异常气候条件下小麦估产方法研究
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作者单位:

1.北京师范大学地理科学学部遥感科学与工程研究院;2.中国地质大学(北京)土地科学技术学院;3.北京师范大学地理科学学部地理数据与应用分析中心

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基金项目:

国家重点研发计划(2019YFAO606901);国家自然科学基金(42077436)。


Study on wheat yield estimation method under abnormal climate conditions
Author:
Affiliation:

1.Beijing Normal University;2.School of land science and technology, China university of geoscience;3.Faculty of Geographical Science, Beijing Normal University

Fund Project:

National Key Research and Development Program of China (2019YFA0606901); National Natural Science Foundation of China (42077436).

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    摘要:

    发展针对异常气候条件下的作物估产模型,对于理解气候变化对作物产量的影响,提高估产模型的适用性具有重要意义。本文提出了一种综合气象灾害指标、遥感植被指数与趋势产量的随机森林回归估产方法,基于该方法构建了中国五大麦区的小麦单产估算模型,并选择典型的灾害年份在县级尺度和麦区尺度上分别进行了精度验证,对比分析了不同麦区输入变量对估产模型构建的重要性。结果显示:五大麦区估产模型拟合精度的R2均在0.95以上,各麦区县级实际单产与预测单产平均相对误差均低于0.060,区级均低于0.049;输入变量在不同麦区的重要性存在差异。标准化降水蒸散指数在五大麦区重要性均较高,干热风指标在西北春麦区与北部春麦区相比其他区域更重要,月平均温度距平与月平均降水距平在各个麦区的重要性差异不大,小麦拔节期与抽穗期的标准化差分植被指数(NDVI)重要性相对其他时间段的NDVI更高。本文构建的估产模型能够满足异常气候条件下估产的精度,可以为极端气候条件下大尺度的估产研究提供参考。

    Abstract:

    The development of crop yield estimation model under abnormal climate conditions is of great significance to understand the impact of climate change on crop yield and improve the applicability of yield estimation model. This paper proposes a random forest regression yield estimation method based on meteorological disaster index, remote sensing vegetation index and trend yield. Based on this method, the wheat yield estimation models of five major wheat growing regions in China were constructed, and the accuracy of wheat yield estimation was verified at both county level and wheat growing region level in selected typical disaster years. The importance of input variables in different wheat growing regions to the establishment of yield estimation model was compared and analyzed. The results show that the R2 of fitting accuracy of yield estimation model in each wheat growing region was all above 0.95. The average relative error between actual and predicted yield in disaster years was below 0.060 at county level, and 0.049 at wheat growing region level. The importance of input variables differs in different wheat growing regions. The importance of standardized precipitation evapotranspiration index is high in all five wheat regions. The dry hot wind index is more important in the northwest spring wheat region and the north spring wheat region than in other three wheat growing regions. The importance of monthly average temperature anomaly and monthly average precipitation anomaly in each wheat growing region has little difference. The importance of Normalized Difference Vegetation Index (NDVI) at jointing stage and heading stage was higher than that at other stages. The yield estimation model constructed in this paper can meet the accuracy of yield estimation under abnormal climate conditions and provide reference for large-scale yield estimation methods under extreme climate conditions.

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朱秀芳,李石波,石振君,任笑,程昌秀. 异常气候条件下小麦估产方法研究[J]. 农业现代化研究, 2022, 43(2): 328-339
ZHU Xiu-fang, LI Shi-bo, SHI Zhen-jun, REN Xiao, CHENG Chang-xiu. Study on wheat yield estimation method under abnormal climate conditions[J]. Research of Agricultural Modernization, 2022, 43(2): 328-339

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  • 收稿日期:2021-09-24
  • 最后修改日期:2021-12-20
  • 录用日期:2021-12-21
  • 在线发布日期: 2022-04-21
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