Couëdel Antoine, Lollato Romulo P, Archontoulis Sotirios V, Tenorio Fatima A, Aramburu-Merlos Fernando, Rattalino Edreira Juan I, Grassini Patricio
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
AIDA, University Montpellier, CIRAD, Montpellier, France.
Nat Food. 2025 Apr 8. doi: 10.1038/s43016-025-01157-4.
Accurate spatial information on yield potential and gaps is key to determine crop production potential. Although statistical methods are widely used to estimate these parameters at regional to global levels, a rigorous evaluation of their performance is lacking. Here we compared outcomes derived from four published statistical approaches based on highest average farmer yields over time and space against those derived from a 'bottom-up' approach based on crop modelling and local weather and soil data for major rain-fed crops in the United States. Statistical methods failed to capture spatial variation in water-limited yield potential, consistently under- or overestimating yield gaps across regions. Statistical methods led to conflicting results, with production potential almost doubling from one method to another. We emphasize the need for well-validated crop models coupled with local data, robust spatial frameworks and extrapolation methods to provide more reliable assessments of production potential from local to regional scales.
关于产量潜力和差距的准确空间信息是确定作物生产潜力的关键。尽管统计方法被广泛用于在区域到全球层面估计这些参数，但缺乏对其性能的严格评估。在此，我们将基于随时间和空间变化的最高平均农户产量的四种已发表统计方法得出的结果，与基于美国主要雨养作物的作物模型以及当地天气和土壤数据的“自下而上”方法得出的结果进行了比较。统计方法未能捕捉到水分限制产量潜力的空间变化，始终低估或高估了各地区的产量差距。统计方法导致了相互矛盾的结果，生产潜力从一种方法到另一种方法几乎增加了一倍。我们强调需要经过充分验证的作物模型，并结合当地数据、强大的空间框架和外推方法，以提供从地方到区域尺度更可靠的生产潜力评估。
