ANALYSIS OF LIVELIHOOD DIVERSIFICATION STRATEGIES AMONG WOMEN CROP FARMERS IN GOMBE STATE, NIGERIA.pptx

Seminar Presented at the Department of Agricultural Extension and Rural Development, Abubakar Tafawa Balewa University, ATBU, Bauchi, Nigeria

1. ANALYSIS OF LIVELIHOOD DIVERSIFICATION STRATEGIES AMONG WOMEN CROP FARMERS IN GOMBE STATE, NIGERIA. BY GARBA, ADO AND TATA , LIBNAH ALGAITA DEPARTMENT OF AGRICULTURAL EXTENSION AND RURAL DEVELOPMENT, FACULTY OF AGRICULTURE, FEDERAL UNIVERSITY DUTSE, JIGAWA STATE, NIGERIA FEDERAL UNIVERSITY, KASHERE GOMBE STATE APRIL, 2024.

2. 1.0 INTRODUCTION 1.1 Background to the study Livelihood is a process whereby people make use of the resources that provide them the capability of building a satisfactory means of living (FAO, 2017 ). Diversification is defined as the series of economic activities an individual gets involved in with shares in the overall economic activity (Olajide et al ., 2022 ). Livelihood diversification is therefore a process of combining both agricultural and non-agricultural activities to survive and improve the standard of living (Martin and Lorenzen, 2016; Pritchard et al., 2019). Livelihood diversification plays a crucial role in promoting economic growth and reducing rural poverty in developing countries ( Loison , 2019).

3. 1.1 Background to the study Cont’d Farming households get their income from farm produce which is not enough to meet their basic needs due to various factors like diseases and natural disasters (Damilola et al. , 2022 ). These consequences of unpredicted natural disasters and shocks in agriculture drives farming households to diversify their means of livelihood (Olajide et al ., 2022). Households across the developing countries are trying to diversify their livelihood activities to secure them from risks and cope with economic and environmental shocks (Baird and Hartter , 2017; Gautam and Andersen, 2016)

4. 1.2 Statement of the Problem Rural economies of developing countries are high dependent on agriculture which is prone to shocks such as weather and natural disasters ( Bezabih et al., 2014), financial risks (Reddy, 2015), price and production risks ( Meuwissen et al., 2015). In the event of a shock to the agricultural sector, livelihoods of agriculture-dependent communities are severely affected (Abid et al., 2016; Imran et al., 2018). Despite the significant roles of women in building the economy, most development literature has neglected their perception on livelihood diversification strategies. From the studies already carried out by some researchers on livelihood there is none with particular interest on the analysis of livelihood diversification strategies among women crop farmers especially in Gombe State.

5. 1.2 Statement of the Problem Contd ’ It is against this backdrop that the research wish to provide answers to the following questions; i . What are the socio-economic characteristics of women crop farmers? ii. What type of livelihood diversification activities are the women crop farmers engaged in? iii. What are the reasons for engaging in livelihood diversification activities? iv. What are the sources of income on livelihood diversification as perceived by women crop farmers? v. What is the impact of livelihood diversification strategies on women crop farmers?

6. 1.3 Objectives of the Study The broad objective of the study was to analyze women crop farmers’ livelihood diversification strategies in Gombe State Nigeria. The specific objectives were to; describe the socio-economic characteristics of women crop farmers; identify the types of livelihood diversification activities engaged by women crop farmers; identify the reasons for engaging in livelihood diversification activities; identify the sources of income of women crop farmers based on their perceived livelihood diversification activities; and determine the impact of livelihood diversification on women crop farmers.

7. 1.4 Justification of the Study Livelihood diversification is the backbone and coping strategy of many households. Understanding the various strategies in this study will result to the following benefits: Inform policy makers to design interventions for women crop farmers empowerment. Make contributions of recommendations on how women crop farmers will help themselves through diversified strategies of livehood Attainment of knowledge to field of academic research and basis for future studies. Serve as opening for further academic research.

8. 1.5 Scope and Limitations The study covered a few number of women groups in the eleven (11) Local Government Areas of Gombe State, Nigeria due to inadequate funds, time and resources. The selection of the study area is due to the available number of women groups engaged in income diversification and agriculture in general. The study is restricted to women crop farmers.

9. 3.0 Methodology 3.1 The Study Area This study was conducted in Gombe State, Nigeria. The State is located in North-East Nigeria. It lies between latitude 90 o 3'N and 120 o N and longitude 80 45'E and 90 o 3'E , GMSG ( 2016) It covers a land area of about 20,365 square kilometers. It has a projected population of 3,960,100 in 2022 using 3.3% annual growth rate as reported by NBS (2022)

10. 3.1 Study Area Cont’d 1,123,157 are female while 1,230,722 are male, National Population Commission, NPC (2006). Major crops grown are maize, guinea corn, millet, cotton, groundnut and cassava, Gombe State Government, GMSG (2016) livestock rearing include poultry, sheep, goat cattle among others, GMSG, (2016). M ajor agricultural business activities include fruits and vegetable selling, oil milling, farming, petty trading, tailoring among others. The climate is characterized by wet and dry seasons. The wet season starts from April / May and ends in November.

11. 3.1 Study Area Cont’d The average rainfall of the study area is 850mm, GMSG (2016). The city averages a yearly temperature of 30.54 °C (86.97 F) Gombe is endowed with both human and natural resources, most of its inhabitants are commercially oriented. Ethnic groups comprises of F ulani, Hausa,Tangale , Tera, Waja , Cham, Dadiya, Tula, Jara, Kamo , kanuri , Pero people.

12. 3.2 Sampling Procedure and Sample Size A multi-stage sampling procedure was used for the study. F irst stage: two wards from the 11 Local Government Areas in Gombe State were selected based on agrarian communities of women crop farmers. Second stage: two groups of women crop farmers were randomly selected from each of the selected LGA making a total of 22 groups. In the final stage 70% respondents each from the selected 22 groups of women crop farmers were randomly selected to make a total of 416 respondents as sample size.

13. Table1: Sample size selection plan of the Study LGA Ward Sample Frame Sample size(70%) Kaltungo Kaltungo west 32 22 Kalaring 30 21 Shongom Lapan 20 14 Boh 20 14 Billiri Billiri north 30 21 Banganje south 30 21 Akko Kumo east 55 38 Kumo centre 25 18 Yamaltu Deba Kuri / lano / lambam 25 18 Deba ward 48 34 Balanga Talasse 30 21 Rema 18 13 Sub-total 363 255

14. Table1:Sample size selection plan of the Study Cont’d LGA Wards Sample Frame Sample size(70%) Dukku Waziri south 21 15 Waziri north 20 14 Funakaye Bage 11 8 Jillahi 30 21 Gombe Jekadafari 26 18 Tudun wada 30 21 Kwami Bojude 25 18 Gadam 25 18 Nafada Nafada east 20 14 Nafada west 20 14 Sub-total 228 161 Total 591 461 Source: Field survey, 2023.

15. F igure 2: Map of Nigeria Showing Gombe State

16. F igure 3: Map of Gombe State showing the study Areas

17. 3.3 Method of Data Collection Primary data was used for the study Data collected were through the administration of structured questionnaires administered to the respondents drawn from the study.

18. 3.4 Method of Data Analysis 3.4.1 Both descriptive and inferential statistics was used to analyze the data. Descriptive statistics such as frequency distribution , percentage, mean, ranking and standard deviation were used to achieve objectives i , ii, iii and iv . Inferential statistics such as Propensity Score Matching was used to achieve objective v.

19. 3.4 Method of Data Analysis Contd ’ 3.4.1 Descriptive Statistics Frequency distribution : It recorded the number of responses for the particular variable during the period of this study. Percentage : this was calculated by dividing the frequency of that particular value by the total number of respondents interviewed multiplied by hundred during the period of this study. Mean : this was simply calculated by the adding up the number of responses and divide by the number of observations. It is also known as the average value. Standard deviation : it describes how scattered the values of the respondent surround the mean derived.

20. 3.4 Method of Data Analysis Contd ’ 3.4.2 Inferential Statistics 3.4.2.1 Propensity Score Matching Propensity scores are an alternative method to estimate the effect of receiving treatment when random assignment of treatments to subjects is not feasible (Rubin, 2001). It is primarily used to compare two groups of subjects but can be applied to analyze more than two groups. In this study, Propensity Score Matching (PSM) was used to pair the treatment (women crop farmers that participate in livelihood diversification activities) and control ( women crop farmers that did not participate in livelihood diversification activities) units with similar values on the propensity score, and possibly other covariates, and the discarding of all unmatched units The estimated propensity score, for subject i , ( i = 1,… N ) is the conditional probability of being assigned to a particular treatment given a vector of observed covariates xi : e(X i ) = Pr (z i =1P|X i ) and Pr (z i …………,|X 1 ……………. X n ) = Oe(X i ) z i {1-e(X i )} 1-z i

21. 3.4.2.1 Propensity Score Matching Contd ’ Where: z = 1, for the respondents that diversified z = 0, for the respondents that did not diversified and X I the vector observed for the i th subject i.e the income Since the propensity score is a probability, it ranges in value from 0 to 1. To explain further, propensity score matching was used in comparing two groups, that is the women crop farmers that had livelihood diversification activities of more than one considered as the treatment group (diversified) while those that had one were considered as the control group (not diversified). The propensity score for each participant in the study was given to be 0.50. This is because each participant would be randomly assigned to either the treatment or the control group with a 50% likelihood, Abdullahi et al., (2021). This model was used in analyzing objective v.

22. RESULTS Table 2: Distribution of Respondents according to Age and Marital (n= 416). ________________________________________________________ Variable Frequency Percentage Mean ____________________________________________________________________________________________________________ Age (years) 18 – 27 58 13.9 28 – 37 146 35.1 4 38 – 47 112 26.9 48 – 57 86 20.7 58 and above 14 3.4 Marital Status Single 55 13.2 Married 317 76.2 Divorced 20 4.8 Widowed 24 5.8 ________________________________________________________________________________ Source: Field Survey, 2023.

23. Table 3: Distribution of Respondents according to Household Size Contd ’ (n=416) _________________________________________________________ Variable Frequency Percentage Mean _________________________________________________________ Source: Field Survey, 2023 Household Size 1-3 172 41.3 4-6 139 33.3 4 7-9 87 20.9 10-13 17 4.1 14 and above 1 .2 __________________________________________________________________

24. Table 4: Distribution of respondents according to Educational level (n = 416) __________________________________________________ Variable Frequency Percentage ___________________________________________________________________ Educational Level Primary education 30 7.2 Secondary education 184 44.2 Tertiary education 149 35.8 Adult and Literacy 27 6.5 Quranic education 11 3.6 No education 15 2.6 ___________________________________________________________________ Source: Field Survey, 2023.

25. Table 5: Distribution of respondents according Farm size Contd ’ (n = 416) __________________________________________________________ Variable Frequency Percentage Mean _________________________________________________________ Farm Size(ha) Less than 1 234 56.3 1-2 107 25.7 1.6 3-4 3 0.7 5-6 52 12.5 7 and above 20 4.8 ____________________________________________________________________ ______ Source: Field Survey, 2023.

26. Table 6: Distribution of respondents according according to Land ownership status Contd ’ (n = 416) __________________________________________________________ Variable Frequency Percentage __________________________________________________________ Land ownership status Inheritance 194 46.6 Purchase 100 24 Communal 12 2.9 Rent 86 20.7 Lease 15 3.6 Gift 9 2.2 ___________________________________________________________________ Source: Field Survey, 2023.

27. Table 7 : Distribution of respondents based on Secondary occupation and Membership (n = 416) __________________________________________________________ Variable Frequency Percentage ___________________________________________________________________ Source: Field Survey, 2023 Secondary occupation Livestock farming 152 36.5 Civil service 128 30.8 Agro -marketing 59 14.2 Agro -Processing 12 2.9 Artisan 5 1.2 Trading 60 14.4 Membership of association Member 394 94.7 Not a member 22 5.3 _______________________________________________________________________________________________

28. Table 8 : Distribution of Respondents based on Benefit of cooperative Contd ’ (n = 416) ________________________________________________________ Variable Frequency Percentage _________________________________________________________________ Benefit Loan/credit 102 24.5 Input 124 29.8 Grant 68 16.3 Education 68 16.3 Marketing Linkages 54 13 _________________________________________________________________ Source: Field Survey, 2023.

29. Table 9: Distribution of Respondents based on the T ypes livelihood of diversification activities. ____________________________________________________________ Types of livelihood diversification Mean Std.Dev . Decision __________________________________________________________________ Food vendor 2.24 0.71 Engaged Operation of grinding machine  2.20 0.69 Engaged Artisan   2.29 0.79 Engaged Agro - processing 2.32 0.76 Engaged Weaving/knitting 2.32 0.77 Engaged Hair saloon 2.35 0.76 Engaged Civil service 2.33 0.81 Engaged Fashion designing 2.40 0.75 Engaged Sales of provisions 2.37 0.80 Engaged Sales of locally made cosmetics 2.44 0.75 Engaged (pomade, soap) __________________________________________________________ Source: Field Survey, 2023.

30. Table 10: Distribution of Respondents based on the T ypes livelihood diversification activities Contd ’. ____________________________________________________________________________________ Types of livelihood diversification Mean Std.Dev . Decision ___________________________________________________________________ Sales of water 2.39 0.79 Engaged Sales of vegetables 2.39 0.77 Engaged Bakery 2.42 0.74 Engaged Lease/financial services  2.43 0.73 Engaged POS 2.33 0.81 Engaged Livestock keeping 1.87 0.69 Engaged Crop production 1.90 0.73 Engaged _________________________________________________________________ Source: Field Survey, 2023.    

31. Table 11: Distribution of the Respondents according to Extent of Livelihood D iversification. __________________________________________________________ Extent of livelihood diversification Frequency Percentage ___________________________________________________________________ Highly diversified 31 7.5 Moderately diversified 322 77.4 Not diversified 63 15.1 ___________________________________________________________________ Source: Field survey, 2023.

32. Reasons Frequency Percentage* 3 6 Table 12: Distribution of the Respondents according to Reasons for Engaging in Livelihood Diversification Activities. _________________________________________________________ _________________________________________________________ Payment of school fees 336 80.8 To pay house rent 73 17.5 Health bills 280 67.7 Risk and uncertainties in agriculture 190 45.7 Large family size 135 32.5 Availability of non-farm opportunities 195 46.9 seasonal nature of agricultural produce 199 47.8 Demand from Husband 217 52.2 Comfortable Life 281 67.5 Lack of Job Security 280 67.3 ___________________________________________________________________ Source: Field Survey, 2023. * Multiple Response

33. Table 13: Distribution of the Respondents according to Reasons for Engaging in Livelihood Diversification Activities C ontd ’ __________________________________________________________ Reason Frequency Percentage* ___________________________________________________________________ Market 286 68.8 Low income from agriculture 196 47.1 Reduce poverty level in the family 210 50.5 For the well-being of the household 268 64.4 Generate sufficient income 287 69 High-cost farm input 253 60.8 Poor productivity from agriculture 287 69 ___________________________________________________________________________________________________________________ Source: Field Survey, 2023. *Multiple Response

34. Table 14: Distribution of the Respondents according to Sources of Income of Women Crop Farmers Based on Their Perceived Livelihood Diversification Activities. _________________________________________________________ Livelihood diversification activities Frequency Percentage* __________________________________________________________________ Agricultural (On-farm) Crop production 228 54.8 Livestock 260 62.5 Off-farm Processing farm produce 226 54.3 Marketing farm produce 272 65.5 __________________________________________________________________ Source: Field Survey, 2023. * Multiple Responses

35. Table 15: Distribution of the Respondents according to Sources of Income of Women Crop Farmers Based on their Perceived Livelihood Diversification Activities Contd ’. ________________________________________________________________ Livelihood diversification activities Frequency Percentage* ___________________________________________________________________ Non agricultural Civil service 235 56.5 Non governmental 260 62.5 Trading 234 56.3 Tailoring 265 63.7 Hair dressing 224 53.8 Grinding 259 62.3 Food vendor 225 54.1 Laundry cleaning job 274 65.9 Basket weaving 284 68.3 ___________________________________________________________________ Source: Field Survey, 2023. * Multiple Responses

36. Table 16: Result of Treatment Effects Estimation Propensity score matching on Impact of Livelihood Diversification activities . _________________________________________________________________ Income Coef. St.Err t-value p—value ** ___________________________________________________________________ Diversification -4907.237 27159.896 0.18 .857 index Mean dependen t 185739.063 Variable SD dependent variable 324930.476 __________________________________________________________________________________________ *** P < 0.01, ** P < 0.05, * P < 0.1 Source: Field Survey, 2023.

37. Table 17: Distribution of the Respondents according to Constraints to Livelihood Diversification Activities. _______________________________________________________________ Constraints Frequency Percentage* Ranking _________________________________________________________ Inadequate capital 366 80 1st Culture 300 72.1 2nd Unstable electricity 293 70.4 3rd Government and economic policy 290 69.7 4th Religious restriction 285 68.5 5th Inadequate access to loan 276 66.3 6th Inadequate Infrastructural facilities 274 65.9 7th Poor access to market 205 49.3 8th _________________________________________________________________________________________ Source: Field Survey, 2023. *Multiple Response

38. 4.0 CONCLUSION The respondents were mostly engaged in more than one type of livelihood diversification activities making them moderately diversified. The reasons for engaging in Livelihood diversification activities include market availability, low income from agriculture and poverty reduction in the household. The source of income of respondents in this study is mostly gotten from on- farm agricultural activities augmented with off- farm activities and non agricultural as livelihood diversification activities. Inadequate capital, culture such as gender issues, unstable electricity and government policies were ranked highest as constraints faced at the time of the study.

39. 5.0 RECOMMENDATIONS i . Young women farmers should be encouraged through skilled trainings of empowerment so as to expand their investment. ii. Stakeholders should provide adequate capital so that women crop farmers can be highly diversified. iii. Women crop farmers should be properly equipped by stakeholders on ways to increase their income. iv . Government should intervene in making policies that would empower more women irrespective of their kind of activities.

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