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黑龙江大宗作物估产模型:以水稻、大豆、玉米为例

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建立黑龙江省的大宗作物的估产模型,对于及时掌握该省的农情有积极作用,同时可以为其他区域的估产建模提供方法参考.首先计算了趋势单产、农业气候指标、遥感类指标、种植波动指标.然后,针对3种作物(大豆、玉米、水稻)进行估产分区,在各分区内联合使用随机森林和留一法筛选建模变量.最后,统计基于留一法建立的所有模型的平均绝对相对精度(mean absolute relative precision,MARE)的最大值、最小值、均值和均方根误差,以此来反映最终建模的精度和稳定性.结果表明:针对大豆、水稻和玉米,黑龙江分别被分成了 3、3和4个估产子区;变量筛选的结果显示趋势单产的重要性>归一化植被指数(normalized difference vegetation index,NDVI)距平的重要性>积温距平的重要性>Z指数的重要性;除玉米1区以外,针对3种作物建立的模型的最终MARE均小于10%,且所有模型的MARE的方差均小于0.05%.所建模型能够准确预测不同农业气候状况下的作物单产,可以为精细化筛选变量进行估产建模提供方法参考.
Yield Estimation Model of Staple Crops in Heilongjiang:Taking Rice,Soybean and Corn as Examples
Establishing the yield estimation model of staple crops in Heilongjiang Province has a positive effect on grasping the agri-cultural situation of this province in time,and can provide a method reference for the yield estimation modeling in other regions.First-ly,the trend yield,agricultural climate indicators,remote sensing indicators,and planting fluctuation indicators were calculated.Then,yield estimation zones were established for the three crops(soybean,corn,and rice),and modeling variables were selected using a combination of random forest and leave one method within each zone.Finally,the maximum,minimum,mean,and root mean square error of the mean absolute relative precision(MARE)of all models established based on the retention method are calculated to reflect the accuracy and stability of the final modeling.The results show that Heilongjiang Province is divided into 3,3,and 4 yield es-timating zones for soybeans,rice,and corn,respectively.The variable screening results show that the importance of trend yield>nor-malized difference vegetation index(NDVI)anomaly>accumulated temperature anomaly>precipitation Z-index.The MARE of the final models established for the three crops are all less than 10%,with the exception of corn yield estimation zone 1.The variance of MARE of all models is less than 0.05%.The model established can accurately predict the crop yield under different agroclimatic con-ditions,and can provide a method reference for fine screening variables for yield estimation modeling.

yield estimationrandom forestyield estimation zoningleave-one-outstaple crops

杨子毅、朱秀芳、代佳佳、姬忠林、潘耀忠

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北京师范大学地理科学学部遥感科学与工程研究院,北京 100875

青海师范大学地理科学学院,西宁 810008

聊城大学地理与环境学院,聊城 252000

估产 随机森林 估产分区 留一法 大宗作物

高分辨率对地观测系统重大专项

20-Y30F10-9001-20/22

2024

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子:0.338
ISSN:1671-1815
年,卷(期):2024.24(9)
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