农业机械学报2025，Vol.56Issue(5)：82-90,9.DOI:10.6041/j.issn.1000-1298.2025.05.008
基于APSIM的新疆棉花生长与产量动态预测方法
Dynamic Predictions of Cotton Growth and Yield in Xinjiang Based on APSIM Model
摘要
Abstract
A process-based cotton growth model could precisely and dynamically simulate the biomass accumulation and yield formation of cotton,so as to provide technical support for smart agricultural decision-making.A dynamic prediction method for cotton growth and yield was developed by integrating meteorological data with the APSIM-Cotton model.Firstly,model parameters were calibrated based on field trial data(2023-2024).Secondly,short-term weather forecasts(ECMWF Open Data)were incorporated for 9 d growth simulations.Thirdly,climate analogue years were used to construct seasonal meteorological datasets to enable the dynamic yield prediction throughout the growing season of cotton.The results showed that the APSIM-Cotton model could accurately simulate the phenology dates(NRMSE was 5.18％),biomass(NRMSE was 19.60％),and yields(NRMSE was 6.08％)of cotton under various planting densities(9～27 plants/m2)in Changji,Xinjiang.Short-term biomass predictions achieved the highest accuracy within 1～3 d(NRMSE was 1.3％),then the errors were increased to about 3.24％at a 9 d forecast.Integrated meteorological data(the dynamic integration of historical meteorological data,short-term weather forecasts,and historical climate analog year data)enabled seasonal yield prediction.Using 18 optimal analogue years minimized prediction errors,stabilizing yield forecast errors below 4％.However,prediction accuracy fluctuated significantly between 90 d and 115 d after sowing(maximum relative error was 10％),which necessitated cautious application of the prediction results during this period.关键词
棉花/APSIM-Cotton模型/气象数据融合/产量/动态预测Key words
cotton/APSIM-Cotton model/weather data fusion/yield/dynamic prediction分类
农业科技引用本文复制引用
陈柏青,张悦,王科,吕智怡,陈茂光,汤秋香..基于APSIM的新疆棉花生长与产量动态预测方法[J].农业机械学报,2025,56(5):82-90,9.基金项目
新疆维吾尔自治区重大科技专项(2022A02011-2)、自治区高校基本科研业务费科研项目(XJEDU2024P031)、新疆农业大学作物学科研项目(XNCDKY2023002)和丝绸之路经济带创新驱动发展试验区乌昌石国家自主创新示范区科技发展计划项目(2023LQJ03) （2022A02011-2）
