Estimation of the weather-yield nexus with Artificial Neural Networks

Lorenz Schmidt, Martin Odening and Matthias Ritter

No 316598, Agri-Tech Economics Papers from Harper Adams University, Land, Farm & Agribusiness Management Department

Abstract: Weather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding the weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products that mitigate financial losses for farmers, but suffer from considerable basis risk. In this study, an artificial neural network is set up and calibrated to a rich set of farm-level yield data in Germany covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, serves as a benchmark. The empirical application reveals that the gain in forecasting precision by using machine learning techniques compared with traditional estimation approaches is substantial and that the use of regionalized models and disaggregated high-resolution weather data improve the performance of artificial neural networks.

Keywords: Agricultural Finance; Crop Production/Industries; Food Security and Poverty; Research and Development/Tech Change/Emerging Technologies (search for similar items in EconPapers)
Pages: 19
Date: 2021-09-21
New Economics Papers: this item is included in nep-agr, nep-big and nep-env
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/316598/files/E ... eural%20Networks.pdf (application/pdf)

Related works:
Working Paper: Estimation of the weather-yield nexus with Artificial Neural Networks (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:haaepa:316598

DOI: 10.22004/ag.econ.316598

Access Statistics for this paper

More papers in Agri-Tech Economics Papers from Harper Adams University, Land, Farm & Agribusiness Management Department Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().