A PARSIMONIOUS, MULTIPLE-REGRESSION MODEL OF WHEAT YIELD RESPONSE TO ENVIRONMENT

By S. Landau, R. Mitchell, V. Barnett, J. Colls, J. Craigon, R. Payne. 2000

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Variability in regional wheat yields as a function of climate, soil and economic variables: Assessing the risk of confounding (2005, result): “The fact that the R values are high compared to previous studies (Landau et al., 2000; Lobell andAsner, 2003) is most likely due to the high aggregation level of both the input and the output data.”
Random Forests for Global and Regional Crop Yield Predictions (2016, methodology): “Simple or multiple linear regressions (MLR) are commonly used statistical models for predicting crop yield [4, 5].”
Yield variation in organic winter wheat: a diagnostic study in the Southeast of France (2005, unknown): “The parsimonious models retained only statistically significant terms, for which the probability values associated with a null hypothesis of a zero coefficient could be rejected at least at a 10% level of significance (Landau et al., 2000).”
Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles (2005, methodology): “Crop modelling approaches are either empirical (e.g. Camberlin and Diop, 1999; Hsieh et al., 1999; Landau et al., 2000) or process-based (e.”