TY - JOUR
T1 - Causal forest approach for site-specific input management via on-farm precision experimentation
AU - Kakimoto, Shunkei
AU - Mieno, Taro
AU - Tanaka, Takashi S.T.
AU - Bullock, David S.
N1 - Funding Information:
We thank Scott Swinton at Michigan State University for providing helpful comments on this publication. This research was supported by a USDA-NIFA-AFRI Food Security Program Coordinated Agricultural Project, titled “Using Precision Technology in On-farm Field Trials to Enable Data-Intensive Fertilizer Management,” (Accession Number 2016-68004-24769), by the USDA-NRCS Conservation Innovation Grant from the On-farm Trials Program, titled “Improving the Economic and Ecological Sustainability of US Crop Production through On-Farm Precision Experimentation” (Award Number NR213A7500013G021), and by the USDA National Institute of Food and Agriculture, Hatch project ILLU-470-333.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/8
Y1 - 2022/8
N2 - Estimating site-specific crop yield response to changes to input (e.g., seed, fertilizer) management is a critical step in making economically optimal site-specific input management recommendations. Past studies have attempted to estimate yield response functions using various Machine Learning (ML) methods, including the Random Forest (RF), Boosted Random Forest (BRF), and Convolutional Neural Network (CNN) methods. This study proposes use of the Causal Forest (CF) model, which is one of the emerging ML methods that comprise “Causal Machine Learning.” Unlike previous yield-prediction-oriented ML methods, CF focuses strictly on estimating heterogeneous treatment effects (changes in yields that result from changes in input application rates) of inputs. We report results of using Monte Carlo simulations assuming various production scenarios to test the effectiveness of CF in estimating site-specific economically optimal nitrogen rates (EONRs), comparing CF with the yield-prediction-oriented ML methods RF, BRF, and CNN. CF's estimations of site-specific EONRs were superior under all scenarios considered. We also show that the model's yield prediction accuracy need not imply EONR prediction accuracy.
AB - Estimating site-specific crop yield response to changes to input (e.g., seed, fertilizer) management is a critical step in making economically optimal site-specific input management recommendations. Past studies have attempted to estimate yield response functions using various Machine Learning (ML) methods, including the Random Forest (RF), Boosted Random Forest (BRF), and Convolutional Neural Network (CNN) methods. This study proposes use of the Causal Forest (CF) model, which is one of the emerging ML methods that comprise “Causal Machine Learning.” Unlike previous yield-prediction-oriented ML methods, CF focuses strictly on estimating heterogeneous treatment effects (changes in yields that result from changes in input application rates) of inputs. We report results of using Monte Carlo simulations assuming various production scenarios to test the effectiveness of CF in estimating site-specific economically optimal nitrogen rates (EONRs), comparing CF with the yield-prediction-oriented ML methods RF, BRF, and CNN. CF's estimations of site-specific EONRs were superior under all scenarios considered. We also show that the model's yield prediction accuracy need not imply EONR prediction accuracy.
KW - Causal forest
KW - Economically optimal input rates
KW - Machine learning
KW - Nitrogen rate
KW - On-farm precision experimentation
KW - Site-specific input management
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U2 - 10.1016/j.compag.2022.107164
DO - 10.1016/j.compag.2022.107164
M3 - Article
AN - SCOPUS:85133410543
SN - 0168-1699
VL - 199
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 107164
ER -