2023
DOI: 10.1016/j.cosust.2023.101278
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Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience

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Cited by 8 publications
(6 citation statements)
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“…Second, previous yield gap analyses in SSA are narrow in geographic scope and range of agronomic practices 8 10 , 35 , 40 , 41 . In contrast, our study includes thousands of farmers from seven countries, across 25 production environments, including farmers with access to inputs which, in turn, extend the range of agronomic practices in our database, increasing the odds to identify yield-improving practices 42 . Finally, the combination of a climate spatial framework, and statistical and machine-learning methods, together with soil and terrain databases, allowed us to control for the confounding effects of varying climate, soil, and terrain factors in relation to the impact of agronomic practices on on-farm yields.…”
Section: Discussionmentioning
confidence: 99%
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“…Second, previous yield gap analyses in SSA are narrow in geographic scope and range of agronomic practices 8 10 , 35 , 40 , 41 . In contrast, our study includes thousands of farmers from seven countries, across 25 production environments, including farmers with access to inputs which, in turn, extend the range of agronomic practices in our database, increasing the odds to identify yield-improving practices 42 . Finally, the combination of a climate spatial framework, and statistical and machine-learning methods, together with soil and terrain databases, allowed us to control for the confounding effects of varying climate, soil, and terrain factors in relation to the impact of agronomic practices on on-farm yields.…”
Section: Discussionmentioning
confidence: 99%
“…About half of the fields in the database comprised farmers who subscribed to the One Acre Fund program whereas the other half did not. We see the inclusion of farmers with varying levels of technology adoption in the database as an advantage as it allows to increase the variation in management practices across farmer fields 42 . Because farmers engaged with the One Acre Fund program have greater technology adoption, it was not surprising that the average maize yield of our database (3 t ha − 1 ) was higher than the average maize yield in those same regions (1.7 t ha − 1 ) 1 .…”
Section: Methodsmentioning
confidence: 99%
“…OR looks for better ways to conduct organizational operations using mathematical, computer-based, or other analytical methods [95]. OR has been divided into two branches, hard OR and soft OR.…”
Section: Discussionmentioning
confidence: 99%
“…Second, previous yield gap analyses in SSA are narrow in geographic scope and range of agronomic practices [38][39][40][41] . In contrast, our study includes thousands of farmers from four countries, across 21 climate zones, including farmers with access to inputs which, in turn, extend the range of agronomic practices in our database, increasing the odds to identify yield-improving practices 42 . Finally, the combination of a robust climate spatial framework and statistical methods, together with soil and terrain databases, allowed us to control for the confounding effects of varying climate, soil, and terrain factors in relation to the impact of agronomic practices on on-farm yields 7-9, 43,44 .…”
Section: Discussionmentioning
confidence: 99%
“…About half of the elds in the database comprised farmers who subscribed to the One Acre Fund program and the other half farmers who did not. We see inclusion of farmers with varying level of technology adoption in the database as an advantage as it allows to increase the variation in management practices across farmer elds 42 . Because farmers engaged with the One Acre Fund program have greater technology adoption, it was not surprising that the average maize yield of our database (2.9 t ha − 1 ) was higher than the average maize yield in those same regions (1.6 t ha − 1 ) 1 .…”
Section: Database Descriptionmentioning
confidence: 99%