CSAP) and poverty status of funning households in North West Nigeria . The study examined the socioeconomic characteristics of :farmers; the constraints of using climate smart agricultural techniques; :factors influencing indicators of CSAP anxmg diflerent crops and livestock enterprises; poverty status for low-users and high-users of climate smart agricultural techniques and the effect of CSAP on poverty status of rural :farmers. This is with a view to establishing the consequences of climate variation and its influence on poverty status among nrral :farmers in North West Nigeria. The multi-stage, sampling techniques was used to select three hundred (300) respondents who provided the relevant primary data for this study through a set of pre-tested structured questionnaires. The data were analyz.ed with descriptive statistics, Foster Greer and Thorberk and Equally Distnbuted Equivalent (EDE) indices, Principal Component Analysis (PCA), Ordinary Least Square regression and instrumental variable of Pro bit regression model The descriptive analysis revealed that average age of high-users of climate smart agriculture (CSA) in Katsina State was 55 years but that of Sokoto State was 52 years. Majority of the respondents (94%) were males; 65% had informal Arabic education Lack of demonstration of climate smart agricultural techniques and lack of processing technology were the major constraints to the use of CSAP. The regression analysis result showed that age, gender, education, marital status, household siz.e, housing materials, connmmication kits and lack of time to use CSAP were significant for maiz.e enterprise. While FGT relative poverty index for food expenditure showed that the low users of CSAP were 8% poorer than high users. And CSAP for both crops and livestock enterprises reduce the probability of being poor significantly. Hence, the study rejected the null hypotheses' which stated that CSAP do not significantly affect poverty status of :farmers in the study area. The study recommended that Governmental and :farmers' organisations can :fashion out a :favourable training workshop to inspire the low-users of CSA to improve on their performance. Also, the female gender should be encouraged to develop interest in CSAP, like the women empowerment programmes instituted by the government through Agricuhura 1 Development Project (ADP) and Katsina State Agricultural and Rural Development Authorit y (KTARDA). The policies on informal education shouki be enhanced and enforced in the curriculum to meet the CSAP challenges. Extension delivery system approach should be upgraded to meet the present information age. Keywords: Climate Change, CSAP, Poverty, Household Famers, North West Nigeria. ii DEDICATION This research study is dedicated to Almighty God fur His grace, mercies, divine :fuvour and :fuithfulness, which enabled me to complete the years of my studies successfully. iii DECLARATION I, the undersigned student, declare that this thesis submitted to the North-West University, Ma:fikeng Campus fur the degree of Doctor of Phibsophy in Agricultural Economics, Faculty of Natural and Agricultural Sciences, School of Agricultural Sciences is my own work.The work contained herein, is my original work wrtb. the exemption of the citations, and I attest that this work bas not been submitted to any other University fur the award of any degree. Name: Daniel Ekpa Signature: ..... ...... ... ... ... ... .... ........ .. . . Date: ... .. ... .... ...... ..... .... .. ... .... .... .... .. . iv ACKNOWLEDGEMENTS I thank God Almighty for His special grace, mercies and divine favour which have made the completion of this research study a reality. My heartfelt gratitude goes to my Supervisors, Pro£ A.S Oyekale and Pro£ O.I Oladele, for their stern principles and persuasive guidelines which greatly improved this work. My special thanks go to all the lecturers present at the Department and School presentations. Their contributions help to improve this research study. I am indeed very appreciative to my wife, Mrs. Mary Owole Ekpa, and our bebveth children Isaac Omachoko Ekpa and Stephen Ojochogwu Ekpa. My profound gratitude ' s goes to our Acting Vice Chancellor Pro£ Bichi Anniyau, Pro£ Ati Friday Dean School of Post Graduate Studies and Dr. Mlllltaka Mamman (Acting Dean), Dr. Hassan Ishaq Ibrahim and Dr. Hassan Yusuf Ibrahim formal and present (HOD) Agric/Econs. (FUDMA), Dr. Akinyemi Mudashiru, Dr. Denis Ylllli, Dr. Mathew Olasupo, Dr. Ruth Nabinta Tumar, Dr Esther Fayemi, Dr. Latifat Kehinde Adebayo, Dr/Mr/Mrs Lanre, Mr. Tiri Gyan, Mr. Emmanuel Ojoko, Mr. Adeola Segun, Mr. Emmanuel Fawole, Mr. Khidri Abdullahi, Mr. Thaddeus Bodaga, Mallam Junaidu Musa, Mallam Sanusi Belli, Mallam Tijani Abu Rimi, Mr Fahad Ibrahim, Mr Ahmed, Madam Esther, Mr/Mrs Torkuma, Brother Timothy, Sister Hope for their support and Mummy (Mrs) Sade Oladele for her motherly advice. I am eternally indebted to all the staff of Federal University Dutsinma (FUDMA), Katsina State Agricultural and Rural Development Authority (KTARDA) and Agricultural Devebpment Project (ADP) Sokoto State for providing me with all the necessary materials and assistance during the field work. My profolllld gratitude goes to my aged parent Mr and Mrs. Edward Ekpa and Barr. Isaac Ekpa, Dr. John Ekpa, Mrs. Anene Onoja, Pastor Joseph, Pastor Noah, Esther, Onoja, CollllSelbr, Senior Jerry, Brother Yokei Sister Ilebaye, Dr/Barr. Shedrack, Iko, Ape, Ukpe, Slllllly, Unekwu, Alady, Ele and Pastor Bamijo. I equally appreciate my Mother in-law Mrs Racheal Agbo, Brothers and Sister In-law: Dr. Daniei Dr/Arch. Gideon, llllcle Joshua, Sister Mathar and Sam5on Agbo. Finally, I also sincerely appreciate the Deeper Life Campus Fellowship (DLCF) brethren ofNorth- West University South Africa, Pastor Joseph Ayodele Adesina, Pastor Tolutope Peter, Pastor Nicholas and my home Church Chapel of Reconciliation (FUDMA) Executives and Rev/Dr. Emmanuel Joseph (Chaplain) for their earnest prayers which brought strength, peace and joy into my life during my studies. Daniel Ekpa Department of Agricultural Economics and Extension North-West University, Mafikeng, South Africa. V TABLE OF CONTENTS TITLE PAGE ............. .. .. ... .... . ................ . ... . ................. ..... ...... ...... ... . ...... .. .. i ABS1RACT ................................ . ...... . ... . .... .. . ....... .. ............. . ........ . .... . ...... ii DEDICAT IO ........ . ........ . ... . .. . ... ............ . ............. . ..... . ..... .... ............. ..... ... iii DECLARATIO ..... . . . .... ..... .... ......... . .... . ............ .. ........................ ..... ........ . .iv ACKNOWLEDGEMENTS .......... .. .. . .................. . . . ......... . ... .. .. . .... . .................. v TABLE OF CONTEN"TS ..... . .. . ... .... . . . ... ...... . .. . ........ . .......... . . . . .. . ... . ........ ... . . .... vi LIST OF TABLES .... ... ...... . .... . ... . ........... . ......... . ......... . . ................ ... ........ .... xi LIST OF FIGURES . ... . .... . . .. ... . .. . ........ . . . ... .. .. . .... .. ............ . . . . .. .... . ................ xiii ABBREVIATIO S ........ . ...................... .............................. . . ... . .................. xiv CHAPTER ONE: GENERAL INTRODUCTION 1. 1 Background of the study .... . ........... . .................... . ............ . .......... . .... . .. 1 1.2 Problem statement. .. ........... . ........................... .. ...... .. .. ..... . ................. 4 1.2. 1 The gap to be filled by the Researcher ............. . . . . . . . ............ .. . .. ......... ... . ... 9 1.3 Research questions . .. ....... ... . ..... .. . . ................ . .. . ....... ... ..... . .. . .............. 11 1.4 Objectives of the study .. . . .. ......... ....... . ............................. . ......... . .... .. .... 11 1.5 Hypotheses of the study . ..... . . . ..... . . . . . .............. . . ....... .. ........................... 12 1.6 Justification of the study . ........ ... . . . .......... .. ...... . .......... . . . ........ ........ ....... 12 1.7 Plan of the study . . .... ... . . ......... .. . . ............. . ... . .... . ...... .. .................... ....... 13 CHAPTER 'IWO: LITERATURE REVIEW 2.0 Introduction .. . . . ... . . . ..... .. . . . . .... .. . . ............................. . ... . . . . ........ .. . .... .... 15 2.1 Theoretical framework . . .... ..... .. ...... . ... .. . . ...... . ................. . .................... . 15 2.2 Conceptual framework .... . . .. ..... . ... . .. .. ......................... ... .. . .... . .. ............. 17 2.3 Climate smart agricultural practices ....... . .. ........... .. ......... . ...... . ......... .......... 22 2.3 .1 Use of organic manure .......................... .. . .... .. . . . .... . .. .. ... . ...... .... .. ... . ...... .2 4 vi 2.3.2 Agroforestry . . . .... ... .. . . . .. .. .. . . . . . . .. . . . . . . . . .... ..... . . ... ... ... . .. . .. . ... . . .. .. .. . ..... . . 25 2.3.3 Conservation agriculture . .. . ... . . . .. .. . . ... . . ...... . .. . ... . . . .. . . . . . . . .... . . . .. ... .. . .. . . .. . .2 6 2.3.4 The use of improved varieties and hybrid of crops/animals .. .... ...... .... .. ........... .. ... ... 27 2.3.5 Integrated crop/ livestock management .... . . . . . . .. .. . .... . .. .. . .. ... . . .. .. .. .... .. . .. .. ... .2 8 2.3.6 Irrigation fur small-holder farmers ... .. .. .. . . . . ........ . .. . .... .. ... . .. .. ... .. ..... . ... .... .2 8 2.4 The concept and nature of poverty in Nigeria . .. . . . . . ........ .. . . ... ..... . . .... . . ..... . . . . 29 2.5 The causes and characteristics of poverty .. ...... . . . .... . . . .. . .. . .. . . . . ..... . ... . . .. .... . . . 32 2.6 Consequences of poverty on Nigeria .. . . . . .. . . ....... . .. . .. . . . . ..... .. ...... . . .. . . ... .. .. . . . 33 2.7 Derivation of poverty line and measurement of poverty ... ... . . .. . .. . . . . .. . . .. . . .. ... . . .. 34 2.8 Multidimensional Poverty Index .. .. . . . . ... . . . . ... . ..... ... .. . .. . .. . .. . ....... . . . .. ... ... .. 38 2.9 Empirical literature review .. ... . . . . . ... . . . .... ... . .. . . . .. . . ... . .. .. . . .. . . . . . . ... . . . ...... . . .. 40 2.10 Conclusion . ... ..... . ... ... . .... . . . ...... . ...... .. .. . . . . . .. . . .... ...... . . .. .. .. .. . ... .... ... . . . .. 45 CHAPTER THREE: RESEARCH MEIBODOLOGY 3 .0 Introduction . .. . ... . . .. . . . .......... . ... ... .... . .. . .. .. . . . .... . . . . . . .. ... . . . ... . ............ .. .... . 46 3 .1 The study area ... . ......... .. .. . ... ... . ... . . ... . . . . .. ... .. . . . . ... . . . .. . .. . ... . . . ... . . . . .. ...... ..4 6 3.2 Research design . ..... . .. . ........... . . . . . ... .. ....... . . . ....... . . . . . . . ...... ... ... .. . ........... 47 3.3 Population of the study .. . . .. . . . . . . ... .. . ... . ... .. .. . ... ... .. ... . . . .. . . . . .. . . . . .... .. . . ... . . .. .. 47 3.4 Sampling procedure and sampling size .. . . . . . ... . .. . . ... . . . . . . . . .. . . . .. . .... . ..... ... .. . . . . . .47 3.5 Research lnstnnnent and Data collection ... . . .. .. . . .. .. . . . .. . . .. ... . . .. . ... . .. . . . . . ... . .. . .. .48 3.6 Method of data analysis . . . ... . . . . . . . . . . . ... . ... . ... . . ... . . . . ..... . .... . ... . . . .. . . ... . . ... ..... ....4 9 3.7.1. Descriptive statistics . . . .. .. ... . . . . . . .. .. ........ . ... . . ..... . ... . ..... ...... .. .. . . .... . . . . . . . ... . .4 9 3.7.1.1 Paired T-Test. .. ... .. ....... . . . ... ..... . .. .. .. . . . ...... . .. .. . .... . .. . .... .. . .. . .. . . . . .. . . . . . . ... ... 49 3.7.1.2 Pearson Chi-Square Test fur Count Data .. .. ...... . . . . . . ... . . . . .. . .. . .. . ... . ... ........ .. .... . 50 vii 3.7.1.3 Model specification fur likert type scale .. .. .. ... . . . .. . . . . . .. ....... . ..... . . .... ... .. . .. . .. 51 3.7.2 Models employed to ascertain objectives two ....... . .................... .. ...... . ...... . 51 3.7.3 Model specification for objective three . . .... . . .. .. . . . . . ....... . ............... ......... . ... 54 3.7.4 Model specification fur objective fuur and five ... . ...... . ........ . ..... . . . . ...... .. .. .... . 56 3.7.4. l The Global Multidimensional Poverty Index . ..... . .. . . ..... . . . . .. .......... .. . . ........ .. 59 3.8 Validity and Reliability ... . .. .... . .. . . . .. ... . ... ... . ... . .. . ...... .. .... . . .. . . ... .......... . . . 61 3.9 Ethical considerations . ..... . ........ . ... . . .... . .. . ... . .... .. .... . ... . .. . .. ..... . ........ . . . .. 61 3 .10 Limitation of the study .. . . ..... . .. . ..... . . .. . .. . . . . . .. . ............. . .. . . . . . .... . .......... .. . 62 3.11 Conclusion. ... . .. . . . ......... . . . ............ .. .................. . . ..... . . . ................... . .. 62 CHAPTER FOUR: RESULTS AND DISCUSSION 4.0 Introduction ... .. . ... ... .......... . ..................... . ............. . . . ... . ......... . .... . .... 63 4.1 Socio-economic Characteristics of Respondents across selected States . .............. 63 4.1.1 Respondents ' Distribution According to Quantitative Variables Employed . . . ....... 63 4.1.2 Respondents ' Distribution According to Gender. .. .... ... ...... ........ ... .. ... ... ..... ..... ..... .. .. 65 4.1.3 Respondents ' Distribution According to Educational level... .. . .. . ....... . . .. ........ .6 5 4.1.4 Respondents' Distribution According to Marital Status . ...... . .. .. . .. .......... . .. ..... 67 4.1.5 Respondents ' Distribution According to Religious Affiliation . . . . ... ... .. .. .. . . ...... . 68 4.1.6 Respondents' Distribution According to Land Ownership . .... . .. . .. . .... .. . .. .... .. .. . 68 4 .1.7 Respondents ' Distribution According to Land Acquisition. .. . ..... . . . .... .. ........ . . . . 69 4.1.8 Respondents ' Distribution According to Major Sources ofLabour. .. ....................... 70 4.1.9 Respondents ' Distribution According to Membership of Association .. . .. . .... . .. ... . 70 4.1.10 Respondents ' Distnbution According to Major means of Transportation ..... . .. ..... 71 4.1.11 Respondents' Distribution According to Housing Materials .... .. ..................... .. 72 viii 4.1.12 Respondents ' Distribution According to Communication Equipment Used ... ....... 73 4.2 The Respondents Source/Access to Water in the Study Area . . . ... . ...... . .......... . .. 74 4.3 Respondents ' Sources of Credit .......... . .... . ... . ... ... . .................................. 75 4.4 Constraints of Climate Smart Agricultural Farming Activities . .. . .................... .. 76 CHAPTER FIVE: DETERMINANTS OF CLIMATE SMART AGRICULTURAL INDICES ON CROP AND LIVESTOCK ENTERPRISES 5.0 Empirical Results and Discussion . .................... . . . ............. .. . ... ......... . ... .. .. 80 5.1 Factors Influencing CSA Practice for the Maize Enterprise .. . ....... ..... . . . . ........... 80 5.2 Factors Influencing CSA Practice for Sorghum Enterprise . .. ...... . .. . .............. ... 84 5.3 Factors Influencing CSA practice for Millet Enterprise . ..... .... . . . .... . ... ........... .. 86 5.4 Factors Influencing CSA Practice for Groundnut Enterprises .. . . . .. .. . . ......... . ...... 87 5.5 Factors Influencing CSA Practice for the Livestock Enterprises... ... . ... .. ..... . ..... 89 CHAPTER SIX: POVERTY DECOMPOSITION AMONG USERS OF CLIMATE SMART AGRICULTURE 6.0 Empirical Results and Discussion . .... . ... ... ................. . ..... . ... ... .. . .............. 92 6.1 Poverty Rates for lligh-Users and Low-Users of CSA . .... . ... . . ..... . . ... ......... ..... 92 6.2.1 Poverty Rates for lligh-Users and Low-Users of CSA for Per Capita Expenditure .. 93 6.2.2 Poverty Rates for lligh-Users and Low-Users of CSA for Food Expenditure ... ... ..... 94 6.3 Effect of CSAP on Poverty status .... . .... . ............ .. . ........... .. .. . . .... ... .. ........ 96 6.4 Empirical Results and Discussion ...... . ..... . ........ .. . . ........ .. .... . .. .. ....... .. ...... 96 6. 4.1 Effect of CSA Practices on poverty status of farmers maize enterprise .. . ... . . . .... 96 6.4 .2 Effect of CSA Practices on poverty status of farmers ' sorghum enterprise .......... . 100 6.4.3 Effect of CSA Practices on poverty status of farmers ' millet enterprise ........ . ..... 102 6.4.4 Effect of CSA Practices on poverty status of farmers ' groundnut enterprise .... .. .... 105 ix 6.4 .5 E:ffuct of CSA Practice on poverty status of farmers ' livestock enterprise .. . ....... . 108 6.4.6 Effect of CSA Practice on poverty status of farmers ' crop/livestock enterprise .... ... 111 6.4.7 The Multidimensional Poverty Index of (2SLS) for Crops and Livestock CSA Enterprises ......... .. ................ . .......... . .... . ........ . .... . . . ....... . . ... . ...... . .. . ... 115 6.4.8 E:ffuct of CSA on Multidimensional Poverty Index of (2SLS) for Crop Enterprises .... . . . .. . .. ...... ···· · · . .. . .. . .............. ... ............ ... .. ....... ................... ... ... .... 115 6.4.9 E:ffuct of CSA on Multidimensional Poverty Index of (2SLS) for Livestock Enterprises ....... . ..... ... .................. . ................................... . . . . ....... ......... ........... 120 6.4.10 E:ffuct of CSA on Multidimensional Poverty Index of (2SLS) for Crops/Livestock Enterprise ... ... . ... .... . . ..... .... .............. . ......... .. . ................................. 123 6.5 Hypotheses Statement ........................ .. ...... . ...... ................................ 127 6.5.1 Evaluation of the Hypotheses for Objective Two ... . ......................... .. ... . ... . 127 6.5.2 Evaluation of the Hypotheses for Objective Four . . ...................................... 127 6.5 .3 Evaluation of the Hypotheses for Objective Five ... . .. . ........ . . ...... . ..... .... . ..... . 128 CHAPTER SEVEN: SUMMARY AND RECOMMENDATION 7.0 Introduction . ... ..... ........ . ...... .. ........ .. . . . . ..... .. ... . ........... . .. . .......... ....... 129 7.1 Surnrnary of Findings ..... . ......... . .... . ... ... ..... ..... . . . .. . . . ................... ... . ..... 129 7.2 Conclusion ofthe Study . .. ... . ...... ..................... ....... ...... . .......... .. ......... 131 7 .3 Policy Implication of Findings . .... . .......... . . ......... ... . . . .... . ...... . ... .. .. ... ... .. .. 132 7.4 Policy Recommendations ............... . ................ . ....... . . . ... . . . ..... .. ............ 134 7.5 Suggestions for further studies .. . ..... .. .... ..... ... ..... ....... ... . .. .... ..... ............. 137 X REFERENCES ..... . .......... . ........... . ........ . . . . . ... .. . . .. ... ......... . ......... .. .... . ...... 13 8 APPENDIX ! :Summary Table of the Study Objectives .. . .. . ....... ..... ......... .. ... ....... ... .. ... 159 APPENDIX 2: The Research Questionnaire .. ... ....... .... ...... .... ..... ........ ....... ....... ....... ..... ..... .160 APPENDIX 3: List of Accepted Manuscript fur Publication from this Thesis ... ...... .. ...... 167 APPENDIX 4 : Langua ge Ed I.l:.J ng ..... .... .............. .. .. ....... ...... ............ .......... .... ... ......... .... ... ... 170 LIST OF TABLES Tables Title Pages 3.1 A priori Expectation fur Regression Model base on Factors Influencing the use of CSA with Crops Enterprise ....... . ..... . .................... . . .. . .. . ..... . ...... . ....... . .. . ... . . . . 53 3.2 A priori Expectation fur Regression Model base on Factors Influencing the use of CSA with Livestock Enterprise .. . . ............... . .......... . .......................... . ... .. . . .. ... 54 3.3 A priori Expectation fur Binary Regression Model based on the Effect of Climate Smart Agriculture on Poverty Status ... ... ............... . .......... . ..... . ... . .. . .................. 58 3.4 A priori Expectation for MPI Model Based on the Efrect of CSA for Crop/Livestock Enterprises ........ . ... . ...... . ..................... . .. . ....... . ....... ... ....... . .. . ....... . . . .6 0 4.1 Summary of statistics of socioeconomic characteristics for actual variable . .......... 64 4.2 Disaggregation of CSA Users by Gender ... ..... .... ..... ....... .. ... ... ... .... .. .. ... ............... ... .. 65 4.3 Disaggregation of CSA Users by Education .... . ..... .. . . ..... . . . .... . .. . . .. ............ .. 66 4.4 Disaggregation of CSA Users by Marital Status .. .. .............................. .. .... .. 67 4.5 Disaggregation of CSA Users by Religion ........ ....... ........ ......... .................... ........... 68 4.6 Disaggregation of CSA Users by Farm-Land Ownership .......... . .... . ......... . .. .... 69 4.7 Disaggregation of CSA Users by Land Acquisition ............ . .... . .......... . .... . ..... 69 4.8 Disaggregation of CSA Users by Sources of Labour ... ........ ......... .. ... .... ... ...... .. ....... 70 4.9 Disaggregation of CSA Users by Membership Association . ...... . . . ........... . . ... ... .. 71 xi 4.10 Disaggregation of CSA Users by Means of Transportation .. . . ...... . .... . .. . . . . . ....... 72 4 .1 1 Disaggregation of CSA Users by Types of Housing Materials ......... . . .... . . . ....... 73 4.12 Disaggregation of CSA Users by Connnunication Equipment... ................. . .... 74 4 .13 Distribution of Respondents by Sources/Access to Water. ........ . .. .... . . ......... .... . 75 4.14 The Sources of Credit . .. . .. . ... . . ....... . . ...... .. ........ . .............. ... . . . ... .. . ..... . .... 76 4 .15 Constraints Perceived with the High-Users and Low-Users of CSA .... . ... . . .... . ... 78 5.1 Multicollinearity Test of Variables .. . .... . .. .. .. . .. .... . . . . .... . ....... . . ............ ..... .. . . 82 5.2 Factors Influencing Indices of CSA Technique on Marze Enterprise . .... . ............. 83 5.3 Factors Influencing Indices of CSA Technique on Sorghum Enterprise . . .... .. ... ...... 85 5.4 Factors Influencing Indices of CSA Technique on Millet Enterprise . .......... .. .. . ... 87 5.5 Factors Influencing Indices of CSA Technique on Groundnut Enterprise ........ . . . . . 88 5.6 Multicollinearity Test of Variables ......... . ..... . .... .... ......... . . ...... ........... . ...... 90 5.7 Factors Influencing Indices of CSA Technique on Livestock Enterprise .. .. ..... ..... 91 6.1 International Poverty Line in Two Different Periods . . . . ..... . . . . . ...... . .. .. . . .... ... ... 93 6.2 Poverty Measurement of Per Capita Expenditure fur High-Users & Low-Users ofCSA .... .. ... . . ..... . . . ......... . .. ........... ... ... ... ....... ... .. . ... . . ... . ... .. . . ...... . . . .. 94 6.3 Poverty Measurement ofFood Expenditure fur High-Users & Low-Users Of CSA . ...... .. ..... . .. . ... ... . .. . ...... .. ... . . . ..... . . . ...... .. ... . ...... . .. . . ... ....... . .... .. 95 6.4 Multi-Collinearity Test of Variables . . ..... .............. ....... ....... . . . . ... . .... .. ..... . .. 98 6.5 IV-Probit Regression Result Detennining the Efrect of CSA Practices on Poverty Status of Farmer' s Marze Enterprise . . ........... . . ........ . . . ...... . ... . ........ . .. . ....... . .......... . 99 6.6 IV-Probit Regression Result Determining the Efrect of CSA Practices on Poverty Status of Farmer's Sorghum Enterprise . .. . ... . . . . ... .. ... . ..... . . . ................... . ... . .. . . . ... 101 xii 6.7 IV-Probit Regression Result Determining the Effect of CSA Practices on Poverty Status of Farmer' s Millet Enterprise .......... .... ........ ......... .. .................. ............. ... ... ..... ....... . 104 6.8 IV-Probit Regression Result Determining the Effect of CSA Practices on Poverty Status of Farmer' s Groundnut Enterprise .............. . ... ... ....... .. .. .. .... . ... ... ... .. .. .. ... . 107 6.9 IV-Probit Regression Result Determining the Effect of CSA Practices on Poverty Status of Farmer's Livestock Enterprise . . . .. . .. ............. . ... . ........... . ......... . ............ 110 6.10 IV-Probit Regression Result Determining the Effect of CSA Practices on Poverty Status of Farmer's Crop/Livestock Enterprise .................. ............ . . . ... .. ............... 114 6.11 Multicollinearity Test of Variable ...................... . ..... . . . . . .. ....... . .. .. .... .... ..... 118 6.12 Multidimensional Poverty Index (2SLS) Analysis for Crops CSA Enterprise ... ... . 119 6.13 Multidimensional Poverty Index (2SLS) Analysis fur Livestock CSA Enterprise .. . 122 6.14 Multidimensional Poverty Index (2SLS) Analysis fur Crops/Livestock CSA Enterprise .. .... . . . .. .. ................... . . . . ............. . . . ...... . . . .. . . . ........... . ........ . ........ . ... 126 LIST OF FIGURES Figures Title Pages 2.1 : Impact pathway of the PAR on climate smart agriculture in West Africa .......... .. .. ..... 22 3. 1: Map ofNigeria showing the geographical zones ...... .. ...................... .. ................ 47 xiii ABBREVIATIONS ADP: Agricultural Development Project AHR: Adjusted Headcount Ratio BNARDA: Benue State Agricultural and Rural Devebpment Authority BNF: Biological Nitrogen Fixation CA: Conservation Agriculture CBN: Central Bank of igeria CCAFS: Climate Change Agriculture and Food Security CGIAR Consultative Group for International Agricultural Research CH4: Methane CO2: Carbon dioxide CSA: Climate Smart Agriculture CSAP: Climate Smart Agricultural Practices DFID: Department for International Development DHS: Demographic and Health Surveys EDE: Equally Distributed Equivalent FAO: Food and Agricultural Organization FANRPAN: Food, Agriculture and Natural Resources Policy Analysis Network FARA: Forum for Agriculture Research in Africa FEI: Food Energy Intake FME: Federal Ministry of Environment FGT: Foster, Greer and Thorbecke Fig. Figure FOS: Federal Office of Statistics GHG: Green House Gas GSM: Global System Mobile HASSP: Harmonized Seed Security Project IAASIBD: International Assessment of Agricultural Knowledge, Science and Teclmology for Devebpment !CARDA: International Centre for Agricultural Research in the Dry Areas IFRC : International Federation of Red Cross IGP: Indo Gangetic Plain IIED: International Institute for Environment and Development xiv IPCC: Inter-Governmental Panel on Climate Change ISFM: Integrated Soil Fertility Management KIPPRA: Kenya fustitute of Public Policy Research and Analysis. KTARDA: Katsina State Agricultural and Rural Development Authority LACM: Lack of Access to Market LAHQHB: Lack of Access to High Quality Hybrid LAICV: Lack of Access to Improved Crop Variety LAIR: Lack of Access to Information from Radio LGA: Local Govermnent Area LPT: Lack of access to Processing Teclmology MAX: Maximum MDG: Millennium Development Goal MICCA: Mitigation of Climate Change in Agriculture MICS : Multiple Indicators Cluster StrrVeys MPCHE: Mean Per Capita Household Expenditure MLE: Maximum Likelihood Estimation MPI: Multidimensional Poverty Index N: Nitrogen NASA: National Aeronautics and Space Administration NBS : National Bureau of Statistics NCCAS : National Climate Change Adaptation Strategy NEPAD: New Partnership for Africa's Development NGO : N on-Govermnental Organisation NIMEf: Nigeria Meteorology Agency NLSS: National Living Standard StrrVey NMA: National Meteorological Agency N20 : Nitrous oxide NRM: Natural Resource Management NPC: National Population Commission NSPFS: National Special Programme for Food Secmity NWN: North West Nigeria OECD: Organisation fur Economic Cooperation and Development OLS: Ordinary Least Square xv PAR: Participatory Action Research PCA: Principal Component Analysis PPP: Purchasing Power Parity RDA: Reconnnended Dietary Allowance SAP: Structural Adjustment Programme SSA: Sub-Sahara Africa SUWBWCA: Source and Use of Water from Borehole for Watering Crops and Farm Animals SUWDI : Source and Use of Water from Deep well fur Drinking SUWPI : Source and Use of Water from Pipe borne water fur Irrigation SUWSD : Source and Use of Water from Stream for Drinking SUWDWCA: Source and Use of Water from Deep Well fur Watering Crops and Farm Animals SUWSWCA: Source and Use of Water from Stream for Watering Crops and Farm Animals TAWASANEr: Tanzania Water and Sanitation Network UNDP : United Nations Development Progrannne VIF : Variance Inflation Factor. WHS : Worki Health Survey xvi CHAPTER ONE IN1RODUCTION 1.1 Background of the study The earth is warming up gradually and the rate at which it heats up keeps on increasing daily and yearly (Purkey and Jolmson 2010). There is an tmdisputable report of the Inter-Governmental Panel on Climate Change (IPCC) 4th assessment report in 2007, which proposed whole enquiry into bow the issue of climate change is impacting on natura~ human and physical structures . Besides, there is a growing concern about the possible significances of climate change on poverty status, livelihood projections, economic development, as well as overall human development. According to Smith et al, (2007) the deprived populace in developing countries are likely to :fuce the influences of climate change, with costs on households projected that surpass billions of dollars in many nations. Evidently, the general impacts of climate change on poverty status of the rural :furmers are colossal such as drought, desertification, flooding, landslides and depletion of the ozone layers . F,qually, the :findings of Assnncao and Chein (2009) showed that on the average, agricultural productivity per hectare could decline as low as 18% by 2040 because of climate change in Brazil It has also been noted that climate change leaves many people vulnerab le to poverty, and it was projected that about half of the world's populace, as well as trost of those who reside in the industrious urban areas located by the region of coastal deha are susceptible to climate tragedies ; International Federation of Red Cross (IFRC, 2000). However, trost of the a:frected comrmmities are mainly futmd where there is concentrated mnnber of nnderprivileged :fumilies, mainly in Sub-Saharan Africa (SSA). As a result, the impacts of climate changes such as desertification, landslides, droughts and flooding, will not only decrease :furm output fur many :furmers, but will also expose them to poverty in due course. Therefore, it is vital to design policies as well as impose practices that will adapt to the current observed climate changes. Besides, in the tmderdeveloped nations, climate change data and suitable reaction can be viewed as an advantage, especially at the general level This is because both can function as catalysts fur the provision of pressing basic amenities such as suitable and efficient health car, standard educational :fucilities, water supply, reliable power and sustainable infrastructural faciliti es. This data information can help the government and non-governmental orgarnzation to fucus t heir attention at the areas and locations where the rural :furming households are trost deprived. 1 However, community base adaptations and awareness are significant features of climate change main stream, and congruently, community adaptation and fucused estimations are important techniques in the community's sustainability which establishes adaptation approaches. As stated by Kijima et al, (2011) the usage of high quality resilient varieties/hybrids of crops and livestock is another adaptation method that could increase agricultural productivity and farm incomes, thereby reducing poverty. However, Local ecosystems provide the key source of livelihood fur many oft he world's poor and roost of the rural poor in Suh-Saharan Africa (SSA) depend on highly climate-sensitive rain-red subsistence or small-scale funning, pastoral herding and direct harvesting of natural ecosystem services such as furests and wetlands. The productivity of this livelihood base, are highly vulnerable to climate-related changes such as, changes in temperature, precipitation and increased incidence of droughts and floods . The vulnerability of the majority of the poor in Africa to climate-related stresses is worsened by widespread poverty and management incapability, education, ineflective institutional arrangements, and lack of social safety nets (IPCC, 2001). Consequentially, emphases are now placed on the implementation of Climate Smart Agricultural Practices (CSAP) in order to meet the subsequent daily needs of individual househokis . CSAP are agricultural practices that sustainably increase agricultural productivity, income, adapt and build resilience to climate change, eliminate or reduce greenhouse gas emission or adapt to changing climate, which heightens the accomplishment of national food security and developmental goals which include poverty reduction; (F AO 2010). However, agriculture is said to be climate smart when it achieves three key objectives which are: buikiing resilience to climate alteration, reduction of greenhouse gas emission and sustainable increase in agricultural productivity (Farren and Adekola 2014). For instance, Mnkeni and Mutengwa (2014) noted that to increase fuod production, CSA stimulates renovation of agricultural systems and agricultural policies in order to improve food security and warrant affurdable food with low input-cost; hence there is a reduction in poverty while conserving the biodiversity and guaranteeing resilience to a changing climatic enviromnent Additionally, existing data revealed that Nigeria suflers from numerous environmental problems which have been directly associated with the ongoing climate change (Adefolalu 2007). The southern part of Nigeria, IOOstly known fur high rainfall, is currently threatened with abnormality in rainfall patterns, while the Savannah zone is slowly increasing temperatures. In the same vein, 2 the northern part also fuces the hazard of desert encroachment at a very high rate armually, brought by a serious decline in the vohnne of smfuce water, wildl:ire resources and vegetation biomass (Obioha 2008). Nevertheless, climate change adaptation, particularly in the rural areas, is vital because the impacts are best felt and understood at the bcal leve~ climate change influences are also experienced at the rural areas where the adaptive capability and susceptibility are very much felt. Consequently, in view of the furegoing, this study seeks to establish the effects of CSAP on poverty status armng :furmers in North West Nigeria. CSA has striving goals that include a wide range of sectors, stakeholders and disciplines, and requires actions that are carried out over several geographical scales and time frames. For this reason, the transition to CSA needs changes at many levels of policy making. To fucilitate these changes, a new, step-by-step guide fur the colllltry- driven enactment of CSA has been devebped. The module introduces a theory of change fur CSA and lays out a recormnended set of steps fur fucilitating the integration of CSA approaches in policy making at the national level Activities that have confirmed, based on evidence, to contribute to the meeting CSA goals can be implemented at the bcal level fur example the promotion of agroforestry. A holistic CSA approaches must encompass all these levels to safeguard the systemic transfurmation of agricultural systetn5 in the fuce of climate change. CSA is rooted in the pursuit to achieve sustainable fuod and agricultural production in a changing climate. The theory of change fur CSA comprises fuur broad areas of action that are based on a country's needs: the development of an evidence base to support and monitor change; continuous dialogue with stakeholders; the fumrulation of tools to enable change; and innovative and multidisciplinary approaches to create and sustain change in fuod and agricultural systetn5 F AO (2017). Forming the theory of change: this is required to delineate in a participatory way, the possible options and their anticipated changes and results chains between activities, expected behaviour changes, outcome and impacts. These can then be reinforce in the intervention design and process, often in the furm of bgical frameworks that outline indicators, assumptions and risks to achieving these changes. These will help define : inputs and activities that is the details and resources of the actual interventions; outputs which is the direct results and deliverables of the interventions that are required fur the outcomes; purpose-level and transitional outcomes which is also the expected external changes from the intervention; and higher-level outcomes or impacts that interventions may contribute to, usually affecting household and individual living states, and changes in the 3 environment CSA in the framework of Feed the Future all of the above around CSA need to be placed in the context of Feed the Future ' s approach and theory of change that places smallho Ider farmers, pastoralists and :fisher-folk at the center of the investment approach. Integration of CSA will be all round solutions that small-hokiers choose to consider and adopt as part of their production decisions. Thus CSA needs to be integrated into best practices and approaches that support overall Feed the Future goals around poverty and nutrition; this is clearly classified in the first pillar. Having stated that, sustainable productivity and income growth benefit from attention to both adaptation and mitigation, the second and third pillars of CSA. CSA adds extra importance to efforts and innovation aimed at uptake of new technologies and practices by smallholders, in particular behavioral change corrnrrunication so that sustainable and rational solutions can reach scale. In this situation, as in others, CSA needs to be integrated into best practices around rural farmer and corrnrnmity engagement as emphasized across Feed the Future Programme F1F, (2016). 1.2 Problem Statement igeria races diverse environmental problems which are directly associated with recent climate change (Ikhile 2007). Obioba (2008) confirmed that- northern Nigeria generally, is under constant attacks and races hazard of desertification, which bas been prompted by decrease in the quantity of rain:full. Empirical studies of the climate change in the North-Western part of Nigeria between 1915 and 2008 revealed that the rain:full of that zone bas fluctuated significantly (Ekpoh and sah 2010). In Africa, CSA offers rrrult:iple benefits in consonant with attaimnent of the goals to sustainably increase agricultural productivity, increase smallholder rarmers ' resilience to the effects of climate change and reduce greenhouse gas (GHG) emissions from agricultural activities and carbon sequestration (N aess 2011 ). This is the process in which trees, crops and other plants absorb carbon dioxide into the soil and after decomposition, oxygen is released into the atmosphere via the process of photosynthesis, which is also, the process in which carbon dioxide and water are converted into oxygen and sugar by the helps of sunlight as energy. The oxygen released in this process help in the reduction of global wanning. Climate change bas been reported to rrrult:iply existing inequalities within corrnrnmities (Dankleman 2008). While exposure to climate variations may be the same fur men and women in a given location, there are varied gender based differences in vulnerability and consequently adaptive capacity and adaptation practices (Adger et al , 2005). Adaptation to climate change such as CSAP is generally costly, largely revolving around adoption of new or improvement of 4 technologies such as improved varieties and hybrids or use of improved crop husbandry practices (Kahmgu et al , 2013) but is a response, many resource poor funning households have embraced despite the low capital hence inefficiency (Thornton et al , 2006). The level of vulnerability of individual :fu.rming househokls is a key component to decision making when considering adaptation to climate change which is a corrnmn challenges to the rural :fu.rming household (Stern 2006). Fanen and Adekola (2014) opined that, although, many nations are projected to embrace CSA, its derronstration in an African perspective is not yet so, neither has its sustainability been evaluated. However, subsequent global wamring and changes in climate are impending dangers to fuod security, with a consequential increase in poverty levels in mnnerous developing nations , including Nigeria due to the high dependence of agricultural system, on some climatic parameters (Bello et al. , 2012). Additionally, the rising problem of climate change effects is a worklwide issue, and the developing nations are particularly the roost vulnerable, because the African nations ' agricultural production system are mainly rainfed and basically relying on the whims of weather, (Nwafur 2007 and Onyenechere 2010). In the northern part of Nigeria, the number of rainy days has dropped by 53% which brings about irregular rainfall arrangement with subsequent increase in temperature in North West Nigeria. The impact of this, drop has resuhed into a rise in desertification, drought and evapo-transpiration which patently can resuh in the reduction of moisture content or the complete dryness of streams, and particularly with continuous annihilation of biodiversity and furest (Adefulalu 2007). According to Akor (2012), there are three main climatic zones in Nigeria, namely: Sudan, Sahel and Guinea Savannahs. Temperature, rainfall and humidity d:i:frer considerably through these three climatic regions. Nevertheless, in the semi-arid behs comprising the Sahei Guinea and Sudan savannahs, extreme temperatures could be as high as 40°C. The report of Nigerian Meteorology Agency (NIMET) (2008), revealed that, the northern part ofNigeria has been subjected to poorer than normal rainfall but gradually became drizzlier than normal Also, the research confirmed that between 1941 and 2000, annually rainfall in the greater parts ofNigeria has declined from eight to two millimeters. Drought and Seasonal rainfall have regularly become perpetual features of northern part of Nigeria which is seriously affecting the livelihood of the rural :fu.rming households in the study area. The dry season is corrnmnly severe, and hence it is very essential for furmers to engage in soil rooisture preservation agricultural practices in order to ease the destructive effect of climate change at this period. The North West region of Nigeria remains an agricultural hub for the nation with a huge proportion of its population in the agricultural sector (Olapojo 2012). Nevertheless, it is the 5 poorest zone in Nigeria according to National Bureau of Statistics (NBS 2013). There is also a prevalence of high-income inequality am.)ng the populace (Action Aid Nigeria 2009). Obayelu (2010) avers that agriculture is the major source of income for many :families in North-West igeria, and weather condition plays an important role in ensuring sustainable agricultural productivity in many parts. In addition, lack of improved agricuhural tools compels wide use of traditional funning system The latest discrepancies in the climate and weather of the area have taken severe toll on crop output with some crop yields now declining (Reddy and Hodges 2000). In 2010, conventional CSAP were introduced to :farmers in North-Western part of Nigeria through a programne called International Institute for Environment and Devebpment (TIED) with the help of the Katsina State Agricultural and Rural Development Authority (KTARDA) and the Sokoto Agricuhural Devebpment Project (ADP). The CSAP that were introduced by the agencies consisted of organic manure, agro-forestry, conservation agricuhure, the use of improved hybrid varieties, integrated crop/livestock management as well as irrigation for small-holder :farmers . Obviously, this was in response to bw agricultural productivity with low agricultural output and high incidence of poverty am.)ng :farmers in North-West Nigeria. In point of fact, presently, desertification and drought have affected several portions of the north, with a resultant daily and yearly extensive decrease in agricultural yields from one bcality to another (Oyekale 2009). Furthefm.)re, Thornton et al , (2009) predicted that by the year 2050, in the sub-tropics, crop yields may decrease by 10 - 20% as a result of gbbal warming but there are some places where the crop yield damages may be ID.)re severe. Meanwhile, Global warming causes erratic and great weather events that impact negatively with a gradually processes of affecting crop growth, as a result of droughts and fbods on soil water and sea level rises with a prevalent diseases (Zoellick and Robert 2009). Then, the restraints set by climate change on agricuhural activities in this region range from prominent variation in precipitation which may be shorter periods of rainfall or irregular rains , (which limits crop production) to repeated droughts. Moreover, the droughts unveil such features as fictional onset of the rains, late onset of the rains, prominent bahs through the rainy season, and early termination of the rains, leading to severe rrodifications in the pattern of seasonal rainfall dissemination (Ayanwale 2007). The report of Tadross et al , (2009) indicated that influence of climate change on crop yield is not restricted to total rainfall effects abne, the intra-seasonal shocks are also very significant because intra-seasonal arid spells may be rmre destructive to growth than short entire rainfall. Very high extreme temperature through the growing season is also considerably unfavourable to crop yields (Thornton and Cramer 2012). Such sequential deviation is anticipated to rise in many parts of 6 Africa nations under rrost climatic change situations (Boko et al., 2007). According to the submission made by United Nation Development Progrannne, high rate of poverty makes majority of the population susceptible to climate change and compromises their adaptation capacity (UNDP 2011). Sirrnlarly, Etim and Udofia (2013) argued that 70% of Africa' s deprived households are rmstly fuund in rural areas and rely solely on agricuhural activities. Englama and Bamidele (1997) also avowed that the majority of the rural dwellers are engaged in farming activities. The implication of this is that, a greater percentage of the rural poor are farmers . Given this, rrost of the poverty deliberations and considerations in Nigeria are always associated with funning households f-N orld Bank 1996). This is due to the fact that funning is still the backbone of the Nigerian economy and it has continued to empby 72% of the people (Ogbalubi and Wokocha 2013) despite its decreased role in providing fureign exchange income to the Federal Government. But these farmers, due to their bw productivity coupled with inadequate access to capital, transportation, storage and processing facilities are usually exposed to negative impacts of climate change and poverty. Nevertheless, despite this alarming consequence of climate change that seems to worsen with time , the poverty statistics of North West Nigeria are equally very worrisome. However, the universal climate changes indicated that SSA will be one of the rrost aflected areas, with anticipated agricultural yield declines of up to 20% fur crops and possibly high levels of poverty and fuod insecurity mainly in the rural coIIIIIll.lillties (Cline 2008). According to Diale (2011) faihrre to be equipped fur the impacts of climate change can results in starvation, malnourishment and a high rate of poverty arrong others. Even though Nigeria is a country that is gifted with vast natural resources, physical and human endowments yet a large proportion of its populace live bebw the relative poverty lines. The national evaluation of between 2003 and 2004 confirmed that slightly above half of the populace, that is, 51.6% live bebw US$1 dollar per day and the relative national poverty incidence was fuund to be 54.4% ational Bureau of Statistics (NBS 2005). Additionally, the Human Development Report by the United Nations Devebpment Programme (UNDP 2007) affirmed that about 83. 7% of the populace live bebw $1 .25 per day. However, this poverty status is worse in the rural comrmmities where over 70% of the population dwell and earn their living via agricuhural activities other than in the urban cities. Moreover, rrore than 86.5% of the rural populace are involved in funning activities (NBS, 2005). Consequently, this consistently defines agricuhure as an important sector capable of affecting majority of Nigerians in various ways. Importantly, the persistence of poverty and hunger in Nigeria, to a large degree must then be attributed to the failure 7 of the agricultural sector to fully impact positively on the largest and most populated nation in Africa. Climate change is a serious risk to poverty reduction and threatens to undo decades of development efforts "the adverse effects of climate change are already evident, natural disasters are more frequent and more devastating and developing countries bke Nigeria are more vulnerable." While climate change is a global phenomenon, its negative impacts are more severely felt by poor people especially those in the northern part of Nigeria being the most poorest wne according to NBS (2013). They are more vulnerable because of their high dependence on agriculture, natural resources, and their limited capacity to cope with climate variability and extremes. Climate change will fi.nther reduce access to drinking water, negatively a:frect the health of poor people, and will pose a real threat to fuod security in many States in the northern part of Nigeria. In some areas where livelihood choices are limited, decreasing crop yields threaten :famines. The macroeconomic costs of the impacts of climate change are highly uncertain, but very hkely have the potential to threaten development in many States in Northern part of Nigeria (Farauta et al , 2011). Mohannnad (2009) reports that desert, which now covers about 35 percent ofNigeria's land mass , is advancing at an estimated 0.6 km per annum while deforestation is taking place at 3.5 percent per anmnn Moreover, the Sudan Sahel region of northern Nigeria bas su:frered decrease in rainfall in the range of3-4 percent per decade since the beginning of the nineteenth century. The Director- Generai Nigerian Meteorological Agency highlights that analyses of rainfall data (1911 -2000) in three 30 years intervals - 1911-1940; 1941-1970; 1971- 2000. Climate Change and Adaptation Measures in Northern part of Nigeria: Empirical Situation and Policy Implications 2000 show that many more places are recording late onset of rains, early cessation of rain, shortened length of the rainy season and reduced annual amount of rain especially in the northern part of the country and there are frequencies of drought, more persistent bamnnattan smog and increasing temperature trends. The concern with climate change is heightened given the linkage of the agricultural sector to poverty. In particular, it is anticipated that adverse impacts on the agricultural sector will exacerbate the incidence of rural poverty. Impacts on poverty are likely to be especially severe in the northern States of Nigeria where the agricultural sector is an important source of livelihood fur a majority of the rural populace. (Mobarrnnad 2009). 8 1.2.1 The Gap to be filled by the Study Existing studies on climate change such as Codjoe et al, (2013); Oz.or et al , (2013) and Anselm et al, (2010) have fucused roostly on adaptation, perception, awareness to climate change, and fuod security. This research study will contribute to knowledge by highlighting the link between climate change, CSAP and poverty status of smallholder farming househokis in the study area. This study will equally, contribute to the knowledge of the policy makers and research scholars , which will offer some significant perspectives that are expected to assist in shaping future policies as well as ongoing research work in this area of study. Additionally, it i<; hoped that this study will help the farmers in the study area to embrace roore CSAP which in tum can reduce their poverty levels through sustainable increase in agricultural productivity and income. Finally, another salient contnbution of this study to knowledge i<; that it will tmderscore the establishment of CSAP as one of the major, and important functional facilities that reduce poverty and GHG emission (mitigation) through the process of carbon sequestration. This is the process in which trees, crops and other plants absorb carbon dioxide into the soil and after decomposition, oxygen is released into the at:roosphere via the process of photosynthesis , which is also, the process in which carbon dioxide and water are converted into oxygen and sugar by the helps of sunlight as energy. The oxygen released in this process help in the reduction of gbbal warming. Global Multidimensional Poverty Index, (2015) posits that 80% of the population in North West live below poverty line, closely fullowed by the North East with 76 .8%, 45.7% in North Centrai 27.4% in the South East, 25.2% noted in South-South and 19.3% recorded in South West. These statistics was an affirmation of the 2010 NBS poverty profile of Nigeria that showed that, the North-West and East bad the upperroost poverty rates in the country in 2010 with 78% and 76% respectively (National Bureau of Statistics, 2010). High rate of poverty in northern Nigeria makes majority of the rural population susceptible to climate variation, which in turn compromises their adaptation capacity (UNDP, 2011 ). Therefure the impact of climate change could be greater due to their inability to adopt CSA as a result of their poverty status. It is therefure pertinent to examine the impacts of difrerent agricultural practices in the context of CSAP in the study area, to improve our understanding of their practices and mitigation impacts and potentials to contribute to fuod security and reduce poverty tmder the specific climate, agro-ecological and socio-economic conditions of the region. A better tmderstanding of the region and its agricultural practices bas broader significance as well 9 and the potential fur expanding cultivated area in the flat regions bas ahmst been exhausted (We zel et al 2002). An attempt to pave the way fur trus study was to (a) discuss six sets of CSAP in north- west Nigeria that have the productive potentia4 (b) evaluate the impact of these CSAP on yields ofcrops and livestock, and (c) map out factors that afrect rural fuming households ' poverty status . There is no one agricultural practice or production system that can be considered CSA, but rather a set of possible options that tmder the specific climate change, socio-economic and agro- ecobgical conditions can increase agricuhure ' s capacity to support food security. That is, the magnitude of the benefits and costs ofvarying practices, as well as the institutional environment necessary to support adoption vary widely across regions (FAO 2010). A robust CSA strategy cannot consist of an individual practice to address the fuod security and climate change challenges in both the short and bng run, therefure, a portfolio of mutua Uy supportive approaches inchlding safuty nets and other improvements in enabling institutions should be expbred. Some scholars argue that the agricultural activities need to be diversified and evaluated in a comprehensive system, one that incorporates CSAP on the farm leve4 diversity of land use across landscapes, and proper management of the interactions among difrerent landscapes (Scherr et al , 2012). Ahhough there are many research and analytical efforts to minimize the impact of climate change on agricuhure and on livelihoods in Africa by various actors, there is however, no coherent documented state of knowledge of CSA practices in Africa. Hence there is a need to detennine the drivers, challenges or opporttmities that may facilitate or hinder scaling up ofCSAP in North West Nigeria. Furthermore, CSA can also have a reverse influence on poverty because of its ability to improve production and subsequently rural wealth. This constitutes the crux of trus study. However, mnnerous studies have been carried out on the subject at regional and state levels. An instance is the research that was carried out by Oz.or et al , (2013). They examined climate change vulnerability and the use of homegrown skills fur adaptation among small bolder farming communities in SSA. Olawale et al, (2016) surveyed the rainfud agriculture system's vulnerability to climate change effect. They empbyed time series data and econometric analytical techniques to quantify the difrerential influences of rainfall and irrigation. Also, Fanen and Adekola, (2014) assessed the applicability of CSA in fighting climate change, desertification and enhancing rural livelihood in an African context; while Branca et al., (2011) boked into CSA as an empirical evidence of fuod security and mitigation benefits from improved cropland management. Equally, 10 Anselem et al , (2010) carried out an examination on climate change and its ecological threat to the fight against malnutrition and poverty in Africa. Furthenrore, Ekpoh, (2010) evaluated adaptation to the effect of climate discrepancies on agriculture by rural fimners in North-West Nigeria. Oyekale and Oyekale, (2010) and Obayelu, (2010) have also examined the poverty status of fimners in Nigeria. Meanwhile, the analysis of the effects of CSAP and poverty status arrong smallholder funning households in North-West Nigeria is yet to be investigated, hence the need fur this study. 1.3 Research Questions This study seeks to provide answers to the fullowing research questions : 1 What are the constraints to the users of climate smart agricultural practices? 11. What are the fuctors influencing indicators of climate smart agricultural practices within different crops and livestock enterprises? Ill. What are the poverty status of those who are low-users and high-users of climate smart agricultural practices in the study area? 1v. What are the effects of climate smart agricultural practices on poverty status of fimners? v. What are the effects of climate smart agricultural practices on multidimensional poverty index (MPI) of the funning household? 1.4 Objectives of the Study The broad objective of this study is to examine effect of climate smart agricultural practices on poverty status arrong smallholder furming households in North-West Nigeria. However, the specific objectives of the study are to: 1 Analyse the constraints on the use of climate smart agricultural techniques. 11. Determine fuctors influencing indicators of climate smart agricultural practices within different crops and livestock enterprises. Ill. Decompose poverty levels across low-users and high-users of climate smart agricultural techniques. 1v. Determine the effects of climate smart agricultural practices on poverty status of furniers . v. Determine the effects of climate smart agricultural practices on MPI of the furming household. 11 1.5 Hypotheses of the Study The Null hypotheses of the study are stated below: 1. Ho: Fanners ' socio-economic characteristics do not significantly influence indicators of climate smart agricultural practices within d:i:frerent crops and livestock enterprises 2. Ho: Climate smart agricultural practices do not significantly a:frect poverty status of furmers . 3. Ho: Climate smart agricultural practices do not significantly a:frect MPI of the funning household. 1.6 Justification of the Study In any country for CSA to be effectively implemented, there must necessarily be clear policies for all the sectors involved (Mnkeni and Mutengwa, 2014). The need for sustainable agriculture in orth-West Nigeria cannot be overemphasised. Not only is there a need for quick climate strategies that will address the e:frect of climate change and its implications in this region, there is also the need to enforce it in order to increase agricultural production and to reduce high poverty incidence rate among the rural furmers . The CSA is therefore very topical especially considering the incidence of droughts in the last decade in the region The CSA bas emerged as the best agricultural development techniques and one of the coherent climate mitigation approaches that bas the potential to sustainably enhance agricultural productivity and build resilence by reducing ermnission of greenhouse gases. This study is conceived on the premises that there is need for such approach to agriculture in Nigeria particularly in the north which is the crux of agricultura 1 production in the nation The study area is prone to acbaic funning methods such as periodic bush burning and deforestation This bas not only led to negative repercursion on :tanner' s productivity but bas also result to depletion of soil nutrients and soil degradation making the soil unsuitable for optimum yield. There is an empirical assertion according to International Assessment of Agricultural Knowleddge , Science and Technology for Development (IAASTD, 2009) which revealed that CSAP will accelerate nutrient recuperation and improve indigenous agricultural system as well as encourage the practice of agro-ecological system In addition, in the context of developing countries, there is need to establish the potential applicability of CSA and its ability to create wider uptake by nrral :farmers and encouragement required for deep transformation within the policies sector. otwithstanding, these benefits, there is a notable dearth on the studies assessing such potentials in CSA approach in the study area, hence this study is very important. 12 Nigeria, similar to other parts of the world, is suffering from the simple features of climate change . 1brough all these abnormalities, all-inclusive tactics on climate change are vital in Nigeria to curtail the tide of the irregular rainfall pattern that is presently bedevilling the cmm.t:ry. CSA should be main-streamed into fimdamental government strategies and programmes, including policy, disbursement and planrung frameworks. Availab le information shows that Nigeria faces diverse natural problems which are openly connected to recent climate change (Adefulalu, 2007). According to Garcia et al , (2006), Nigeria's main challenges include, reducing poverty, diversifying its economy from the oil and gas sector towards more labour intensive sectors such as agriculture which involve CSAP and improving health and education. The oil has increased economic volatility and inflation while those living in poverty being most vulnerable to volatility and inflation. The northern region are seriously facing the hazard of desert infringement at a very fast rate prompted by wanton reduction in the quantity of natural water, furest ecosystem and wikl- lifu resources on land (Obioha, 2008). However, it is worrisome that the farmers that are meant to carry out the CSAP may be incapacitated due to their high poverty status. Nevertheless much has been written about climate change, agriculture and poverty as a distinct subject matter, but the link between them has received little or no consideration. Therefore, understanding this relationship is very important fur the construction of effective policy responses to climate change. Hence, this makes it imperative to ascertain to what extent poverty and its related characteristics constitute constraints to CSA practices, and to examine if there is a significant difference in practising CSA between deprived and non-deprived househokls. It is also vital to find out to what extent CSAP alleviate poverty in this high poverty rate region via its influence on yield improvement. This study will therefore, improve understanding on CSAP, ways of using it to decrease poverty level in the study area and to furm policies which border on CSA in North-West-Nigeria. It will therefore be a working document to administrators , policy makers as well as other interested groups that aim at improving CSAP in the region or reducing poverty. It will also con.tribute to the bulk of existing literatures and also suggest areas fur further research where applicable fur researchers and students. 1.7 Plan ofthe Study Chapter one has been discussed. Chapter two expounds on the theoretical framework, conceptual frame work, and the review of related literatures. The third chapter discusses the methodology of the study. This comprises of the narrative discussion of the study area, the sources for data, the sampling procedure, scope of the study and the analytical teclmiques used. Chapter fuur presents 13 the cliscussions of the resuhs of data analyses. Chapter five deals with the ordinary least square (OLS) statistical analysis of the :factors influencing indicators of CSAP wit:mn diffurent crops and livestock enterprises. Chapter six advances the decomposition of poverty status ofbw-users and lrigb.-users of CSAP so as to ascertain the inferential statistical analysis and presents the effects of CSAP on poverty state of funners in the study area. Lastly, chapter seven concentrates on the summary of the major :findings, conch.is ion, policy reconn:nendations and suggestions fur further study areas. 14 CHAPTER lWO LITERATURE REVIEW 2.0 INTRODUCTION This chapter reviews relevant literature based on the objectives of the study. It commences by the theoretical and conceptual framework, also analysing the various CSAP by the household farmers . Additionally, it identifies and explains some selected CSAP that are connmnly practised in the areas under study. It highlights the approaches of CSA activities and the various types of poverty. Lastly, it provides the link between climate change, CSAP and poverty. 2.1 Theoretical Framework The study applies utility theoiy which is concerned with households ' decisions and choices. The theory provides a methodological framework for estimation of the alternative choices made by persons, companies and govermnents. Utility refers to the satisfaction that each choice provides to the decision maker. Thus, this theory assumes that :farmers' decisions are made for utility maximization. Utility theory is often used to explain the behaviour of distinct funning households who are the consumers. In this case, :farmers plays the role of the decision maker who must decide how much each of the many available climate smart agricultural activities adaptation strategies to be use so as to secure the highest possible level of total utility subject to the households available income, prices, and other :factors of production. CSAP are risk mitigation strategies for which their contributions to :farmers' incomes could be evaluated given some expectations of income losses should the :farmers fail to make those adaptation decisions. The traditional framework of utility theory bas been extended over the past three decades to multi- attribute cases, in which decisions are taken by numerous principles. The following six major types of CSAP were studied, and they are: the practice of using organic manure, agro-forestry, conservation agriculture, integrated crops and livestock management, the use of improved varieties/hybrid of crops/animals and the use of irrigation for sma1Il10 lder :farmers, each is taken in as X1 to X6:- In all cases the utility that the decision maker either the CSA :farmers gets from selecting a specific choice of CSAP adaptation strategy is measured by a utility :function U, which is a mathematical representation of the decision maker's the climate smart agricultural system to the :farmers household scale of preferences such that: U(,¥1) > U(X2), where choice ofCSAP X1 is preferred over choice X2 or U X1 = U X2, where choice X1 is indifferent from choice X2 - both choices are equally preferred. 15 Fanners are assumed to use the combination of CSAP that maximi:zes their expected utility over their planning horizon Let Uij be the i t h farmers expected utility from using the combination of practices j , j (i=l.. ..J ), with respect to using a lternative combination of practices m : . . ... . . ....... . . . .. . ... . .. . . .. .. . .. .... . . . ... ..... . . . ..... . ... .. (2.1 ) Where Xi is observed exogenous variables (education, marital status, househoki size, furmland ownership and experience) and Eij is unobserved characteristics. The farmer's utility from choosing a combination of CSAP is not observable but the choice is, CSAP combination (j) is chosen if Uij the rughest is for househoki size (i) . Therefore, the funner will choose combination of using practices (j) in prefurence to adopting any other combination of the practices m if li ff u11 > max(Ui~) or Tii i max(Ui~ ) orniJ U{F(So)} . . .... . . . .. .. . .. ..... . ..... . . . . ..... . ....... .. . . . . .. .. . . . ..... (2.4) It is further assumed that farming househokis adopting two or more strategies have rugher utility levels compared to househokis having adopted only one strategy and so on . .. ......... . .. .. ... .. . . ......... .. ........... ..... . .. ..... (2.5) 16 2.2 Conceptual Framework Universally, agricultural activities and forestry systems are anticipated to change corniderably in reaction to prospective climate change (Morton, 2007). Worldwide demand fur agricultural goods, be it fuod and fibre, continues to escalate because of population explosion, variatiorn in diet related to rise in per capita income and the need fur marginal energy sources, while there is less extra land accessilile fur agricultural development. The agricultural system thus needs to produce rrore on the unchanged aggregated land, while responding to changes in climate and extreme weather such as floods , desertification and droughts. The term CSA represents a set of approaches that can help to meet these tasks by increasing resilience to weather condition excesses, adapting to climate change and decreasing agricultural GHG emissiorn that contriliute to global warming (World Bank, 2011). However, by the few previous eras, crop yield has been reduced because of global warming and the results of rrodelling indicated that climate change will decrease crop yiek:l possilily and predominantly in the tropical and mid-latitude natiorn (Cline, 2008). CSA places emphasis on meeting the prerequisites of the people fur fuod, fibre and timber, and through science-based engagements, reducing poverty and cornerving, improving the productivity, fuod security, adapting to climate change and resilience to both natural and agricultural ecosystem fimctiorn . It involves an endless, interactive practice fur stakeholders, researchers and policy makers to meet the tasks presented by climate change and jointly alter agriculture and fuod systems towards sustainability objectives. Increased cornciousness and adaptive management are vital mechanisms fur the CSA policy (Neufeldt et al , 2013). The relatiornhip between vulnerability, adaptation and resilience is very essential to CSA. While vulnerability is defined as exposure and capacity to respond to the influence of climate change, adaptation involves a process in which :farmers are able to reduce their susceptiliility to climate change, resilience is on the other hand termed as the capability to bear disturbance, diminish vulnerability and prorrote adaptation to climate change. It also means to undergo alteration and retain the same necessary utilities, structure, identity and feedback, without reverting to the prior state of uneasiness or disturbance (Walker et al , 2004). Equally, CSA seeks to add to the mitigation and reduction of greenhouse gas emission, rrostly carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) releases. Radntion fluxes at the earth's surface show vital roles in mnnerous environmenta~ climatological, and hydrological systems. Biotic activity is strongly refunt on radntive transfer both directly, via the interaction between phyto-elements and radiant energy produced by the sun, and indirectly through 17 micrometeorological controls. Understanding the spatial dissemination of photosynthetically active radiation (PAR) is important fur forecasting patterns of ecosystem fimctioning within a furest (Vierling & Wessman, 2000). Additionally, it aims to stabilise trade-off with food security and livelihoods of individual However, merging agro-furestry and conservation efforts with agriculture to meet global fuod demand will help to diminish greenhouse gas emissions , consistently reserve ecosystem services and support biodiversity (Mbow, 2014). CSA is said to be a furm of agriculture that sustainably increases agricultural production and incomes, adapts, builds resilience to the impact of climate change, reduces or eliminates greenhouse gas emission which heightens achievement of national fuod security and devebpmental goals which include poverty reduction (FAO, 2010). Additionally, the link between climate change, agriculture and poverty has been registered earlier by NBS, (2013) which affirmed that, North-West Nigeria has the highest rate of poverty, while their major occupation is notably agriculture. Subsequently and meanwhile the major causes of climate change are agricultural activities and one of the consequences of climate change is poverty (Belb et al, 2012). According to IPCC, (2007) agriculture is among the causes ofclimate change because it involves the process of releasing greenhouse gas emission into the atmosphere. At the same time agriculture is the major soh.rt:ion to climate change through the process of CSA and carbon sequestration, which is the process in which trees, crops and green plants absorbed carbon dioxide into the soil and after decomposition oxygen is been released into the atmosphere through the process of photosynthesis. 1bis is the process through which carbon dioxide and water are converted into oxygen and sugar by the help of sunlight as energy. The oxygen released helps in the reduction of global warming. However, agricultural activities accounts fur the larger part of land used in the third world countries and this is perhaps the single most dominant impact on environmental quality. At the same time, funning activities remains the major livelihood of the rural poor household farmers (Malik, 1999). The relationship between environment and poverty can be traced back to the original Malthusian theory which postulated that the poor subsistence :farmers of developing nations over-exploited their environment out of sheer necessity and that, the subsequent degradation :fiu1:her aggravates poverty. Mahhus anticipated an impending doomc;day situation where excessive human population growth would overtax a limited supply of natural resources (Malthus, 1798). More recently however, the realization of climate change has triggered huge research on its consequences and 18 remedies. Evident in this research is that, climate change directly or indirectly affects poverty. For instance, OECD, (2016) examined the influence of climate change and came to two conclusions : First, climate change is happening and will progressively affect the poor. Second, adaptation is required and there is a need to incorporate responses to climate change and adaptation measures into strategies fur poverty reduction to ensure justifiable development. According to UNDP, (2004) poor nations and people tend to be particularly susceptible to demtions from average climatic extremes. OECD, (2016) :further posited that climate change will :further reduce access to drinking water, negatively affect the health of poor societies, and will pose a real threat to food security in many African cotmtries, Latin America and Asia. It will also cause fish grOlmds to deplete, floods, increase droughts, and storms destroying entire annual harvests in affected areas and consequently leading to increased food insecurity and hence poverty. The theoretical relationship between climate change and poverty difrers from other theories that establish cause of poverty, such as the poverty theory of individualism which postulates that poverty is caused by the rate of individual bard work and responsibility to acquire basic needs including food, health care and educational :facilities (Rank, 2004). Likewise, cuhural and neighbourhood theory postulate that diflerent cuhural beliefs and lire styles are the cause of poverty in some areas, as well as the structural causation of poverty that associates poverty to the economic structures such as capitalism Apparently, climate change goes beyond the traditional theories of poverty to explain the causes of poverty, particularly in its rrrultidimensional perspective. Demonstrably, climate change means that the regular trend of the climate is now uncertain; which then impacts on agricultura 1 production It also directly makes some places hotter and causes droughts in extremes, increase floods and causes acid:ic rains armngst others that reduce agricultural production or causes disasters that affect life, property and fa.nm. Patently, this might lead to limited food supply or food insecurity in the affected areas. In :fact, OECD, (2016) assert that food security is a fimction of several interrelating :factors, including agricultural productivity as well as food purchasing power, and above and beyond, climate change couki worsen the prevalence of hunger through direct negative effects on production and indirectly influences on purchasing powers. Therefore , poverty is unequivocally a :factor of climate change as evident in our individual abilities, cultura 1 beliefs and structure of the economics, to mention just these few. The CSA addresses the relationship between agriculture and poverty which continues to be the major process of producing food, empbyment and income fur many households in developing countries. Indeed, it is estimated that about 75% of the poor in the world are generally bcated in the rural areas, with agricultural 19 activities being their mainstream source of income (Lipper et al , 2014). On account of this , fuming activities are uniquely placed to push people out of poverty. Compatibly, it has been observed that agricultural and economic growth is often the most effective and equitable strategy for reducing poverty, as well as a means of increasing food security (CCAFS and F AO, 2014 ). Practicing CSA is often considered a "no-regrets" approach in that its benefits of sustainable improvements in agriculture will accrue even if climate change impacts tmn out not to happen or be as severe as expected. Moreso, it is important to consider climate-smart management of agriculture within a broad socioecological :framework, recognizing that climate change is one of the many drivers of change influencing the agricultural sector. Climate-smart interventions have varying costs and environmental economic benefits. So quality decisions in CSA technologies and policies should be based on relevancy in current and future scenarios of climate and economic impacts. However, the seaweed fuming is a low-carbon activity that brings many benefits to the coIIll1llIDity and the environment, and is resilient to climate change. Adopting CSA options also implies a need for increased investments at the :farm level Extended transition times may be needed to realize the benefits to CSA in the form of productivity or in- creased resilience. During the transition, the returns to agriculture are low or negative, and thus some form of financing to support this transition is necessary. For instance, if we find that agrofor- estry practices improve the adaptation indicators more in coIIll1llIDities with a higher historic a 1 rainfull/temperature variation, we can conclude that CSA provides adaptation benefits in these coIIll1llIDitie s (Branca et al , 2 0 12). Consequently, CSA should not be regarded as a set of practices and technologies but should be seen as an approach which has plausible and positive numerous entry points, ranging from the development of technologies and practices to the expansion of climate change models and scenarios, information technologies, value chains and the strengthening of institutional and political enabling environments . However, beyond single technology at the farm leveL CSA includes the incorporation of multiple interventions at the food system, biodiversity and value chain or policy level Contrary to conservative agricultural development, CSA analytically incorporates climate change into the development of sustainable agricultural schemes which increase income and in tmn reduce poverty (Lipper et al , 2014). Additionally, it endeavours to improve the activities of biodiversity with the hereditary assets and ecological functions which transfer essentials. The active process of biodiversity strengthens food security, increase sustainable livelihoods, ecosystem resilience, the management ofbiological processes needed for 20 sustainable agricultural productivity and insurance for the future; CSAP is the best approach to meet these roles ofbiodiversity target PAR, (2010). Besides, climate change is likely to affect species ' distnbution and abundance, aside increasing the risk of total annihilation and loss of biodiversity (IPCC, 2001). Subsequently, vicissitudes in the accessibility and dissemination of the several apparatuses of biodiversity allied with food security will aflect home-grown supplies of raw materials vital for improving output of ecosystem :facilities. Ultimately, fluctuations in dissemination of pollinators, beneficial and hannful soil micro-organisms may have a perceptive impact on agricuhural output. However, the estimated loss of cuhivated lands as a resuh of increase in salinization will make the need for rrore proficient production on the residual cropland ever rrore vital Additionally, the rural :farmers will need to adjust existing agrononnc practices, which include how to adapt and mitigate the effects of climate change. These adjustments will also include aherations in water use like inigation activities, use of improved crop varieties development, better adapted livestock breeds and diversification of production strategies to improve capacity to :face risk. There is previously considerable endorsement that rural smallliolder :farmers in agrarian :farming system are adapting to the impact of climate change, predominantly via the use of local varieties and the adaptation of traditional :farming practices (PAR, 2010). 21 Improved livelihoods/food ecurlty/reduced poverty amongst farmers In Africa Enabling policy t Eventual wider environment created 8. Community livelihood adoption improvements t t Outcomes t Changes In knowledge 7. Adopting farmers/ Change In knowledge and attJtudes of communities enjoy higher & & attitudes of farmers/ stakeholder more stable Income communltJe t Scaling-up 4. Adoptlot of Scaling-out t 6. Stakeholders ◄ technologies & .. 6.Adoptionln teaming of NRM changes In_ practices other village 3. Farmer communitie ~-Change In farmers'/ odl and Innovate communities' attJtude & perceptions 1. Improved knowledge of ~ farmers t PAR to adap~ develop and Outputs valldat/etceh nologl~::.::~ of knowledge & tool (diagnosis, vulnerablllty assessmen trade-offs between Participatory diagnosis to identify adaptation & mitigation, technologies, pilot site and scope of options practice & policies) Figure 2.1 : Impact pathway of the PAR on climate sma rt agriculture in Wes t Africa Source: PAR, (2010). 2.3 Climate Smart Agricultural Practices The CSAP offer an environmenta l res ilience which ass ist farmers to move from poverty to prosperity and it also increases the so il nutrient, and simultaneous ly reduces the negative impact of climate change. In the same vein, CSA also safeguard the defense from vulnerab ii ity and unbearab le risks for htu11an and environ me nta I influences. This is vital in igeria because of the ris ing concern over the pollution of agricultura I lands, an upshot of the in:flue nee of inorga nic ferti lizers' app licatio n and organic waste materials, which contributed to a serious damaging effect on biodiversity. However, CSA emphasizes the viable use of the environme nt particularly ecosystems and forest as an essential sink for carbon fluxes on surfuce of the earth. There is also emphas is on the ro le that tree planting can play in mitigat ing mechanism on climate change 22 through carbon sequestration processes. Nigerian farmers :face myrids of challenges ocassioned by vagaries of climate change among other :factors. A substantial literature provides empirical evidence on climate-induced choices among crop types and livestock selection with their decision to irrigate under variable climate conditions in difrerent parts of the work:l (Seo and Mendelsohn 2008). They consider the application of various combinations of CSAP . One salient area fur research on teclmobgy adoption that has not been very thoroughly studied is that of rrrultip le teclmology adoption (Teklewok:l et al , 2013). Farmers may :face teclmology alternatives that can be adopted either as substitutes or in combination as complements or supplements to deal with their overlapping constraints such as low soil fertility and moisture stress (Moyo and Veeman 2004). That is why, fur the purpose of this study, if a respondent used three and more types of CSAP, the researcher classified the :farmers as high-users of CSAP, while the respondent who used two or less types ofCSAP, the researcher classified the farmers as low-users of CSAP. According to Ngigi et al, (2005) and Arslan et al , (2013) in their studies, they consider three CSAP and their first was the adoption of agricultural water management practices. However, while previous studies of choice and impact of adaptation have fucused on either a single practice or a set of practices considered as a single unit (Deressa et al , 2010 and Di Falco et al , 2012). There is a limited infurmation on how adoption of rrrultiple strategies by smallholder farmers responds to climate change or on the synergies between various adaptation practical strategies in improving agricultural productivity and farm income. While individual CSAP provide rrrultiple benefits, there are complementarities and synergies when more than one practice is adopted together. Treating :farmers' adoption choices as bundle of practices , rather than as isolated decisions, is important in order to better understand the synergistic effect of inter-related practices. Furtherroore, a joint analysis may still be needed fur determining the total effect of the simultaneous application of the practices (Teklewok:l et al , 2013). This will enable policy makers and development practitioners to promote combinations of practices that perfurm well together. This however, has imposed greater heights in agricultural production and application of CSAP in devebped work:l to proffered solutions, to :farmer' s problem in the area of use of improved drought tolerant seeds, and effectually, there has been a significant increase in the number of :farmers using irrigation; CSA has enabled expansion in the use of agro-furestry and conservation agricultural techniques. By the same token, it has activated the promotion and the reclamation of 23 degraded agro-pastoral land, livestock and other high potential value chain while improving smallho Ide r' s access to market. In addition, CSA bas the potential to reduce hunger, increase agricultural productivity, improve rural people ' s lives and conserve biodiversity using an irmovative whole landscape in the area of study. Considering the afuresaid, this study fucuses on the e:flect of CSAP on the poverty state of farmers . Following this, six major types of CSAP were studied, and they are: the practice of using organic manure, agro-furestry, conservation agriculture , integrated crops and livestock management, the use of improved varieties/hybrid of crops/animals and the use of irrigation fur smallholder farmers . A brief discussion on each is taken in turn: 2.3.1 Use of organic manure Organic manure is essentially livestock waste consisting of organic materials from residues of plants that were digested by animals housed in an enclosure. In addition, inorganic fertilisers require energy and thus increase greenhouse gas emissions. Organic manure is relatively less harmful and expensive compared to inorganic fertilisers. It also stimulates activities of micro - organisms that release nutrients (F AO/WFP, 2007). Though there are some sorts of CSAP such as the use of organic manure, however such practices are not fully mechanised and eflicient due to several reasons aroongst which is the lack of adequate finance . Manure from cow dimg under standard mid-hill funning condition produces 1,825 kg fresh dimg and 1,460 litres urine per annum (Subedi et al , 1993). The bulk of nitrogen of about 97% that is consumed by livestock is defecated in the furm of organic nitrogen in urine and faeces (Mc-Crory and Hobbs, 2001). In agricuhural schemes, some types of microorganisms can carry out Biological Nitrogen Fixation (BNF) as free- living organisms, while other micro-organisms can only fix nitrogen through a shared relations hip with plants, mainly leguminous plant species. On the other band, some elements including adverse temperature and droughts that affect plants (BNF) cannot be controlled. Also, some legume species are greater at fixing nitrogen than others. In recurrent temperate, furage legumes and Lucernecan fix 200 - 400 kg of nitrogen per hectare (F AO, 2009). Organic resource management practices as part of Integrated Soil Fertility Management (ISFM) o:fler vital benefits fur the water use efliciency of crops by improving water holding and decreasing vaporisation (Bationo, 1996). Agricuhural activities are sources and sink fur GHG emission notably through the storage of carbon in the top soil organic matter and in biomass. Regarding the sink side of emissions, agriculture and furestry, unlike other economic sectors, have the capacity to fix atmJspheric carbon by photosynthesis and to sequestrate it in the soil and in biomass. Grassland, humid zones and furests in particular can fix carbon in large quantities. However, these carbon 24 stocks can also be bst. For instance, through land use change either defurestation, pbugb.ing of grassland, drainage of humid z.ones or by exceptional climatic events either through storms or fire leading to a rapid release of the stocked carbon to the at:rmsphere as carbon dioxide (CO2) (Regmi et al, 2009). Biomass produced in agriculture and furestry is used as energy or as raw material which is another way to increase the bio-sequestration of carbon. The nitrogen controlling approaches have to be reassessed in light of the advancement of organic fertilisers which are exceptionally energy demanding fur their synthesis and contribute to 12% of the emissions , because it bas been a systematically established fuct that most crops prefer the N-amroonium furm present in manure and biogas digestive instead of the N-nitrate furm (inorganic fertiliz.er) which is highly disposed to be percolated to the water table (Allen et al, 1998). Organic manure increases soil organic carbon base and also increases the soil organic matter via carbon dioxide (CO2) in bng term by absorption, soil quality and water quality. Practices used to spread organic manure or biogas digestive on the field or in meadow land have extreme influence on GHG emissions. Ideally organic fertilisers should be spread in a liquid furm to penetrate rapidly into the soil or if solid, should be rapidly incorporated (PAO, 2010). The advantages of using organic manure as CSAP comprise addition of nutrients to the top soil and the sequestration of carbon dioxide, thus reducing its adverse efl:ects on gbbal warming through the CSAP. 2.3.2 Agroforestry This is a type of land used fur management system, in which trees or shrubs are grown-up around or within crops or pasture land. It is also the practice that involves the growing of perennial trees with arable crops, perennials with livestock or a combination of woody perennials with arable crops and livestock. In summary, agro-furestry is an incorporated land use system merging trees and shrubs with crops and livestock. Like conservation agriculture, it is an old land use system that furmers have practised traditionally, as a necessity, fur increasing soil fertility by relying on standing vegetation, mostly trees, through the slash and burn shifting cultivation method in most African countries (World Agrofurestry Centre, 2013). Agriculture and furestry expertise combine to create more dynamic, wealthy and susta:inab le land use system The most comroon types of agrofurestry are: agro-sylvicultural (trees with crops) and sylvo- pastoral (trees with pasture and livestock) (Manyats~ 1999). According to Sileshi et al , (2008), agro-furestry schemes in Africa have increased maize yiekls by 1.3 and 1.6 tons per hectare per year. Agroforestry epitomises a price e:frective and viable supplement fur the use of inorganic chemical fertilisers (Ajayi et al , 2008). However, the leguminous agrofurestry leaves are usually 25 used as a result of their capability to atmospheric nitrogen :fixation in the soil in a furm available fur plants use. Ahhough agrofurestry goes some way towards di:f:Iering deforestation, estimated in Malawi to occur at a rate of 1.0 to 2.6% annually, but it also offers potential fuod security aids (F AO, 2005). Agroforestry system5 for furage are also lucrative in developed countries. For example, in the northern agricultural region of Western Australia, using tagasaste to improved earnings to rural furmers whose livestock formally furaged on yearly legume grasses (Abadi et al , 2003). However, the use of trees and shrubs in agricultural system5 help to tussle the triple challenge of securing food security and decreasing the vulnerability and increasing the adaptability of agricultural system5 to climate variation. Agroforestry plants help in the carbon sequestration, a process of absorbing carbon dioxide (CO2) into the soil by trees, other plants and crops, later after decomposition, releases oxygen (0 2) into the atmosphere through the process of photosynthesis thereby, reducing the adverse effect of climate change and global warming. 2.3.3 Conservation agriculture Conservation agriculture is a climate resilient, environmentally sustainable and profitable :furmmg technology. This allows nature to regenerate and retain soil structure thus improving water and nutrient availability to plants and reducing soil erosion. Additional benefits include : reduction in the cost of labour and machinery use, reduction in the need for utilising agro-chemicals, reduction in soil compaction, improvement in the yiekls of crop, as well as improvement in timing of planting (Workl Bank, 2012 and PAO, 2013). The system involves dry-season on land preparation using minimum tillage methods, using fixed planting stations; preservation of crop residue from the previous harvest in the field or use of other nrulches covers; and rotation of crops in the field. Over 180,000 furmers used this scheme at the end of 2010, and this figure was projected to rise to 250,000 furmers by 2011 representing 30% of the populace of small-scale furmers in .zambia. These practices have been fuund to be rugb.ly profitable and not only because of their effect on soil health but by eliminating the need fur labourious land preparation (FAO, 2007). In conservation agriculture, the organic matter in the top 30cm of the earth's soil comprises virtually as much carbon as there is in the whole atmosphere. When poorly managed, soils not only provide meagre crop yields, they also discharge significant quantity of carbon (Fowler et al , 2001). However, furming also bas the prospective to sequester vast quantities of carbon through practices such as conservation agriculture, which improves the nutrients and water-holding capability of the topsoil. The agricultural conservation also increases water use efficiency, reduces land and water pollution 26 and leads to reduction in emission of greenhouse gases (Dumanski et al , 2006). The reduction of the greenhouse gas emissions was achieved through the process of CSAP. 2.3.4 The use of improved varieties and hybrid of crops/animals The improved crop seed variety and animal hybrid which are drought and disease resistant were as a result ofcrop tecbnologis ts and animal breeders who used biotechnology to produce improved seeds and animals, making breeding easier and it produces additional fuod fur the large populace. The livestock breeders can expand the breeding mechanism through the processes like genetic engineering, cloning and artificial insemination. Animals are part of Mother Nature, and hence native breeds of furm animals are often kept in natural parks because several of the world 's native livestock breeds are in jeopardy of massive reduction as cormnercial breeds take over, according to a workiwide record of animal diversity (Mason, 1982). Cultivar testing is an important means of improving crop varieties. A wide series of trials are taking place on sites all over the worki , addressing subjects such as drought tolerance, heat stress and soil management. Scientists used 250 open Agricultural Trial Datasets to construct crop rmdels specific to the West African Region. The rmdels are used to project the local effects of climate change, and describe breeding programmes fur adaptation (CGIAR, CCAFS, 2015). Growing pulses sideways with cereals leads to nutrient reutilizing. Legumes are productive leaf shedders, and act as a natural manure to preserve soil fertility and sustain soil rmisture (Tiwari, 2001 ). Exchanging livestock species that are rmre adapted to water scarcity and resilient to disease can include buying or breeding such animals or even changing the type or species of animals produced. For example, Zebu cattle and small ruminants are rmre tolerant of water scarcity (Thornton and Herrero, 2010). Livestock are responsible for methane and nitrous oxide discharges from ruminant digestion and manure management and is the largest workiwide source of methane secretions. However, the carbon foot print of livestock varies considerably armng production organisations , regions and comrrodities, mainly due to variations in the worth of feed, the feed conversion proficiencies of difrerent animal species and influences on deforestation and land degradation. The animal breeders scientifically breed animals that will produce less methane emission and increase other elements of organic substances that will accelerate manuring when their waste are decomposed into the soil which help in carbon sequestration, hence decreasing the adverse effect of climate change through the process ofCSAP (FAO, 2001). 27 2.3.5 Integrated crop/ livestock management The integrated funning scheme comprises ofa range ofresource, valid practices that target suitable profits and sustainable production levels, while curtailing the negative impacts of climate change through agricultural activities and stabilising the at:rrosphere. As a principle of improving natural biological processes above and below the grotrnd, the integrated system signify a wirming mixture that dimirtish erosion, improves crop yiekls, soil biological activity and nutrient recycling, intensifies land use, increases profit and can therefore help reduce poverty and support ecological sustainability (Delgado et al , 1999). The main dairy animals are goats, sheep and cattle; each of these has a habitation in mixed crop-livestock schemes and they share a gastrointestinal system that allows them to consume coarse feeds like hays, pastures and tree leaves. Vegetables can be watered from the :fish ponds and their remains can be used for feeding livestock (Chen, 1996). Practical inventions have connected collaborations between livestock, crops and agroforestry production to improve economic and ecological sustainability of agricultural schemes and at the same time off.er a flow of valued ecosystem services. Through improved biological variety, proficient nutrient recycling, enhanced soil health and forest preservation and contribute to climate change adaptation and mitigation They also heighten livelihood divergence and knowledge by enhancing production inputs, including labour. In this method, incorporated systems also increase producers ' resilience to economic strains (F AO, 2011). Livestock nutrition, strengthening through agroforestry, the ruminant diets that are higher in quality result in reduced methane output per unit of milk and meat as flourishing as in higher meat and milk production 2.3.6 Inigation for small-holder farmers Irrigation is the artificial application of water to land with growing crops. Irrigation can be carried out using power rotary sprinklers, spray lines or by channelling water along trndergrotrnd pipes or small irrigation canals from reservoirs or river. Irrigation water can be roore successfully used than the corresponding aroount of rainfa ll because a regular supply is confirmed. The benefits of integrated irrigation include increase of crop yiekls, improved water management, heightened synergies, increased income, rooderate environmental friend ly and poverty reduction The main factor preventing :farmers from irrigating their crops was the lack of access to water and this was followed by high cost of irrigation The other :factors were lack of funding for developing irrigation fur small scale funning, to secure land tenure to warrant investment and soil's not being suitable for irrigation (F AO, 2007). Irrigation request in South Africa is expected to increase over the next few eras WJ.der constrained emissions set-ups, as a result of increase in temperature and 28 vaporisation Under a warmer and damper climate situation, a modest increase of 4 - 6% in inigation demand is proposed, with restricted implications fur the agricultural sector. For catchments along the eastern coastline, an increase in evaporation is projected to be oflset by the increase in rainfall, with no change in inigation demand. Increases of about 15 - 30% in inigatio n demand are possible under a hotter and drier scenario presenting substantial risk fur the sector and with important cross-sectorial consequences due to the rugh proportion of sur:fuce water consigned to agriculture in South Africa (FAO, 2013). Appropriate and well-timed water application method, such as drip inigation, bas been acknowledged as one of the paramotmt approaches to increase water use proficiency. It requires less water than sprinkling. Kbanal et al , (2013) reported that drip inigation, as likened to conventional inigation, resuhed in 31 % water saving in cauliflower and improved cauliflower production/area by 46% with the same source of water. Moreover, average production/plant increased by 14%, it reduced inigation labour expenses by 40% and that for weeding and earthling-up by 33%. Ngigi, (2009) also revealed that shifting from sur:fuce inigation to drip inigation enhanced inigab le area by 200%, effective inigatio n proficiency by between 25 and 75% using the same capacity of water. Regmi et al , (2009) also recotmted that the local societies in Kaski district of Nepal found drip inigation to protect water, enhance yields and decrease losses during droughts, thereby battling water shortage. Similarly, sprinkling on coffee plants encouraged flowering when the rain failed in the flowering season, and sprinkling warm water on plants in coffee nurseries maintained and reduced warmth which as a consequence reduced the adverse effect of climate change through this CSAP. However, despite all the indicators of CSAP in the study area, our presun:iption was that, the poverty rate will be reduced drastically or eliminated but that was not the case hence, the need to know why poverty keeps on persisting in North-West Nigeria. 2.4 The Concepts and Nature of Poverty in Nigeria The concept of poverty does not offer itself to an easy and exact definition Any logical investigation of the concept of poverty is fraught with a number of complications. This is because poverty touches various parts of the lnnnan state of living; comprising psychological, physical and moral life that a concise and generally accepted definition is obscure (Blackwood and Lynch, 1994). A concise and tmiversally accepted explanation of poverty is erosive basically because it affects many phases of the human conditions, inchrling physicai moral and psychologically. However, different benchmarks have been used to conceptualize poverty in various places. Most analyses fullow the conventional view of poverty as a resuh of inadequate income fur securing 29 basic goods and services. Some persons view poverty, in part, as a function of education, health, lifu expectancy and child mortality. Blackwood and Lynch (1994), identify the poor, using the standards of the levels of consumption and expenditure. Furthermore, Sen (1983), relates poverty to entitlements which are taken to be the various parcels of goods and services over which one bas command, taking into perception, the means by which such goods are acquired (fur example , m:mey and coupons) and the availability of the needed goods. Hitherto, other experts see poverty in very broad term5, such as being incapable to meet ' 'basic needs" (physica~ (food, health care, education, shelter and non-physical; par-ticipation and identity) requirements fur a meaningful life (World Bank, 1996). The UNDP, (2006) proposes a multidimensional poverty concept that is causally associated to command over economic resources. They contends fur an income poverty line that reflects the cost of realizing basic human need. Poverty is a muhifuceted concept, which could be broadly categorized into two groups; absorute and relative poverty. Absorute poverty otherwise referred to as extreme poverty refers to the complete lack of the means necessary to meet basic personal needs such as fuod, clothing and shelter. The determination of those who are absorutely poor is given by an income threshold; below which one is considered poor. Universally, the most corrnnonly used are $1 , $1.25 or $1.9 per day. However, most coillltr:ies/economies have locally generated thresholds that suit their economies. Relative poverty is perceived more as socially defined and dependent on social context, which is subjective to the society/country one lives in. This occtrrS when a person in a society/country does not enjoy certain minimum basic necessities as will the rest of the population Relative poverty is largely regarded as a measure of inequality in a cmmtry like Nigeria. The living standards of individuals is generally measured using current consumer spending or income. A measure of current consumer spending is mostly preferred to income as a measure of current living standards fur two reasons. First, current consumption is often taken to be a better indicator than current income because immediate utility depends directly on consumption, not on income per-se. Second, current consumption may also be a decent indicator of long-term average well-being, as it will expose infurmation about incomes at other dates, in the past and future. This is because incomes (including those of the poor) frequently vary over time in fairly predictable ways mainly in agrarian economies such as Nigeria. Aklerman and Paxson (1992). A search of the appropriate literature shows that there is no universal agreement on any meaningful explanation of poverty by individuals or households. This is because of its muhidimensio nal 30 nature. While an economist would approach the subject from the view point of want, needs and actual demand, the psychologjst may look at it from the view point of deprivation, appreciation and personality. But from whatever viewpoint it is perceived, it is obvious that, it is not a desirable situation of l:i:fu. Streeten, (1979); Ogwurrnke, (1987) and others have defined poverty in very broad terms such as not being able to meet basic needs particularly the physical needs such as food, clothing, health care, education, housing, electricity and transportation for meaningful life. Amagh:ionyeodiwe and Osinub~ (2004) argued that poverty is not only a term that is connnonly used by the generality of the people but also one that has no particular content as concept. Besides, it is a multi-dimensional and cultural state that excels economic explanation and investigation. Ftuthermore, poverty is both existing and relative. For any particular society, poverty and the poor are very existing phenomena and can be easily recognised, that is why poverty is easily recognised than defined (Foster et al , 2010) yet it is also relative. The population that may be categorised as poor in a developed economy would be regarded as materially well off in less developed countries. In spite of the complications, poverty is hereby defined in terms of inadequacy of disposable possessions to support a minor standard of decent living. Ravallion, (1991) nevertheless, upholds that though poverty is a multifaceted concept, its features of poor dietary status and incapability to work are adequately well correlated with income and consumption expenditure to allow us focus on these two variables. Ajakaiye and Adeyeye, (2001) conceptualised poverty as a function of food, education, health care :facilities and other demographic variables. Olaitan et al, (2000) defined poverty as the inadequacy ofhrnnan basic needs such as food, shelter, clothing and the incapability of an individual or society to obtain human basic needs for existence. He fi.nther asserted that poverty could affect individuals or a group or community or nation Individual poverty is one that results from individ ua 1 misfortunes or inability to obtain the basic requirements of lire expectancy like essential clothing, three meals a day that are nutritious; habitable house and a means of transportation either by land , sea or air (Olaitan et al , 2004). But Anyanwu, (1997) defined poverty as dearth of the means of meeting the basic and customary needs of individuals or households which include good shelter, food, clothing, security, health care, education and freedom to participate in social activities and this group of people are the most exploited economically and manipulated politically. Likewise, poverty has been classified by some economists as chronic or transitory (Mckay and Lawson, 2002; Oduro and Aryee, 2003). The dissimilarity between the two depends on time frame if the household is poor for the entire reference period; it is referred to as chronic. It is transitory if households move in and out of poverty throughout the reference period. And he fi.nther stated 31 that in the early times, poor people were regarded as those who coukl not go fur weddings, manage large funilies and own domestic animals bke goats, cattle and poultry. Olaitan et al , (2000) also claimed that these are characteristics of affluence on which individuals idolised as wealth. But nowadays, rrodem generation identifies these attnbutes to be of short span with bw materials and sustainable value when human needs are in question. For example, if somebody at present marries many wives and gets children without work or habitable houses that connnensurate to the large :families, good education fur the children and important means of transportation, he is still regarded as poor. The ational Bureau of Statistics (NBS, 2007) reported that poverty as futmd in Nigeria is basically a rural social phenomenon, the bulk of those in poverty are inexplicably situated in the rural parts, where they are predominantly involved in agricultural activities and the allied accomplishments. 2.5 The Causes and Characteristics of Poverty Obadan, (1997) acknowledged some elements as the reasons fur poverty. These comprised inadequate access to empbyment prospects, insufficient access to markets, obliteration of natural resources and lack of power to contnbute in designed devebpmental progrannnes, lack of physical assets and lack of willingness to assist those living at the margin. The characteristics of poverty include: (D Increase in households ' size: The choice to have large :family can be a poverty pointer because large househokl size is a trait of bw income per capita in the society. Large number of children :facilitates high rate of dropout and malnutrition, and consequential limited resources (Olaitan, 2004); (ii) Low productivity: This is evident that productivity implies efficiency and where there is scarcity ofcorresponding resources, poverty elements are inevitable (Olaitan, 2000). Still, as stated by Olaitan, (2002), other cases are: lack of appropriate training, poor attitude to work and the use of rudimentary tools. On the other band, the literature is also full of confirmation that large households are connected with poverty (Lanjouw and Ravallion, 1994; Anyanwu, 2012; and Gang, Sen and Yun, 2004). The absence of well-devebped social security systeID5 and bw savings in devebping comrtries tends to increase fertility rates, particularly arrong the poor, in order for the parents to have some economic backing from children when parents reach okl age. This is one of the rationales for parents to increase the number of children so that they will have high probability of getting support when they are okl; (iii) High rate of dependence ratio: The incapability to produce a connnensurate income fur the poor except from the generosity of the wealthy individuals to assist via aids in order to eliminate lack, so that the rich produce rrore fur their sustainability (Olaitan et al , 2000). Livelihood resilience ofhousehokls and comrmmities can also be enhanced through access to diverse sources of fuod, feed and employment during episodes 32 of adverse climatic conditions. Wild plant species in farm;, forests, savannabs and wetlands contribute significantly to the diets of many of the poor in developing cotmtries, and these food sources, particularly the famine foods such as wild greens, tree fruits, and roots, play an important role in supplementing diets during periods of climate induced scarcity Bharucha and Pretty, (2010). Poverty is nrulti-dimensional and even though it is difficult to separate the various dimensions of poverty from the various causes of poverty, in the case of Nigeria, we have shown that tmemployment, corruption, the non-diversification ofNigeria' s economy, inequality, laziness, and a poor education system are some of the key determinants of poverty. These determinants are many times related to each other and also enforce each other. For example, tmemployment, poor education and poverty can be seen as vx;ious cycle. Today, people who are not educated lack the opportunity of being hired for good jobs, and the poor masses today still cannot affurd to go to school which makes them the poor tmemployed masses tomorrow. Hence, all these factors are correlated and must all be tackled together if any progress wants to be made Oluwatayo, (2008). The reduction of poverty rate in Nigeria between year 1985 and 1992 was the product of the Structural Adjustment Programme (SAP) which was introduced in 1986 (World Bank, 1996). Some of Nigeria' s earlier anti-export prejudice in industry vanished, with the manufacturers in agro-processing and fabric industries swapping from imported materials to local inputs. This made the industrial output dropped annually by 4.8% on average from 1981 to 1986 which grew by 5% per year from 1987 to 1992. However, in the same period, production of traditional food crops and cash crops improved and agricultural productivity grew at 3 .5% per year on average, compared with only 0.6% from 1981 to 1986. 2.6 The Consequences of Poverty It has been noted that poverty has a negative effect on the society, and as well as the government, even the nation' s economy. Moreso, Aku et al , (1997) revealed that, there is a universal loss of self.confidence in a society afllicted by poverty and this renders government policies unproductive. Poverty also results in collective tmcertainty and vulnerab ility of members of the society to peripheral impacts. Additionally, poverty makes production remain principally subsistence due to lack of capital required for expansion. Labour becomes exhaustive and margina 1 productivity remains low. Similarly, the implication of poverty on the people cannot be overstated. The vx;ious sequences of poverty stated before, means that lasting handicaps and difficulties are passed on from one generation to another. The detriments of poverty include lack of education, 33 lack of simple hygiene, child labour to help the parents and transmission of infectious diseases . Joblessness and very low earnings produce a situation where parents cannot afford to send their children to school to acquire education. Besides, even those who can really go to school simply do not see how bard work can advance their lives, mainly because they see their parents rail at the task every day. The future of a developed cotmtry for example is the presence of a middle class, but lately we have seen even Western nations gradually losing their middle class, hence the increase number of protests. In the society, poverty is a very hazardous feature that can threaten a whole nation. The Arab dem:mstration is another noble example, in all the nations concerned, the uprisings started because of the lack of employments and high poverty rates among the people, and this bas led to most governments being ousted. In Nigeria, widespread and severe poverty is a reality. It is a reality that depicts a lack of food, clothes, education and other basic amenities. Severely poor people lack the most basic necessities of lire to a degree that it can be wondered how they manage to survive. There are several efrects and deficiencies associated with poverty in Nigeria. One of the main effects of poverty is poor health, as is reflected in Nigeria' s high infunt mortality and low lire expectancy. Poor people in Nigeria race several health issues as they lack basic health amenities and competent medical practitioners. Most children do not have the opportunity of being immunized and this leads to certain physical defects in some of the children. Their health bas become low priority and as they have little or no choices, they live with whatever they are provided with, whether healthy or not Nnand~ (2008). 2.7 The Derivation of Poverty Line and Measurement of Poverty The poverty line can be seen as worth of consumption expenditure or mcome essential fur at least a standard of diet and other requirements. The specification of the poverty threshokl or consumption pattern or norms regarded as basic or minimum for which anybody with income or consumption payments below the line is considered poor while those with income or consumption payments equal to or above it are regarded as non-poor which shows the bench mark to difrerentiate the poor from the non-poor. The poverty line definition is frequently seen as a standard of consumption expenditure or income, which is estimated to epitomize the least, prereqU1Site fur a productive and active lire (Okunmadewa, 1999). Although poverty line can be regarded as the level of individual or househokl income or consumption expenditure, below which 34 one is classified as poor according to government standards , that is the estimated rmmmum level of income needed to secure the necessities of lire. Nevertheless, there is lack of certified poverty line in Nigeria, and as such, previous studies used poverty lines, which were set as magnitudes of the average per capita expenditure (Canagarajah and Thomas, 2001 ). The poverty line that is realised for any poverty study centres on the kind of investigation that the scholar is concerned with. In a measureable analysis, the poverty lines are objective while for qualitative analysis, it is generally subjectively determined. Objective poverty lines are usually of two types. These are relative and absohrte poverty lines. However, some studies are using the relative poverty line technique due to its ease of calculation (Manyong et al, 2009). FOS (1999), used the relative poverty line of 1/3 or 2/3 Mean Per Capita Household Expenditure (MPCHE). The 1/3 MPCHE was referred to as the core poverty lines while the 2/3 MPCHE was the IDJderate poverty line. The National Bureau of Statistics (NBS, 2007) apart from using the 2/3 MPCHE also used the absohrte poverty line required to meet 2,900 calories and a minimum non- food items, US$1.25 per person per day as poverty lines and subjective poverty measure by means of a self.assessment of poverty. When measuring poverty over time, analysts must choose the value of the income elasticity of the poverty line, which essentially determines whether an absolute or relative poverty line is being used and the choice of this parameter is ultimately a value judgement which bas some empirical basis. The absohrte poverty denotes a state in which an individual or household lacks resources crucial for subsistence. It is not only lack of income but also not having access to social services such as electricity, water, health care, education and political discrimination (Suleiman et al , 2013). While relative poverty refers to a state in which individuals or households lack assets in comparison to members of another society because definition of poverty in one place is not the same as in another environment. For example de:finitio n of poverty in Dutsinma town in Katsina State is not the same as in Abuja or Lagos State capitals. That is why poverty line is considerably higher in developed countries than the developing nations (Witt, 2007). Although relative poverty varies with income or economic growth, the poverty line from this approach is connnonly expressed as a fixed percentage of the mean mcome or expenditure (Awoyemi et al , 2011). Poverty line is also known as threshold . The $1.25 international standard of poverty line is difEcult to set as connnon international poverty line since different countries have d:ifl.erent poverty lines. 35 Furthermore, some devebping colllltries are blessed with enormous physicai natural resource and human endowments yet the majority of its people live below both the absolute and relative poverty lines. The national survey pibted between 2003 and 2004 indicated that slightly above half of the population in Nigeria 52% live bebw US$1 dollar/day and the relative national poverty incidence was fulllld to be 54.4% National Bureau of Statistics (NBS, 2005). According to 2004 Human Development report, the percentage of Nigerians living bebw the poverty line of one dollar/day has increased radically during the last two decades. The report ranks Nigeria mnnber 151 and places the colllltry among the 26 poorest colllltries in the world. In the year 2000, the statistic connotes that more than 70% of Nigerian' s were projected to be living bebw the internationally defined poverty line of one dollar/day (Workl Bank, 1995). This view was sustained by other studies like Sorudo, (2006). (NBS, 2005) also set a poverty line at deflated dollar/day. This was conducted using a :familiar measure of the 2002 World Bank Purchasing Power Parity (PPP) for 2003 PPP with inflation rates and exchange rate changes. Typically, the per capita expenditure of each household was transformed into an index ranging from greater than zero to less than one using the standard of normal distribution in statistics. Thereafter, the mean of the Slllil of the probable index was taken to be the vulnerability index (Oyekale and Oyekale, 2010). Abt of models have been designed to measure poverty. These include the Sen Index (Sen, 1976), Pa weighed poverty extent (Foster et al , 1984) the Physical Quality of Life Index (PQLI), (Morris , 1979), Human Devebpment Index (HDI), and Relative Welfare Index (IFAD, 1993). However, of all these, the most obviously used in poverty assessment is the Foster, Greer and Thorbecke (FGT) Pa weighted poverty extent that is based on income or consumption expenditure of the household (Aigbe and Isiorhovoja, 2013). Ravallion, (1996) in support of the income or expenditure method to poverty measurement upholds that though poverty is a multi-faceted concept, its characteristics such as poor nutrition, lack of physical assets and inability to work are sufficiently correlated with income and consumption expenditure to albw us to fucus on these two variables. Foster, Greer and Thorbecke offered the weighted measure of poverty in 1984. It is a class of parametric poverty measure that satisfies Sen' s axioms and which incrude fundamentals that are sensitive to changes in disparity among the poor, changes in income deficiency and changes in the mnnber of the poor. Having established the poverty line, there is the need to carry out poverty analysis decomposed into various indices. According to Foster et al , (1984) and Ravallion, (1996) the most frequently used measurements are poverty headcount, poverty gap and poverty severity. 36 Pa, _N1 ~q (Z-Yi)a L.i=1 -z- ..... . ......... ... ... .. ... . .... ... ..... .. ... ... (2.6) Where: Pa= Foster, Greer and Thorbecke index (O0 ................ . ..... . ................. . . .. ..... (3 .8) EDE FGT is the Equally Distributed Equivalent form ofFGT. EDE FGT applies only when a =1 and a = 2. This means that it does not measure head count as is the case with the FGT index. According to Levine et al, (2012) the multidimensional index by Alkire and Foster is made up of two components : the poverty headcount, 'H', and an adjustment measure, 'A' that represents the number of deprivations suflered, on average, by the poor. MPI = H x A .............. . ... . .......... . . .. .. . ... . .............. . . . .............. .. . ... .. . . ... (3 .9) Therefore : H = 9_ ... .... ... ....... .. .. .. .. .... ....... .. ........ ... .. ..... ... .... ... .. ... ... ........ (3 .10) n Where : q = The number of respondents below the poverty line, n = The total number of respondents sampled. 3.7.4 Model Specification for Objective Four and Five The Instrumental Variable (IV) - probit regression rmdel was used to ascertain the fourth objective which is to examine the effect ofCSAP on poverty status of the rural farmers. The PCA was used to develop composite indices for the practice of CSA which includes : the use of organic manure, agro-forestry, conservation agriculture, integrated crops and livestock management, the use of improved varieties/hybrid of crops/animals and the use of irrigation for smallholder farmers. The Probit :fimction follows a normal distribution Following the assumptions ofthe normal distribution where Zi is the standard normal variable, P1 is the intercept and P2 the coefficient of x, while 'x' represents explanatory variables and Z~N (0,82), Lis the mobserved index also known as the latent variable which is determined by the stated explanatory variables (such that, the larger value of L, greater the probability of being poor) and 'F ' is the standard cumulative distribution frontier, which could be written as; 56 1 I; _,.2 1 ~, +~2X; _,.2 2 F(I;)= ✓2flle dz= ✓2TT l e2 dz ... ...... .. .... ........... .......... ..... ......... ......... .... ..... (3.11) Then to be able to examine the effects of CSA on the probability that a household JS poor, we estimate. : . = f (Bi+ B2X ;) B2· ········· ·· ··· ························· ······· ······ ················ ·· ································ ····· (3 .12) I dP; = Di:frerential for the dependent variable dX = Di:frerential for the explanatory variable f = Standard ClDlllllative distribution frontier /31 = Intercept /32 = Coefficient Xi= Explanatory variable Extending the IlX)del and including other socioeconomic variables: use of CSA dummy (ucsad) , age, (age), dummy for sex (dsex), dummy for education (deduc), dummy for marital status (dmats) , households size (hbsiz.e), furmsize (fsiz.e), experience (exp), dummy for ownership of :farmland (downfI), dummy for land acquisition (dlaq), dummy for type of labour (tlab), dummy for membership of association (dmas), dummy for transportation (dtrnsp), dummy for housing materials (d bmtrQ, dummy for cotmll.llllCa tio n ( dcoioctn), dummy for access to credit (dace) and dummy for States (dstate). Then the instrumental variables include lack of access to CSA, sources and use of water from borehole for drinking and quantity of millet per yield. The Instrumental variable IlX)del was therefore estimated based on the following equations. For each of the crops and livestock climate smart agricultural generated indexes under consideration. All five dimensions were regressed based on the specification as follows: .... .... .. . ....... .... . (3.13) ucsad = a 1lacsapi + a2suwbdi + a3qtymilpyieldi + µi .. .... ......................... .( 3.14) Where povi represents IlX)netary poverty wherein 1 is assigned to respondents with IlX)nthly income less than US$1.9 and O otherwise. The use of CSA dummy is regressed independently m 57 six estimations fur the production of mll7.e, mille t, sorghum, groundnut, livestock and crop/lives tock. Table 3.3: A Priori Expectation for Binary Regression Model Based on the Effect of CSA on Poverty Status Variables Category Coding System Expected References Sign CSA Indices dmnmy 1 if high users, 0 -/+ James et al , otherwise (2013) Age continuous Number in years -/+ Sokoya, (2006) Sex dummy 1 if mile, 0 if not. -/+ Nil Educational level dmnmy 1 educated in Arabic, 0 if + Prasad et al , not. (2006) Marital status dmnmy 1 if mimed, 0 if not. +/- Aliyu et al., (2016) Househoki size continuous Member of househokis +/- Mathonze, (2000) Farm size continuous Number in hectares + Sadiq et al., (2015) Experience continuous Number in years +/- Asogwa et al , (2012) Ownership of farm dmnmy 1 if yes, 0 if not. -/+ Nil land Land acquisition dmnmy 1 if inherited, 0 if not. + Nil Type of labour dmnmy 1 if both, 0 if not. +/- Nil Membership of dmnmy 1 if yes, 0 if not. + Agbamu, association (2006) Transportation dmnmy 1 if animils, 0 if not +- Nil Housing rmterials dmnmy 1 if concrete/block ZlllC , +- Devendra and 0 if not Chantalakhan a, (2002). Communication kits dmnmy 1 if GSM, 0 if not +- Johnson, (1999) Access to credit dmnmy 1 if access, 0 if not. + Nil States dummy 1 if Katsina, 0 if Sokoto +/- NBS, (2013) 58 3.7.4.1 The Global Multidimensional Poverty Index In addition, Irnltidimensional poverty assessments aim to measure the non-income based dimensions of poverty, to provide a more comprehensive assessment of the extent of poverty and deprivation The Global Multidimensional Poverty Index (MPI) was developed in 2010 by the Oxford Poverty and Human Development Initiative (OPHI), and the United Nations Development Programme and it uses different :factors to determine poverty beyond income-based lists. It replaced the previous Human Poverty Index. OPHI descnbes the MPI as a high resolution lens on poverty: it can be used as an analytical tool to identify the mo st prevailing deprivations . To measure acute poverty, the study looked at the proportion of people who experienced multiple deprivations and the intensity of such deprivations. The MPI is a very versatile methodology that can be readily adjusted to incorporate alternative indicators, cut-ofls and weights that might be appropriate in regionai nationai or subnational contexts (Alkire and Santos, 2010). Where MPI represents multidimensional poverty indices wherein 1 is assigned to respondents with monthly income less than US$1.9 and O otherwise. The use of CSA dummy is regressed independently in three estimations for crops, livestock and crops/livestock enterprises. The utilization of multidimensional poverty index of two stage least square (2SLS) to ascertain the effect of CSAP on the multidimensional poverty index of the respondents in the study area. The post-estimation tools for the multi-dimensional poverty index of 2SLS identified the selected instrumental variables such as expenditure on inorganic mamrre, education and State to be strong and not weak which can give an expected good result, to instrument CSA fur crops, livestock and crop/livestock enterprises. CSA for crops, livestock and crop/livestock enterprises are the key independent variables in each of the three scenarios for testing the effect of CSAP on the multidimensional poverty index of the respondents in the study area, while in contrast, the MPI was developed to be the dependent variable in the regression model Additionally, CSA for crops, livestock and crop/livestock as the key independent variables which are quantitative variables that have been achieved with the aid of the PCA. Extending the model and including other socioeconomic variables : use of CSA dummy (ucsad) , age (age), dummy for sex (dsex), dummy for marital status (dmats), religion (reQ, households size (bhsiz.e), :failll5ize (fsiz.e), experience (exp), dummy fur ownership of :farmland (downfl), dummy fur land acquisition (dlaq), dummy for type of labour (tlab), dummy for membership ofassociation (dmas), dummy for transportation (dtrnsp), dummy for housing materials (dhmtrQ, dummy for cormnunication (d comctn), number of extension contacts (nextc) and dummy for access to credit 59 (dace). Then the instrumental variables inch.Jde State, education, and expenditure on inorganic manure. The Instnnnental variable model was therefore estimated based on the following equations. For each of the crops and livestock climate smart agricuhural generated indexes under consideration. All three dimensions were regressed based on the specification as follows: ··· · · · ..... ... ... .. ........ . (3.15) ucsad = a1statei + a2dmedlli + a 3expinorgnicmi + µi ....................................... . (3 .16) Where mpi represents multidimensional poverty index wherein 1 is assigned to respondents with monthly income less than US$1.9 and O otherwise. The use of CSA dummy is regressed independently in three estimations for the production of crops, livestock and crops/livestock. Table 3.4: A Priori Expectation for MPI Model Based on the Effect of CSA for Crops/lives tock Enterprises Variables Coding System Category Expected References Sign CSA Indices 1 if high users, 0 dummy -/+ otherwise Age Number in years continuous -/+ Rogers, (2003) Sex 1 if male, 0 if not. dummy -/+ Nil Marital status 1 if married, 0 if not. dummy +/- Nil Religion 1 if Muslim, 0 if no dummy +/- Apata and Awe, (2014) Househokl size Number of siblings dummy +/- Nil Farm siz.e Number in hectares continuous + Nil Experience Number in years continuous +/- Nil Ownership of furm land 1 if yes, 0 if not. dummy -/+ Ez,e et al , (2011) Land acquisition 1 if inherited, 0 if not. dummy + Nil Type of labour 1 if both, 0 if not. dummy +/- Nil Membership of association 1 if yes, 0 if not. dummy + Franklin and Patience, (2014) Transportation 1 if animals, 0 if not dummy +- Nil Housing materials 1 if concrete/block dummy +- Jadhav et al , zinc, 0 if not (2011) Con:m:nmication kits 1 if GSM, 0 if not dummy +- Workl Bank, (2006) Extension contacts Number of contacts continuous - Nil Access to credit 1 if access, 0 if not. dummy + Nil 60 The Ins1nnnental variables of two stage least square (2SLS) estimation is used when the model has an endogenous variables (Y = ~o + ~1X + u). The instrumental variable can be used to address the problem of omitted variable bias. In order for a variable z, to serve as a valid instrument for x, the following must be true: The instrument must be exogenous, that is cov (z, u) = 0 and the instrument must be correlated with the endogenous variable x, that is cov (z, x) I- 0 The general concept is that of the instrumental variables estimator, a popular form of that estimator, often used in the context of endogeneity is known as two stage least square (2SLS). A weak correlation between x and z will bring a sizable bias in the estimator. If there is any correlation between z and u, a weak correlation between x and z will render instrumental variable estimates inconsistent. Although, the researcher catm0t observe the correlation between z and u, we can empirically evaluate the correlation between the explanatory variable and its instrument. It shoukl also be noted that an R2 measure in the context of the instrumental variable estimator is not the percentage of variation explained measure that we are fumiliar within OLS term5. 3.8 Validity and Reliability The race validity of the questiormaire was done by a panel of specialists in Agricultural Economics and Extension in order for them to judge the extent to which the instrument measures the highlighted issues regarding CSA and poverty, coupled with the rate at which they conveyed the intended meaning to the rarming households. To ensure the reliability of the questionnaire, a split half technique was used to determine the reliability of the instrument. A rngh reliability coefficient ofr = 0.83 was derived which showed the instrument was consistent and rnghly reliable. 3.9 Ethical Considerations 0 bservations and :face-to- race interviews were used to collect data for the study. Interviewees were fully informed of the aims and objectives of the study. Participation was vohmtary and interviews were only conducted after oral consent had been obtained not by coercion. Households were approached to obtain consent to carry out research on their premises. All documents and information that were provided by the respondents were kept confidentiai and only used for the pUipose of this study. As was the case for other vulnerable population groups, there was a need for increased gwdance for the ethical conduct of research. This will not only have implications for the people that are being studied, but will also affect the quality of the resuhs being obtained. 61 3.10 Limitations of the Study 1. There was a deficiency and lack of proper record keeping by the househok:l farmers and as result of this, it was a serious challenge in the course of this research as most of the respondents rely solely on memory recall, hence to minimise error, information on relevant variables were strictly used in this study. 2. The issue of general ability and capacity of the research work, large population randomly sampled size was used, and therefore making the data representative and sufficiently robust to all rural househok:ls in the villages, Districts and bcal government level This aimed at drawing a general conclusion about the whole processes in the study area. 3. The problems of finance, ethics and time frame were all senous constraints to this research. However, the Author received assistance from North West University, my place of duty. More energy and time were devoted to data collection than the budgeted period in the research time frame. 4. Finally, as a result ofall the above challenges and limitations, the output of the research work is worthwhile and greatly reliable. 3.11 CONCLUSION This chapter bas presented an in-depth explication on the research methodobgy, which includes the data collection procedure and design adopted for this study. Data was collected from the households in the study area, North-West Nigeria. The sampled primary data significantly enabled the development of the appropriate models for CSAP and the poverty status of farmers in the study area. A questionnaire was designed as a tool for data collection. The collected data was coded and entered into Microsoft excel and later transferred to SPSS and Stata for proper analysis through the use of Stata command. 62 CHAPTER FOUR RESULTS AND DISCUSSIONS 4.0 INTRODUCTION Tiris chapter presents the results of the analysed data. The chapter covers information on socioeconomic and demographic characteristics, as well as sources/access to water, sources of credit and constraints to climate smart agricultural activities. In addition, the chapter highlights the nature of the utilised sampled data, besides providing and outlining the rationale for the behaviour of some variables as discussed in the interpretation of subsequent results . 4.1 Socioeconomic Characteristics of Respondents in Katsina and Sokoto States Tiris section is dedicated to the examination of the socioeconomic characteristics of respondents. Summary statistics such as means, percentages, t-tests and chi-square tests were empbyed to fully understand the socio-economic characteristics of the farmers. 4.1.1 Respondents' Distribution According to Quantitative Variables Employed Table 4.1 shows the analysis of socio-economic characteristics of the actual variables in relation to :farmers who practice CSA in the study area. Considering the actual (continuous) variables for Katsina State, Sokoto State and the pooled data, the high-users of CSA were more in Katsina State than Sokoto State while the bw-users of CSA were more in Sokoto State than Katsina State. Nevertheless, there was no significant diflerence between high-users and bw-users across household sizes in Sokoto State and for the whole samples. Other variables were not significantly diflerent between high-users and low-users of CSA as shown by the t-test table. Furthermore, the mean average age of high-users of CSA in Katsina State was 54.6 years old but that of Sokoto State was 51. 7 years. Tiris shows that more experienced :farmers are into CSAP in Katsina State than Sokoto State which bad lower average age. These results aligned with that of experience variable wherein, average mean for high-users of CSAP in Katsina State (27.44 years) was higher than that of Sokoto State which was (24.64) years. The summary of the statistics revealed that the average mean age was 53 years for both high-users and low-users of CSAP in the study area. Additionally, in terms ofexpenditure, high-users of CSA bad consistently higher expenditure amoUI1ts for both Katsina and Sokoto States. The survey suggested that, high-users of CSA spent much more than bw-users of CSA. Tiris result apparently supports the :findings ofFanen and Adekola (2014) that, there was a wide outcome disparity among the participants of the climate smart agricultural practitioners in Nigeria. Tiris might be due to the :fact that the operational level of each :farmer F 0.0000 R-Squared 0.2119 RootMSE 1.1509 Source: Computer Printout of Regression Analysis ote: *,**and*** means 10%, 5% and 1 % level of significance respectively 83 5.2 Toe Factors Influencing Climate Smart Agricultural Practice for the Sorghum Enterprise Additionally, the Ordinary Least Square (OLS) Regression result of sorghum enterprise in Table 5.3 signified that the F-statistics (0.0442) of the regression model was significant (p<0.05), an affirmation that the model was fitting. The muhiple coefficient of detennination in R-Square of 0.0867 revealed that 8.70% of the variation in sorghum enterprise can be explained by the explanatory variables included in the model The Ordinary Least Square (OLS) estimation results in Table 5.3 disclosed that education, :farm size and housing material were significant (p< 0.05). This implied that respondents who bad informal education bad significantly lower indices of CSA fur sorghum production by (-0.1527) than their counterparts who bad furrnal education. These results was contrary to the priori expectation and accentuated the importance of furrnal education. Correspondingly, 0 kpachu et al , (2014) emphasised that education could have a positive impact on the :farmers if the curriculum is enriched and applicable to their :farming activities. Also, Ayinde et al , (2010) fuund that the educational level of :farmers bad a significant influence on adoption and Oladele, (2006) noted that introduction of improved maize variety is not enough without a suitable complementary training and practices such as planting distance, seed dressing, weed control method and storage teclmiques to aid better perfurmance of agricultural technology. Also, the result shows that a l.Illit increase in :farm size will decrease the corresponding CSAP indices by (-0 .0589) on sorghum enterprises and this was in contrary to the :findings ofN ambuya et al , (2005) who found that adoption of improved varieties is positively correlated to :farm sizes and that most :farms with small land holding capacity were not growing improved varieties. Housing material parameter was significant (p<0.01). This means that the :farmers with mud/thatched and mud/zinc houses bad their indices of CSA being lower by (-0 .5707) when compared with those who owned brick/zinc and concrete block zinc houses. According to Stephen, (2010) on d:i:frerent management practices and approaches that can contnbute to the improvement of productivity and economic sustainability, the quality of housing materials was discovered to promote good environment and enhance productivity of the :farmers. The :farmers in the study area lived in mud/thatched buildings as a result of their poverty leve~ and this bas a consequence on their commitment to CSAP in the study area. 84 Table 5.3 Factors Influencing Indices of Climate Smart Agricultural Techniques on Sorghum Enterprise Sorghum CSA enterprise Coefficient Standard error t-value p-value Age -.0033532 .0128667 -0 .26 0.795 Gender .374837 .3812064 0.98 0.326 Education - .152724 .0776972 -1.97 0.050** Marital -.5625115 .4265004 -1.32 0.188 Religion - .118351 .276596 -0.43 0.669 Households - .0042876 .0282198 -0 .15 0 .879 Farm s:iz.e -.058918 .0238867 -2.47 0.014** Experience .0090153 .0121938 0.74 0.460 Ownership - .1548924 .4692801 -0.33 0 .742 Land acquisition .0329311 .4313515 0.08 0.939 Labour -.1803822 .1917326 -0 .94 0.348 Membership .0960629 .169133 0.57 0 .571 Transportation - .0862034 .2165393 -0.40 0 .691 Housing material -.5706555 .1919158 -2.97 0 .003*** Corrn:rn.mica tio n .0489703 .1536928 0.32 0.750 Extension contact -.0473868 .0567902 -0.83 0.405 Lack of access to credit -.2207313 .1567902 -1.41 0 .159 Lack oftime -.0930056 .1473308 -0.63 0 .528 State .099974 .1601718 0.62 0.533 Expenditure -18600000 581 ,000,000 -0 .32 0.749 Constants 1.244299 .7860683 1.58 0 .115 Number of Obs: 294 F (20, 273) 1.64 Prob> F 0.0442 R-Squared 0.0867 RootMSE 1.1403 Source: Computer Printout of Regression Analysis Note: *,**and *** means 10%, 5% and 1 % leve l of significance respectively 85 5.3 Factors Influencing Climate Smart Agricultural Practice for the Millet Enterprise The Ordinary Least Square (OLS) result in Table 5.4 shows that the F-statistics is significant at p<0.05, hence the model was well fitted. The regression results indicated that five variables were significant. Age and education were significant at p<0.01. It implied that a unit increase in age will lead to a corresponding increase in the indices of CSA in millet enterprise by 0.3282. In the same manner, Mobann:ned, (2008) postulated that age influences farmers ' abilities to practice more agricultural :fanning activities, adding that commercial ability and farm management skills increase with experience; as such a :farmer's ab ility is a :function ofage. Also, the results advocated that those who bad informal education (Arabic education) bad their indices of CSAP on millet enterprise being significantly bwer by (-0 .2862) when compared with their counterparts with furmal education. Ismaila et al , (2010) identified non-:fimctional education among the cereal :farmers in northern Nigeria and suggested that extension education should be participatory fur it to deliver the desired objectives. Religion and housing materials parameters were significant (p<0.10). Muslims bad significantly bwer indices of CSAP on millet enterprise than Christians and Traditionalists by (-0.4846). This is in line with Macaulay's (2014) work, who opined that northern farmers' performance fell below potentials because of their episodic religious and ethics problems. The parameter of :farmers' lack of access to credit was significant (p<0.05). This is an indication that :farmers who lacked access to credit bad their indices of CSAP on millet being significantly higher by 0.0779 when compared with those who did not lack access. Per se, it contrasted with the work of Oni et al , (2009) fur they avowed that credit :facilities contribute to household production effic iency and enhance their farming operations. Nevertheless, there exists a unifurmity of results between this current study and Cresensia et al ' s (2013), because they discovered that credit :facilities produce a multiplier effect fur Tunisian farmers. Housing materials bad significantly bwer indices of CSAP on millet enterprise. This means that :farmers with bbck/zinc housing materials were being signifJCantly bwer by (-0.357765) when compared with those with mud/thatched housing materials. This was in contrary to the apriori expectation that support a good proper housing materials will increase the millet enterprise. And this was in tuned with the :findings of Cbaipa and King, (1998), which they stated that fuod gardening in Zimbabwe is predominantly practiced by house owners with regular other income sources and who are in most cases better off and have better access to resources like storage :facilities. This illustrates how household production is constrained by poor resource base such as housing type. Household production is also limited by lack of appropriate production infrastructure such as proper constructed builling for housing crops and farm animals . 86 Th.is refers to lack of fencing, irrigation system, on funn electricity and lack of storage and related building :facilities. The provision of necessary production infrastructure needs attention. Infrastructure such as fencing, buildings and irrigation systems is needed to :facilitate and improve agricultura l productivity. Table 5.4 Factors Influencing Indices of Climate Smart Agricultural Techniques on Millet Enterprise Millet CSA enterprise Coefficient Standard error t-vahre p-vahie Age .0318158 .0118787 2.68 0.008*** Gender - .3824433 .3884031 -0.98 0.326 Education -.2861645 .1010043 -2.83 0.005*** Marital .550824 .4169381 1.32 0.188 Religion -.48462 .2701536 -1.79 0.074* Househokis -.0306577 .0283781 -1.08 0.281 Fann size .0055162 .0239478 0.23 0.818 Experience - .014989 .011696 -1.28 0.201 Ownership .1757601 .3844383 0.46 0.648 Land acqwsitio n .1692809 .3350572 0.51 0.614 Labour .0257794 .2302996 0.11 0.911 Membership -.0304396 .1652455 -0.18 0.854 Transportation -.4341823 .232163 -1.87 0.911 Housing material - .3942465 .2159231 -1.83 0.069* Communication -.1610436 .1471098 -1.09 0.275 Extension contact - .0217481 .058981 -0.37 0.713 Lack of access to credit .0779438 .1582457 0.49 0.048** Lack of time .2922038 .1575878 1.98 0.691 State .2347578 .1575878 1.49 0.691 Expenditure 2.36-06 5.92e-06 0.40 0.691 Constants -1.001871 .6347732 -1.58 0.116 Number of Obs: 294 F (20, 273) 1.67 Prob> F 0.0374 R-Squared 0.1092 Root MSE 1.1231 Source: Computer Printout of Regression Analysis Note: *,**and*** means 10%, 5% and 1 % level of significance respectively 5.4. Factors Influencing Climate Smart Agricultural Practice for Groundnut Enterprises The Ordinary Least Square (OLS) Regression result of groundnut enterprise in Table 5.5 signified that the F-statistics (0 .0320) of the regression rrodel was significant (p<0.05), an affirmation that the rrodel was fitting. The multiple coefficient of determination in R-Square of 0.1111 revealed 87 that 11 .11 % of the variation in groundnut enterprise can be explained by the explanatory variables included in the Irodel The results also depicted that age was significant (p<0.01). This implied that a tmit increase in age of the furmers will lead to a corresponding increase in the indices of CSA for grm.mdnut farmers by 0.0372. This result maintained a match with that of Zalk:uwi et al , (2013). Their study confirmed that the older furmers were IIDre prompt in their perception and response to risk management than ym.mger furmers. Additionally, the examination deironstrated that education was negatively significant at p<0.05 . Evenly, it pointed to the fuct that those who had infurmal education (Arabic education) had their indices of CSAP in groundnut enterprise being significantly lower by (-0.2102) when compared to their counterparts with formal education. For instance, Ansehn and Taofeeq, (2010) confirmed that groundnut farmers lack requisite education that can make them productive and effect positive changes in their productive activities. Again, farm size was positively significant (p<0.10), meaning, that a tmit increase in furm size will lead to a corresponding increase in the indices of CSAP for grm.mdnut furmers by 0.0487. The result maintained a par with the work of Ani, (2013) who hypothesised that increase in farm size coupled with ecological flexibility, will guarantee productivity in groundnut furming. Table 5.5: Factors Influencing the Indices of Climate Smart Agricultural Techniques on Groundnut Enterprise Groundnut CSA enterprise Coefficient Standard error t-value p-value Age .0372305 .0 123537 3.01 0.003*** Sex -.435532 .4039346 -1.08 0.282 Education - .2101668 .1050433 -2 .00 0.046** Marital .178331 .4336107 0.41 0.681 Religion .2281706 .2809566 0.81 0.640 Households -.0138106 .02951 29 -0.47 0.417 Farm size .0487316 .0249054 1.96 0.051* Experience -.0177124 .0121637 -1.46 0.146 Ownersrup .2138104 .3998113 0.53 0.593 Land acquisition .4268788 .3484556 1.23 0.222 Labour -.1263712 .2395089 -0 .53 0.598 88 Membership .0719808 .1718534 0.42 0.676 Transportation -.2455936 .2414468 -1.02 0.310 Housing material - .3052228 .2245575 -1.36 0.175 Comrmmication -.0748344 .1529925 -0.49 0.625 Extension contact -.0685198 .0613396 -1.12 0.265 Lack of access to credit .0202029 .1645736 0.12 0.902 Lack of time .0570541 .1531026 0.37 0.710 State .1558512 .1638895 0.95 0.432 Expenditure 1.99e-06 6.15e-06 0.32 0.747 Constants -1.393806 .6601566 -2 .11 0.036 Number of Obs: 294 F (20,273) 1.71 Prob> F 0.0320 R-Squared 0.1111 RootMSE 1.1681 Source: Computer Printout of Regression Analysis Note:*,** and*** means 10%, 5% and 1 % level of significance respectively 5.5 Factors Influencing Climate Smart Agricultural Practice for the Livestock Enterprises Table 5.6 shows the mean Variance Inflation Factor (VIF) of 1.10, denoting that that there was no serious multi-collinearity problem among the independent variables. Also, heteroscedasticity was automatically corrected fur, hence robust estimates were used . Table 5.7presents F-statistics value (0.0002), which was significant atp<0.01, and a consequent indication that the model was apropos. The muhiple coefficient of determination R-Square value of0.1286 revealed that 12.86% of the variability in the indices of CSAP was accounted fur by the independent variables. Regression results show that education and communication equipment's were negatively significant (p<0.01). This implies that those who bad informal education (Arabic education) bad their indices of CSAP fur livestock enterprise being significantly lower by (-0 .3359) when compared with their counterparts with furmal education. The resuh was echoed in the work of Ofuoku and Isi:fe, (2009) fur they fuund setbacks in livestock production as a resuh of dominance of infurmal education among the :farmers in the northern part of Nigeria. The study finther suggested that the use of CSA fur livestock by respondents who were cmnmrnicated by handsets 89 was significantly bwer by (-0.4830) than those who were informed by radios, televisions and videos. In the same vein, Adamu and Odion, (2000) observed that lack of communication caused a great bss in animal output. Farm size was positively significant (p<0.05). Thus, this connotes that a unit increase in :farm size will lead to corresponding increase in the indices of CSAP by 0.0664 for livestock :funners. A similar result was registered in Philips, (2010) research, who concluded that livestock production is directly linked with competition of animal for space, natural resources and food and feeding supplement which was a function of :farm size. Extension contact and lack access to high quality hybrid were negatively significant (p<0.05). It denoted that respondents who bad no extension contact, besides experiencing lack of access to high quality hybrid bad significantly lower use of CSA for livestock by (-0.1887) and (-0.4626) respectively. Philip et al, (2009) obtained a similar result and attributed the poor performance to lack of funding in the extension delivery system to the :funners and paucity in coordination of the extension programme in Nigeria. Table 5.6: Multi-Collinearity Test of Variables Variable VIF Tolerance Eigenvalue Education 1.08 0.9260 8.5056 Religion 1.12 0.8950 1.0884 Households 1.17 0.8513 0.9528 Farm size 1.20 0.8339 0.8854 Land acquisition 1.06 0.9430 0.6006 Labour 1.03 0.9693 0.4165 Membership 1.16 0.8632 0.3597 Communication 1.19 0.8377 0.3422 Extension contact 1.02 0.9768 0.2737 Lack of access to credit. 1.04 0.9652 0.2335 Lack of high quality 1.08 0.9228 0.1604 hybrid. Lack of time to practice 1.05 0.9556 0.0971 CSA. Lack of processmg 1.12 0.8902 0.0669 technology 0.0170 Mean VIF 1.10 Source: Computer printout ofMulticollinearity Test 90 Table 5.7 Factors Influencing Indices of Climate Smart Agricultural Techniques on Livestock Enterprise Livestock CSA enterprise Coefficient Robust t-value p-value Standard Error Education -.3358789 .1258248 -2.67 0.008*** Religion .2011503 .3282965 0.61 0.541 Households .0294844 .0303353 0.97 0.332 Fann size .0663634 .0290053 2.29 0.023** Land acquisition - .376809 .3582446 -1.05 0.294 Labour -.2778648 .2885837 -0 .96 0.336 Membership - .1244127 .1798012 -0.69 0.490 Comm.mication -.483034 .1839629 -2.63 0.009*** Extension contact - .1887444 .0741602 -2.55 0.011** Lack of access to credit. -.1012549 .2 1064 -0.48 0.631 Lack of high quality -.4625896 .2115944 -2.19 0.030** hybrid. Lack of time to practice - .2073913 .1784449 -1.16 0.246 CSA. Lack of processmg .0799576 .3432333 0.23 0.816 technology Constant .6366 101 .583967 1.09 0.277 Number of Obs: 294 F (13 , 280) 3.18 Prob> F 0.0002 R-Squared 0.1286 Adj R-Squared 0.0881 RootMSE 1.4301 Source: Computer Printout of Regression Analysis Note: *,**and*** means 10%, 5% and 1 % level of significance respectively 91 CHAPTER SIX POVER1Y DECOMPOSITION AMONG USERS OF CLIMATE SMART AGRICULTURE 6.0 The Empirical Results and Discussion This chapter expounds on the result on poverty rates fur high-users and low-users of CSA. The study computes relative poverty indices with Foster Greer and Thorbecke (FGT) and Equally Distributed Equivalent (EDE) using households ' expenditure. The expenditure dimensions include overall per capita expenditure as well as expenditure on fuod. All the results illustrated the headcount of poverty {P(0)} , poverty gap {P(l)} and the poverty severity {P(2)} in every decomposition techniques that was considered. 6.1 Evaluation of Poverty Rates for High-Users and Low-Users of CSAP Several furm5 of expenditure shall be used to ascertain the income dimension of poverty; per capita expenditure and fuod expenditure. Expenditure is often used as a proxy to income fur two key reasons: First, experience bas shown over time that individuals are rrore comfortable and hence rrore likely to be truthful about their expenditures than their incomes. Secondly, expenditure captures all streams of income other than just the salary that is usually thought of when posed with the question of income earned. It is on this note therefore that this study employs various expenditure profiles to analyse poverty. Preponderantly, the essence of poverty, in relative term, is ' inequality', an implication that poverty can also be described as relative deprivation (Bradshaw, 2006). The general popular poverty line was one US dollar ($1) per day, ahhough according to Pound, (2016) one dollar per day using the exchange rate of naira to dollar in 2016 which was $1 to Nl99.28 at the con:nnercial bank rate while the parallel market rates, referred to as the black marketers operate a black market rate at $1 to N201.68. However, the report of Vanguard News in August, 2017 shows that the exchange rate of one naira to a dollar was $1 to N359.70, that is, con:nnercial bank rate; while the parallel market rates was $1 to N364.90 . According to World Bank, (2016) the current international poverty line is ($1.9) per day. 92 Table 6.1 International Poverty Line in Two Different Periods Poverty Date (Year) International Vendor ($) Government ($) Line poverty line ($) (parallel market rate (N) rates) (N) February, 1.00 201.68 1.5883 199.28 1.6077 2016 August, 201 7 1.00 364.90 0.8780 359.70 0.8907 February, 1.90 383 .23 0.8360 378.10 0.8473 2016 August, 2017 1.90 693.31 0.4621 683.43 0.4688 Source: Nefflpapers and bulletins 6.2.1 Poverty Rates for High-Users and Low-Users of CSAP for Per Capita Expenditure Table 6.2 represented the total expenditure per head of each household which was calculated as total household expenditure divided by household size. Per capita expenditure therefore represents overall expenditure per head in the household. The results fur all the two measurements of poverty evidenced that poverty head cm.mt was higher for b w-users of CSA than high-users, despite the :fact that these varying techniques of measurements have different fumrulations. Poverty head cm.mt according to the FGT index fur the total population was 9.13% fur relative poverty. The scrutiny of the poverty rate showed that, bw-users aIJd high-users of CSA were deprived of basic human needs such as fuod and health care services; subsequently, the FGT relative poverty values ofl0.23% aIJd 8.76% for bw-users aIJd high-users ofCSAP respectively were recorded. Consequently, as represented by the individualised percentages, the deduction is that both bw- user and high-users of CSA were poor. In the case of FGT relative poverty index, when disaggregated, the low-users of climate smart agricultural :farmers were relatively 2% poorer than high-users of climate smart agricultural :farmers. This was resonated in the work of Olowa and Olowa, (2010) that the major cause of poverty in Nigeria was the reh.ictance of the :farmers to adopt new farming techniques that will enhance their productivity, aIJd CSA was one of such new farming tedmiques. A related thought was also pointed out in the research carried out by Samuel et al , (2014). They attested that the archaic farming methods of rural :farmers make them to be 93 vulnerable to poverty and that the situation needs government ' s intervention for them to escape the scourge of poverty. This trend could be explained in two ways. First, poor farmers are tmable to practise CSA due to the extra costs for inigation, organic manure and procurement of high quality hybrids. Second, the avoidance of CSA could further impoverish farmers since CSAP are the only sustainable means of funning in this era of climate change. Poverty depth and severity as shown for FGT and EDE- FGT followed the same trend as the poverty head cmmt, therefore implying that low-users ofCSAP were worse off than high-users. This further buttressed the need for conscious eflorts to be made to enfurce the practice of climate smart agricultural activities, principally as a means of alleviating poverty. Table 6.2: Poverty Measurements of Per Capita Expenditure for Low-Users and High-Users of CSA Measurement Population P(0) P(l) P(2) Poverty Poverty Poverty Teclmique line (N) line ($1) line ($1 .9) per/m:mth per/day per/day FGT Relative Low-user CSA 0.102326 0.024100 0.011050 6520 .62 0.5966 0.3135 Index High-user 0.087639 0.018310 0.005462 6504.63 0.5942 0 .3127 CSA Total 0.091256 0.019732 0.006838 6508 .57 0 .5945 0.3129 Population EDE-FGT Low-user CSA 0.024100 0.105120 6520.62 0.5966 0.3135 Relative High-user 0 .018310 0.073906 6504.63 0 .5942 0 .3127 Index CSA Total 0.019732 0.082693 6508.57 0.5945 0.3129 Population Source: Author's Computation 6.2.2 Poverty Rates for High-Users and Low-Users of CSA for Food Expenditure Table 6.3 presented the food expenditure poverty wherein we consider only a portion ofhousehold expenditure on food per head. The resuhs for food dimension of poverty manifested the same trend as with that of per capita expenditure. It pictured a wider gap between low-users and high-users , with low-users of CSAP having consistently higher poverty head count than high-users of climate smart agricultural farmers . Also, poverty gaps and poverty severity for food dimension followed 94 the same trend with equally higher gaps than those of per capita expenditure. The resuhs fur all the measurements of poverty status shows that poverty head count is higher for bw-users ofCSAP than the high-users, despite the :fact that, all these various tecbruques of measurement have different formulations. Poverty head count according to the FGT index for the total population is 12.87% for relative poverty. It connotes that the average CSAP :farmers have deprivation of food. However, in the case of FGT relative poverty index the relative poverty values of 17.83% and 9.88% indicate that 18% and 10% of bw-users and high-users of CSA were poor respectively. And when disaggregated, the bw-users of CSAP :farmers were relatively 8% food poorer than high-users of climate smart agricultural :farmers. This was in consonance with the resuhs of Robert et al , (2016) that CSA will be impactful when the strategies of the :farmers brings devebpment and this will be visible and evident on the :farmers productivity. Additionally, this result was confirmed in the Obayehi and Orosile, (2015). The study contended that fuod poverty is more pronounced armng nrral livelihood :farmers who are basically subsistence :farmers. This again aptly proved that bw-users of CSAP :farmers have higher poverty rates than the high-users for food expenditure and per capita expenditure. This is the same for relative poverty measurements ofFGT and EDE-FGT. Table 6.3: Poverty Measurements of Food Expenditure for Low-Users and High-Users of CSA Measurement Population P(0) P(l) P(2) Poverty Poverty Poverty Tecbruque line (N) line ($1) line per/month per/day ($1.9) per/day FGT Relative Low -user CSA 0.178295 0.079972 0.044431 993.41 0 .0905 0.0478 Index IEgb.-user CSA 0.098784 0.035594 0.020157 1152.03 0 .1052 0.0554 Total 0.128675 0.048499 0.027527 1112.96 0.1017 0.0535 Population EDE-FGT Low -user CSA 0.079972 0.210787 993.41 0.0905 0.0478 Relative Index IEgb.-user CSA 0.035594 0.141974 1152.03 0 .1052 0.0554 Total 0.048499 0.165914 1112.96 0 .1017 0.0535 Population Source: Author's Computation. 95 6.3 Effect of Climate Smart Agricultural Practices on Poverty Status 6.4 Empirical Results and Discussion To ascertain the fuurth objective which is to determine the effect of CSAP on poverty status of nrral farmers, the researcher used an instrumental variable (IV) probit regression model The post- estimation tools fur IV-Probit analysis identified these selected instrumental variables (lack of access to CSAP, sources and use of water from borehole for drinking and quantity of millet per yiekl) to be strong and not weak fur the analysis which were meant to cater fur the potential problem of endogeneity in the analysis. In addition, the choice of instrument was binged on the need to allow consistent estimation as required when the explanatory variables (covariates) are correlated with the error terms in a (2SLS) probit regression relationship . The study employed variables such as lack of access to CSAP, sources and use of water from borehole fur drinking and quantity of millet per yiekl to instrument CSAP fur maize, sorghum, livestock, and crop/livestock enterprises, while lack of access to CSAP, quantity of sorghum per yiekl and use of irrigation were used to instrument CSAP fur millet and groundnut (which are the key independent variables in each of the six setups fur testing the effect of of CSAP on poverty status of farmers). It is worth noting that, CSAP fur maize, sorghum, millet, grmmdnut, livestock and crop/livestock (the key independent variables) are quantitative variables that have been achieved with the aid of the Principal Component Analysis (PCA). In addition, the test fur nrulti-collinearity among the variables was carried out with Variance Inflation Factor (VIF), the mean VIF was found to be 1.55 in the analysis and the high level of tolerance computed fur the variables indicates that there was absence of serious nrulti-collinearity in the analysis. Variables with negative parameters imply a negative relationship with dependent variable while those with positive ones indicate a positive and direct relationship with dependent variable. Meanwhile, the dependent variables represent poverty status wherein one represents farmers whose monthly income is less than $US 1.9 and rero otherwise. To that end, this chapter shows to what extent CSAP fur maize, sorghum, millet, grmmdnut, livestock . and both crop/livestock enterprises affect poverty status of farmers in the study area. 6.4.1 Effect of CSAP on Poverty Status of Farmer's Maize Enterprise The study examined the effect of CSAP fur maize enterprise on poverty status of farmers in the study area and the results have a probability chi-square of 0.000, showing that the overall model is significant. The findings validated the fact that CSAP fur maize enterprise, age, education, housing materials and State were significant determinants of poverty status, given that their z- values are greater than 1.96 and their probability values are less than 0.05 . Table 6.5 presents the 96 outcomes of analyses for the eflect of CSAP on poverty status of the respondents using Two-Stage Least Square (2SLS) Probit roodel The instrumented results were meant to correct for the likely problem of endogeneity. Additionally, Wald test of exogeneity was statistically significant (p<0.01) in the fitted roodel The resuh indicates that the inchision of maize climate smart agricultural enterprise as an endogenous variable was reasonable and the selected ios1ruments satisfied the necessary conditions (Gujarati, 2004). The Wald chi-square statistics was also statistically significant (p<0.01), showing that the roodel produced was a good fit and estimated parameters were jointly unequal to :zero. The results showed that CSAP was significant and had a negative relationship with the odds of being poor for farmers involved in maize enterprise. In fact, the resuh suggests that a unit increase in CSAP reduces the probability of farmers involved in maize production to be poor by 0.7582123. This is expected a priori, as CSAP should generally improve production and consequently reduce poverty. This was expected because CSAP had impacted greatly to the farmers ' means of livelihood and also changed their poverty status. This findings was confirmed in the research carried out by FAO, (2013) that agricultural policies such as CSA are the basis for achieving food security and improving livelihoods and that an eflective combination of sustainable agriculture and climate change policies can boost green growth, protect the environment and contnbute to the eradication of lnmger and reduce high poverty rate in the rural areas. Equally, education was significant and inversely related to the probability of being poor. Possession of informal education, decreases the probability of being poor by O. 2624578. This is in agreement with Prasad et al ' s (2006) study. The researchers discovered that cereal farmers who practiced CSA in Tanzania had higher yield compared with their counterpart because education accelerated the rate of comprehension of the technicality associated with the used ofclimate smart agricultural techniques. On the other hand, age was significant and had positive relationships with the probability of being poor. The resuh shows that a unit increase in age, increases the probability of farmers being poor by 0.0328457. Then, it follows that the older the farmers become, the higher the possibility for them to be poor. This corroborated the findings of Sokoya, (2006) that farmers tend to be less productive as they advance in age because their natural strength diminishes, likewise their mental capability. The results showed that housing materials was significant and had a negative relationship with probability of being poor for farmers in maize enterprise. The analysis indicated that a unit increase in the mrrnber of farmers that used bricks or concrete (c ement)/zinc materials for building reduces the probability of the respondents being poor by 0.7496268. This is equally articulated in the 97 findings of United Nation, (2007) which stated that households are considered to be "above standard" if their dwelling pla.ce do meet the predetermined standards such as suitability, adequacy, and affurdability norms that means a house built with concrete block and roodern roof for a comfortable living. Additionally, the result illustrated that State was significant and had a positive relationship with the probability of being poor for farmers engaged in maize enterprise. This result also established the fact that the poverty rate in Katsina State is higher than Sokoto State, cinching a match with the report of National Bureau of Statistics (NBS, 2013) and Sabina et al , (2015). They discovered that poverty increased at the rate of 8. 7 and 4.1 in Katsina and Sokoto respectively, between 2013 and 2015. Table 6.4: Multi-Collinearity Test of Variables Description VIF Tolerance Eigen Value Age 2.54 0.3931 11.3794 Sex 1.95 0.5115 1.2463 Education 1.08 0.9241 0.9948 Marital status 1.76 0.5695 0.8250 Housebokls size 1.53 0.6552 0.6612 Farm size 1.31 0.7618 0.4625 Experience 2.36 0.4230 0.4009 Ownership funnland 1.81 0.5526 0.3420 Land Acquisition 1.49 0.6701 0.2466 Labour 1.06 0.9436 0.1287 Membership 1.58 0.6333 0.0946 Transportation 1.14 0.8781 0.0751 Housing materials 1.25 0.8008 0.0619 Communication kits 1.21 0.8245 0.0365 Access to credit 1.41 0.7086 0.0252 State 1.32 0.7571 0.0125 0.0068 Mean VIF 1.55 Source: Computer printout ofMulticollinearity Test 98 Table 6.5: IV-Pro bit Regression Results Determining the Effect of CSA Practices on Poverty Status of Farmer's Maize Enterprise Description Coefficient Standard Error Z-value P>/:z/-value Marginal Effect {dy/dx} Maiz.ecsainde x -.7582123 .105982 -7.15 0.000*** -.7582123 Age .0328457 .0120891 2.72 0.007*** .0328457 Sex .2774317 .3951599 0.70 0.483 .2774317 Education -.2624578 .1299249 -2.02 0.043** -.2624578 Marital status .0686225 .4565479 0.15 0.881 .0686225 Households Stze .0739883 .0545875 1.36 0.175 .0739883 Farm size -.0077441 .0277985 -0.28 0.781 -.0077441 Experience -.0170221 .0 119261 -1.43 0.153 -.0170221 Ownersmp furmland -.3644852 .3898637 -0.93 0.350 -.3644852 Land Acquisition -.0119436 .3542807 -0 .03 0.973 -.0119436 Labom .0905952 .2345065 0.39 0.699 .0905952 Membersmp -.1971802 .1846458 -1.07 0.286 -.1971802 Transportation .2167461 .243268 0.89 0.373 .2167461 Housing materials -.7496268 .2162328 -3.47 0.001 *** -.7496268 Comrmmication kits -.1668879 .1530503 -1.09 0.276 -.1668879 Access to credit -.0918715 .1603149 -0.57 0.567 -.09 18715 State .9221478 .1641148 5.62 0.000*** .9221478 Constant -2.274299 .6531988 -3.48 0.000 Number of Observation 294 Rho .8822057 Sigma 1.092749 Wald chi2(17) 224.00 Prob > chi' 0.0000 Log likelihood -587.57785 Source: Computer Printout of IV-Probit Regression Analysis. ote: *,**and*** means 10%, 5% and 1 % level of significance respectively 99 6.4.2 Effect of CSAP on Poverty Status of Farmer's Sorghum Enterprise The Table 6.6 has expanded upon the effect of CSAP for sorgln.nn enterprise on poverty status of farmers in the study area. The probability chi-square of 0.000 shows, that the overall rmdel is significant; given that at least one of the regression coefficients in the rmdel is not equal to zero. The results disclosed that CSAP for sorgln.nn enterprise, marital status, farm size and housing materials were significant determinants of poverty status, considering that their z-values were greater than 1.65, hence they were significant, at least 10% significant level Again, the results of the Table 6.6 attested that CSAP was significant and had a negative relationship with the probability of being poor for farmers involved in sorghum enterprise . Apparently, the inspection divulged that a unit increase in CSAP resulted in a reduction of the probability of farmers involved in sorgln.nn production to be poor by 0.8615877 as expected a priori and consequently maintaining a parallel with the output of James et al ' s (2013) work, for they asserted that CSA has a multiplier effect on farming household 's livelihood and the comrmmity in which they are living. Contrary to the previous case, farm size had a significant and negative relationship with the probability of being poor. This is in accordance with the expected assumption, given that as farm size increases, production should increase thereby reducing the odds of being poor. A unit increase in farm size, reduces the probability of the farmer being poor by 0.0679061, thus, portraying a compatible upshot with Sadiq et al ' s (2015) research. They avowed that, farm size produced the incremental effect on sorghum production in Niger State because the process involved the employment of rmre input factors. Also, the results depicted that marital status was significant and has a negative relationship with the probability of being poor for farmers involved in sorgln.nn enterprise. It was recorded that a unit increase in the number of farmers that are married reduces the probability of farmers involved in sorghum CSAP to be poor by 0.7033051. This was analogous to the report presented by Aliyu et al, (2016) they voiced that majority of the respondents were married and this may be due to the fact that married respondents are rmre engaged in the strategies because they have to farm in order to feed their :fu.mily. Therefore they have to go to the farm and engage in these activities to prevent the effect of agricultural deforestation on their farms and concurrently improve their productivity, and invariab ly leading to increase in their income, social and economic requirements. Additionally, housing materials was significant, posing a negative relationship with the probability ofbeing poor for farmers in sorgln.nn enterprise. The result indicated that a unit increase in the number of farmers that used bricks or concrete zinc reduces the probability of the respondents being poor by 0.5943448 . A matching result was ascertained in the findings of Devendra and Chantalakhana , 100 (2002). The analysts reported that capital investment in housing materials such as iron sheets (zinc) and concrete cement blocks improves agricultural funning income and productivity. Table 6.6: IV-Probit Regression Results Determining the Effect of CSA Practices on Poverty Status of Fanner's Sorghum Enterprise Description Coefficient Standard Z-value P>/z/-value Marginal Effect Error {dy/dx} Sorghum:::saindex -.8615877 .0584338 -14.74 0.000*** -.8615877 Age .0017924 .0114586 0.16 0.876 .0017924 Sex .239223 .3573523 0.67 0.503 .239223 Education -.1547731 .1009767 -1.53 0.125 -. 1547731 Marital status - .7033051 .385484 -1.82 0.068* - .7033051 Households size .0559366 .0424006 1.32 0.187 .0559366 Farm size - .0679061 .0229405 -2.96 0.003*** - .0679061 Experience .0043374 .0110189 0.39 0.694 .0043374 Ownership furmland - .2399118 .3519926 -0 .68 0.496 -.2399118 Land Acquisition -.0717922 .3140232 -0.23 0.819 -.0717922 Labour - .0953503 .2147268 -0.44 0.657 -.0953503 Membership -.0271669 .1681772 -0.16 0.872 -.0271669 Transportation .0008668 .2208 0.00 0.997 .0008668 Housing materials - .5943448 .1989946 -2.99 0.003*** - .5943448 Cor1111 11mication kits .0688813 .1341723 0.51 0.608 .0688813 Access to credit -.2317755 .1444702 -1.60 0.109 -.2317755 State .2674109 .1636097 1.63 0.102 .2674109 Constant .5237744 .6514246 0.80 0.421 Nlilllber of Observation 294 Rho .9725071 Sigma 1.095577 Wald chi.2(17) 410.00 Prob > chi2 0.0000 Log likelihood -587.08602 Source: Computer Printout of IV-Probit Regression Analysis. Note:*,** and*** means 10%, 5% and 1 % level of significance respectively 101 6.4.3 Effect of CSAP on Poverty Status of Farmer's M illet Enterprise Table 6.7 attempt to estimate the effect of CSAP on poverty status, the study then introduced the climate smart agricultural index fur millet enterprise. The results eventually showed a probability chi-square of0.000, a token that the overall model was significant. The results also indicated that CSAP fur millet, age, education, housing materials and State were significant determinants of poverty status, considering that they were all significant at 10% significant level The results from the Table 6.7 show that CSAP was significant and manifested a negative relationship with the odds of being poor for farmers involved in millet enterprise as that of maize and sorghum In point of fact, the result suggests that a unit increase in CSAP fur millet, reduce s the probability of farmers involved in millet production being poor by 0.8618127. This was equivalent with the report of Worki Bank, (2011) the body affirmed that, experience bas shown that growth in the agricultural sector which include CSAP are highly effective in poverty reduction and increasing fuod security. In the meantime, age bad a significant and positive relationsrup with the probability of being poor. The result inferred that a unit increase in age, increases the probability of farmers being poor by 0 .0333073 . Hence, it tallied with the studies of Maurice , (2005); Amaza et al , (2006) and Zalkuwi, (2013) fur they reported a significant relationship between :tanners' age and efficiency in agricultura l production, and that younger farmers have the tendency to operate more efficiently than okier ones. Furthermore, the result presented State as significant and also with positive relationship and the probability of being poor for farmers that participated in millet enterprise. Additionally, education is significant and inversely related to the probability ofbeing poor. Respondents who bad infurmal education, decreases the probability of being poor by 0.2404237 . This is probably because education contributed to knowledge in the area of technical know-how, hence more productiv ity and income. This is in conformity with the work of Mohammed et al , (2014) who stated that climate change adaptation is greater fur those who have higher educational fulfilment compared to illiterate farmers . Patently, it is evident that educated farmers have more knowledge and respond to anticipated changes promptly, are better able to furecast future scenarios and overall, have greater access to information and opportunities than others, which might inspire adaptation to climate change. Moreover, several studies fuund out that education also positively and significantly affects agricultural productivity and the adoption of new technology (Quay um and Ali, 2012). Additionally, the examination evinced that housing materials was significant and a resultant negative relationship with the probability of being poor for farmers in millet enterprise . The result indicated that a unit increase in the mnnber of farmers that used bricks or concrete zinc 102 reduced the probability of the respondents being poor by 0.357765 . 1bis result revealed that the rate of poverty in Katsina State is higher than Sokoto State as a result of high population in the funner; hence the State with high dependency ratio tended to be more vulnerable to poverty than the one wttb. less dependency ratio . 1bis was in agreement wttb. the findings of Achem et al , (2013) which stated that farmers are :faced wttb. family welfare and other living expenses fur such a large dependants and these expenses justified the low saving at the end of every harvest season aside the :fact that most :farm produce are consumed by the large househoki members. 103 Table 6.7: IV-Pro bit Regression Results Determining the Effect of CSA Practices on Poverty Status of Farmer's Millet Enterprise Description Coefficient Standard Error Z-value P>/:d-value Marginal Effect {dy/dx} Milletcs ainde x -.8618127 .0746003 - 11.55 0.000*** -.8618127 Age .0333073 .0112111 2.97 0.003*** .0333073 Sex -.2310507 .3492081 -0.66 0.508 -.2310507 Education -.2404237 .0932965 -2 .58 0.010*** -.2404237 Marital status .1385795 .4241677 0.33 0.744 .1385795 Househokls size .0253905 .0521846 0.49 0.627 .0253905 Fann size -.0105633 .0251334 -0.42 0.674 -.0105633 Experience -.0155156 .0108839 -1.43 0.154 - .0155156 Ownership furmland .2115872 .3581873 0.59 0.555 .2115872 Land Acquisition .0531429 .3274727 0.16 0.871 .0531429 Labour .1024686 .213681 0.48 0.632 .1024686 Membership -.0993023 .1693368 -0.59 0.558 - .0993023 Transportation -.2986372 .227099 -1.32 0.189 -.2986372 Housing materials -.357765 .1970982 -1.82 0.069* -.357765 Communication kits -.1078572 .1340477 -0 .80 0.421 - .1078572 Access to credit - .0396649 .1485683 -0.27 0.789 -.0396649 State .3212522 .1746636 1.84 0.066* .3212522 Constant -1.278858 .6282223 -2 .04 0.042 Number of Observation 294 Rho .9817122 Sigma 1.092558 Wakl cbr(l 7) 393.09 Prob > chi2 0.0000 Log likelihood -583.41157 Source: Computer Printout of lV-Probit Regression Analysis. Note:*,** and*** means 10%, 5% and 1 % level of significance respectively 104 6.4.4 Effect of CSAP on Poverty Status of Farmer's Groundnut Enterprise The study finther investigates how CSAP fur groundnuts enterprise afrect poverty status of fu.rmers in the study area. The estimation resuhs in Table 6.8 show that the overall IIX)del is significant given the probability chi-square of 0.000. The resuhs also indicated that CSAP fur grmmdnut enterprise, age, education, househokis ' size, experience, housing materials and State were significant determinants of poverty status at 10% level of significance. This is very similar to the maize scenario but difrers from that of the sorghum and millet scenario . The resuhs finther show that CSAP fur groundnut is inversely proportional with the probability of being poor fur farmers involved in groundnut enterprise. As such, the resuhs suggest that a unit increase in CSAP reduces the probability of farmers involved in groundnut production to be poor by 0.719364. Again, education is significant and inversely related to the probability of being poor. This time, it is significant at 10% level of significance. The respondents who bad infurmal education, decreases the probability of being poor by 0 .2350159. This was in consonance with Okunmadewa and Omonona's (2005) submission, that extension education was good machinery through which education can be implemented on the fu.rmers fur their sustainable agricultural productivity. Additionally, age bad positive relationship with the probability of being poor. The result shows that a unit increase in age, increases the probability of farmers being poor by O. 0428294. This was in line with the findings of Rekwot, (2014) who discovered that older groundnut farmers were poor and were not able to optimize their production efficiency because of their inability to update useful infurmation that are pertinent to groundnut production. The resuhs show that household size was significant and bad a positive relationship with the probability of being poor for farmers involved in grmmdnut enterprise. Subsequently, a unit increase in farmer' s household size increases the probability of farmers involved in rrn11et CSAP to be poor by 0.1262777. This is not surprising given that the larger the household size, the higher the dependency ratio. This was in line with the report ofMathonze, (2000) who stated that the size of the household was fuund to have a negative impact on fu.rm income. A large household size which is actively involved in farming is useful to provide farm labom but household size that is big and roost members are just dependants, makes the farming household to be vulnerable to poverty, a view supported by N ganga , (2009). The study attnbuted large household sizes to the desire to have enough family labom which in roost cases end up having roore children to care fur their daily needs. This may be understood from the view point of the African's polygamous tendencies and also the extended family setting. In fact, most of the respondents are Muslim; and polygamy is not abhorred by its adherents. Experience was another variable that exhibited a significantly mverse relationship with the probability of being 105 poor with grormdnut enterprise. Obviously, a unit increase in experience reduces the probability of being poor by 0.0236805 . This is expected a priori given that experience should improve expertise and consequently optimize production A similar knowledge was noted in Asogwa et al ' s (2012) findings, for the researchers discovered that experienced :fu.rmers were more productive because they were able to learn from their past experience and a consequent ability to solve present challenges and boost their production and income. As regards the analysis on housing materials, the investigation depicted that it was significant, aside mani:iesting a negative relationship with probability of being poor for farmers in grormdnut enterprise. The result indicated that a unit increase in the number of farmers that used bricks or concrete zinc reduces the probability of the respondents being poor by 0.4015493, dermnstrating a uniform outcome with the study of Ajibefun, (2006). According to Ajibefun (2006), lack of proper management in terms of housing and health care used by farmers armng other factors are responsible for the low agricultural productivity, adding that inadequate housing is one of the major problem.5 detrimental to the improvement of production and productivity in Nigerian agricultural sector. Finally, the coefficient of respondents State of origin is positive (0.4897395) and significant at p<0.01 level of significance. This indicated that farmers had significantly high rates of poverty in Katsina State fur groundnut climate smart agricultural enterprises than their fellow farmers in Sokoto State. 106 Table 6.8: IV-Probit Regression Results Detennining the Effect of CSA Practices on Poverty Status of Farmer's Groundnut Enterprise Description Coefficient Standard Z-value P>lz/- Marginal Effect Error value {dy/dx} Groundnutcsaindex -.719364 .1192245 -6 .03 0.000*** -.719364 Age .0428294 .0124203 3.45 0.001 *** .0428294 Gender -.4463375 .39563 14 -1.13 0.259 -.4463375 Education -.2350 159 .1351789 -1.74 0.082* -.2350159 Marital status -.3865291 .4566571 -0.85 0.397 -.3865291 Househokis Stz.e .1262777 .0519257 2.43 0.015** .1262777 Farm size -.0041589 .0299557 -0.14 0.890 -.0041589 Experience -.0236805 .01 21905 -1.94 0.052* -.0236805 Ownership furmland -.1640579 .4174058 -0 .39 0.694 - .1640579 Land Acquisition -.0363482 .3786044 -0 .10 0.924 -.0363482 Labour .0475729 .2445703 0.19 0.846 .0475729 Membership -.1953063 .1906244 -1.02 0.306 - .1953063 Transportation .001493 .2571589 0.01 0.995 .001493 Housing materials -.4015493 .218418 -1.84 0.066* -.4015493 Cormmmication kits -.0253465 .1526643 -0.17 0.868 -.0253465 Access to credit - .1185087 .1671323 -0.71 0.478 -.1185087 State .4897395 .1884638 2.60 0.009*** .4897395 Constant -1.93956 .6765611 -2 .87 0.004 Number of Observation 294 Rho .8341323 Sigma 1.103418 Waki chi2(17) 186.97 Prob > cbr 0.0000 Log bkelihood -590.92947 Source: Computer Printout of IV-Probit Regression Analysis . Note:*,** and*** means 10%, 5% and 1 % level of significance respectively 107 6.4.5 Effect of CSAP on Poverty Status of Farmer's Livestock Enterprise 1bis segment explicates the e:frect of CSAP fur livestock on poverty status of the rural funners . To that e:frect, the study in Table 6.9 shows that the chi-square probability is 0.000, hence the overall rmdel was significant. The results authenticated the fact that CSAP fur livestock, education, household size, :farm size, membership of association and extension contact were significant determinants of poverty status of funners in the study area at 10% significant level The negative sign of the climate smart agricultural livestock index pointed to an inverse relationship between the practices and probability of being poor. 1bis means that fur all the crops (maize, millet, sorgbrnn and groundnut) considered and then the livestock, CSAP significantly and inversely relates with the probability of being poor as expected a priori The pract£e of CSA fur livestock production reduces the probability of being poor by 0.6438993. 1bis finther buttresses the need fur funners in crops and livestock enterprises to take the pract£e fur CSA rmre seriously, in order to minimise poverty and this was in agreement with the report of FAO, (2015) which revealed that livestock productivity increases the efficiency of soil and other agricultural inputs which o:frers higher returns to :farmers, hence increasing fuod availability and reducing high poverty rate in rural coIIllllJ.l[ljty. Education is equally significant and inversely related to the probability of being poor. Respondents who bad informal education, reduces the probability of being poor by 0.312885. 1bis was in relation to adoption of agricultural innovations which is also easier and :faster armng the educated farmers, as orchestrated by Njoku, (1991) and Amaza et al, (2006) and thus, rmves them closer to the frontier of agricultural output. Household size and :farm size bad a signifoant and direct relationship with the probability of being poor. A tmit increase in household size signifoantly increased the probability of being poor by O. 144 7 5 81. 1bis is not surprising given that the larger the household size, the higher the dependency ratio. 1bis was in line with the findings of Anyanwu, (2013) who reported that poverty is high for a large household's size because of the high rate of dependency ratio with little or no resources to care for the larger households ' size. Membership association was another variable that indicated a significantly inverse relationship with the probability ofbeing poor with livestock enterprise. 1bis implies that a tmit increase in membership association will reduce the probability of being poor by 0.338705. 1bis is expected a priori given that membership of an association by the respondent will be more knowledgeable than other members as a result of the interaction, and this was in agreement with the :findings of Agbamu, (2006) who argued that the greater the part£ipation of a farmer in social organization and the more interaction with others :farmers, then the earlier his adoption of innovations . Furthermore , 108 col1llillIDication equipment also ilh.Jstrated a significant inverse relationship with the probability of being poor with livestock enterprise. The inspection manifestly depicted that a unit increase in col1llillIDication equipment will reduce the probability of being poor by 0.2586087. It is also an expected a priori, knowing that proper infurmation is a stimulant that affects and naturally eventuates in proper transfunnation of resources such as agricultural productivity. This was also in agreement with the :fiodipgs of Johnson, (1999) who reported that, Tele-connnunica tio n opportunities allow spatially parted production, using infurmation technology to synchronize production activities, splits the traditional workshop and E-commerce, which can lead to rapid economic growth and brings about IIDre output and income. The results show that State is significant and has a positive relationship with the probability of being poor fur :farmers engaged in livestock enterprise. This result indicated that poverty rate in Katsina State is higher than Sokoto State as earlier discussed due to the high rate of dependency ratio. 109 Table 6.9: IV-Pro bit Regression Results Determining the Effect of CSA Practices on Poverty Status of Farmer's Livestock Enterprise Description Coefficient Standard Z-value P>lzJ- Marginal Effect Error value {dy/dx} Livestockcsaindex -.6438993 .0680927 -9.46 0.000*** -.6438993 Age .0154656 .0127362 1.21 0.225 .0154656 Gender -.3072264 .3845474 -0.80 0.424 -.3072264 Education -.312885 .1343072 -2.33 0.020** -.312885 Marital status - .3376178 .4333781 -0 .78 0.436 -.3376178 Households size .1447581 .047361 3.06 0.002*** .1447581 Farm size .0149597 .0282894 0.53 0.597 .0149597 Experience -.0117404 .0121103 -0 .97 0.332 -.0117404 Ownership furmlan::l -.3235444 .39463 -0 .82 0.412 -.3235444 Land Acqu:isitio n -.4923594 .3450545 - 1.43 0.154 -.4923594 Labour -.0522675 .2392773 -0.22 0.827 - .0522675 Membership -.338705 .1816927 -1.86 0.062* -.338705 Transportation .2153821 .2494875 0.86 0.388 .2153821 Housing materials -.2649417 .2175976 -1 .22 0.223 - .2649417 Commmication kits - .2586087 .1544379 -1 .67 0.094* -.2586087 Access to credit - .0711528 .1628259 -0.44 0.662 -.0711528 State .3921002 .1874637 2.09 0.036** .3921002 Constant -1.024671 .7029864 -1.46 0.145 Number of Observation 294 Rho .8695716 Sigma 1.397093 Wald cbi2(17) 260.40 Prob > ch¥ 0.0000 Log likelihood -659.79176 Source: Computer Printout of IV-Probit Regression Analysis. Note:*,** and *** means 10%, 5% and 1 % level of significance respectively 110 6.4.6 Effect of CSAP on Poverty Status of Farmer's Crop/Livestock Enterprise This section articulates the e:frect of CSAP for the combination of the two major enterprises , namely, crop and livestock enterprises in the study area. In this case ofcrop/livestock CSAP, the researcher tried to use separate enterprise as an endogenous variable to run the instrumental variable fur the probit model analysis but the analysis was not converging, which means the continuous output results was not concave; hence the need fur combining the individual CSAP fur each crop and livestock fur the Principal Component Analysis (PCA) to generate the crop/livestock climate smart agricultmal index. Table 6.10 displayed the results of the analysis for the e:frect of CSAP on the poverty status of :farmers using (2SLS)-probit model The instrumented results were intended to correct the possible problem of endogeneity. However, Wald test of exogeneity was statistically significant at 1% in the fitted model The result extrapolated that the presence of crops/livestock climate smart agricultural enterprise as an endogenous variable was satisfied and the selected instruments justified. The Wakl chi-square statistics was also statistically significant 1% , showing that the model produced a good fit and estimated parameters were jointly wequal to zero. The probability chi-square of 0 .0000 , shows that the overall model is significant fur the combined analyses. The results demonstrated that CSAP for crop/livestock enterprises, age, education, household size, experience, housing materials, comnnmication equipment and State were significant determinants of poverty status of :farmers in the study area at 10% significant level The negative sign of the climate smart agricultmal crop/livestock index shows an inverse relationship between the practices and probability ofbeing poor. This means that fur all the crops and livestock considered, the CSAP significantly and inversely relates with the probability of being poor as expected a priori. The practice of CSA fur crop/livestock production reduces the probability of being poor by 0.5121226. This was in line with the report of FAO, (2013) which stated that, agricultmal sectors such as crops and livestock have transformed themselves in order to feed a growing gbbal population and provide the basis for economic growth and poverty reduction and this transformation accomplished natural resource base such as organic manure from agricultural waste. Furthermore, age, households ' size and :farm size had a positive relationship with the probability of being poor. The result shows that a wit increase in age of the :farmers increases the probability ofrespondents being poor by 0.0318688. This aligned with the findings ofMagx.inga et al , (2005) which stated that as a :farmer's age increases, it becomes more di:flicult to respond to opportunities including accessing the local market. Age can, to a large extent, also affect the response to modern irmovations in :farming activities such as the CSAP. Similarly, this result 111 coincided with the :findings of Anyanwu, (2013) which reported that the level of poverty increases as we go up the age ladder. Additionally, Gan, Sen and Yun, (2004) put forward a life-cycle hypothesis, which stated that poverty is relatively rugh at ymmg ages, decreases during middle age and then increases again at okl age when the law of diminishing returns start to manifest. The households ' size presented a similar categorization with respect to age; an increase in households ' size will also lead to a corresponding increase in the probability of funners being poor by 0 .1177 648 . This result also agreed with the a priori expectation because as dependency ratio increases in the fumily, the househokl becomes poorer due to the number of fumily members to be fed, in addition to taking care of their well-being. This was in agreement with Gan, Sen and Yoo, (2004) that large househokls are associated with poverty. Yet still, according to Achem et al , (2013) large fumily size can sometimes be an asset to the furmers in tenns of available work force/labour, even though often times a funner is fuced with the challenges of providing social and welfure fucilities such as feeding, education, shelter, health care and other living expenses for such a large number of dependants. Ultimately, these expenses accooot for low saving at the end of every harvest season, aside the fuct that most furm produce are consllllled by the large household members. Furthermore, education is significant and inversely related to the probability of being poor, the furmers who had informal education decrease the probability ofbeing poor by 0.3717899, showing a correspondence with Joseph and Bayei's (2014) study. They stated that majority had attended Arabic education and could read and/or write in Hausa or Fulani language. This can be attributed to the :fact that the Arabic education is usually taught to the youth at tender ages, thus enabling an improvement in tenns of agricultural activities, and a resultant reduction in poverty. In addition, on the word of Anyanwu, (2013) the level of education is an important determinant of poverty. The research he carried out in 2010 was a proof that Nigerians who had little or no education experienced greater poverty. He also stated that education increases the stock of human capital, which in turn increases labour productivities and wages; hence a decrease in education will result in a corresponding increase in the poverty level of an individual Moreover, the survey showed that experience was significant and had a negative relationsrup with the probability of being poor for the crop/livestock enterprise. Thus, a unit increase in the mnnber of :farmers' years of experience decreases the probability of the respondents being poor by 0.0192952. The implication is that, the funners will be more knowledgeable due to the experiences 112 they acquired from vanous past events. Evenly, Olayemi, (1998) verified that experience JS proportional to age because as the ages of the farmers increase, simultaneously years of experience also increases. It is believed that the higher the years of funning experience of a farmer, the rrme the ability of such farmer to make good farm management decisions giving him all the necessary fi.mding at his disposal Equally, housing materials was significant and bas a positive relationship with probability of being poor fur farmers in crop/livestock enterprise. The result indicated that a tmit increase in the mnnber of farmers that used bricks or concrete zinc increases the probability of the respondents being poor by 0.5346467. This may be as a result of the high cost of the building materials such as cements and zinc which sometimes the rural farmers cannot affurd due to inadequate income from their agricultural productivity. The result maintained a unifurmity with the :findings of Okoli et al., (2009) and Ajala, (2007) who reported that 62 % of livestock farmers in northern Guinea Savannah of Nigeria constructed their livestock house (pens) with brick and roofed with thatches. The floor spacing per animal was fuund to be inadequate and insufficient good roofing with iron zinc coukl expose the animals to ill health and :facilitate the spread of contagious diseases leading to high livestock mortality rate and poor perfurmance (Bastianelli et al , 2002). Additionally, the result of the analysis indicated that comrrrunication equipment was significant and bas a negative relationship with probability of being poor fur farmers of crop/livestock enterprise. The result revealed that a tmit increase in the mnnber ofcomrrrunication equipment will reduce the probability of the respondents being poor by 0.2776469. It harmonized with the report of World Bank (2008), which stated that promoting modern teclmology in infurmation and comrrrunication system.5 helps enabling producer groups or farmers to access market infurma tio n and acquire professional advice necessary fur modern supply chain management and effective participation in the policy discussion Also BNARDA, (2005) reported that after the implementation of National Special Programme fur Food Secmity (NSPFS) in selected comrrrunities in Benue State, beneficiaries enjoyed GSM cormnunication handsets bought through increased income from NSPFS intervention and which in turn helped rural farmers in their agricultural productivity. Equally, the result revealed that poverty rate in Katsina State is higher than Sokoto State as shown by previous indicators, especially the high-dependency ratio. 113 Table 6.10: IV-Probit Regression Results Determining the Effect of CSA Practices on Poverty Status of Farmer's Crop/Livestock Enterprise Description Coefficient Standard Z-value P>h/- Marginal Effect Error value {dy/dx} Crops/Livestockcsaindex -.5121226 .0548524 -9.34 0.000*** -.5121226 Age .0318688 .0119232 2.67 0.008*** .0318688 Sex -.2957636 .3746506 -0.79 0.430 -.2957636 Education -.3717899 .1238905 -3.00 0.003*** -.3717899 Marital status -.0938366 .4288513 -0.22 0.827 -.0938366 Households SJZ.e .1177648 .0467431 2.52 0.012** .1177648 Farm siz.e .0131584 .0274241 0.48 0.631 .0131584 Experience -.0192952 .0116287 -1.66 0.097* -.0192952 Ownership furmland -.1925776 .3822434 -0.50 0.614 -. 1925776 Land Acquisition -.0663599 .341614 -0.19 0.846 -.0663599 Labour .0018454 .2306574 0.01 0.994 .0018454 Membership -.173865 .1799275 -0.97 0.334 - .173865 Transportation .0120589 .242582 0.05 0.960 .0120589 Housing materials .5346467 .2090827 -2.56 0.011 ** .5346467 Comrmmication kits -.2776469 .1501989 -1.85 0.065 -.2776469 Access to credit -.1027178 .1563716 -0 .66 0.511 -.1027178 State .6562722 .169357 3.88 0.000*** .6562722 Constant -1.725303 .6554086 -2.63 0.008 Number of Observation 294 Rho .9067014 Sigma 1.721423 Wald chi2(17) 270.20 Prob > cbr 0.0000 Log likelihood -721.00893 Source: Computer Printout of IV-Probit Regression Analysis. Note: *,**and*** means 10%, 5% and 1 % level of significance respectively 114 6.4.7: The Multidimensional Poverty Index of (2SLS) Analysis for Crops and Livestock CSA Enterprises This section of the study fucuses on the utilization of rrrultidimens ional poverty index of two stage least square to ascertain the effect of CSAP on the rrrultidimensional poverty index of the respondents in the study area. The post-estimation tools fur the rrrulti-dimensional poverty index of 2SLS identified the selected instrumental variables such as expenditure on inorgaruc manure , education and State to be strong and not weak, to instrument CSAP fur crops, livestock and crop/livestock enterprises. CSAP fur crops, livestock and crop/livestock enterprises are the key independent variables in each of the three scenarios fur testing the effect of CSAP on the rrrultidimensional poverty index of the respondents in the study area, while in contrast, the MPI was developed to be the dependent variable. However, CSAP fur crops, livestock and crop/livestock (the key independent variables) are quantitative variables that have been achieved with the aid of the Principal Component Analysis (PCA). The estimation resuhs show a high R- Square of 75% fur both crop and livestock enterprises, and 77% fur crops/livestock enterprises, suggesting a very high predictability level of the dependent variables by the independent variables . Also, all the three probability chi-square of 0.000 shows that the overall models were significant at 1% level of significance. 6.4.8: Effect of CSA on Multidimensional Poverty Index of (2SLS) Analysis for Crops Enterprise This study examined the effect of CSAP of crops enterprises on the rrrultidimensional poverty index using (2SLS), and Table 6.12 showing the probability chi-square of0.000, meaning that the overall model is significant, and the mean VIF is 1.52, which implies that the model is void of any serious rrrulticollinearity problem The result of the analysis showed that CSAP for crop enterprises, age, religion, ownership of £:u-mland, membership of association, housing materials and communication equipment were significant determinants of deprived rrrultidimensional poverty index of the respondents in the study area, given that their z-values were greater than > 1.96 and their probability values were less than <0.05 . The resuhs of the Table 6.12 depicted that CSAP with crops enterprise was significant and had a negative relationship with MPI. In :fact, the result suggests that a unit increase in climate smart agricultural practice, reduces MPI of :furmers involved in crop production by 0.3007364. This is expected because CSAP should generally improve agricultural productivity and consequently reduced poverty. This was expected because CSAP had impacted greatly to the respondents ' 115 means of livelihood and also changed their poverty status. This was in support of Aggarwal et al. (2000) who in their research work titled, 'Economic benefits fur CSAP to smallholder :farmers in the Indo-Gangetic Plains of Inclia' stated that CSAP improve crop yiekls as well as net returns . The econometric analysis indicates that implementations ofCSAP and technobgies in smallholder :fum5 in the IGP of India, have significant impacts on the changes in total production costs and the yiekl of crops. However, the age of the respondents who engaged in crops enterprise was significant and bad positive relationships with MPI. The result showed that a unit increase in the age of the :farmers increases the MPI by 0.0211582. This implied that the older :farmers become, the higher their MPI. This was not in consonant with the :findings of Rogers , (2003) who upheld that the perceived attributes of an innovation in agricultural activities such as agricultural techniques woukl vary according to individuals different personal characteristics such as age, expenence and comnrunication channels. Perception about these attributes of innovation will influence adoption of behaviour and the perception about such new techniques, the age and experience are major :factors that play out. In addition, the result showed that religion was significant and bad a negative relationship with MPI fur :farmers in crop enterprises. The result indicated that practicing Islamic religion, bad their MPI being reduced by 0.3908004; when compared with non-Muslims. This goes in line the findings of Apata and Awe, (2014). They stated that religion is significant to production and their coefficient of significance was negative implying thus, people that bebng to Islamic religion are rrore productive than those that bebng to other religion. This may be due to different level of access to production inputs and opportunities given to the major religious sect in the study area. Additionally, the result of the analysis derronstrated that ownership of furmland was significant and bad a positive relationship with MPI. The result revealed that a unit increase in the number of :farmlands' ownership increases the MPI by 0.6637031. In the same way, Eze et al , (2011) confirmed that greater than half of the rural furmers in his study area had full ownership of furmland. By implication, because of the less practices of the land tenure system in the area, land ownership is obviously by inheritance and purchase arrong other forms. This revealed the fuct that, constant transfer of land rights via inheritance led to land fragmentation thereby reducing furm size, and a fulbwing reduction in productivity and income. However, membership of an association was also signu.icant and bad a negative relationship with MPI for the respondents in crop enterprises. The result indicates that a unit increase in the number of membership of an association reduces the MPI by 0.874686. Relatedly, the :findings of Franklin and Patience, (2014) 116 confirmed that membership in farmers ' cooperative societies is veiy high among farmers in their study area. Tilis was attributed to the benefits that members derived from their cooperatives' organization. The most comrron benefits include financial assistance, access to essential and scarce production inputs, and a form of insurance cover against crop failure on the members' farms . Furthermore, housing materials was significant and bad a positive relationship with MPI fur farmers in crop enterprises. The result of the analysis revealed that a i.mit increase in the mn:nber of farmers that used bricks or concrete cement/zinc materials for building increases the MPI by 1.732706. This was in contradiction to the findings of Jadbav et al , (2011) fur they stated that majority of the furmers dwelled in stone and mud houses (81 %) and nearly 18% owned houses constructed with cement and bricks. The flooring of the houses made with stone slaps ranked 60% of the farmers ' houses, while the ones made with mud was 40%. Likewise, the roofing of the houses was mainly clay tiles, thus attracting a ratio of 45%, while houses with mud roof was a fraction of 18%, 5% bad metal sheets and only 3% bad modem roof 'Equally, the results ascertained that comrrnmication kits was significant and bad a positive relationship with MPI fur respondent, in crop enterprises. A i.mit increase in the use of GSM fur comrrnmication will increase the MPI by 1.109964, which however contravened the report submitted by World Bank, (2006). Contrariwise, the findings (World Bank, 2006) revealed that the importance of using targeted public and private research investments in comrrnmication to resolve tecbnobgical bottlenecks in the supply chain, the presence of social media and environmental sustainability criteria, a fucus on outcomes in terms of poverty reduction and an emphasis on collaboration among actors are fimctionally a driver ofcompetitiveness. Additionally, the consistent provision of key infrastructure services such as comrrnmications, roads, water and electricity is a central element of an enabling environment, as they are relevant public policies to maintain sustainable market competitivenes s. 117 Table 6.11: Multi-Collinearity Test of Variables Description VIF Tolerance Eigen Vah.ie Age 2.52 0.3966 11.0655 Sex 1.99 0.5035 1.3194 Marital status 1.76 0.5684 0.9841 Religion 1.16 0.8641 0.8954 Households size 1.54 0.6500 0.7467 Farm size 1.14 0.88 10 0.6124 Experience 2.37 0.4222 0.3974 Ownership furmland 1.84 0.5433 0.2757 Land Acquisition 1.50 0.6689 0.2434 Labour 1.06 0.9431 0.1329 Membership 1.55 0.6451 0.1071 Transportation 1.12 0.8941 0.0778 Housing materials 1.17 0.8521 0.0620 Co 1111,11micat:ion kits 1.21 0.8258 0.0363 Extens:io n contacts 1.03 0.9689 0.0243 Access to credit 1.44 0.6963 0.0127 0.0068 Mean VIF 1.52 Source: Computer printout of Multicollinearity Test 118 Table 6.12: Multidimensional Poverty Index (2SLS) Analysis for Crops CSA Enterprise MPlindex Coefficient Standard Error Z-value P>/z/-value Cropscsaindex -.3007364 .0663187 -4.53 0.000*** Age .0211582 .0079913 2.65 0.008*** Sex -.2358375 .2434869 -0.97 0.333 Marital status .3059399 .2591988 1.18 0.238 Religion -.3908004 .1658785 -2.36 0.018** Househokis SJZe .0069469 .0172686 0.40 0.687 Fann siz.e .0172747 .016104 1.07 0.283 Experience -.0081623 .0075621 -1.08 0.280 Ownership farmland .6637031 .24074 2.76 0.006*** Land Acquisition .0286285 .2182321 0.13 0.896 Labour -.0365408 .1453235 -0 .25 0.801 Membership -.8746846 .1032507 -8.47 0.000*** Transportation -.0426148 .1450852 -0 .29 0.769 Housing rmterials 1.732706 .1357505 12.76 0.000*** Communication kits 1.109964 .0972432 11.41 0.000*** Extension Contacts - .0453184 .0379546 -1.19 0.232 Access to credit .142326 .0994695 1.43 0.152 Constant -2.015972 .4231245 -4.76 0.000 Number of Observation 294 Waki cbt(l 7) 923.28 Prob > cltr 0.0000 R-Square 0.7484 RootMSE .70989 Source: Computer Printout ofMPI (2SLS) Regression Analysis Note:*,** and *** means 10%, 5% and 1 % level of significance respectively 119 6.4.9: Effect of CSA on multidimensional poverty index of (2SLS) Analysis for Livestock Enterprise The examination of the effect of CSAP of livestock enterprises on the multidimensional poverty index using 2SLS and the Table 6.13 showing probability chi-square of 0 .000 meaning that the overall rmdel is significant, and the mean VIF is 1.52, which implies that the rmdel is void of any serious multicollinearity problem The result of the analysis ascertained that CSAP fur livestock, religion, ownership of :farmland, membership of an association and housing materials were significant determinants of deprived poverty status of the :farmers in the study area. CSAP was significant and had a negative relationship with MPI fur :farmers involved in livestock enterprise in the Table 6. 13 . In :fuct, the result suggests that a unit increase in climate smart agricultural practice reduces MPI by 0.2420245. This is in line with the :findings of Callaway, (2004) who substantiated in his research work that many empirical studies conducted in the IGP region, also indicated that the implementation of CSAP increases livestock production, crop yields , :furm income and input use efficiency which was as a result of crops/livestock integrated management. And this was also validated with the findings of F AO of the United Nations, (2015) which reported that :farmers needed a collective action to adopt rmst of the CSAP so as to achieve discernible impact on agricultura l production in the :fuce of climate change and enhance resilience of rural livelihoods because the adoption of CSAP will help to entrench and harness rmre economic and environmental benefits to be realiz,ed by many :farmers. Also, the result showed that religion was significant and had a negative relationship with MPI fur :farmers in livestock enterprises. The result revealed that a unit increase in the number of :farmers that practice Islamic religion will reduce their MPI by 0.3101763. An equivalent result was noted by Ekong, (1988). The study confirmed that the institutions of :fumily religion, rmrality, marriage, state, and property have been altered and the adoption of techoology led to the transfurmation in the economy with the evohrtion of new social classes which has altered rmdes of life, institutions of :fumily religion, rmrality, marriage and property. Many fimctions of the :fumily have been taken away by other agencies, religious and traditional believers have lost hold over their members. Conflicts or disequilibrium has crept in between the new techoology and the old social organization resulting in social lag in the rural coIIlIIlllllities. On the other hand, the result of the analysis show that :furm1and ownership was significant and had a positive relationship with MPI fur respondents in livestock enterprises. 120 It was also observed that a unit increase in the number of funnland ownership increases the MPI by 0.6308056. This is in agreement with the findings ofFasona and Ormjola, (2005). They noted that, the guinea savannah :farmers who were already funning close to the margins of cultivation would naturally resist any attempt of invasion of their farmlands by cattle herdsmen who were continually in search of greener pastures that were only in existence within the limit of arable funnland. These conflicts led to burning and destruction of farmlands and houses which in turn bad tremendous adverse effect on the environment; hence, the rmre funnland a :funner bas, the rmre conflict with herdsmen. Congruently, Oyekale, (2012) observed that land conflict increased unsustainable land use in Niger Delta region ofNigeria, as well as noted that where there is land conflicts, investment in sustainable land management practices cannot be prormted. It is therefore evident that land conflict prormtes environmental degradation. Similarly, membersrup association was also significant and bad a negative relationship with MPI for the farmers in livestock enterprises. The result revealed that a unit increase in membership societies will increase MPiby 0.9119656. This is in consonant with the findings of Ade:fila, (2012) who piloted a study on the factors influencing the performance of :farmers ' cooperative organizations in Gurara area, Niger State of Nigeria. The outcomes from the regression analysis indicated that fumers ' cooperative organizations are involved in agricultural development and that factors influencing their role performance include annual income, experience in funning and leadership training. The study concluded that cooperatives in whatever form are seriously viewed as catalyst in the process of rural socioeconomic development and the law should empower cooperatives to perform certain fimctions, such as strengthening their negotiating power as effective agents of socio-economic rural transformation. Likewise, housing materials was significant and bad a positive relationship with MPI for respondent in livestock enterprises. The result indicated that a unit increase in the number of farmers that use bricks or concrete/zinc materials for the construction of their houses increases the MPI by 1.868803. Contrariwise, the findings of Attah, (2012) asserted that housing cooperatives is involved in the building of low cost houses or even renting them on behalf of members, and they rent these buildings on owner occupier basis. This makes it possible for members to become eventually the landlords of their own houses without spending a huge armllllt of rmney. 121 Table 6.13: Multidimensional Poverty Index (2SLS) Analysis for Livestock CSA Enterprise MPlindex Coefficient Standard Error Z-value P>/zl-value Livestockcsaindex -.2420245 .0712911 -3.39 0.001 *** Age .0071547 .0073537 0.97 0.331 Sex -.2505577 .2424615 -1 .03 0.301 Marital status .201189 .2564993 0.78 0.433 Religion -.3101763 .1661474 -1.87 0.062* Households size .0221361 .0173994 1.27 0.203 Farm size .0027504 .0153043 0.18 0.857 Experience -.0010308 .007332 -0 .14 0.888 Ownership funnland .6308056 .2393362 2.64 0.008*** Land Acqui<;ition -.2931967 .2110767 -1.39 0.165 Labour - .0670254 .1453036 -0.46 0.645 Membership -.9119656 .1032153 -8 .84 0.000*** Transportation .0248209 .1439458 0.17 0.863 Housing materials 1.868803 .1303798 14.33 0.000*** Communication kits 1.130305 .0983726 11.49 0.000*** Extension Contacts - .0470573 .0386808 -1 .22 0.224 Access to credit .1315647 .0988692 1.33 0.183 Constant -1.387317 .3927443 -3.53 0.000 Number of Observation 294 Wald cht(l 7) 924.07 Prob > chi2 0.0000 R-Square 0.7511 RootMSE .70605 Source: Computer Printout of MPI (2SLS) Regression Analysis Note:*,** and*** means 10%, 5% and 1 % level of significance respectively 122 6.4.10: Effect of CSA on multidimensional poverty index of (2SLS) Analysis for Crops/Livestock Enterprise The effect of CSAP of crop/livestock enterprises on the multidimensional poverty index using 2SLS, and the Table 6. 14 showing probability chi-square of 0.000 which was obtained, meaning that the overall model is significant, and the mean VIF is 1.52, an index that the model is void of any serious multicollinearity problem The result of the analysis ascertained that CSAP for crop/livestock enterprises, age, ownership of :farmland, membership of association, housing materials and coIIllI1Llllication kits were significant determinants of:MPI of the respondents ' in the study area given that their z-values were greater than > 1.96 and their probability values were less than <0.05 . In addition, Table 6. 14 shows that CSAP was significant and had a negative relationship with MPI fur :farmers involved in crops/livestock enterprise. In fact, the result suggests that a unit increase in climate smart agricultural practice reduces the MPI by 0.2177158. An equivalent submission was made by Aggarwal et al , (2013) they upheki that the implementation of CSAP and technologies could improve crop yiekis, livestock productivity, bring abandoned land under cultivation and increase the income of smallholder :farmers. Besides, CSAP revolve around seed, water, energy, nutrients and some risk averting instruments that help :farmers in reducing climatic risks in agricultural activities. Additionally, the age of the :farmers who are into crops/livestock enterprise was significant and bad positive relationships with the MPI. The result shows that a unit increase in the age of the respondent, increases the MPI by 0.014705. This implies that the okier :farmers get to, the more possibility fur their MPI to increase. This was in agreement with the finding of Yusuf et al (2011), that age is positively related to participation in 'gandu ' and it significantly influenced participation in 'gandu '. Thus, as the age of the farmer increases, the tendency to participate in 'gandu' also increases and vise-versa. Pertaining to religion, it was discovered that religion was significant and bad a negative relationship with MPI fur :farmers in crops/livestock enterprises. The result indicated that a unit increase in the number of :farmers that practice Islamic relig10n, will reduces the MPI by 0.3279175 . This was however contrary the findings of Ukeje, (2008) who stated that the frequent communaVreligious crisis in some region of the country is a major constraint to food security in Nigeria. The crises occur either during planting, weeding or harvesting period and with the flight of farmers from the areas irrespective of the stage of farming, food security is threatened as most, if not all the crops are lost. 123 Unlike religion that portrayed a negative relationsmp with MPI for farmers in crops/livestock entetprises, the examination of the ownership of farmland was significant and bad a positive relationship. The resuh revealed that a unit increase in the mnnber of funnland ownership increases the MPI by 0.6637031. The findings contrasted the a priori expectation because according to the study ofRaufus, (2010) in Nigeria' s agriculture, the excellence factor stands out as a major cause of land productivity. This is due to the problems related with sourcing artificial amendments that can improve the productivity of land especially by majority poor subsistent farmers that dominate the arable crop production landscape. The alternative structures to this, are the intensive use of the pbts of land which typically woukl resuh in farmland nutrient degradation, bw yield restricted farms and continuous poverty folbwing bw output. However, increase in farmland use intensity without corresponding plans to complement the soil with sustainable nutrients coukl be detrimental to the national policy on self. food sufficiency in the bng nm. Also, bss of ecosystem fi.mctio ning could be other consequences of irrational use of agricultural land (Geist et al, 2005). However, membersmp of an association was also significant and bad a negative relationship with MPI for the respondents in crops/livestock entetprises. A unit increase in the mnnber of membersmp of an association, reduces the MPI by 0.874686. This is in consonant with the findings ofVeerakumaran, (2005) who revealed that cooperatives serve as essential tool for achieving food security at household level Co-operatives are the best influential intervention for achieving food security in any country. The devebped nations like United States of America, Canada, ahnost all European countries have attained food self-sufficiency through cooperatives (Cbambo, 2009). Furthermore, housing materials was significant and bad a positive relationship with MPI for farmers in crops/livestock entetprises. The resuh of the analysis revealed that a unit increase in the number of farmers that used bricks or concrete cement/zinc materials for buikiing increases the MPI by 1.732706. Diffurently, Breisinger et al , (2008) avowed that homegrown manufacturing and service sectors take full advantage of the country' s comparative advantage in the ex.pans ion process and are also likely to lead to a broad-based growth in order to accommodate the raw materials such as blocks, roofing sheet, flooring and other buikiing materials in order to make the buikiing materials easily accessible to the middle class .. The results showed that commrnication kits was significant and bad a positive relationsmp with MPI for respondent, in crops/livestock entetprises. It was ascertained that a unit increase in the use of GSM for commrnication will increase the MPI by 1.109964. Nevertheless, this study's result negated the :findings of World Bank, (2011) because the organization reported that many 124 devebping countries have been registering real progress in the last few years by expanding ICT infrastructure and universal access of corrn:rn.mication technologies. For instance, the report made by ITU, (2010) on mobile phone coverage of Africa revealed that, only the mobile penetration rates abne will reach an estimated 41 % growth at the end of 2010 (compared to 76 % gbbally) leaving a significant potential fur growth. This can be counted as a promissory development in dropping down the existing gap of ICT application level known as "digital divide" between countries. Oppositely, the spread of these technologies might worsen the digital divide as companies might become too dependent on the tecbnobgy and service they are providing. Per se, this must be addressed by a well-organized and effective ICT policy. Generally, it is considerably important to expand ICT infrastructures as this advancement improves the overall economic, social and political transformation of the poor living in the rural areas. 125 Table 6.14: Multidimensional Poverty Index (2SLS) Analysis for Crop/Livestock CSA Enterprise MPlindex Coefficient Standard Error Z-value P>/z/-value Crop/livestockcsaindex -.2177158 .0492873 -4.42 0.000*** Age .014705 .0073017 2.01 0.044** Sex -.2678662 .2345769 -1.14 0.253 Marital status .243013 .2483791 0.98 0.328 Religion -.3279175 .1599406 -2.05 0.040** Households SIZ.e .018677 .0166469 1.12 0.262 Farm size .0113607 .0151013 0.75 0.452 Experience - .0050341 .0071642 -0 .70 0.482 Ownership furmland .6675424 .2316268 2.88 0.004*** Land Acquisition -.1172788 .2044856 -0.57 0.566 Labour -.0529275 .1399946 -0.38 0.705 Membership -.8823083 .0993309 -8.88 0.000*** Transportation -.0096466 .1392061 -0.07 0.945 Housing materials 1.783062 .1284655 13.88 0.000*** Comm.m.ication kits 1.107219 .0944422 11.72 0.000*** Extension Contacts - .0551526 .0372423 -1.48 0.139 Access to credit .1185266 .0955386 1.24 0.215 Constant -1.727015 .3903677 -4.42 0.000 Number of Observation 294 Wald chr(l 7) 995.02 Prob > chl2 0.0000 R-Square 0.7671 RootMSE .68289 Source: Computer Printout of MPI (2SLS) Regression Analysis Note:*,** and*** means 10%, 5% and 1 % level of significance respectively 126 6.5 Hypotheses Statement The study employs three of the hypotheses which are the second, fuurth and fifth objectives because they can be statistically measured: The null and alternative hypotheses fur objective two are specified thus: Ho : Farmers socio-economic characteristics do oot significantly influence indicators of CSAP within different crops and livestock enterprises. H1: Farmers socio-economic characteristics significantly influence indicators of CSAP within different crops and livestock enterprises. 6.5.1 Evaluation of the Hypotheses for objective two The probability vah.ie of education socio-economic characteristics : 0.005, 0.008 , 0.009, 0.046 and 0.050 fur nnllet, livestock, maize, groundnut and sorghum enterprises respectively, are all less than 0.05 and absoh.ite t - vah.ie greater than 1.96 shows that CSAP are significant at the standard of 1% except 5% for the sorghw:n enterprises significant level Therefure, the researcher rejected the null hypothesis and accepted the alternative hypothesis which stated that :farmers' socio-economic characteristics such as education significantly influence indicators of CSAP within different crops and livestock enterprises. The null and alternative hypotheses for objective four are specified thus: Ho: Climate smart agricultural practices do not significantly affect poverty status of :farmers in the study area. H1: Climate smart agricultural practices significantly affect poverty status of furmers in the study area. 6.5.2 Evaluation of the Hypotheses for objective four The probability vah.ie of 0.000 fur maize, sorghum, nnllet, groundnut, livestock and crop/livestock climate smart agricultural indices which are all less than 0.05 and absoh.ite t - vah.ie greater than 1.96 shows that CSAP are significant at the standard of 1% . Therefure, the researcher rejected the null hypothesis and accepted the alternative hypothesis which stated that CSAP are significant and determinant of poverty status of :farmers in the study area. 127 The null and ahernative hypotheses fur objective five are specified thus: Ho: Climate smart agricuhu.ral practices do not significantly aflect :MPI of the furmmg househokl in the study area. H1: Climate smart agricultural practices significantly aflect :MPI of the funning househokl m the study area. 6.5.3 Evaluation of the Hypotheses for objective five The probability value of 0.000 fur crop CSA indices, livestock CSA indices and crop/livestock climate smart agricultural indices which are all less than 0.05 and absoh.rte t - value greater than 1.96 shows that CSAP are significant at the standard of 1% . Therefure, the researcher rejected the null hypothesis and accepted the ahernative hypothesis which stated that CSAP are significant and bas an eflect on :MPI of funning househokl in the study area. 128 CHAPTER SEVEN SUMMARY OF MAJOR FINDINGS, CONCLUSIONS AND POLICY RECOMMENDATIONS 7.0 Introduction This is the concluding chapter of the study and it gives a complete summary on the objectives investigated and the outcomes observed. This chapter bas five sections: the summary of the study, conclusion, policy implications of the :findings, policy recommendations and suggested areas fur further studies. 7.1 Summary of the Findings This research work was motivated and encouraged by the increasing consequences of climate change and its bearing on the poverty status on the rural farmers . The current poverty situation in Nigeria is alarming, and it is exacerbated by fuod insecurity, an upshot of climate change. This necessitated the examination of CSAP and poverty level of the smallholder farming households in North-West Nigeria. The selected research questions addressed were: what are the socio-economic characteristics of farmers in the study area? What are the constraints to the high-users and low- users of CSAP in the study area? What are the factors influencing indicators of CSAP within different crops and livestock enterprises? What is the poverty status of those who are high-users and low-users of climate smart agricultural techniques in the study area? And what are the effects of CSAP on poverty status of farmers? The study employed descriptive statistical analysis, likert scale technique, PCA, ordinary least square regression analysis , Foster, Greer and Tborbecke , EDE-FGT index, IV-Probit and MPI of Two Stage Least Square (2SLS) models to address these research questions. The resuhs showed that the high-users of CSAP are more in Katsina State than Sokoto State. The study further showed that males (94%) are more involved in CSAP than the females, and that the high-users of CSAP are mostly knowledgeable through informal education. Many of the respondents who are high-users of CSAP were married as against those who are low-users of CSAP with a significant difference between the groups. Additionally, 91 % of the respondents were Muslims. Furthermore, the study evinced that land was principally owned through inheritance and about 69% of the farmers employed both family and hired labour. Likewise, it was discovered that streams/rivers as well as deep well water were mostly used for watering crops, animals and washing their household items, while bore holes were mostly used for drinking and washing. The research showed that public water was seldom used by the respondents as not up to (13 %) used it 129 fur irrigation, drinking, washing and watering crops/animals. This result depicted a parallel with Adekoya' s (2014) findings , that majority (70.9%) of the :farm households did not have access to potable water. The findings further showed that only 48% bad access to credit and the credit source with the highest average credit received was from :family/relations, fullowed by cooperative societies and then microfinance banks. In terms of constraint, the results showed that IIX)re than 50% of the respondents agreed that the lack of access to credit, lack of access to education, lack of access to improved crop varieties, lack of access to high quality breeds, lack of time to practise CSA, lack of technical knowledge, lack of infurmation from rad:io, lack of awareness of CSAP, cost of labour was high, cost of input was high, lack of social interaction, lack of deIIX)nstration of climate smart agricultural techniques and lack of processing technology were constraints to the practice of climate smart agriculture, and majority of them said they were very serious constraints. The study indicated that age (p<0.10), gender (p<0.10), education (p<0.01), marital status (p<0.10), households (p<0.10), housing material (p<0.01), comrmmication (p<0.05), lack of time (p<0.01) and State (p<0.01) were significant determinants of the use of CSAP for the maize enterprise. Also, education (p<0.01), :farm size (p<0.05), commmication equipment (p<0.01), extens:ion contact (p<0.05) and lack of access to high quality hybrid (p<0.05) were significant determinants of the use of CSAP for the livestock enterprise. The study employed several decomposition techniques, and consequently, the investigation deIIX)nstrated that as regards all the IIX)netary dimensions (per capita expenditure and food) low- users of CSA bad higher poverty rates and higher poverty severity than high-users ofclimate smart agricultural :farmers. In the case of FGT relative poverty index fur per capita expenditure, when clisaggregated, the low-users of climate smart agricultural :farmers were relatively 2% poorer than high-users of climate smart agricultural :farmers. Also in the case of FGT relative poverty index fur fuod, when disaggregated, the low-users of climate smart agricultural :farmers were absolutely 8% fuod poorer than high-users of climate smart agricultural :farmers. Finally, the CSAP in all the crops (maize, sorghum, millet and grmmdnut) livestock and crop/livestock enterprises decreases the odds or probability of being poor significantly. 130 7.2 Conclusion of the Study The research work was IlX)tivated by the increasing popularity of climate alteration consequences being witnessed all over the world. Projections show that developing countries who are least prepared fur this changes in the climatic situations will be the IlX)St affected. However, the present poverty in Nigeria is frightening and climate change brings about fuod insecurity and poverty to a large extent. It was on this note that the study assessed CSAP and its implication on poverty status of small holder :funning households in North-West Nigeria. The :findings showed that lack of access to credit, lack of access to education, lack of access to improved crop varieties, lack of access to high quality hybrid, lack of time to practice CSA, lack of teclm:ical knowhow, lack of information from radio, lack of awareness of CSAP, cost of labour was high, cost of input was high, lack of social interaction, lack of demonstration of climate smart agricultural techniques and lack of processing technology were constraints to the climate smart agricultural activities and majority of them said they were very serious constraints. Additionally, the study showed that age, education, :farm size and type of housing materials were the significant determinants on the use of CSA fur IlX)St of the enterprises under consideration. Furthefm)re, the results also showed that poverty rate was higher fur low-users of CSAP than fur high-users fur all dimensions and using all the decomposition techniques. The findings also showed that high-users of CSAP bad reduced odds of being fuod poor significantly. It is worth noting that while some of the techniques measure all the poverty status of the farmers , others such as the EDE FGT measure only the poverty gap and severity. The poverty line in each case is two- third of the mean of each dimension as discussed in chapter three. This arbitrary poverty line is not completely bias as the fucus is to compare poverty status between farmers of high-users and b w-users of CSAP. And CSAP in all the crops (maize, sorghum, millet and groundnut) and livestock enterprises decrease the odds or probability of being poor significantly. Also training and integration on climate change adaptation tactics at the various national levels will bring about a wider ownership of climate response and albw sketches on a wider pool of human resources and financial implementation, with supporting institutional structures and extensive dimensions . Folbwing this, agriculture system must therefure include climate change effects as a benchmark to safeguard sustainable production in agricultural activities. As a result, farmers, particularly the younger generation who are anticipated to be future agriculturalists, need to be equipped not only with CSA knowledge, but climate threat management approaches such as timely atmospheric information, crop and livestock insurance, credit facilities and institutions, such as farmers ' 131 cooperative societies. CSAP bas been proposed as an approach that can combat climate change and desertification comprehensively by emphasizing adaptation to climate change. Having assessed the approach through the prism of the framework, we futmd that broadly speaking it fits with what can be termed as a sustainable technology. Admittedly, there are many aspects , such as the emphasis on culhrral functions that will need to be addressed and that is why CSAP in a societies like Nigeria where the poor are often cheated out of programmes should integrate all the needs of the disadvantaged into the policy befure its final adoption. Such a review bas become necessary because the approach, as currently conceived, does not do enough justice to some of the critical issues in the agricultural sector in Nigeria. There is need for an all-inclusive approach that would not only enhance environmental protection fur the c0tmtry but also respect social values. The outcomes of some of the current practices adopted to manage adverse environmental impacts were futmd to provide coping strategies that fit with the concepts of CSAP although, still not very widespread enough. However, here we contribute to the existing empirical literature on whether a combination of multiple CSAP is more resilient against climate change and to reduce poverty. In addition, we simulate the possible effects of CSAP on poverty based on both the choice of practices. Our results indicated that the current choices of alternative combinations of practices influenced poverty status of rural households ' farmers in the study area. The study therefure recommends that massive campaigns be made by government, ·civil societies and the media to create awareness about CSAP and to proffer indigenous solutions that address the significant constraints being raced. There is equall.y a need to boost the role of :financial institutions in teIIllS of the volume and frequency ofcredits given out and to reduce or eliminate their interest rate fur the rarmers, especially fur those that practise CSA 7 .3 Policy Implication of Findings The findings of the study suggest several policy implications as fullows: (~ The socio economic characteristics show that majority of the respondents are old matured funners with an average age of fifty three (53). This implies that youths in the study region were not really involved in CSAP which might create an age gap in rarmi.ng activities. This could be due to the ract that the business was not lucrative enough and hence it did not attract their business acumen, or there was a misconception about the furmi.ng enterprises, and as such they might prefer white collar jobs. Notwithstanding the standpoints of the youths, there was a need for the ageing generation to handover to the yotmger generation. 132 (ii) The study equally shows that majority (94%) of the :farmers were male and this was not surprising as the study area bas a clear male dominance that stems from their culture and religion believed. There was still need for policy formulation so that even the women whose husbands are involved in other sectors could delve into :farming and in the long nm can increase productivity and profitability. (iii) The statistics infers that education was very low in the study area given that only 24% bad post primary education and 56% bad Arabic education which was an informal system of education. Therefore, it is needful to stinrula te policies to encourage an advancement in education which will in tum reflect on their acceptability of CSAP. (iv) The study shows that only 47% of the respondents are involved in membership associations in form of cooperative societies as against 53% who are not members of any. This was alarming considering the relevant information that was circulated in these associations that could be of benefit to the :farmers. Moreover, the highest source of credit was from cooperative societies which was one of those associations that could help them expand their :farming enterprise if given the required credit :facilities and could enable them better their :farming activities in the CSAP. (v) The report shows that public water was sekiom used by the respondents as not up to 13% used it for irrigation, drinking, washing and watering animals. This will definitely increase the cost of production and restrain the :farmers from employing irrigation which was one of the CSAP, given that the :farmers may not be able to affurd boreholes or may not be around or near streams. Public water was generally perceived to be better because it was considered to be subsidised by the govenment and properly distributed to all households at a moderate price rate. (vi) The study examined sixteen (16) indicators to check the extent to which they are constraints and concruded that lack of access to credit, lack of access to education, lack of access to improved crop varieties, lack of access to high quality hybrids, lack of time to practice CSA, lack of technical knowledge, lack of awareness of CSAP, cost of labour was high, cost of input was high, lack of demonstration of climate smart agricultural techniques and lack of processing technology were very serious constraints to CSAP in North- West ofNigeria. (vii) The results show the significant determinants of the use of CSAP for five enterprises (maize, sorghum, millet, groundnut and livestock). The most recurring significant variables were age, education, :farm size and type of housing materials. These 133 determinants could therefore constitute the benchmark for adequate policies aimed at improving CSAP for each enterprise. (viii) Furthennore, the finding shows that poverty rate was higher for low-users of CSAP than for high-users. This implies that the practice of CSA was an antidote to poverty elimination in the study area. Farmers should make conscious effurts to practice climate CSA regardless of their poverty status due to the fact that poverty resides more with low-users. It could also be as a result of the fact that high-users make their production sustainable by practising CSA and consequently accrue high yields that might in turn reduce their poverty status. (ix) Finally, CSAP in the selected crops (maize, sorgm.nn, millet and groundnut) and livestock enterprises decrease the odds or probability of being poor significantly. 7.4 Policy Recommendations Recommendations based on the policy implications are outlined: ( i) The study recommends that massive campaigns be carried out in educating the youths regarding the importance of agriculture production activities and how sustainable it can be when using CSAP. The importance could equally be emphasised in the school curriculum so as to diffuse the misleading urge for white collar jobs and focus on where the talents lead you. Formal education and Arabic educational curriculum should be enriched with climate smart agricultura 1 information and should be agricultura 11 y focused. This will serve as bait for household funners in the study area because education was one of the major significant variables. (ii) Govennnent, Non-Govennnental Organizations (NGO) or Civil Society Groups can advance campaigns promoting and encouraging women to develop interest and their involvement in climate smart agricultural farming in the North-West Nigeria ; disclosing the advantages ofbaving both heads of the family earn income from various agricultural activities. The media also have a huge role to play in encouraging women to set up associations and unite to break off from the 'purdah' system being practised in the study area and this limitation is caused by male dominance in the study area because marital status was one of the significant variables. (iii) There is need to invent new ways of encouraging education in the study area. The govennnent bas done a considerable work in this aspect, the most significant being the universal basic education Nonetheless, it is still necessary to make such considerations for post primary educational levels so as to get those who eventually become farmers 134 I1X>re knowledgeable about the business which is becoming I1X>re lucrative and scientific with CSAP and not just planting and then wait fur harvest time activities. In the educational sector, there is very urgent need to re-orientate the thinking and value system ofboth parents and their children through mass educational campaign regarding the importance of education and the need fur parents to insist on their chikiren going to school at least up to first degree befure seeking employment or going into business because majority were found to be infurmally educated. (iv) There is equally a need fur public water to be provided to furmers. If getting pipe borne water will be difficult, dams and canals coukl be built and water circulated across the furms to encourage irrigation and settle other forms of water needs in the study area. Investing in the agricultural sector to reduce poverty shoukl be a matter of great priority and there is also need to encourage productivity and access in both furm and non-farm occupations through direct input to strengthening the agricultural research and extension fucilities, adapting agricultural technobgy and taking extension services to poor furmers, and by improving physical infrastructure such as rural roads and irrigation. Since poverty in Nigeria does have important spatial implications , geographic targeting especially in the North West and rural areas can play an important role in government anti-poverty eflorts. Moreover, geographically targeted programmes are attractive partly because they are I1X>re cost-efrective than untargeted programs. Thus, making financial capitai physical infrastructure especially roads, water supply, electricity and tecbnobgical innovation available in poor zonal and rural areas will lead to important contribution to government's efforts to reduce poverty in the study area. (v) There is need fur massive campaigns in creating awareness about existing CSAP and ways to domesticate these practices by using their immediate environments to show them ways of keying into CSAP. Lack of awareness was seen to be a very serious constraint and therefure clearly needs increased efforts in that direction. And extension delivery system services should emphasise on househok:l education and training of furmers on how to practise CSA because extension contact was one of the significant variables. (vi) There is the need fur birth rate to be controlled even aI1X>ng funning househok:ls. This 15 because, contrary to expectation, poverty level increases with a rise in the mnnber of chik:lren in the households, if the birth rate is reduced, the child dependency ratio 135 will be reduced and the funning households will be better off Hence, it is suggested that funning households should be educated on the need and on how to use contraceptive measmes and devices to control explosive population which is growing geometrically on daily and yearly basis in the study area. Given that poverty increases with the number of household size, there is trrgent need to intensify fumily planning services efforts and activities in Nigeria so as to improve knowledge and practice of fumily planning. Om results and analyses above suggest that policy interventions are necessary to reduce poverty in Nigeria because monogrumus marriages tend to reduce poverty although government cannot legislate mamage structure given the heterogeneous nature of the country and the need to promote freedom ofcboice, public and private sector policies can be used to increase the proportion of high quality monogrumus marriage rates among rtrral farmers, which will be an important strategy fur poverty reduction in the study area. (vii) It is equally needful to address all the identified challenges or constraints of practising CSAP as they stand as stumbling bbcks and until they are addressed, the practice will not be encotrraged. Toe study therefore recommends that conscious eflorts be made at the three tiers of government to address all the constraints highlighted in this study. (viii) There is need fur significant empowerment of the farmers. Some of the CSAP have cost implications and require extra money to fimd it. Insufficient credit facilities will not encotrrage farmers to practice CSA as some of them can barely aflord seeds and tools more or less of hybrid seeds and other furms of CSAP. It is important that the rtrral capital market be opened up to the poor to albw them to borrow money on regular market terms. In addition, the difficulties and btrreaucracies (like suitable collateral and education of the applicants involved in formal lending procedtrres must be removed). In line with this, furmal financial institutions should be ready to collect and invest the small amount of potential savings that are available in the rtrral areas. This is to curb the pronm.mced tendency to consmne conspicuously which is evident in connection with birthday ceremonies, marriages or burial activities of the farmers in the study area. (ix) Government and farmers' organisations should create an environment to motivate the bw-users of CSA to increase and improve on the level of their usage ofCSAP because CSA was one of the significant variables that needed to be improved fur more productivity and profitability. 136 (x) The practice of CSA can be greatly encouraged to the rural farmers since they are getting involved in the practices gradually. Moreover the consequences of climate change effect, on all and sundry, bas been alleged to increase poverty levels among farmers . It is therefure only logical to practise CSA so as to be sustainably increasing in agricultural productivity, income, adapt and buiki resilience to climate change, reduce or eliminate green-house gas emission which enhances achievement of national fuod security and reduce poverty in the long nm. 7.5 Suggestions for Further Studies CD Studies on CSA are very intriguing and fulbwing ctnTent trends and so many more , empirical studies need to be done in this line. Studies could relate CSA and other indicators bes:ides poverty such as output productivity and input materials amongst others. (ii) The study for CSAP could equally be done fur small and medium sized enterprises or large scale agro-based :fum5 especially to investigate if the economies of scale enjoyed by these :fum5 permit or ease the practices of CSA. (iii) This study was done in North-West Nigeria but could be done for other regions and nations. It could equally be scaled down to States and Local Government Areas. 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Very serious =5 Serious = 4 Undecided =3 Not serious =2 Not very serious = 1 2 Determine factors influencing The Principal Component Principal Component indicators of CSAP within Analysis of the crops and Analyses and Ordinary different crops and livestock livestock climate smart Least Square Regression enterprises. agricultural indices regressing Model (On STATA with the socio-economic version 13 Software). characteristics. 3 Determine poverty status fur Low-users are respondents who Foster, Greer and bw-users and high-users of used two or less CSAP while Thorbecke (1984) which climate agricultural techniques. high-users are respondents who is widely known as the used three or above CSAP. FGT poverty measurement. 4 Determine the efrect of CSAP The respondents who are poor = 1 Instnnnental Variable of a Probit Model on poverty status of farmers . while the non-poor= 0. Regression (On STATA version 13 Software). 5 Determine the efrect of CSAP The respondents who are poor = 1 MPI of (2SLS) while the non-poor= 0. regression model (On on MPI of the househokls. STATA version 13 Software). Source: Authors Computation, 2016 159 APPENDIX 2 QUESTIONNAIRES QUESTIONNAIRE ON CSAP AND POVERTY STATUS OF SMALL HOLDER FARMING HOUSEHOLDS IN NORTH WEST NIGERIA. State: .......... . .......... ... . .......... . ........................... ... ......... ....... ............. . LGA: ..... . ..... .. ... ...... .... ....... ..... .. .. . ...... . ........... . . .... . ... ... . ... .. . .. . .... ....... . Identification: Dear Sir/Ma, I am EKP A, Daniel currently emolled in a Ph.D. programme. This questionnaire was drawn to elicit information on eflect of CSA practices on poverty status am:mg :farmers in North West Nigeria. The exercise is designed fur research only. We therefore plead fur yotrr cooperation in supplying the acctrrate information. Yotrr input is considered very important. We promise to treat yotrr contribution with absohrte confidentiality. Thank you BACKGROUND INFORMATION: Name of Emnnerator. ... . .. .. ...... . .. . .... ... Name of Supervisor. . . ........ . . ..... ...... ...... . .... . Please identify with a tick ({) A. Socioeconomic Characteristics of Farmers 1. What is yotrr age? . ...... . ........ . ........ .. . . ........ ......... . 2 . Gender? (a ) Male D (b) Female D 3. Educational level attained? (a) Arabic education D (b) Primary education D (c ) Secondary education D (d ) Tertiary Education D 4. Wbat is yotrr marital Status? a) Married-rmnogam y D b) Married-polygamy D c) Divorced D d) Single D e) Widowed D 5. Wbat is yotrr Religion? 160 a) Christian 0 b) Muslim 0 c) Traditional Religion D 6. How many persons live in your househokl on a regular basis? ... .. .. ..... ........... .......... . 7. What is your estimated personal monthly expenditure (N)? ............................ . 8. What is the size of your farm land (ha)? .. .. . . . . .... .. ........ .. . . ........................ . 9. How long have you been involved in farming activities (years)? 10. Are you ownership of:furm land? Yes D , No D 11. 1.and acquisition methods (a) Rent D (b) inherited D (c) Purchased D (d) Gift D 12. Type of labour (a) Family D (b) Hired D (c) both D 13 . Membership of any farm group/association? (a) Yes D (b) No D 14. Means of transportation (a) Bicycle D (b) Motorcycle D (c) Ca1O (d) Pickup van D (e) Animals D 15. Types of housing materials (a) Mud/thatched D (b) Mud/zinc D (c) Brick/zinc D (d ) concrete bbck zinc D 16. What is your comrrn.mication equipment? (a) Radio D (b) Television D (c) GSM 0 (d) Video□ 17. How many times do you have contact with extension agents per year? ..... . ..... . 18 . Kindly indicate the source(s) and the use of water as indicated in the table below: Source of Irrigation Drinlcing Washing Watering Water crops/animals S trea.m/river Deep well Borehole Public water 161 19. Wbat is the quantity of your crop production that were produce using the six smart index as folbws, zero if not? (D Use of organic manure: ( ii) Agro-forestry: ( iii) Conservation agriculture: (iv) The use of improved varieties and breeds: (v) Integrated crop/ livestock management ( vi) Irrigation for small-holder :farmers: S/No: Crops Hectares Yield 0 CD (n) (in) (iv) (v) (YI) (ba) (kg) I Maize Ii Sorghlllll Iii Millet Iv Ground nut 20. Wbat is the quantity of your animal production on the average using the CSAP, zero if not? S/No: Livestock Herds(#) 0 (n) (iv) (v) I Cattle Ii Sheep Iii Goat Iv Donkey V Camel 21. Do you have access to credit? (a) Yes D (b) No D I Amount (N) 162 Ii Micro-Finance Iii Non-Governmental Organisation Iv Cooperative society V Family /Relations B. Constraints in implementing the use of CSA SIN Description Yes No Very Serious Neutral Not Not Serious Serious Very Serious 1 Lack of access to market 2 Lack of access to credit 3 Lack of access to education 4 Lack of access to extension services 5 Lack of access to improved crop varieties 6 Lack of access to high quality breeds 7 Lack of time to practise CSA 8 Lack of teclmical knowledge 9 Lack of information from radio 163 10 Lack of awareness of CSA practices 11 Cost of labour is high 12 Cost of input is high 13 Lack of social interaction 14 Lack of willingness 15 Lack of demonstration of CSA teclmiques 16 Lack of processing teclmology C. CSAP and non-practices (a). Do you practise CSA on your farm? (a) Yes (b) No. /No Description YES NO 1. Do you use organic manure? 2. Do you practise agro-furestry? 3. Do you practise conservative agriculture 4. Do you use improved drought resistant seeds varieties? 5. Do you use improved breeds of livestock? 6. Do you practise crop/livestock management? 7. Do you practise irrigation in your furm? 8. Do you practise mulching on your farm? 164 9. Do you practise crop rotation on your farm? 10. Do you practise intercropping on in your farm? 11. Do you based your farm activities on information about weather forecast for planting periods? 12. Do you vaccinate or use medication on your livestock against diseases? 13 Do you have any mechanism 1Il your farm to reduce or remove greenhouse gas emissions (via plants that absorb carbon dioxide)? 14. Do you experience sustainable mcrease in output over the last few years? 15. Do you practise conventional tillage on your farm? 16. Do you use inorganic manure (fertiliz.ers) on your farm? 17. Do you use bcal varieties of crops during planting season? 18. Do you use bcal breeds of animals as livestock? 19. Do you cut down trees fur firewood from the fures t in your area? 20. Do you burn grass residue materials on your farm before cultivation? 21. Do you rely on history and expenences to determine your funning activities? C. What are the household farmers' expenditure on monthly bases? S/No List of expenditures Amount (N) 1. On fuod 2. On health 3. On education 4. On improved seeds 5. On improved animal breeds 6. On organic manure 7. On agro- forestry 165 8. On crop/livestock management 9. On irrigation 10. On forecast information 11. On vaccination of livestock against diseases 12. On tillage process 13 . On inorganic manure (f urtiliz.ers) 14. On bcal seeds varieties 15. On bcal animal breeds. 'Thank you fur your contribution to this survey. We hope to use these results to determine how best to provide affordable and desirable service to the people of your corrnnunity. 166 APPENDIX 3 LIST OF PUBLICATION 1. Evaluation of Factors Influencing Indicators of Climate Smart Agricultural Practice on crops in North-West Nigeria. World Journal of Agricultural Research 2017. Vol 5, No: 5, pp 273 - 278. Authors: Ekpa Daniel, Oyekale Abayomi Samuel and Oladele Oladimeji Idowu. Candidate's Contribution : The researcher designed the study, searches fur the materials, managed the literature review and wrote the first of the manuscript. American Journal ofR ural Development, 201 7, Vol. 5, No. 5, 138-143 Available online at http J/pubs.sciepub.com/ajrd/5/5/3 ©Science and Education Publishing DOI:10.12691/ajrd-5-5-3 2. Poverty Status of Climate Smart Agricultural Farmers in North West Nigeria: Application of Foster Greer and Tborbecke model American Journal of Rural Development, 2017, Vol 5, No : 5, pp 138 - 143 . Authors: Ekpa, D, Oladele 0. I and Akinyemi, M. Candidate 's Contribution : The researcher designed the study, searches fur the materials, managed the literature review and wrote the first of the manuscript. (1) Evaluation of Factors Influencing Indicators of Climate Smart Agricultural Practice on Crop in North-West Nigeria. Ekpa, D.1, Oyekale, A.S .2, Oladele, 0 .12 1Department of Agricultural Economics and Extension, Federal University Dutsin-Ma, Katsina State, Nigeria. 2Department of Agricultural Economics and Extension, North West University, Ma:fikeng, South Africa. *Corresponding author: dekpa@ fudutsinma.edung ABSTRACT: The current state of poverty in Nigeria is alarming and climate change threatens fuod security and increases poverty directly and indirectly. This research established a link that exists between CSAP and poverty status of small househoki farmers in North-West geopolitic a 1 zone of Nigeria; and based on this, examined CSAP and poverty status of small holder funning househokis in the zone. The specific objective of the study was to determine :factors influencing indicators of CSAP on crop enterprise in the study area. Multi-stage, purposive and random sampling techniques were used to select three hwdred (300) :farming households in the study area, and using a set of pre-tested structured questionnaires, relevant data were collected. The study employed Ordinary Least Square (OLS), regression models to ascertain the objective. The regression analysis of maize enterprise shows that age, gender, marital status and househoki size were significant (p < 0.10), with age positively significant inferring that a WJ.it increase in age will result in corresponding increase in the practice of climate smart agriculture fur maize enterprise by 0.0264; also, the results showed that many more male :farmers used climate smart agriculture in the maize enterprise than their female cowterparts by 0.6001. Education, housing materials , lack of time and State option were significant (p < 0.01 ). The study concludes that crop production is greatly influenced by climate Smart Agriculture in the study area. The results suggest that those who had infurmal education (Arabic education) had significantly lower indices of climate smart agriculture fur maize production than their cowterparts who had furmal education primary, 167 secondary and tertiary. Housing material was also negatively significant (p < 0.01 ), meaning that the fanners with mud/thatched and mud/zinc houses had significantly lower usage of climate smart agriculture in the production of mai?.e when compare with those with brick/zinc and concrete block zinc houses. The study concrudes that socio economic variables influenced climate Smart Agriculture in the study area. It therefore recommends that women be encorn-aged to develop interest in climate smart agricultural farming activities through women empowerment programme instituted by government and private bodies because men dominate the CSAP in the study area; Government, Non-Governmental Organizations and funner associations should create a conducive learning environment to encorn-age the fanners of climate smart agriculture in the study area to embrace formal education which can improve their perfurmance rapidly; and finally, policy on informal education shoukl be enriched and developed in the curriculum to meet the ctnTent climate smart agricultural challenges. Keywords: climate, smart, agriculture and poverty in north-west Nigeria. 1. Introduction The earth is warming. This is the unequivocal conchision of the Fourth Assessment Report of the Inter-Governmental Panel on Climate Change (IPCC) in 2007 [1 ], which offers a complete investigation into how climate change is affecting natrn-al and human system;. (2) Poverty Status of Climate Smart Agricultural Farmers in North West Nigeria.- Application of Foster Greer and Thorbecke Model Ekpa, D. 1, 0 ladele, 0. 1.2, Akinyem.i, M. 1 , 1Department of Agricultrn-al Economics and Extension, Federal University Dutsin-Ma, Katsina State, Nigeria 2Department of Agricultrn-al Economics and Extension, North-West University Mafikeng, South Africa *Corresponding author: ekpadaniel3322@ gmail com, dekpa@fudutsin.ma.edung Abstract This research established a link that exists between climate smart agricultural practices and poverty in North-West geopolitical wne of Nigeria. The study was motivated by the increasing consequence of climate change and its impact on poverty status among fanners in the study area. Farming households changing agricultrn-al practices as a result of global observation of climatic and environmental changes. It was based on this that the study examines the impact of climate smart agricultrn-al practices on poverty status among fanners in North West Nigeria. A multi-stage sampling techniques was used to select two hundred and ninety four (294) farming households in the study area who provide the relevant primary data information for the study through a set of pre-tested structured questionnaires. The objective was to decompose poverty status for high-users and low-users of climate smart agricultrn-al techniques in the study area. Foster Greer and Thorberk mode~ Watt' s index, Sen, Shorrocks and Thon index were used to ascertain the objective. Poverty head count according to the FGT index for the total population is 35 .89% for absohrte poverty and 9.12% for relative poverty. This means that the average climate smart agriculture :fanners had about 36% deprivation of basic hun:Jan needs such as food, safe drinking water, health, shelter, education and information. On the other band, for the absohrte poverty of 9%. It means the average climate smart agricultrn-al fanners had 9% deprivation to maintain the average standard of living. It connotes that the average climate smart agricultural :fanners bad 33% deprivation of food and 13% deprivation of average standard of living. Analysis of health poverty reveals that the absohite poverty is 42.38% and relative poverty 27.64%. It implies that the average climate smart agricultrn-al :farmers were deprived of health by 42% and by average standard of living by 28%. Further, analysis on education poverty reveals the absohrte poverty and relative poverty vahie of 4 7 .10% and 28 .26%. This signifies that about 4 7% of the climate smart agricultural fanners were 168 deprived of basic education and about 28% of climate smart agriculture farmers were deprived of average standard of living. The study concludes that poverty is evident in the study area. It therefore reconnnends that Government, Non-Governmental Organizations and funner associations should create a conducive knowledge exchange environment to encourage the bw- users of climate smart agriculture to improve on their perfurmance. Spouses especially should devebp interest in climate smart agricultural funning. Women empowerment programme can be embarked upon by government and private individual Policy on formal education should be enriched and devebped in the curriculum to meet the climate smart agricultural challenges . Keywords: climate-smart-agriculture, poverty, North-West, Nigeria 1. Introduction Climate change equally leaves many rmre people vulnerable to poverty. [21], estimated that above half of the world's population as well as rmst of the productive lands and urban areas are situated in coastal and delta regions where the climate related disasters are prominent. 169 Editorial ~ Suite 1, Albarakah Plaza, Favos, New Bodija, Ibadan Tel: 08068500568 Email: todionl508@yahoo.com Website: www.ttlameconsults.webs.com Date: 20 November, 2017 Prof. A. S. Oyekale, Department of Agricultural Economics and Extension, Faculty of Natural and Agricultural Sciences, North-West University, South Africa. Dear Prof. Oyekale, EDITORIAL REPORT ON THE MANUSCRIPT: "Climate Smart Agricultural Practices and Poverty Status of Smallholder Farming Households in North West Nigeria" The above manuscript was received for editing on Thursday, 16th November, 2017 with the following details: Type of manuscript: Ph.D. project Author: D. Ekpa Institution: North-West University, South Africa Number of pages: 165 Total word size: 51,712 The manuscript was thoroughly edited for grammatical and stylistic errors. The details of the corrections are provided below: Categories of errors found: Concord, spelling, om1ss1ons and wrong use of grammatical words, articles and connecting items, tautology, wrong word choices, wrong phrasing of expressions and punctuation. Broad report on corrections done: Corrections were effected on spelling; grammatical words; and punctuation marks, and articles that were omitted were inserted, and the wrong ones were equally corrected. Connecting items were corrected, as well as inserted where necessary. Errors of concord, including subject-verb agreement and pronominal agreement were also corrected. Method of correction: All corrections are effected as track changes in the manuscript, and are attached as document, in addition to a clean, submittable copy of the same document. Two versions of the edited works are prepared: a version showing the changes made, and a clean version in which the changes have been accepted. Concluding remarks: Generally, the candidate demonstrates an average level of competence m English. With the errors corrected, the manuscript reads well and can be subjected for examination. Yours sincerely, Taiye Odionkhere. T'fl,AM£ CONSUL iS ... We, Edu cuul,, Pr-oofvead,,