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報告摘要：In recent years,the use of machine learning algorithms for solving regression and classification problems has increased dramatically,including in the Earth and environmental sciences. Machine learning has become a dominant approach for predicting soil and agronomic properties from a wide range of explanatory variables. In this presentation, I will use the example of predicting maize yield in Ghana based on climate, soil, crop, management, and fertilizer data. A random forest regression model trained on more than 3,000 paired observations explained over 80% of the observed variability in maize yield. The trained model can also be used to evaluate how fertilizer application rate influences yield by generating so-called fertilizer–yield response curves. From these curves, we can determine the optimal fertilizer rate by identifying the rate that maximizes profit. I will explain how this can be done, but I will also raise the question of whether this approach is scientifically sound and valid, since it effectively uses an empirical, correlation-based model as if it were causal. I will not provide answers but aim to initiate a discussion. The key issue appears to revolve around achieving unconfoundedness among explanatory variables—a condition typically ensured through well-balanced experimental designs, but which may also be attainable through thoughtful combinations of observational data.

報告人：Gerard Heuvelink?教授 荷蘭瓦赫寧根大學

報告人簡介：

Gerard Heuvelink是ISRIC–World Soil Information的高級研究員、荷蘭瓦赫寧根大學土壤地理與景觀研究組的計量土壤學與數字土壤制圖特聘教授。他的博士研究聚焦于地理信息系統空間建模中的誤差傳播，標志著其在空間不確定性分析領域科研生涯的開端，主要研究方向為土壤應用。2014年，他因推動計量土壤學發展而獲得國際土壤科學聯合會授予的理查德·韋伯斯特獎章；2020年，獲得國際空間精度研究協會授予的彼得·伯勒獎章。他在地統計學、空間不確定性分析、計量土壤學、數字土壤制圖以及農學與土壤科學機器學習領域發表了200余篇SCI論文，被科睿唯安評選為“全球高被引科學家”，其論文在Web of Science上被引用超過17,500次（H指數為55）。Gerard Heuvelink現任《European Journal of Soil Science》副主編，并擔任《Geoderma》《Spatial Statistics》等五本SCI期刊的編委。多年來，他已指導45余名碩士研究生和25名博士研究生。2011年至2022年，他擔任中國科學院地理科學與資源研究所資源與環境信息系統國家重點實驗室客座教授。

主持人：葛詠 研究員

時??間：2025年10月17日（星期五）10:00 -12:00

地??點：A332會議室

主辦單位：地理信息科學與技術全國重點實驗室