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Estimation of Spring Maize Planting Dates in China Using the Environmental Similarity Method

文献类型： 外文期刊

作者： Sheng, Meiling 1 ; Zhu, A-Xing 3 ; Ma, Tianwu 3 ; Fei, Xufeng 1 ; Ren, Zhouqiao 1 ; Deng, Xunfei 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Peoples R China

2.Minist Agr & Rural Affairs China, Key Lab Informat Traceabil Agr Prod, Hangzhou 310021, Peoples R China

3.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China

4.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China

5.Univ Wisconsin Madison, Dept Geog, Madison, WI 53706 USA

关键词： maize; planting dates; environmental similarity; the third law of geography; spatial prediction

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2024 年 14 卷 1 期

页码：

收录情况： SCI

摘要： Global climate change is a serious threat to food and energy security. Crop growth modelling is an important tool for simulating crop food production and assisting in decision making. Planting date is one of the important model parameters. Larger-scale spatial distribution with high accuracy for planting dates is essential for the widespread application of crop growth models. In this study, a planting date prediction method based on environmental similarity was developed in accordance with the third law of geography. Spring maize planting date observations from 124 agricultural meteorological experiment stations in China over the years 1992-2010 were used as the data source. Samples spanning from 1992 to 2009 were allocated as training data, while samples from 2010 constituted the independent validation set. The results indicated that the root mean square error (RMSE) for spring maize planting date based on environmental similarity was 10 days, which is better than that of multiple regression analysis (RMSE = 13 days) in 2010. Additionally, when applied at varying scales, the accuracy of national-scale prediction was better than that of regional-scale prediction in areas with large differences in planting dates. Consequently, the method based on environmental similarity can effectively and accurately estimate planting date parameters at multiple scales and provide reasonable parameter support for large-scale crop growth modelling.

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