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Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy by João Vasco Silva; Joost van Heerwaarden; Pytrik Reidsma; Alice G. Laborte; Kindie Tesfaye; Martin K. van Ittersum is a Agricultural and Biological Sciences article available to read on EtoBox.

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## Context Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. ## Objective The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. ## Methods A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. ## Results Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop man

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Author

João Vasco Silva; Joost van Heerwaarden; Pytrik Reidsma; Alice G. Laborte; Kindie Tesfaye; Martin K. van Ittersum

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Agricultural and Biological Sciences (Life Sciences)