000			02697nab|a22003257a|4500
001			64379
003			MX-TxCIM
005			20211015213745.0
008			202102s2021||||xxk|||p|op||||00||0|eng|d
022			_a2045-2322 (Online)
024	8		_ahttps://doi.org/10.1038/s41598-021-97221-7
040			_aMX-TxCIM
041			_aeng
100	1		_aAnsarifar, J. _923959
245	1	3	_aAn interaction regression model for crop yield prediction
260			_aLondon (United Kingdom) : _bNature Publishing Group, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aCrop 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.
546			_aText in English
650		7	_aRegression analysis _2AGROVOC _95834
650		7	_aModels _2AGROVOC _94859
650		7	_aCrop yield _2AGROVOC _91066
650		7	_aMachine learning _2AGROVOC _911127
700	0		_aLizhi Wang _922618
700	1		_aArchontoulis, S.V. _919736
773	0		_gv. 11, art. 17754 _dLondon : Nature Publishing Group, 2021. _x2045-2322 _tNature Scientific Reports _wa58025
856	4		_yClick here to access online _uhttps://doi.org/10.1038/s41598-021-97221-7
942			_cJA _n0 _2ddc
999			_c64379 _d64371