| 冬小麦产量结构要素预报方法 |
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| 引用本文： | 张佩,陈郑盟,刘春伟,王福政,江海东,高苹.冬小麦产量结构要素预报方法[J].农业工程学报,2020,36(8):78-87. |
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| 作者姓名： | 张佩 陈郑盟 刘春伟 王福政 江海东 高苹 |
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| 作者单位： | 江苏省气象局,南京 210008;福建省烟草公司龙岩市公司,龙岩 364000;江苏省农业气象重点实验室、南京信息工程大学应用气象学院,南京 210044;勤耕仁现代农业科技发展(淮安)有限责任公司,淮安 223001;南京农业大学农业部作物生理生态与生产管理重点实验室、江苏省现代作物生产协同创新中心、国家信息农业工程技术中心,南京 210095 |
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| 基金项目： | 国家重点研发计划课题（2018YFD1000900）;2019年国内外作物产量气象预报专项;江苏省"333工程"高层次人才培养科研项目;江苏省气象局科技项目（KM201905） |
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| 摘 要： | 为优选出最佳的冬小麦产量结构要素预报方法,该研究选择冬小麦成穗数、穗粒数及千粒质量为预报目标,综合考虑种植品种、密度及地区因子,并对气象因子进行膨化统计,得到126个自变量因子,分别采用多元线性回归、因子分析-线性回归及BP(Back Propagation)神经网络等3种方法进行建模分析。结果表明,直接采用各因子进行回归分析无法解决不同自变量间存在的多重共线性问题,而因子分析虽然消除了不同自变量间的多重共线性,但采用因子优化后的10个综合因子分别对3个产量结构要素进行线性回归,得到的预报模型决定系数(R^2)均不足0.500。运用BP神经网络对冬小麦3个产量结构要素进行预报,结果发现,当输入层为126、隐含层为16、输出层为3时,BP神经网络结构最佳,在此结构下,模型的决定系数为0.644,明显优于多元线性回归及因子分析-线性回归法。同时,基于BP神经网络模型对冬小麦产量结构要素的预报精度平均达85.3%。因此,推荐采用BP神经网络模型对冬小麦产量结构要素进行预报。
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| 关 键 词： | 作物 冬小麦 产量结构要素 多元线性回归 因子分析 BP神经网络 |
| 收稿时间： | 2019/12/12 0:00:00 |
| 修稿时间： | 2020/2/29 0:00:00 |
Method for the prediction of wheat yield components |
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| Zhang Pei,Chen Zhengmeng,Liu Chunwei,Wang Fuzheng,Jiang Haidong,Gao Ping.Method for the prediction of wheat yield components[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(8):78-87. |
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| Authors: | Zhang Pei Chen Zhengmeng Liu Chunwei Wang Fuzheng Jiang Haidong Gao Ping |
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| Institution: | 1. Jiangsu Meteorological Bureau, Nanjing 210008, China;,2. Longyan Company of Fujian Provincial Tobacco Corporation, Longyan 364000, China;,3. Jiangsu Provincial Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China;,4. QinGengRen Modern Agricultural Science and Technology Development Co., Ltd., Huai''an 223001, China;,5. Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Jiangsu Collaborative innovation Center for Modern Crop Production, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China; and 1. Jiangsu Meteorological Bureau, Nanjing 210008, China; |
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| Abstract: | Accurate determination of yield components can assist in predicting the final crop yields, revealing the physiological significance of yield estimation. Research on the direct prediction of crop yield components is still lacking, because the feature data of yield components for long sequence are difficult to obtain, and some highly variable factors influence each other on the accuracy of the estimation. In this study, the spike quantity per plant (SQ), grain number per spike (GN), and 1000-grain weight (1 000 GW) of winter wheat were taken as prediction targets, to determine the optimal method for the prediction of winter wheat yield components. 126 independent factors were achieved using the puffing technology for meteorological factors after assessing the factors of planting species, density and region. A multivariable linear regression was used to analyze the crucial factors correlated to the concerned crop yield, and thereby to determine the quantitative relationship between the factors and yields. Three multiple regression models for the yield components of winter wheats were constructed after the 126 independent factors were regressed step by step. The determination coefficient R2 of the three multiple regression models were 0.515, 0.178 and 0.368, respectively, all at a low level than before. In collinearity diagnosis, if the characteristic values of multiple dimensions in 3 models were approaching to be zero, or the corresponding condition indexes were greater than 10, the time-delay prediction can occur due to the multicollinearity relation between factors. To solve this collinearity among factors and verify the data structure, a factor analysis was conducted to transform various observed variables into a few typical comprehensive factors. The optimized 126 independent variables made it possible to reduce the factor dimension. After factors optimization, 10 comprehensive factors were obtained to establish the three multiple regression predicting models of yield components, and the determination coefficient R2 were 0.376, 0.111 and 0.288, respectively, all less than 0.5. Based on neural network principle, a back-propagating neural network (BPNN) model was established between multiple independent factors and dependent variables, due to its ability for an approximate representation without restricting the input-output data. The determination coefficient R2 of the proposed model was 0.644 under the optimal model structure (126-16-3), indicating much better than that from the multiple linear regression and factor analysis. The overall prediction accuracy of BPNN model was 85.3%. The average prediction accuracies of grain number (GN) and 1000-grain weight (1 000 GW) were 88.1% and 89.5%, respectively, showing significantly higher than that of spike quantity per plant (SQ). In the prediction regions, the average prediction accuracies of the BPNN model were more than 80% in 6 regions, with the highest prediction accuracy of 89.6% in the east coast of Jiangsu. The results demonstrate that the nonlinear feature of BPNN model can be used to improve the approximation ability when dealing with multiple factors. The BPNN modeling is strongly recommended to predict yield components of winter wheat. |
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| Keywords: | crops winter wheat yield components multiple liner regression factor analysis BPNN |
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