Machine learning methods for crop yield prediction and climate change impact assessment in agriculture

By Andrew Crane-Droesch. Environmental Research Letters, 2018

396 citations recorded by the source corpus.

Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield modeling that uses a semiparametric variant of a deep neural network, which can simultaneously account for complex nonlinear relationships in high-dimensional datasets, as well as known parametric structure and unobserved cross-sectional heterogeneity. Using data on corn yield from the US Midwest, we show that this approach outperforms both classical statistical methods and fully-nonparametric neural networks in predicting yields of years withheld during model training. Using scenarios from a suite of climate models, we show large negative impacts of climate change on corn yield, but less severe than impacts projected using classical statistical methods. In particular, our approach is less pessimistic in the warmest regions and the warmest scenarios.

Subfields: Ecology, Evolution, Behavior and Systematics, Global and Planetary Change, Soil Science

View Paper: https://www.semanticscholar.org/paper/039c1148106113a9f723e3bab22a07d7fad00a33

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Citations in the corpus

Machine learning for large-scale crop yield forecasting (2020, methodology): “Among applications of deep learning, Crane-Droesch (2018) applied semiparametric deep neural networks to predict corn yield in the US; You et al. (2017) leveraged representation learning ideas to predict soybean yield in the US; and Pantazi et al. (2016) used self-organizing maps (Von der Malsburg…”