Technology adoption, impact, and extension in developing countries’ agriculture: A review of the recent literature

Takahashi, Katsumi; Muraoka, Rie; Otsuka, Keijiro

doi:10.1111/agec.12539

Cited by 242 publications

(184 citation statements)

References 129 publications

(161 reference statements)

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“…In most cases, technologies and skill demands in poor countries are not as advanced as in high-income countries like the United States, Western Europe, or Japan. Nonetheless, studies from developing countries reinforce the need to train workers for more skill-intensive employment, not only on farms but throughout the food supply chain, as the agricultural transformation unfolds and digital agriculture takes hold ( Takahashi et al, 2020 ). The COVID-19 crisis may present an opportunity to accelerate the digitization of the agri-food system, helping players across the globe in all nodes of the AFS become more efficient and informed while bridging the rural-urban divide by improving participation in modern markets ( FAO, 2020 ).…”

Section: Productivity-enhancing Technology Is Keymentioning

confidence: 99%

Viewpoint: The future of work in agri-food

Christiaensen

Rutledge²,

Taylor³

2021

Food Policy

110

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Highlights As countries develop, job creation in agri-food shifts beyond the farm. Jobs in the agri-food system, on and off the farm, remain key for youth in Africa. Agri-food in developed countries relies on migrants. Technology reduces this. COVID-19 fosters automation and reduces agricultural migration and trade. Policies must promote inclusive agricultural chains and social insurance.

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Section: Productivity-enhancing Technology Is Keymentioning

confidence: 99%

Viewpoint: The future of work in agri-food

Christiaensen

Rutledge²,

Taylor³

2021

Food Policy

110

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“…In particular, it contributes to explaining the low maize yield and economic returns in Nigeria and SSA in general [5][6][7][8][9]. Addressing this challenge through the traditional fallow systems has become almost impossible for smallholder farmers given the decline in per capita landholding associated with rising population pressure, which results in continuous cropping and soil exploitation [10,11]. Soil conservation practices offer smallholder farmers an opportunity to improve soil fertility and sustainably increase yields while also conserving the environment, i.e., these are considered as climate-smart practices [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Interdependence and Temporal Dynamics of Smallholders’ Adoption of Soil Conservation Practices: Evidence from Nigeria

et al. 2020

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The adoption of soil conservation practices is widely recognized as essential in improving soil fertility and promoting climate-smart agriculture in general. Yet, smallholders’ adoption of soil conservation practices in Sub-Saharan Africa has not been adequately documented, especially in relation to the interdependence and temporal dynamics of adoption decisions. In this paper, we analyze the interdependence and temporal dynamics of smallholders’ adoption of soil conservation practices, such as animal manure, crop residue retention, intercropping, and crop rotation in northern Nigeria. We use data from two rounds of a farm-household panel survey among maize-based farming households and estimate econometric models, including pooled multivariate probit and random effects ordered probit. We found that there is a significant positive correlation between the soil conservation practices, suggesting that adoption decisions for these practices are interrelated and the practices are considered complements by the farmers. We found evidence of inter-temporal variability in the adoption of soil conservation practices, which suggests that some farmers do switch in and out of these practices and may likely explain the often-reported variability in maize yields. Also, we found that the farmers’ decisions to adopt soil conservation practices and the intensity of adoption are influenced by several factors, including farmer-, household-, farm-, institutional-, and biophysical-level factors. Yet, the factors that significantly influence the likelihood of adoption differ slightly from those that influence the intensity of adoption. Policy interventions to enhance the adoption intensity of conservation practices should strongly leverage important factors, such as contract farming, crop–livestock integration, and off-farm income diversification.

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“…In addition to the self-learning component, the literature has also pointed out the importance of learning externalities from other mechanisms such as from the success of neighbors, membership to farmer groups, social networks, formal information dissemination programs/training, extension services, and education (Bandiera & Rasul, 2006;Liverpool-Tasie & Winter-Nelson, 2012;Matuschke & Qaim, 2009;Mekonnen, Gerber, & Matz, 2018;Shikuku, 2019). Moreover, two reviews by Foster and Rosenzweig (2010) and Takahashi, Muraoka, and Otsuka (2020) assess technology adoption choice as a result of social learning through social networks or farmer-to-farmer technology extension. Studies by Foster and Rosenzweig (1995), Maertens and Barrett (2013), and Manski (1993b) equate social network to membership to a village and examine the adoption impact from village level adoption.…”

Section: Conceptual Framework and Hypothesesmentioning

confidence: 99%

Gender and the dynamics of technology adoption: Empirical evidence from a household‐level panel data

Mishra

Sam

Diiro

et al. 2020

Agricultural Economics

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Very few empirical studies account for the dynamic nature of the agricultural technology adoption decision and none of these explores if this dynamic nature depends on the gender of the decision maker. Using four waves of a householdlevel Ugandan panel data, this is the first empirical analysis to account for self-learning (one's own adoption experience) in explaining current adoption decision in a developing country context, and the first to study the interaction between self-learning and gender. Technology adoption is defined as adoption of hybrid seed, inorganic fertilizer, or pesticides. Our results indicate that the dynamic panel data Probit model is superior to its static counterpart in the sense that self-learning, captured by lagged technology adoption indicators, is by far the most important determinant of technology adoption. We also find a weaker impact of self-learning for female-headed households than male-headed households. Female-headed households face fewer learning opportunities, which produce a lower self-learning impact in later periods, further exacerbating the gap in technology adoption among male-and female-headed households.

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Technology adoption, impact, and extension in developing countries’ agriculture: A review of the recent literature

Cited by 242 publications

References 129 publications

Viewpoint: The future of work in agri-food

Viewpoint: The future of work in agri-food

Understanding the Interdependence and Temporal Dynamics of Smallholders’ Adoption of Soil Conservation Practices: Evidence from Nigeria

Gender and the dynamics of technology adoption: Empirical evidence from a household‐level panel data

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