Determinants of adoption of multiple sustainable agricultural practices among smallholder farmers in Nigeria

Oyetunde-Usman, Zainab; Olagunju, Kehinde Oluseyi; Ogunpaimo, Oyinlola Rafiat

doi:10.1016/j.iswcr.2020.10.007

Cited by 104 publications

(67 citation statements)

References 35 publications

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“…Gender of Household Head: Results obtained in Table 3 are partially in tandem with previous findings [46,50,79,80] that male-headed households often have a higher likelihood of adopting agricultural innovations and thus are better adapted to climate change. Being a male-headed household increases the chances of practicing soil conservation compared to their female counterparts.…”

Section: Irrigation Soil Conservation Crop Diversification Diversificsupporting

confidence: 81%

“…The selection of the variables was motivated by previous literature [4,25,[45][46][47], availability of data [48], and economic theories [4] on factors influencing the choice of CCA and are presented in Table 2. Farm and household attributes were included as explanatory variables for assessing determinants of CCA options.…”

Section: Panel Probit Modelmentioning

confidence: 99%

“…Resource constraint was also considered while accounting for the factors influencing CCA options. Quantity of household assets can act as a proxy in measuring the wealth status of farm households [4]; access to credit facilities extension services was included as one of the explanatory factors since extension services provide crucial education and information needed to adopt CCA options [25,46].…”

Section: Panel Probit Modelmentioning

confidence: 99%

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Impact of Climate Change Adaptation on Household Food Security in Nigeria—A Difference-in-Difference Approach

2021

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Studies have shown that climate change adaptation options (CCA) are implemented to buffer the unfavorable climatic changes in Nigeria causing a decline in food security. Against the background of measuring the impact of CCA options using cross-sectional data, this study assessed how CCA had affected food security using panel data on farming households from 2010–2016 obtained from Nigerian General Household Survey (GHS). Data were analyzed using the Panel probit model (PPM), Propensity Score Matching (PSM), and Difference-in-Difference (DID) regression. PPM showed that the probability of adopting CCA options increased with farm size (p < 0.01), extension contact (p < 0.01), and marital status (p < 0.01), but decreased with the age of the household head (p < 0.01). Credit facilities (p < 0.05), ownership of farmland (p < 0.01), household size (p < 0.01), years of schooling (p < 0.01), household asset (p < 0.01), and location (p < 0.05) also had a significant but mixed effect on CCA choices. PSM revealed that farming households that adopted CCA strategies had 9% higher food security levels than non-adopters. Furthermore, the result of the DID model revealed a significant positive effect of CCA on household food security (β = 5.93, p < 0.01). It was recommended that education and provision of quality advisory services to farmers is crucial to foster the implementation of CCA options.

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Section: Irrigation Soil Conservation Crop Diversification Diversificsupporting

confidence: 81%

Section: Panel Probit Modelmentioning

confidence: 99%

Section: Panel Probit Modelmentioning

confidence: 99%

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Impact of Climate Change Adaptation on Household Food Security in Nigeria—A Difference-in-Difference Approach

2021

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“…As reported in Fig 1, maize productivity in Nigeria and Africa as a whole has steadily lagged behind the world average, while in the last 10 years the mean yield of maize in Nigeria has been lesser than Africa's average [1]. Given that the maize farming sector in Nigeria is mainly dominated by small scale rural farmers, the country's performance in terms of yield may be attributed to constraints such as unavailability of modern technologies [30,31], access to finance and extension services, and productive inputs [4,32] which may have implications on farm incomes and food security status of rural households. To address these constraints, rural farmers have resorted to forming self-help groups, an action that dates back to 1926 in Nigeria when cocoa…”

Section: Context and Related Literaturementioning

confidence: 99%

Does agricultural cooperative membership impact technical efficiency of maize production in Nigeria: An analysis correcting for biases from observed and unobserved attributes

et al. 2021

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The formation of agricultural cooperatives has been widely promoted as an agricultural development policy initiative to help smallholder farmers cope with multiple production and marketing challenges. Using a nationally representative survey dataset of smallholder maize producers from rural Nigeria, this study assesses the impact of agricultural cooperative membership on technical efficiency (TE). We based our estimation approach on the combination of a newly developed sample selection stochastic production frontier model with propensity score matching to control for possible selectivity biases from both observables and unobservables. We estimate stochastic meta-frontiers to examine TE differences between cooperative members and non-members. Our results reveal that TE levels of members are consistently higher than that of non-members. This calls for continued policy incentives targeted at encouraging farmers to form as well as participate in agricultural cooperatives.

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“…Although ordered probit and logit models yield similar inferences, they assume standard normal and logistic distributions of the error terms, respectively. An ordered probit model was used in this research as it is a commonly used tool in other related research such as [37][38][39][40][41][42].…”

Section: Specification Of the Econometric Modelmentioning

confidence: 99%

Determinants of Farmers’ Level of Interaction with Agricultural Extension Agencies in Northwest Ethiopia

et al. 2021

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This research identifies critical determinants for interactions between farmers and extension agencies. Cross-sectional farm household-level data from three hundred household heads were collected between September 2019 and March 2020 and triangulated with data from workshops with farmers and extension agents. The data were analyzed using Spearman’s correlation coefficient, Kruskal–Wallis analysis of variance and the ordered probit model. Farmers’ socio-economic characteristics significantly affect their degree of interaction with extension agencies. Recognition of the determinants of the level of farmers’ interactions can inform policymakers about how to formulate and improve the effectiveness of extension programs, enhance information and knowledge dissemination and facilitate development in collaboration with local communities by focusing on a better interaction between farmers and extension agencies. The level of a farmer’s interactions is based on a systematic decision-making process. Although personal and demographic characteristics are important, farmers’ interaction levels require conducive institutional and household assets, groups/social capital and access to extension agents’ contexts. These contexts will differ by household, country and region. Therefore, extension agencies should create and design contextually appropriate strategies for substantial interactions with farmers for the dissemination of farm information. This research is original and valuable in identifying the factors associated with the level of farmers’ interactions with extension agencies in the Amhara region, Ethiopia. It also provides a new pathway for operationalizing farmer-oriented agricultural extension policies and strategies and to help agricultural policymakers formulate extension service programs.

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Determinants of adoption of multiple sustainable agricultural practices among smallholder farmers in Nigeria

Cited by 104 publications

References 35 publications

Impact of Climate Change Adaptation on Household Food Security in Nigeria—A Difference-in-Difference Approach

Impact of Climate Change Adaptation on Household Food Security in Nigeria—A Difference-in-Difference Approach

Does agricultural cooperative membership impact technical efficiency of maize production in Nigeria: An analysis correcting for biases from observed and unobserved attributes

Determinants of Farmers’ Level of Interaction with Agricultural Extension Agencies in Northwest Ethiopia

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