基于COSIM模型的棉花冷害预测研究

 为了防御和减轻新疆地区棉花低温冷害,对冷害发生进行可行性预测,本文运用COSIM棉花模型逐年模拟石河子地区乌兰乌苏1961－2005年、喀什地区莎车1961－2009年的棉花生长状况,对模拟结果进行统计学分析。结果表明,棉花吐絮日期和7月1日、8月1日、9月1日等关键日期的生物量与冷害是否发生具有显著的相关关系;运用模型预测的准确率、漏报率、空报率对冷害预报因子进行优度评价,认为吐絮期日期、7月1日的发育期指数、8月1日的叶质量占总干物质质量比例和9月1日的生殖器官质量均可以作为冷害预报的要素指标,在新疆棉区应用取得较好的结果,其中用于南疆棉区的冷害预测结果符合度达到97%以上。这说明基于作物模型进行冷害预测是可行的。     

新疆棉花物候期对气候变化的响应及敏感性分析

为了研究气候变化背景下棉花物候的变化规律,对棉花生长模型COSIM进行参数调校,验证实现本地化,利用数理统计及作物模型模拟的方法,分析了棉花物候期对气候变化的响应及其敏感性。结果表明：1980—2019年,新疆棉区棉花出苗期、现蕾期、开花期分别提前0.5~7.0 d/10a、0.1~5.8 d/10a、0.3~3.9 d/10a,吐絮期推迟0.1~4.7 d/10a。COSIM模型模拟棉花物候变化趋势结果与实际较一致,出苗期、现蕾期、开花期、吐絮期实际值与模拟值的RMSE分别为0.9、0.7、0.6、0.7天。以1981—2010年气候条件为基准,增温0.5℃、1℃、1.5℃、2℃,各棉区棉花出苗期、现蕾期、开花期和吐絮期分别提前0~2天、1~9天、2~12天和3~31天,营养生长、生殖生长分别缩短0~6天、2~22天。在气候变暖的趋势下,通过选择生育期较长的品种、调整播期等措施,可充分利用热量资源,实现棉花优质高产。     

基于COSIM模型的新疆棉花延迟型冷害指标分析

 应用新疆石河子和莎车两地的棉花试验资料,对棉花模型COSIM进行参数调试及有效性验证,模型可较好地模拟石河子和莎车两地的棉花生长发育过程及产量水平。利用COSIM模型45年连续模拟结果及2004年不同播种期的模拟结果进行统计学分析,结果表明：棉花生育期≥12℃有效积温、开花期和子棉产量有显著的线性关系。提出：棉花生育期≥12℃有效积温较多年平均的减少量和开花期较多年平均的推迟日数是定量评估新疆棉花延迟型冷害的指标。通过检验,延迟型冷害指标对严重气候减产年的拟合率达到80%。     

Dynamic Prediction of Cotton Regional Yield Based on the COSIM Model - A Case Study of Akesu City,Xinjiang

[Objective] Dynamic prediction of crop yield using a crop growth simulation model is the focus of increasing research attention. [Method] Based on meteorological, cotton yield, and cotton phenology data recorded at Akesu in Xinjiang from 1991 to 2014, this study aimed to improve the accuracy of crop yield prediction by the COSIM model. The average sowing date for each study year, as well as multiple sowing dates during the suitable sowing period, was imported into the COSIM model, and the two yield prediction methods were compared and analyzed. [Result] The accuracy of both yield prediction methods was higher than 90.0%, indicating that the two methods showed good applicability at Akesu. However, the method using multiple sowing dates during the suitable sowing period showed higher prediction accuracy when cotton yield was dynamically predicted in each month and the actual sowing date was uncertain. [Conclusion] The two prediction methods based on the crop growth simulation model are suitable for prediction of cotton yield at Akesu. In addition, according to the characteristics of different forecast years, the appropriate forecasting method can be used to improve the accuracy of prediction. The results also provide a reference for dynamic prediction of cotton yield in other cotton-producing areas.