Investigating agricultural drought in Northern Italy through explainable Machine Learning: Insights from the 2022 drought

Aug 5, 2026

Agricultural drought is a complex hazard shaped by multiple interacting variables, increasingly threatening food security worldwide as climate change intensifies drought events. Existing research often emphasizes precipitation or evapotranspiration and may miss additional drivers linked to crop stress, while large-scale analyses can obscure mechanisms behind different drought severities. This study builds an integrated agriculture drought index (IADI) using multi-source remote sensing and applies explainable ensemble machine learning with SHAP to clarify drivers for Northern Italy’s 2022 drought.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Agricultural drought types and significance

Remote sensing approaches for monitoring

Computers and Electronics in Agriculture 227 (2024) 109572 Contents lists available at ScienceDirect Computers and Electronics in Agriculture journal [homepage:](homepage: www.elsevier.com/locate/compag)[ www.elsevier.com/locate/compag](homepage: www.elsevier.com/locate/compag) | Investigating agricultural drought in Northern Italy through explainable Machine Learning: Insights from the 2022 drought | | | | | --- | --- | --- | --- | | Chenli Xuea,b, Aurora Ghirardellib, Jianping Chena, Paolo Tarollib,*
a School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
b Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, PD 35020, Italy | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Agricultural Drought Monitoring Ensemble Learning Explainable AI
Northern Italy
Shapley Additive Explanation | | Agricultural drought is a complex natural hazard involving multiple variables and has garnered increasing attention for its severe threat to food security worldwide. In the context of climate change and the increased occurrence of drought events, it is crucial to monitor drought drivers and progression to plan the subsequent efforts in drought prevention, adaptation, and migration. However, previous studies on agricultural drought often focused on precipitation or evapotranspiration, overlooking other potential drivers related to crop drought stress. Additionally, macro-level analyses of drought-driving mechanisms struggle to reveal the underlying contexts of varying drought intensities. Northern Italy is one of the most important agricultural regions in Europe and is also a hotspot affected by extreme climate events in the world. In the summer of 2022, an extreme drought struck Europe once again, causing significant damage to the agricultural regions of Northern Italy. However, no studies to date have revealed the potential impacts and extent of extreme drought on this crucial agricultural area at a regional scale. Therefore, a comprehensive understanding of agricultural drought still requires further clarification and differentiated driver analysis. This study proposed a novel framework to comprehensively monitor agricultural drought with ensemble machine learning by constructing an integrated agriculture drought index (IADI) with remote sensing-related data including meteorology, soil, geomorphology, and vegetation conditions. Additionally, the Shapley Additive Explanation (SHAP) explainable model was applied to reveal the driving mechanism behind the drought event that occurred in northern Italy in the summer of 2022. Results indicated that the proposed explainable ensemble machine learning model with multi-source remote sensing products could effectively depict the evolution of agricultural drought with spatially continuous maps on an 8-day scales. The SHAP analysis demonstrated that the extreme and severe agricultural drought in the summer of 2022 was closely related to meteorological indicators especially precipitation and land surface temperature, which contributed 68.88% to the drought. Moreover, the new findings also highlighted that soil properties affected the agricultural drought with a contribution of 28.3%. Specifically, in the case of moderate and slight drought conditions, higher clay and soil organic carbon (SOC) content contribute to mitigating drought effects, while sandy and silty soils have the opposite effect, and the contributions from soil texture and SOC are more significant than precipitation and land surface temperature. The proposed research framework could effectively contribute to improving the methodology in agricultural drought research, potentially bringing more instructive insights for drought prevention and mitigation. | | 1. Introduction Drought is a globally pervasive hazard with complex causative factors, and it can be categorized into multiple types based on various focal poi

FAQs

What does the study aim to achieve for Northern Italy’s agricultural drought?

It proposes a framework to comprehensively monitor agricultural drought and to clarify differentiated drivers behind drought progression and intensity in Northern Italy during summer 2022.

How is explainability introduced in the monitoring framework?

The study applies the Shapley Additive Explanation (SHAP) model to identify the driving mechanisms and quantify the contributions of different indicators.

Which factors contributed most to the 2022 drought according to the SHAP results?

Meteorological indicators, especially precipitation and land surface temperature, contributed the largest share (68.88%). Soil properties also had a substantial contribution (28.3%).

How do soil characteristics relate to drought severity in the findings?

For moderate and slight drought, higher clay content and soil organic carbon help mitigate drought effects, while sandy and silty soils show the opposite tendency. The influence from soil texture and SOC is reported as more significant than precipitation and land surface temperature in these cases.

Aug 5, 2026

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