Codes and Data for Causal Forest Approach for Site-specific Input Management via On-farm Precision Experimentation
Description
We present here the R and Python codes and data related to manuscript "Causal Forest Approach for Site-specific Input Management via On-farm Precision Experimentation." by Kakimoto, S., Mieno, T., Tanaka, T.S.T., & Bullock, D.S. In this study, we propose 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 quality of a model's yield predictions provides little if any information about the quality of its EONR predictions.
Files
Steps to reproduce
README.md is included. This dataset is also available as a Github repository at: https://github.com/Shunkei3/VRA_with_CF.git