Studies from Virginia Polytechnic Institute and State University (Virginia Tech) Provide New Data on Agricultural Losses (Predicting the Impact of Extreme Weather On Agricultural Losses In the Delmarva Peninsula Using Multi-step Machine …): Agriculture – Agricultural Losses

2026 FEB 05 (NewsRx) -- By a News Reporter-Staff News Editor at Food Daily News -- Investigators publish new report on Agriculture - Agricultural Losses. According to news reporting from Blacksburg, Virginia, by NewsRx journalists, research stated, “The increasing occurrence of extreme weather events due to climate change presents significant challenges for agricultural production. Existing research on climatic impacts to agriculture has predominantly focused on changes in yield for major crops, providing limited insights into overall losses and impacts on diverse regional agricultural systems.”

Financial support for this research came from This work was supported by a seed grant from the Delmarva Land Grant Cooperative Research Program and the Virginia Tech Agricultural Experiment Station. Author Julie E. Shortridge has received research support..

The news correspondents obtained a quote from the research from Virginia Polytechnic Institute and State University (Virginia Tech), “This study addresses this gap by using financial crop loss data and crop insurance payouts to gain a more comprehensive understanding of agricultural impacts in diverse agricultural regions. To address the irregular data structure of financial loss data, we developed multi-step machine learning models to quantify the relationship between weather-related financial crop loss and contributing climatic factors. The Delmarva Peninsula in the Eastern United States is used as a case study location to demonstrate this methodology over the period from 1980 to 2018. Multi-step configurations of linear regression, random forest, and support vector machine approaches are compared in terms of their classification and estimation accuracy using a repeated hold-out cross-validation analysis. Results indicate that machine learning methods, particularly random forest, outperform both statistical approaches and our null baseline model, demonstrating superior generalizability in agricultural damage estimation. Multistep configurations that address irregular data distributions are shown to have a significant influence on models’ capacity to detect and estimate damage occurrence.”

According to the news reporters, the research concluded: “The study reveals a preference for simpler modeling approaches that minimize variance in handling unseen data, as well as the importance of accounting for seasonal patterns, spatial groupings, and persistent weather phenomena in accurately estimating agricultural losses.”

This research has been peer-reviewed.

For more information on this research see: Predicting the Impact of Extreme Weather On Agricultural Losses In the Delmarva Peninsula Using Multi-step Machine Learning and Financial Crop Loss Data. Stochastic Environmental Research and Risk Assessment, 2026;40(1). Stochastic Environmental Research and Risk Assessment can be contacted at: Springer, One New York Plaza, Suite 4600, New York, Ny, United States. (Springer - www.springer.com; Stochastic Environmental Research and Risk Assessment - http://www.springerlink.com/content/1436-3240/)

Our news journalists report that additional information may be obtained by contacting Zahra Nourali, Virginia Polytechnic Institute, Dept. of Biological Systems Engineering, Blacksburg, VA 24061, United States.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1007/s00477-025-03157-z. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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