Research Article | Volume 2 Issue 1 (Jan-June, 2022) | Pages 1 - 4
Impact of Adoption of Organic Fertilizer on Poverty Reduction among Farming Households in North-Central, Nigeria
1
Finance and Accounts Department, Agricultural and Rural Management Training Institute (ARMTI), Ilorin, Nigeria
Under a Creative Commons license
Open Access
Received
Jan. 17, 2022
Revised
Feb. 22, 2022
Accepted
March 21, 2022
Published
April 27, 2022
Abstract

This paper examined the impact of adoption of organic fertilizer on poverty reduction among farming households in North-central, Nigeria. Three hundred and eighty-nine (389) farming households were selected from villages across North-central, Nigeria. Data were collected using structured interview schedule and were analyzed through the use of frequency count, simple percentages and propensity score matching technique. The results showed that the income level of farmers who adopted organic farming was significantly higher than that of non-adopters. Furthermore, farm size, and years of education negatively influence poverty, while, it was also revealed that adoption of organic fertilizer has led to poverty reduction among adopting farming household hence such initiative as organic farming should be encouraged and sustained among farming households in the study area.

Keywords
INTRODUCTION

The long history of African food insecurity has worried concerned minds. To avert this prevailing hunger situation in Africa, especially in the sub Saharan region, food production must be increased to meet up with the teeming population. This could be achieved through expansion of land area or improving the yields of crops on cultivated land area [1]. As a result of population pressure on land, expansion is difficult; moreover, after cropping for some years, soil depletion sets in, resulting in low crop productivity. According to [2], population pressure exacerbates and intensifies land degradation and this has resulted in the use of different intensification technologies by farmers to improve yields. Soil is usually degraded due to its constant use and there is the need to replenish it either by the use of organic fertilizer or inorganic fertilizer. Chemical fertilizers often produce plants products at a fast rate and in large quantities, but over time they become less effective and eventually pollute the land with some unwanted chemicals resulting in increased soil depletion. [3] observed that the rapid decline of plant nutrients, low organic matter content and poor physical condition of the soil constitute strong limitation to crop production.

Consequently, attention is gradually shifting towards organic manure as soil amendment material for crop production [4].In Nigeria, crop production is mostly practiced on subsistence scale and most farmers are poor, and as a result they are faced with several constraints in the use of fertilizers. Some of the constraints include among others: constant increasing price of fertilizers, unavailability or late arrival of fertilizers, cultural barriers, insufficient quantities of fertilizers, political interference in distribution, no soil test, poor fertilizer recommendation or lack of appropriate information on correct usage, lack of incentives and unintended subsidies [5]. Recognizing the myriad of deficiencies inherent in the use of fertilizer in developing countries, many agricultural based agencies have advocated the use of organic manures as alternatives to inorganic fertilizer [3]. Many studies have identified the increasing use of organic manure in vegetable cultivation in Nigeria [5]. The commonly used organic manures by vegetable farmers in the country are: cow dung, goat droppings, pig and poultry dropping. Poultry dropping (poultry litter) seems to be the most preferred among them probably due to its relative affordability and availability on demand. Poultry manure is the organic waste material from poultry consisting of a mixture of poultry excreta, spilled feed, bird’s feathers, and material (wood shavings or sawdust) used as bedding in poultry operations.

Farmers in rural areas of North-central Nigeria have been facing the challenge of declining agricultural productivity. One of the reasons for this is decrease in soil fertility. Since 1970s, the Nigerian government has intervened in agricultural sector to overcome this problem through promotion of various agricultural technologies such as organic fertilizer. However, soil degradation has continued leading to decline in agricultural productivity. In lieu of this, government at different times have made efforts at encouraging adoption of organic fertilizer, with the rate of adoption relatively above average among farming households [6]. However, there is a dearth of information on the impact of adoption of organic fertilizer as it affects poverty among rural households. It is against this background that this study was designed to specifically: examine the poverty status of the adopters and non-adopters of organic fertilizer among the farming households, assess the poverty status and evaluate the impact of adoption of organic fertilizer on poverty reduction in the study area. 

METHODOLOGY

The study was conducted in North Central, Nigeria. North Central Nigeria is situated in the southern Guinea savannah agro-ecological zone and consists of six states, namely Plateau, Nasarawa, Benue, Kogi, Niger and Kwara as well as the Federal Capital Territory, Abuja [7]. The region covers a land area of about 251,425 square kilometres with a population of about 20,266,257 inhabitants (National Population Commission, 2009) and has a high degree of ethnic diversity. Among the dominant ethnic groups are Tiv, Igala and Eggon. Subsistence agriculture is the principal activity in the study area. Farms are generally small, usually less than five hectares and rely on the use of manual labour and crude implements such as hoes and machetes.

 

The study population comprised all the vegetable farmers in North-central Nigeria. A multi-stage sampling technique was adopted to select sample for this study. The first stage involved purposive selection of three states namely: Kwara, Niger and Benue based on the prevalence of vegetable productions in those states. The second stage involved a random selection of two local government areas per state, making a total of six selected local government areas for the study. The third stage involved random selection of three (3) villages from each of the selected LGAs, making a total of eighteen villages selected for the study. The fourth stage involved the use of 10% of the sample frame thus, a total of 389 respondents were selected as sample size from the 3,890 registered vegetable farmers in the selected villages in the study area. Based on the estimated proportion of the adopters and non-adopters of organic fertilizer in the study area, whereby adopters in the area were about 62 percent and non-adopters were about 38 percent of the population. Therefore, proportionally, 241 farming households (62 percent of the sample) and 148 farming households (38 percent of the sample) were selected from the lists of adopters and non-adopters respectively. The data for the study were collected using structured interview schedule. 

Percentages was used to estimate the socioeconomic characteristics of the respondents while per capita household consumption expenditure was used as a proxy for per capita household income in this study. This was to overcome the problem of overstating or understating household income.

Annual per capita Expenditure = Annual expenditure of households

 


 

 

Propensity score matching was used to verify the effect of adoption of organic fertilizer on poverty reduction.

Average Treatment Effect (ATE)

In the statistical analysis of observational data, Propensity score matching is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. In other words, propensity score matching can be used. Rosenbaum and Rubin (1983) proposed propensity score matching as a method to reduce the bias in estimating treatment effects with observational datasets. Propensity score matching is a way to evaluate direct causal effects of programs based on the idea that bias is reduced when the outcomes are compared using treated and control subjects who are as similar as possible [8]. In literature, one of the most widely used matching method of the propensity score to estimate the ATE is the Nearest-Neighbour Matching. The average treatment effect shows the difference of a unit (person) being assigned to a particular treatment given a set of observed covariates.

In this study average treatment effect (ATE) method was used to determine the effect of the adoption of organic fertilizer on adopters’ poverty reduction. Also, to compare the yield and return on farmland adopters and non-adopters of organic fertilizer in the study area.

RESULTS AND DISCUSSION

Household Income of Adopters and Non-adopters 

Table 1 shows the household income of adopters and non-adopters of organic fertilizer. The mean income of adopters and non-adopters was N9205.50 and N4975.32 respectively. This showed that the use of organic fertilizer significantly impacted on the mean income of adopters as compared to non-adopters. This may be due to the fact that the use of organic fertilizer is considerably cheaper when compared to the use of inorganic fertilizers. Furthermore, organic product is increasingly gaining popularity and acceptance by consumers . When disaggregated by year of education, the result revealed that income increases as year of education increases by both adopters and non-adopters. However, adopters of organic fertilizer had significantly higher income. This could be because the educated farmers are good adopters of new technologies[6]. This in turn led to increased yield, and hence more income. This is in consonance with findings [9] who showed that income level of rural households increases with increase in education. The result also shows that as farm size cultivated increases, mean income increases with that of adopters of organic fertilizer higher than that of non-adopters. This also implies that scale of production affects the mean income of farmers.

 

Table 1: Level of income by year of education and farm size

Characteristics                                   Score                                                          Adopter                           Non-adopter

All                  9205.50                             4975.32

                        (7825.56)                         (4925.51)

Year of Education                                0 – 5                                                             9745.22                             3325.57

                        (3892.45)                         (1925.42)

6 – 11          10225.75                          5650.35

                        (2132.68)                         (1822.15)

12 – 17       11235.01                          6545.31

                        (3112.56)                         (1902.43)

≥ 18              14512.22                          8012.03

                        (5872.12)                         (2943.19)

Farm size                                                                          ≤ 2ha                                                            5822.03                             3891.21

                        (1978.32)                         (1892.29)

3 – 5ha        6452.45                             4822.51

                        (1985.45)                         (1522.45)

6 – 8ha        7335.41                             5022.56

                        (1957.55)                         (1588.56)

Note: Figures in Parenthesis are standard deviation 

Source: Field Survey, 2021

 

Household Poverty Level 

Poverty lines were computed for respondents using the two-thirds of mean per capita household expenditure. The poverty line is N8175.14 per month. The poverty situation among the respondents is presented in Table 2. Based on the analysis, 34.2% of adopters of organic fertilizer live below the poverty line while among non-adopters, it increased to 69.2%. This shows that non-adopters of organic fertilizer (farmers) tend to be poorer. This attests to the fact that organic fertilizer significantly increases income level of adopters thereby, placing them above the poverty level. Also implicit in this finding is that as year of education increases, poverty reduces among adopters and non-adopters of organic fertilizer. Although poverty reduces among respondents as educational attainment advanced, that of adopters of organic fertilizer reduces more than that of non-adopters. This again implies that years spent in acquiring formal education affects poverty. In similar vein, as farm size increases, poverty decreases. This is an indication that farmers with small scale of production tend to be poorer when compared with their counterparts that cultivate large farm sizes.

Table 2: Poverty profile by year of education and farm size

Variable   Score          Adopters                                                 Non-adopters

                                   p0           p1                   p2 p0            p1                     p2                                                  

All                        0.3421     0.0201    0.0001   0.4210    0.0692     0.0092

Years of 0 – 5  0.3441    0.0382  0.0022         0.578   0.0291    0.0031

education 

6 – 11  0.2210   0.0079   0.0009 0.1412  0.0082     0.0008

  12 – 17 0.1334 0.0065  0.0008 0.0882    0.0015    0.0000

                        ≥ 18  0.0000   0.0000   0.0000     0.0000 0.0000     0.0000

Farm size  < 2  0.6022     0.0167  0.0020      0.5834  0.0087    0.0006

                          2 – 4  0.6512    0.0176  0.0007  0.4987  0.0182    0.0005

                            > 4  0.3012    0.0388   0.0041   0.1822  0.0701    0.0079

Source: Field Survey, 2021

 

Effect of adoption of organic fertilizer on Poverty Reduction 

The results for average treatment effect are given in Table 3 using the nearest neighbour propensity score matching method with 241 treated adopters against 148 controlled non-adopters of organic fertilizer. The balancing test was satisfied as well as the common support imposed. The Average treatment effect (ATE) shows that adoption of organic fertilizer increases the output of the farmers by 6032.1 Kg and was significant at 1% significant level. The result also shows that adoption of organic fertilizer increases the yield and returns on land of the farmers by 899.22kg/ha and ₦4214.2 respectively and was significant at 1% significant level. Overall, the result shows that adoption of organic fertilizer has a positive impact on poverty reduction among farmers in the study area.

 

Table 3: Average treatment estimation of poverty reduction among rural households

Variable   Treated    Control      ATE              Std. error                        t-statistics

 Farm size (ha)241     148               0.46              0.0399192    0.81

Output (kg)    241        148               6032.1        0.0000612   4.88***

Yield (ha/kg) 241        148               899.22        0.0000401  6.15***

Return on land 241    148               4214.2        0.0000223  5.02***

p<0.05

CONCLUSION AND RECOMMENDATIONS

High level of education and large farm size helps to raise the farmers’ income and their probability of escaping poverty. These support the fact that improvement in education and farm size can contribute to raising the income of farming households and alleviate poverty in the study area. The policy implication is that farmers’ income can be greatly increased through education, and direct increment of cultivated farm lands. Adoption of organic fertilizer has a positive impact on poverty reduction among adopting farming households in the study area. Creation of efficient production and market infrastructures, improved extension services, as well as assist farmers in time of inadequacy or loss so that the adopters can fully meet up with the continued increase in the cost of living and possibly invest the income accruable in vegetable farming in petty trading where they can get more income. 

Conflict of Interest:

The authors declare that they have no conflict of interest

Funding:

No funding sources

Ethical approval:

The study was approved by the Agricultural and Rural Management Training Institute (ARMTI), Ilorin, Nigeria.

REFERENCES
  1. Sarker et al. "Determinant of Adoption Decisions: The Case of Organic Farming in Bangladesh" Extension Farming Systems Journal, 5.2 (2011), pp. 39-46.

  2. Alalade et al. "Farmers’ Perception of the Activities of Lower Niger Basin Authority in Ilorin East Local Government Area of Kwara State, Nigeria" Journal of Tropical Agriculture, Food, Environment and Extension, 16.2 (2017), pp. 17-22.

  3. Babasola et al. "Factors Affecting the Use of Organic Fertilizer Among Vegetable Farmers in Kwara State, Nigeria" Tanzania Journal of Agricultural Sciences, 16.1 (2017), pp. 46-53.

  4. Akpan and Aya. "Determinants of Fertilizer Use Among Small–Holder Farmers in Wetland Region of Cross River State" Global Journal of Agricultural Science, 8.2 (2012), pp. 195-201.

  5. Udoh. "Technical Inefficiency in Vegetable Farms of Humid Region: An Analysis of Dry Season Farming by Urban Women in South-South Zone, Nigeria" Journal of Agricultural & Social Science, 1 (2013), pp. 80-85.

  6. Ayinde et al. "Impact of Village Alive Development Initiative on Farming Household’s Productivity in Kwara State: A Comparative Analysis" Nigerian Journal of Agricultural Economics, 7.1 (2017), pp. 36-44.

  7. National Bureau of Statistics (NBS). Reform Programme, 2012, www.nigerianstat.gov.ng.

  8. Harris et al. "The Effect of Gender on Productivity Status in U.S. Agriculture" Selected Poster prepared for presentation at the 2015 Agricultural and Applied Economics Association and Western Agricultural Economics Association Joint Meeting, San Francisco, CA, 26-28 July 2015.

  9. Ijioma and Osondu. "Agricultural Credit Sources and Determinants of Credit Acquisition by Farmers in Idemili Local Government Area of Anambra State" Journal of Agricultural Science and Technology B, 5 (2015), pp. 34-43.

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