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DOI Number:10.1016/j.agrformet.2022.108810

Journal:Agricultural and Forest Meteorology

Key Words:maize, yield prediction, weather analogue, within-season, the Loess Plateau

Abstract:Within-season crop yield prediction with a dynamic crop model can provide valuable references for field management practices and regional food security. However, weather ensembles containing the unknown future weather conditions occurring after prediction dates, that are usually estimated by local historical weather data, are essential for such predictions using crop models. Two strategies were established for selecting analogue weather years as the target growing season based on a five-year maize experiment conducted at eight sites in the Loess Plateau of China. The first strategy tried weather data from different lengths of years ahead the planting year. The second strategy used the k-nearest neighbor (k-NN) algorithm to select analogue weather according to different combinations of weather variables with daily or accumulative values. The results showed that satisfactory predictions could be obtained after maize tasseling (about 50 d prior to maturity). The mean absolute relative error (ARE) and coefficient of variation (CV) of the daily yield predictions after tasseling were 6.6% and 5.7%, respectively, in 2010 at the Yulin site. In the leading-year strategy, the most reliable predictions were obtained by the weather data from the 10 years ahead of planting, with an overall average ARE of 11.7%. In the k-NN strategy, the most reliable predictions were obtained by using the analogue weather selected with only accumulative precipitation, with an overall average ARE of 11.5%. Additionally, both of the two optimal strategies improved the original predictions in most cases. However, the k-NN strategy was more likely to generate worse predictions in the early part of the growing season. Generally, it was more convenient to use the weather data of 10 leading years before the planting year to represent the unknown weather data after the prediction dates. This strategy provided reliable prediction accuracy without complex programming and requirement for long-term weather records.

All the Authors:Dong Wenbiao,He Liang

First Author:Chen Shang

Indexed by:Journal paper

Correspondence Author:He Jianqiang

Volume:315

Page Number:108810

Translation or Not:no

Date of Publication:2022-01-03

Included Journals:SCI

Date of Publication:2022-01-03