Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China
文献类型： 外文期刊
作者： Li, Linchao 1 ; Wang, Bin 2 ; Feng, Puyu 5 ; Wang, Huanhuan 1 ; He, Qinsi 2 ; Wang, Yakai 1 ; Liu, De Li 4 ; Li, Yi 8 ; He, 1 ;
作者机构： 1.Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Shaanxi, Peoples R China
2.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China
3.Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing Agr, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
4.Wagga Wagga Agr Inst, NSW Dept Primary Ind, Wagga Wagga, NSW 2650, Australia
5.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China
6.Univ Technol Sydney, Fac Sci, Sch Life Sci, POB 123, Broadway, NSW 2007, Australia
7.Univ New South Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia
8.Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling 712100, Shaanxi, Peoples R China
9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
关键词： Crop yield forecast; Climate extremes; Remote sensing; Random forest; Lead time; Variable importance
期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：5.734； 五年影响因子：5.964 ）
ISSN： 0168-1923
年卷期： 2021 年 308 卷
页码：
收录情况： SCI
摘要： Accurate and timely crop yield forecasts can provide essential information to make conclusive agricultural policies and to conduct investments. Recent studies have used different machine learning techniques to develop such yield forecast systems for single crops at regional scales. However, no study has used multiple sources of environmental predictors (climate, soil, and vegetation) to forecast yields for three major crops in China. In this study, we adopted 7-year observed crop yield data (2013-2019) for three major grain crops (wheat, maize, and rice) across China, and three major data sets including climate, vegetation indices, and soil properties were used to develop a dynamic yield forecasting system based on the random forest (RF) model. The RF model showed good performance for estimating yields of all three crops with correlation coefficient (r) higher than 0.75 and normalized root means square errors (nRMSE) lower than 18.0%. Our results also showed that crop yields can be satisfactorily forecasted at one to three months prior to harvest. The optimum lead time for yield forecasting depended on crop types. In addition, we found the major predictors influencing crop yield varied between crops. In general, solar radiation and vegetation indices (especially during jointing to milk development stages) were identified as the main predictor for winter wheat; vegetation indices (throughout the growing season) and drought (especially during emergence to tasseling stages) were the most important predictors for spring maize; soil moisture (throughout the growing season) was the dominant predictor for summer maize, late rice, and mid rice; precipitation (especially during booting to heading stages) was the main predictor for early rice. Our study provides insights into practical crop yield forecasting and the understanding of yield response to environmental conditions at a large scale across China. The methods undertaken in this research can be easily implemented in other countries with available information on climate, soil, and vegetation conditions.
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