河北省冬小麦生产空间格局及其控制因素

Spatial structure and control factors of winter wheat production in Hebei Province

在气候变化与水资源短缺的背景下,华北平原冬小麦生产面临巨大的挑战,明确冬小麦生产的空间格局及其控制因素,可为本区冬小麦的科学规划管理决策和高效生产提供依据。基于河北省各县市2004—2013年冬小麦单位面积产量与农情资料,以主产区（以县市平均冬小麦播种面积大于总播种面积的20%为标准选取）101个县市为基本研究单元,采用系统聚类分析对河北省冬小麦主产区进行区域划分;利用因子分析方法对冬小麦生产要素进行主成分分析,并利用逐步回归分析方法建立冬小麦产量与主要控制因素主成分之间的关系。结果表明,河北省冬小麦主产区分为4个（Ⅰ~Ⅳ）,各区冬小麦产量水平从Ⅰ区向Ⅳ区依次递减,产量变异依次增大,且各区差异均达显著水平（P〈0.05）。冬小麦产量（Y）与化肥因子（F1）、灌溉因子（F3）、年降水量因子（F4）、年降水量下限因子（F5）有显著的线性回归关系（R2=0.685,P〈0.05）,其中F1、F3分别解释了Y的21.7%、37.4%,F4、F5解释了Y的9.4%。据此说明灌溉是影响河北省冬小麦产量区域差异的首要因素,其次是化肥使用量,而年降水量对产量区域差异影响较小。农药因子（F2）的回归效应不显著,说明冬小麦病虫害发生及防治具有区域随机性变化特点,对冬小麦生产的区域差异不形成显著影响。另外,全省冬小麦播种面积比重与产量水平有较好的区域吻合度（R2=0.409,P〈0.05）,说明在现有管理模式下冬小麦区域布局基本合理,只是在东部低平原沿海的极个别低产县的播种面积明显偏高,结合冬小麦对灌溉条件的依赖性,认为这些县市的冬小麦布局需要慎重审视。

Winter wheat is one of the most important staple crops in the North China Plain. With intensifying challenges of climate change and water shortage, the need for sustainable and high efficiency winter wheat production is becoming more pressing. Clear understanding of the control factors of wheat production can provide the necessary basis for the regulation of a smart winter wheat production policy. Hebei Province is one of the major winter wheat production regions in North China. Annual records（2004–2013） of wheat yield and agricultural information for each county in the province were used to analyze the spatial distribution of yield variation of winter wheat along with the control factors. The major winter wheat production area（which consists of 101 counties with winter wheat area greater than 20% of total cropping area of the county） in Hebei Province was selected and divided into 4 zones（Ⅰ–Ⅳ） using hierarchical clustering analysis based on maximum, minimum and average yields. A principal component analysis was conducted using the average and variation indexes（minimum, maxi-mum, bottom and top boundaries of the 95% confidence interval） of the agricultural variables for winter wheat production, including irrigated area, fertilizer and pesticide amount as well as annual rainfall in each county. Five derived principal factors represented fertilizer（F1）, pesticide（F2）, irrigation（F3）, annual rainfall（F4） and minimum rainfall（F5）. Based on the factor score values of the factor models, a stepwise regression analysis model was developed to assess the spatial variation of winter wheat yield as an independent factor（Y）. The results showed that from zone Ⅰ to zone Ⅳ, averaged winter wheat yield decreased, whereby temporal yield variation increased significantly（P 0.05）. Zone Ⅰ mostly covered central Hebei Plain including Gaocheng and Luancheng Counties to the east of Shijiazhuang City. Zones Ⅱ, Ⅲ and Ⅳ diverged from the central to the peripheral of Hebei Plain in that sequence, especially zone Ⅳ which only covered the southwest and northeast corner with the lowest yield and the highest variation. Model analysis showed that Y was significantly correlated with F1, F3, F4 and F5（R2 = 0.685, P 0.01） and these factors explained 68.5% of Y, where irrigation（F3） was the most important factor explaining 37.4% and fertilizer（F1） explaining 21.7%. Annual rainfall F4 and F5 together explained only 9.4% of the whole model result, indicating a minor effect of annual rainfall on the spatial distribution of winter wheat production in Hebei Province. Pesticide（F2） was not factored into the Y regression model, showing that the use of pesticide had no significant effect on winter wheat production. It underlined the point that plant diseases and pests happened at random and with no regional tendency in winter wheat production. Regression analysis on winter wheat yield and the ratio of winter wheat area to total cropped area（F6） showed that F6 coincidently varied with winter wheat yield among different counties（R2 = 0.409, P 0.05）. Only a few counties to the far east of the low plain close to the coast belonging to zone Ⅳ with the lowest winter wheat yield but abnormally high F6. The results showed that winter wheat production was basically more proper under the existing management mode. However, under severe water shortage conditions, the abnormally high F6 value with low yield spots needed careful examination to insure a healthy and sustainable high-efficiency cropping system. Otherwise winter wheat production should be reduced in low yield areas because irrigation was the most important controlling factor of production. Nevertheless, in this study, only 68.5% of the production variables were explored. This implied that there were other factors not only taken into account in the analysis but also affected winter wheat production.