An interaction regression model for crop yield prediction

Abstract Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agro...

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Autores principales:	Javad Ansarifar, Lizhi Wang, Sotirios V. Archontoulis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d04f2b76f8b248cd9faf8b0a344c5624
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spelling	oai:doaj.org-article:d04f2b76f8b248cd9faf8b0a344c56242021-12-02T17:19:16ZAn interaction regression model for crop yield prediction10.1038/s41598-021-97221-72045-2322https://doaj.org/article/d04f2b76f8b248cd9faf8b0a344c56242021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97221-7https://doaj.org/toc/2045-2322Abstract Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.Javad AnsarifarLizhi WangSotirios V. ArchontoulisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Javad Ansarifar Lizhi Wang Sotirios V. Archontoulis An interaction regression model for crop yield prediction
description	Abstract Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.
format	article
author	Javad Ansarifar Lizhi Wang Sotirios V. Archontoulis
author_facet	Javad Ansarifar Lizhi Wang Sotirios V. Archontoulis
author_sort	Javad Ansarifar
title	An interaction regression model for crop yield prediction
title_short	An interaction regression model for crop yield prediction
title_full	An interaction regression model for crop yield prediction
title_fullStr	An interaction regression model for crop yield prediction
title_full_unstemmed	An interaction regression model for crop yield prediction
title_sort	interaction regression model for crop yield prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d04f2b76f8b248cd9faf8b0a344c5624
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_version_	1718381050326941696

An interaction regression model for crop yield prediction

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