Analysis of the temporal and spatial changes of photo-thermal resources in winter wheat growing regions in china | 中国冬小麦种植区光热资源及其配比的时空演变特征分析

中国人. 【目的】明确中国冬小麦种植区小麦生长季及关键生育阶段光热资源及其配比的时空演变特征，为从依据光热资源变化调整种植制度与作物布局的角度，应对气候变化提供参考依据。【方法】利用中国冬小麦种植区264个气象站点47a（1961―2007）的逐日气温与日照时数观测数据，分别计算每日活动温度和太阳辐射总量。根据冬小麦完成各个生育时期所需要的活动积温（＞0℃），推算出各地每年冬小麦主要生育时期出现的理论日期，并计算冬小麦生长季以及关键生育时期的光（太阳辐射）、热（有效积温，热时间）资源及其配比（辐热积）。采用ArcGIS (v9.3)以及反距离权重空间插值方法获得空间上连续分布的栅格数据，实现数据网格化。【结果】1961―2007年间，中国冬小麦理论播种期平均推迟0.5 d•10a-1，抽穗、成熟期分别平均提前1.6和1.7 d•10a-1,全生育期平均缩短2.2 d•10a-1。冬小麦生长季内，有效积温平均增加7.8 ℃•d•10a-1，热时间平均增加0.6 d•10a-1，太阳总辐射平均减少39.69 MJ•m-2•10a-1，下降率为-2.34%•10a-1，辐热积平均减少10.18 MJ•m-2•10a-1，下降率为-1.22%•10a-1。播种至抽穗期，有效积温平均增加6.2 ℃•d•10a-1，热时间平均增加0.57 d•10a-1，太阳总辐射平均减少27.7 MJ•m-2•10a-1，下降率为-2.72%•10a-1，辐热积平均减少5.2 MJ•m-2•10a-1，下降率为-1.18%•10a-1。抽穗至成熟期，有效积温平均增加1.6 ℃•d•10a-1，热时间平均增加0.05 d•10a-1，太阳总辐射平均减少12 MJ•m-2•10a-1，下降率为-1.78%•10a-1，辐热积平均减少4.97 MJ•m-2•10a-1，下降率为-1.26%•10a-1。【结论】1961―2007年间，中国冬小麦生长季内辐热积降低和生育期缩短较多的地区是北部冬麦区和位于黄淮麦区的山东中东部，光热资源的变化趋势对这些地区冬小麦生产潜力呈负面影响。在其它麦区（西南麦区、长江中下游麦区、黄淮麦区和华南麦区），冬小麦生长季内辐热积的增加与生育期缩短对冬小麦生产潜力的正负面影响同时存在。

英语. 【Objective】To identify the temporal and spatial changes of photo-thermal resources during the growing season and key development stages of winter wheat in China so as to mitigate the impacts of climate change on wheat production in China by adjusting cropping system and crop distribution according to changing trends of photo-thermal resources.【Method】Daily data of temperature and sunshine hours of 47 years (1961-2007) from 264 meteorological stations in the winter wheat growing regions in China were used to calculate daily active temperature (＞0℃) and daily total global radiation. The theoretical dates when winter wheat reaches its main development stages were determined based on the accumulated active temperature required by each development stage. Photo-thermal resources (the growth degree days (GDD), the thermal time (TT), the total global radiation, and the product of thermal effectiveness and global radiation (TEP)) for the whole growing season and the key development stages were then calculated. The grid data were obtained by means of inverse distance weight spatial interpolation using ArcGIS (v9.3).【Result】During the 1961-2007, the theoretical sowing date of winter wheat delayed averagely by 0.5 d•10a-1, that of heading and ripening became earlier averagely by 1.6 d•10a-1 and 1.7 d•10a-1, respectively, and the whole growing season was shortened averagely by 2.2 d•10a-1. During winter wheat growing season, the GDD and the total TT increased averagely by 7.8℃•d•10a-1 and 0.6 d•10a-1, respectively, the total global radiation decreased averagely by 39.69 MJ•m-2•10a-1 (by -2.34%•10a-1), and the TEP decreased averagely by 10.18 MJ•m-2•10a-1 (by -1.22%•10a-1). From sowing to heading, the GDD and the total TT increased averagely by 6.2 ℃•d•10a-1 and 0.57 d•10a-1, respectively, the total global radiation decreased averagely by 27.7 MJ•m-2•10a-1 (by -2.72%•10a-1), and the TEP decreased averagely by 5.2 MJ•m-2•10a-1 (by -1.18%•10a-1). From heading to ripening, the GDD and the total TT increased averagely by 1.6 ℃•d•10a-1 and 0.05 d•10a-1, respectively, the total global radiation decreased averagely by 12 MJ•m-2•10a-1 (by -1.78%•10a-1), and the TEP decreased averagely by 4.97 MJ•m-2•10a-1 (by -1.26%•10a-1).【Conclusion】During the 1961-2007, in the North region and in Mid-eastern Shandong Province of Huang-Huai region for winter wheat growing, the decreases of both TEP and the length of wheat growing season indicate that changes in photo-thermal resources had negative effects on winter wheat potential productivity in these regions. In the rest regions (the Southwest, the Middle and Lower Reaches of the Yangtze River, the Huang-Huai, and the South) for winter wheat growing, the increased TEP had a positive impact on winter wheat potential productivity whereas the shortened growing season had a negative impact on winter wheat potential productivity, hence, the impacts of changes in photo-thermal resources on winter wheat potential productivity is positive or negative can only be determined by resorting to quantitative analysis using crop growth simulation models.