Abdulazeez et al. Nigerian Journal of Agricultural Economics (NJAE). Volume 9(1), 2019: Pages 56-69

Economic Diversification Strategies of Farmers Under the Kogi Accelerated Rice Production Programme (KARPP)

1Abdulazeez, R. O., 2Abdulrahman, S. and 2Oladimeji, Y. U.

1Department of Extension and Rural Development, Ahmadu Bello University, Zaria, 2Department of Agricultural Economics, Ahmadu Bello University, P.M.B. 1044, Zaria, Nigeria Correspondence author e-mail: [email protected]

Abstract

The scope of this study was to examine profitability and determine the factors influencing diversification strategies among rice farmers that participated in Kogi Accelerated Rice Production Programme (KARPP). Primary data were obtained through multistage sampling procedure from 216 participating farmers. Descriptive statistics, gross margin, Gini coefficient, Lorenz curve and Tobit models were employed for data analysis. The results showed that the average total cost (TC) incurred by the farmers was ₦127,981.00/ha, with a gross margin of ₦182,092.20/ha and net farm income of ₦159,592.00. The return on investment (ROI) was 1.25 implying that for every one naira invested in rice production, a profit of ₦1.25 was recorded. This indicates that rice production is profitable in the study area. The study also revealed that 52.3% of the farmers had an income greater than ₦90,000. The Gini Coefficient recorded was 0.729 which implied a greater degree of income inequality among the participant. The findings revealed that variables such as sex (p<0.01), age (p<0.01), marital status (p<0.01), household size (p<0.1), farming experience (p<0.01) and extension contact (p<0.01) were the main determinants of diversification strategies. It is recommended that extension service delivery to rice farmers should be strengthened by government and non-governmental organisation through increased funding of extension agencies. Finally, it is suggested that any policy aimed at improving the livelihood strategies of the rice farmers should at least for now target both primary and secondary occupations. There is also the need for Kogi government state to scale up the programme to other non- beneficiary communities. ______Keywords: Diversification strategies, Farmers income, Gini-coefficient,

Introduction

Nigeria’s agriculture at present is characterized by a weak and inefficient production system, decaying infrastructure, and risk and uncertainty. As a result, most farmers and their households in Nigeria earn an increasing share of their income from non-farming sources. In addition, farmers are constrained to develop strategy to cope with vulnerability of agriculture production system through livelihood diversification (Abdulrahman et al., 2016). This is why most farmers are involved in a combination of agricultural activities such as animal and crop production as their primary source of livelihood, while engaging in other income-generating activities to improve their primary source of income (Oladimeji et al., 2015). Only very few farmers in most developing countries are involved in just one activity (Bernard et al., 2014, Oladimeji et al., 2015, Femi and Adelomo, 2016).

Livelihood diversification plays a very significant role in the socio-economic life of the rural farming households and so, the need to analyse rural farmers’ livelihood becomes imperative. Most farming households’ diversification strategies are driven largely by push factors such as diversification undertaken to manage risk, cope with shock, or escape from agriculture in stagnation or in secular

56 decline (Reardon et al., 2007). In other words, to sustain their livelihood or cushion food shortage experienced by the rice farmers, settle domestic obligations and buy back some inputs needed for farming operations (Oladimeji et al., 2015). In addition, livelihood diversification by the farmers has the potential to foster local economic growth and exhibits higher potentials of reducing rural unemployment rate as well as increasing farmers’ income.

Rice has become a highly strategic and priority commodity for food security in Africa, and its consumption is growing faster than that of any other major staple on the continent because of high population growth and rapid urbanization. Rice is adjudged as the single most important source of dietary energy in West Africa and the third most important for Africa as a whole (Seck et al., 2013). Although local rice production increased rapidly after the 2007-2008 food crisis, a key problem facing the rice sector in Africa, in general, is that domestic production has never caught up with demand. The continent, therefore, continues to rely on importation to meet its increasing demand for rice (International Crops Research Institute for the Semi-Arid Tropics, ICRISAT, 2015).

The Kogi Accelerated Rice Production Programme (KARPP) is an initiative of the Kogi State Government under Kogi Agricultural Transformation Agenda. The KARPP which started in December 2011 aims to make the State self-sufficient in rice production with particular emphasis on irrigated rice production and create wealth for farmers through opportunities in rice processing and export. Information obtained from the State ADP revealed that there were 287 registered farmers’ cooperative groups, each comprising of 10 farmers as participants in the KARPP (Agricultural Transformation Agenda, ATA, 2011).

The Project goal is the reduction of poverty through improving the livelihoods and living conditions of the rural poor by empowering and supporting them to effectively manage their own development activities. The project objective is also to contribute to the country’s food security efforts, increase access to rural infrastructure facilities in the project areas using participatory community demand driven approach. In other words, this is also seeking to produce sufficient rice that will fill the gap in Nigeria Rice Need Demand (NRND) in order to liberate Nigeria from rice importation, enhance household food security and improve the livelihood of the people in the state. Its specific aobjective is to make the state self-sufficient in rice production and create wealth for farmers through opportunities in rice production, processing and export (ATA, 2011).

As part of the drive to boost rice production, the state government cleared 1,500 hectares of land at Seriki Noma, Galilee, Okumi in and Koton-Karfi Irrigation Project sites and prepared/allocated them to farmer’s group free of charge. Lands were allocated to them on the basis of membership of the cooperative groups. Assistance were also rendered to the farmers in the form of provision of farm input like improved rice seeds, fertilizers, and herbicides. The State Government under the programme also provided rice farmers with pumping machines for irrigation, diesel for the machine, maintenance and supplementary labour. Farmers were also trained on irrigated rice production (Ezekiel, 2015).

The livelihood diversification strategy was design in a broader perspective to achieve a hunger-free Nigeria through an agricultural sector that drives income growth, accelerates achievement of food and nutritional security, generates employment and transforms Nigeria into a leading player in global food markets to grow wealth for millions of farmers. However, job creation in rice production if sustained, is expected to improve farmers livelihood through primary production and value chain and to reduce non-farm activities.

Following from above, the broad objective of this study was to assess economic diversification strategies of farmers under the Kogi Accelerated Rice Production Programme (KARPP). The specific objectives were to:

(i) describe socio-economic characteristics of rice farmers under KARPP, (ii) estimate the profitability of rice farmers under KARPP, (iii) determine income distribution and the inequality of participated rice farmers and,

57 (iv) determine the factors influencing diversification strategies among rice farmers that participated in the programme.

Methodology

Study Area: This study was conducted in Kogi State, Nigeria. The State has an area of 29,581.9 sq. km. with a population of 4.37 million in 2015 and the State has an average maximum and minimum temperature of 33.2°C and 22.8°C respectively (Kogi State Government, 2007, and Olatunde and Adejoh, 2017). The State has two distinct kinds of weather – dry season; which spans from November to February and the raining season that lasts from March to October. In addition, the State’s annual rainfall ranges from 1016 to 1524 mm (Olatunde and Adejoh, 2018). According to Kogi State Ministry of Agriculture, the main rice-producing areas in Kogi State are and (zone A), Bassa (zone B), Lokoja (zone C) and Ejiba (zone D). As part of the drive to boost rice production, the State has established rice farms in Koto, Okumi, Galele, and Sarkin Noma Irrigation Project sites. Data were collected from KARPP farmers in 2016 farming season and the questionnaire captured the objectives of the study.

Sampling procedure: This study employed a multi-stage sampling procedure in the selection of respondents for data collection. In the first stage, simple random sampling was adopted to pick two zones (B and C) out of four zones in Kogi State. The two zones (Zone B & C) out of the four (4) zones in Kogi State were randomly selected through the use of ballot technique method. In the second stage, Bassa Local Government Areas (LGA) was selected from zone B while Lokoja LGA was selected from zone C. Bassa and Lokoja LGAs was purposively selected each from Zone B and Zone C, respectively, based on the volume of rice cultivation and where irrigated rice production is currently taking place under the KARPP. In the third stage, the villages in the two LGAs were stratified according to the highest irrigated rice output. Thereafter, two villages from Bassa LGA (Koriko 1 and Koriko 2) and four villages from Lokoja LGA (Kabawa, Sarkin Noma, Okumi 1 and Okumi 2) were randomly selected to make a total of six villages from the two LGAs. Forty percent of the farmers’ cooperative groups were selected randomly using the ballot technique, in each of the villages thus, giving a total of 45 farmers’ cooperative groups as shown in Table 1. The total respondents sampled through simple random sampling at the last stage were picked using the Slovian formula (Sani and Oladimeji, 2017) for estimating minimum sample size based on the assumption of 5% expected margin of error, that is, 95% confidence interval and applying the finite population correction factor. In other words, the size of minimum respondents that could be selected for statistical analysis was determined using Slovia formula expressed as follows:

(1)

Where: is the sample size without considering the finite population correction factor; = 0.05; = total number of observation.

Therefore, a total of 216 irrigated rice farmers were randomly selected using the card method and interviewed as participants in the KARPP (Table 1).

58 Descriptive statistics, gross margin, gini coefficient and Tobit regression model were used to achieve the objectives of study. Descriptive statistics such as frequency, percentage, mean and coefficient of variation was used to describe the socio-economic characteristics of rice farmers (objective i). The Gross Margin (GM) and Net Farm Income (NFI) were used to achieve profitability of rice farmers under KARPP (objective ii). The gross margin analysis is expressed as:

GM = GI – TVC (2)

Where: GM = Gross margin (N/ha); GI= Gross farm income (₦/ha); TVC = Total variable cost (₦/ha). The Net farm income analysis is expressed as:

NFI = GI -TC (3)

Where: NFI = Net farm income of the rice farmers (₦); GI = Gross income of the rice farmers (₦) and TC = Total cost incurred in rice production by farmers (₦) and is expressed as:

TC = TVC + TFC (4)

Where: TFC = Total fixed cost incurred by the rice farmers (₦)

Total fixed cost involved depreciated value of pumping machine, generator and farm tools. Return on Investment, ROI (NFI/TC) was employed to explain the extent to which a naira invested in rice production contributed to the net farm income.

Gini coefficient: Gini coefficient and Lorenz curve were used to determine income distribution and the inequality of participated rice farmers (objective iii). The Gini coefficient ranges from 0 to 1, where 0 implies perfect equality of income in the distribution, while 1 implies perfect inequality in farmer’s income. The closer the Gini coefficient is to zero, the greater the degree of equality in income. Similarly, as the Gini coefficient approaches unity, the greater is the degree of inequality, the higher the level of income disparity. Mathematically, the Gini coefficient (Oladimeji, 2013) is expressed as follows:

k =n GC = 1- (Xt - Xt -1)(Yt + Yt -1 ] (5) åk =0 [ )

Where: GC= Gini coefficient; X = Cumulative proportion of population variable; Y= Cumulative proportion of income variable and ∑= Summation sign

The Lorenz–curve: The Lorenz curve was used to determine the inequality of participated rice farmers in the study area. Lorenz curve is a graphical representation of the distribution of income derived from the the cumulative proportions of income from the smallest to the largest against the cumulative proportions of their income earnings. The extent of deviation of these curves from the equality line further indicate the level of inequalities in income distribution. If the distribution is totally equitable, the curve will pass through the 45-degree line. The greater the inequality, the greater the deviation from the 45-degree line. The degree of income inequalities are shown by the curves which form an arc with the 450 line or equality line (Afolabi, 2007).

Gini coefficient was determined from the Lorenz curve as the ratio of the area above the Lorenz curve to the sum of the area above and below the curve. the Lorenz curve is obtained, by plotting the cumulative proportions of rice farmers from the smallest to the largest against the cumulative proportions of their income earned from rice production. If the distribution is totally equitable, the curve will pass through the 45-degree line. The Lorenz curve estimates the Gini coefficient as:

59 A GC = (6) A + B

Where: GC = Gini coefficient; A= Proportion of the population above the Lorenz curve; B = Proportion of the population below the Lorenz curve; GC = 0 implies perfect equality; GC = 1 implies perfect inequality and GC = 0 < 1 implies a degree of inequality.

Livelihood diversification index: This was used to operationalized the dependent variable in the Tobit model. This approach estimates the shares of incomes of individual rice farmers by finding the share of each income source from total farmers’ income (Bernard et al., 2014). The mean share for each income source for all rice farmers was calculated. The general Mean of Income Shares (MIS) formula is given as:

# !" ∑"$% �� = " (7)

Where: I = The income source, Y = Total Income (Naira, ₦), y = Income from particular activity (Naira, ₦), and n = The number of farmers.

The total farmers’ income (Oladimeji, 2018) is given as:

� ��� = ∑�=1 �� (8)

Where: TFI = Total farmers’ income, thus income coming from all sources J

J = 1 , 2, 3, 4….n,

The total farmers’ income (TFI) comprises of rice farmers’ income, off-rice farming and non-farming incomes. Therefore, livelihood diversification index (Yi) was measured as:

Yi = Income from rice production Total farmers’ income (9)

Tobit Regression Analysis The Tobit regression model was used to determine the factors influencing diversification strategies among rice farmers that participated in the programme. It is pertinent to note that the rice farmers’ participation in KARPP was not the only livelihood strategies; hence, the level / intensity of livelihood strategies in addition to KARPP was estimated using a truncated Tobit model, expressed in equations 10 and 11:

(10)

Where, = observed response on the level of livelihood strategies measured by indexing or ratio, = vector of explanatory variables, = vector of parameter estimates, = error term. Explicitly, the Tobit model (Oladimeji et al., 2015) was operationalized in the form:

60 (12)

(13)

(14) (15) i = 1, 2… n

Where: Yi* = Livelihood strategies index of farmers, X1 = sex (male = 1 and female = 0), X2 = age in years, X3 = marital status (married=1, single=0), X4 = house-hold size in numbers, X5 = education in years, X6 = farmers experience (years of rice production) and X7 = extension contact (number of visit). The α = constant while the e = error term. β1 – β7 = regression coefficients.

Result and Discussion

Socio-Economic Characteristics of Respondents: The result in Table 2 shows the distribution of farmers based on socio-economic characteristics. The result shows that majority of the respondents (82.5%) were within the productive age bracket of 20-49 years. The mean age of 42 years shows that the respondents were active and agile to participate in rice production. This is expected to have positive influence on the farmer’s productivity and efficiency, an indication that that there will boost in rice production.

The coefficient of variation of age (CV) was 19.1%, and this indicates a low level of variation in age among the farmers in the study area. This implies that irrigated rice farming in the study area is embraced predominantly by the middle-aged farmers, which implies that given available production incentives, the KARPP programme beneficiaries have the capacity to boost rice production.

The distribution of respondents’ sex in KARPP skewed towards male (about 65%). This implies that the male folks still play dominant roles in agriculture and related agri-business in the study area. The result of the study also shows that a larger proportion (72.2%) of respondents had a household size between 1-5 persons with an average household size of 4 persons while the minimum and maximum household size are 1 and 7 persons, respectively. The size of the household may enhance labour availability that can be used for different agricultural activities (Oyewole, 2012). The educational level among the respondents indicated that most of the respondents were educated as 64.36% had at least secondary education. The average farming experience of the respondents (8 years) is an indication of farmers’ experience which would enhance the output per unit effort and invariably productivity in output.

The distance covered to the farm as well as proximity to the source of water may affect farmers’ choice of participation in irrigated rice production, cost of transportation and efficiency. Results in Table 2 show that the mean distance covered to farm across the participants is 2.5 km. Majority of the sampled irrigated rice farmers (90.6%) covered a distance of less than 4 km to their farms while only 9.4% of the farmers covered from 4 - 6 km. This implies that farmers who live very close to the source of water are more likely to participate in irrigated rice farming than their counterparts who live far off.

The extension agents serve as a link between the research institutes and farmers. The study revealed that all the KARPP participants had access to extension contact, although 84.3% had only 1 - 3 contacts per season. In many rural settings, access to adequate knowledge, improved technology, financial services and other relevant social services (e.g. drinking water, education and health services) remain a critical issue (Oladimeji, 2013). There are still significant challenges in providing extension and advisory services (EAS) in these areas. These range from insufficient funds for supporting the public extension, poor resourcing, a high extension to farmer ratio, limited involvement of rural farmers and

61 populations in extension processes to the lack of appropriate strategies for effective research and adequate extension methods (IFPRI–World Bank 2010). The KARPP made use of extension agents from the Kaduna Agricultural Development Project (KADP) to facilitate its activities to improve the productivity of the participating farmers.

The Profitability of Rice Farmers Under KARPP: Table 3 shows the costs and returns of rice farmers under KARPP. The viability of an enterprise is indicated by the amount of profit realised per period of time. As part of the drive to boost rice production, the Kogi State government provided land, improved rice seeds, fertilizers and pumping machines for irrigation to the farmers free of charge. The TVC (₦105,481.00) accounted for 82.43% of TC. The total cost incurred by the farmers was ₦127,981.00/ha, with a gross margin of ₦182, 092.2 / ha and NFI / ha of ₦159,592.42. The return on investment (ROI) was 1.25, implying that for every one naira invested in rice production, there is a profit of ₦1.25. This indicates that rice production was profitable in the study area. Labour was sourced from both family and hired. Labour cost accounted for 43.4% of the TVC, while agrochemical and fuel costs accounted for 4.0% and 6.9% respectively. The analysis revealed that labour is the most utilized variable among the respondents. This conforms to the study of Okam et al. (2016) where labour cost (61% of the TVC) dominated the total variable cost of rice production.

Gini Coefficient Estimation: The gini coefficient estimation result in Table 4 reveals the income distribution and the inequality of the participants in the KARPP. The difference in the value of productive goods was large and significant among the participants. The result revealed that the majority (52.3%) of the farmers had a net farm income greater than ₦90,000 with a minimum of ₦10,000 and maximum of ₦800,000.

The result further shows that the estimated Gini Coefficient (GC) of income among the participant was 0.729 which depicts a higher level of inequality in the income distribution of rice farmers that participated in KARPP. The Lorenz curves (figures 1) show the degree of income inequalities which form an arc with the 450 lines or equality line.

The deviation of these curves from the line of equality indicates the level of inequalities in income distribution in the study area. There is, therefore, a need for the Kogi accelerated rice production programme to engage in activities that will lower the current higher inequalities among the beneficiaries.

Livelihood Strategies Employed by Rice Farmers under the KARPP: The result in figure 2 revealed that in all the income sources of livelihood diversification, seven sources of income categorized as farm and non-farm sources were identified. The key reasons given for participation in non-farm activities include the ability to cater for the needs of the household such as food security, payment of school fees, and accumulation of income to manage risks associated with farming, among others. The number of farm households engaged in both farm and non-farm income sources in the study area is shown in Figure 2.

Result revealed that the total farm income share (62.8%) comprising of food crop, cash crop livestock income, and farm wages represents large proportion of total farmers’ income. The results indicate the importance of farming, and it’s related activities in the study area. Non-farm wage activities contributed insignificant portion (about 7.2%) of the total farm household’s income, and these include teaching, construction works, masonry, quarry and mining works. About 28% of the respondents practice self- employment and these include tailoring, carpentry, barbing, oil processing, transport business operations (achaba {motorcycle} and taxi). The result also revealed that remittances account for paltry of 1.6% of the total farm household income. These remittances were received from spouses living in cities and it is used to expand farming, cater for household needs, and purchase farm inputs. This finding on the shares of income coming from farm and non-farm source is at variance with Abdulrahman et al. (2016), who found 32.92% share of farm income in total household income and 67.08% of non-farm income amongst rural farm households in southern Nigeria.

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Factors Determining the Diversification Strategies Adopted by Rice Farmers under KARPP Programme:

The Tobit regression analysis was used to determine the factors influencing diversification strategies among rice farmers that participated in the KARPP programme. The results of this analysis is presented in Table 5. The estimated log-likelihood function of 292.67, AIC information criterion of -2636 and ANOVA based fit measure of 1.58 as well as the sigma square (statistically significant at 1%) which implies that the explanatory variables used in the model were collectively able to explain the determinants of diversification strategies. The results depict that the signs of most of the estimated parameters conformed to a priori expectations except for education that was not statistically significant though positive.

The coefficient of sex (0.034) and marital status (0.091) were both significant (p<0.01) and positively related to diversification strategies. The implication of this is that males and married farmers tend to diversify more compared to their other counterparts. Oladimeji et al. (2015) observed that male farmers’ headed households were more engaged in off-farm activities to enable them take care of their homes, wife (s) and children.

Result also revealed that the coefficient of variable age (0.002) was also positive and statistically significant at 1%. Though variable age has a divergence interpretation in regression analysis, the result obtained could implies that farmers that are married and relatively young would undergo more diversification to cater for their household. Correspondingly, the coefficient of household size variable (0.008) was also statistically significant (p<0.10) implying that there is need for farmers to seek for or diversify into other enterprises to cater for the household members who may be young or attending formal schooling and hence participate less in family enterprises.

The coefficient of farming experience (0.009) and extension contact (0.013) were both statistically significant (p<0.01). The number of extension visits made to the households by extension agents, the higher the likelihood that such farmer will be exposed to improve agricultural practices most especially due to accumulated farming experience which was also statistically significant at 1% probability level. This is so because during such visits, the farmer will be trained and feedbacks will be expected during the previous visit. The result is comparable to studies by Oladimeji et al. (2015) and Oladimeji (2018) that found socio-economic variables influencing income diversification among farming households in North Central and , Nigeria respectively.

Conclusion and Recommendations The study concluded that rice production under KARPP was profitable with a return of ₦1.25 kobo for every ₦1 invested. The study also established that rice farmers under KARPP had numerous livelihood diversification strategies. There was high level of inequalities in income distribution among sampled respondents. The study revealed that the coefficients of sex, age, marital status, household size, farming experience and extension contact were the factors determining the diversification strategies adopted by rice farmers under KARPP Programme in the study area. Therefore, it could be concluded that income from both rice production and non-farm activities could be combined to minimize income stress, fluctuation and shocks and the proceeds from non-rice activities could be valuable for remedying consumption particularly during off-farm season and buy rice inputs.

It is recommended that extension service delivery to farmers should be strengthened by government through increased funding of extension agencies. Finally, in view of the fact that farmers engages in secondary occupations partly due to volatile nature of agricultural production system, suggests that any policy aimed at improving the livelihood strategies and standard of living of the rural farming households in the study area should at least for now target both primary and secondary occupations. There is also the need for Kogi State Government to scale up the programme to other non-beneficial communities.

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Table 1: Population and sample size of irrigated rice farmers in the study area Zone LGA Villages Cooperative Cooperative sample Sample Sample size groups size (40%) frame 1 Bassa Koriko 1 20 8 80 36 Koriko 2 20 8 80 36 2 Lokoja Kabawa 20 8 80 36 Sarkin Noma 18 7 70 36 Okumi 1 18 7 70 36 Okumi 2 18 7 70 36 Total 2 6 120 45 450 216

65 Table 2: Distribution of socio-economic characteristics of participated farmers Variables Range F % Min. Max. Mean CV (%) Age (years) 20 – 29 12 5.6 20 58 42 19.1 30 – 39 65 30.1 40 – 49 101 46.8 50 & above 38 17.5 Sex Male 140 64.81 Female 76 35.19 na na na Na Marital status Single 9 4.17 Married 160 74.07 Widow 45 20.83 Divorced 2 0.93 Household size 1-5 156 72.2 1 7 4 29.7 6-10 60 27.8 Education No formal 41 18.98 Primary 28 12.96 Secondary 63 29.17 Tertiary 76 35.19 Adult 8 3.7 Farming experience 1-5 47 21.76 5 15 8 32.6 6-10 130 60.19 11-15 39 18.05 Distance 0.1-2 89 40.1 0.1 6 2.5 41.7 2.1-4 119 50.5 4.1-6 8 9.4 Extension contact 1-3 182 84.3 1 9 3 33.3 4-6 28 13.0 7-9 6 2.7 Total 216 100 Note: ‘na’ denote not applicable, Min, minimum, Max., maximum, CV, coefficient of variance

66 Table 3: Costs and returns of rice farmers under Kogi Accelerated Rice Production Programme Cost items Unit price (₦) Quantity Value (₦/ha) % TVC / %TC Variable cost TFC Seed (kg) 250 25 6,250 5.90 4.88 Fertilizer (kg) 120 350 42,000 39.80 32.82 Agro-chemical (litre) 1500 2.83 4,245 4.00 3.32 Labour (man-day) 800 47.17 45,736 43.40 35.74 Fuel (litre) 145 50 7,250 6.90 5.67 Total variable cost 105,481 100 82.43 Fixed cost depreciation Pumping machine 8,500 37.78 6.64 Generator 10,500 46.67 8.2 Other fixed items 3,500 15.55 2.73 Total fixed cost 22,500 100 17.57 Total cost 127,981 - 100 Output (Kg/ha) 90 3,195.26 287,573.4 Gross Margin /ha 182, 092.2 Net Farm Income 159,592.40 GM / kg (₦) 56.99 NFI /kg (₦) 49.95 Return to Investment 1.25

Table 4: Income distribution and inequalities (Gini Coefficient) among the Participant Income Frequency Percentage Average income Gini coefficient 1-10000 3 1.4 10000.00 0.99 10001-20000 11 5.1 19090.91 0.94 20001-30000 4 1.9 27750.00 0.92 30001-40000 11 5.1 38181.82 0.87 40001-50000 49 22.7 49510.20 0.64 50001-60000 8 3.7 56000.00 0.60 60001-70000 9 4.2 70000.00 0.56 70001-80000 6 2.8 80000.00 0.53 80001-90000 2 0.9 85000.00 0.52 >90000 113 52.3 180663.7.00 0.72 Total 216 100 616196.60 0.729 Note: GC= Gini Coefficient

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Figure 1: The Lorenz curves of income distribution among rice farmers

Figure 2: Livelihood diversification strategies of KARPP rice farmers

68 Table 5: MLE Tobit regression result determinants of diversification strategies KARPP farmers Variable Coefficient Standard error t-value P > / Z / Sex 0.0337*** 0.0102 3.31 0.0009 Age 0.0022*** 0.0005 4.31 0.0000 Marital status 0.0909*** 0.0244 3.72 0.0002 Household size 0.0077* 0.0040 1.93 0.0541 Education 0.0062 0.0039 1.561 0.1186 Farming experience 0.0088*** 0.0017 5.32 0.0000 Extension 0.0133*** 0.0034 3.90 0.0001 Diagnostic statistics Sigma 0.0624*** 0.0030 20.785 0.000 No. of observation 216 Log likelihood function 292.67 AIC Information criterion -2.636 ANOVA based fit measure 1.58 DECOMP based fit measure 0.398 Note ***; * significance at 1% and 10% respectively

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