Seeing Growth from Space: Assimilating crop stress into macro TFP for endogenous growth and climate adaptation

This project combines advances in agricultural data assimilation (Wang et al., 2024) with crop-specific climate sensitivity (Adekanmbi et al., 2023 on potatoes) to construct high-frequency, spatially resolved agricultural TFP series. These series feed into an endogenous growth model with sectoral R&D and adaptation choices. Initial calibration is for Mexico, where physical risks are well characterized, with plans to generalize. Unlike typical crop simulation and remote sensing used for yield forecasting, this creates a data-assimilation bridge that replaces ad hoc damage functions with observed biophysical signals. The methodology uses ensemble Kalman filters and hierarchical Bayesian assimilation to update sectoral productivity states and links adaptation R&D productivity to reductions in remotely sensed stress (e.g., LAI recovery after drought). This approach identifies where adaptation investments endogenously raise TFP versus simply offset losses, capturing unexpected positives like transient CO2 fertilization in cool regions. The impact is a policy tool to target adaptation subsidies and ag-tech R&D by region and crop, quantifying macro growth payoffs and improving welfare comparisons in IAMs and SFC macro models that currently rely on stylized damage elasticities.

References:

1. Climate change impacts on global potato yields: a review. Toyin Adekanmbi, Xiuquan Wang, S. Basheer, Suqi Liu, Aili Yang, Huiyan Cheng (2023). Environmental Research: Climate.
2. Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects. Jun Wang, Yanlong Wang, Zhengyuan Qi (2024). Agronomy.