Skip to Main content Skip to Navigation
Poster communications

What can crop modelers learn from machine learning models about corn, sorghum and soybean?

Abstract : An ingenious statistical analysis by Schlenker and Roberts (2009) of the county-level grain yield of cotton, corn and soybean in response to climate showed that these non-controlled experiments contain valuable and somewhat hidden information. Critically, these authors identified a temperature range over which grain yields increase (≈10 to 29°C for corn for corn) after which grain yields decrease sharply. Analogous analyses have been presented by Lobell et al. (2014) and Hoffman et al. (2017). Our goal was to apply machine learning (ML) tools to data panels like those used by Schlenker and Roberts (2009) to reveal the relationship between grain yield and climate variables for specific phases of the crop cycle. We analyzed the rainfed yield of corn, sorghum and soybean in response to climate in the US using Random Forest (RF), a non-parametric machine learning (ML) tool (Breiman, 2001). Unlike other ML tools, RF allows gleaning the functional form between predicted and predictor variables.
Document type :
Poster communications
Complete list of metadatas

Cited literature [4 references]  Display  Hide  Download

https://hal.inria.fr/hal-02950260
Contributor : Christophe Pradal <>
Submitted on : Sunday, September 27, 2020 - 5:19:26 PM
Last modification on : Tuesday, September 29, 2020 - 3:04:16 AM

File

Kemanian_S1-Poster.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02950260, version 1

Citation

Armen Kemanian, Alexis Hoffman, Forest Chris. What can crop modelers learn from machine learning models about corn, sorghum and soybean?. ICROPM2020: Second International Crop Modelling Symposium , Feb 2020, Montpellier, France. ⟨hal-02950260⟩

Share

Metrics

Record views

21

Files downloads

97