Training for transformation: the impact of investor-led training on yields, farm output, and food security among Ghanaian rice farmers

This study examines the impact of agronomic training provided by large-scale private investors (LSPIs) involved in land acquisitions on smallholder rice farmers in northern Ghana, where public extension services are limited. Using panel data from 2017/2018 and 2020/2021, combined with spatial data, the analysis applies Correlated Random Effects-Control Function Approach (CRE-CFA), Dose–Response Function (DRF), and Multinomial Endogenous Treatment Effect (METE) models to address selection bias. Training was measured both as total hours and by intensity levels. Results show that LSPI-led training increases rice yield by 42%, farm output by 12%, food consumption scores by 15%, and reduces food insecurity by 30%. The DRF results highlight nonlinear gains, especially beyond two hours of training, while METE confirms these findings across categories. The study concludes that investor-led training can complement public services and improve productivity and food security, offering valuable policy insights for agricultural development in sub-Saharan Africa.

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Publication Details

Journal
Journal of Applied Economics
Published
2025-11-04
DOI
https://doi.org/10.1080/15140326.2025.2573098
Primary Topic
Agricultural Innovations and Practices
Type
article
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article

Training for transformation: the impact of investor-led training on yields, farm output, and food security among Ghanaian rice farmers

Abdul-Hanan Abdallah
Journal of Applied Economics
Agricultural Innovations and Practices
article

Training for transformation: the impact of investor-led training on yields, farm output, and food security among Ghanaian rice farmers

Abdul-Hanan Abdallah
article en

Abstract

This study examines the impact of agronomic training provided by large-scale private investors (LSPIs) involved in land acquisitions on smallholder rice farmers in northern Ghana, where public extension services are limited. Using panel data from 2017/2018 and 2020/2021, combined with spatial data, the analysis applies Correlated Random Effects-Control Function Approach (CRE-CFA), Dose–Response Function (DRF), and Multinomial Endogenous Treatment Effect (METE) models to address selection bias. Training was measured both as total hours and by intensity levels. Results show that LSPI-led training increases rice yield by 42%, farm output by 12%, food consumption scores by 15%, and reduces food insecurity by 30%. The DRF results highlight nonlinear gains, especially beyond two hours of training, while METE confirms these findings across categories. The study concludes that investor-led training can complement public services and improve productivity and food security, offering valuable policy insights for agricultural development in sub-Saharan Africa.

Journal of Applied EconomicsVol. 28(1)
University for Development Studies (GH)
Openalex Percentile: Top 52%
Agricultural Innovations and Practices
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