CY-Bench: a comprehensive benchmark dataset for sub-national crop yield forecasting

作者：Michiel Kallenberg, Dilli Paudel, Stella Ofori-Ampofo, Hilmy Baja, Ron van Bree, Aike Potze, Pratishtha Poudel, Abdelrahman Saleh, Weston Anderson, Malte von Bloh, Andres Castellano, Oumnia Ennaji, Raed Hamed, Rahel Laudien, Donghoon Lee (252291), Inti Luna, Dainius Masiliū̄nas, Michele Meroni, Janet Mumo Mutuku, Siyabusa Mkuhlani, Jonathan Richetti, Alex C. Ruane, Ritvik Sahajpal, Guanyuan Shuai, Vasileios Sitokonstantinou, Rogério de S. Nóia-Júnior, Amit Kumar Srivastava, Robert Strong, Lily‐belle Sweet, Petar Vojnović, Allard de Wit, Maximilian Zachow, Ioannis N. Athanasiadis · 发表于：Earth system science data · 年份：2026 · DOI：10.5194/essd-18-3997-2026 · 被引用次数：1 · 研究领域：Climate change impacts on agriculture、Remote Sensing in Agriculture、Smart Agriculture and AI

Abstract. In-season, pre-harvest crop yield forecasts are essential for enhancing transparency in commodity markets and improving food security. They play a key role in increasing resilience to climate change and extreme events and thus contribute to the United Nations’ Sustainable Development Goal 2 of zero hunger. Pre-harvest crop yield forecasting is a complex task, as several interacting factors contribute to yield formation, including in-season weather variability, extreme events, long-term climate change, soil, pests, diseases and farm management decisions. Several modeling approaches have been employed to capture complex interactions among such predictors and crop yields. Prior research for in-season, pre-harvest crop yield forecasting has primarily been case-study based, which makes it difficult to compare modeling approaches and measure progress systematically. To address this gap, we introduce CY-Bench (Crop Yield Benchmark), a comprehensive dataset and benchmark to forecast maize and wheat yields at a global scale. CY-Bench was conceptualized and developed within the Machine Learning team of the Agricultural Model Intercomparison and Improvement Project (AgML) in collaboration with agronomists, climate scientists, and machine learning researchers. It features publicly available sub-national yield statistics and relevant predictors, such as weather data, soil characteristics, and remote sensing indicators, that have been pre-processed, standardized, and harmonized a...