Comparing and combining process-based crop models and statistical models with some implications for climate change
By Michael J. Roberts, Noah Braun, Thomas R. Sinclair, David B. Lobell, W. Schlenker. 2017
167 citations recorded by the source corpus.
We compare predictions of a simple process-based crop model (Soltani and Sinclair ), a simple statistical model (Schlenker and Roberts ), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.
Subfields: Ecology, Evolution, Behavior and Systematics, Plant Science, Soil Science
View Paper: https://www.semanticscholar.org/paper/65fba333ca07069b57a3cd07454446c239333581
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In Ecology, Evolution, Behavior and Systematics
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Citations in the corpus
- Machine learning methods for crop yield prediction and climate change impact assessment in agriculture (2018, methodology): “Recent work has sought to fuse crop models with statistical models, variously by including cropmodel output within statistical models (Roberts et al 2017), and by using insights from cropmodels in the parameterization of statistical models (Roberts et al 2012, Urban et al 2015).”
- Machine Learning Applications for Precision Agriculture: A Comprehensive Review (2021, background): “[110] have discussed that a sensor-based control system might create some bottleneck in terms of reducing the reliability of decision support tools on process-based crop models, which further may require costly calibration and affect in generating an uncertain representa-”