1980–2011年中国农作物种植结构时空变化（英文）

Understanding the spatial and temporal variations of cropping systems is very important for agricultural policymaking and food security assessment,and can provide a basis for national policies regarding cropping systems adjustment and agricultural adaptation to climate change.With rapid development of society and the economy,China's cropping structure has profoundly changed since the reform and opening up in 1978,but there has been no systematic investigation of the pattern,process and characteristics of these changes.In view of this,a crop area database for China was acquired and compiled at the county level for the period 1980–2011,and linear regression and spatial analysis were employed to investigate the cropping structure type and cropping proportion changes at the national level.This research had three main findings:(1) China's cropping structure has undergone significant changes since 2002;the richness of cropping structure types has increased significantly and a diversified-type structure has gradually replaced the single types.The single-crop types—dominated by rice,wheat or maize—declined,affected by the combination of these three major food crops in mixed plantings and conversion of some of their planting area to other crops.(2) In the top 10 types,82.7% of the county-level cropping structure was rice,wheat,maize and their combinations in 1980;however,this proportion decreased to 50.7% in 2011,indicating an adjustment period of China's cropping structure.Spatial analysis showed that 63.8% of China's counties adjusted their cropping structure,with the general change toward reducing the main food types and increasing fruits and vegetables during 1980–2011.(3) At the national level,the grain-planting pattern dominated by rice shifted to coexistence of rice,wheat and maize during this period.There were significant decreasing trends for 47% of rice,61% of wheat and 29.6% of maize cropping counties.The pattern of maize cropping had the most significant change,with the maize proportion decreasing in the zone from northeastern to southwestern China during this period.Cities and their surroundings were hotspots for cropping structural adjustment.Urbanization has significantly changed cropping structure,with most of these regions showing rapid increases in the proportion of fruit and vegetables.Our research suggests that the policy of cropping structural adjustment needs to consider geographical characteristics and spatial planning of cropping systems.In this way,the future direction of cropping structural adjustment will be appropriate and scientifically based,such as where there is a need to maintain or increase rice and wheat cropping,increase soybean and decrease maize,and increase the supply of fruit and vegetables.     

1949–2014年中国三种主要作物的地理重心时空迁移分析（英文）

Spatial distribution changes in major crops can reveal important information about cropping systems.Here,a new centroid method that applies physics and mathematics to spatial pattern analysis in agriculture is proposed to quantitatively describe the historical centroids of rice,maize and wheat in China from 1949 to 2014.The geographical centroids of the rice area moved 413.39 km in a 34.32° northeasterly(latitude 3.08°N,longitude 2.10°E) direction at a speed of 6.36 km/year from central Hunan province to Hubei province,while the geographical centroids of rice production moved 509.26 km in the direction of 45.44° northeasterly(latitude 3.22°N,longitude 3.27°E) at a speed of 7.83 km/year from central Hunan province to Henan province.The geographical centroids of the maize area and production moved 307.15 km in the direction of 34.33° northeasterly(latitude 2.29°N,longitude 1.56°E) and 308.16 km in the direction of 30.79° northeasterly(latitude 2.39°N,longitude 1.42°E),respectively.However,the geographical centroids of the wheat area and production were randomly distributed along the border of Shanxi and Henan provinces.We divided the wheat into spring wheat and winter wheat and found that the geographical centroids of the spring wheat area and production were distributed within Inner Mongolia,while the geographical centroids of winter wheat were distributed in Shanxi and Henan provinces.We found that the hotspots of crop cultivation area and production do not always change concordantly at a larger,regional scale,suggesting that the changing amplitude and rate of each crops' yield differ between different regions in China.Thus,relevant adaptation measures should be taken at a regional level to prevent production damage in those with increasing area but decreasing production.     

滇西石鼓杂岩南部早白垩世以来剥露隆升的锆石和磷灰石裂变径迹证据

石鼓杂岩位于青藏高原东南缘经历了多期变质变形作用叠加。为了揭示杂岩体的低温热演化与浅部剥露历史,采集了石鼓杂岩南段石鼓镇-拉巴支村剖面变质岩中的锆石和磷灰石,开展裂变径迹分析。结果表明,石鼓杂岩从早白垩世(133~145Ma)到渐新世(31Ma)经历了一次缓慢的剥露(1.08℃/Ma),而从渐新世开始,其南部经历了较快速的剥露过程(3.23℃/Ma)。磷灰石热史模拟也反映出第二阶段较为快速的冷却过程。结合区域构造分析认为,拉萨与羌塘板块碰撞的远程效应影响早白垩世以来藏东地区地壳结构的调整,导致石鼓杂岩南部出现了第一阶段的剥露作用;而印度与欧亚板块碰撞与后碰撞过程对于石鼓杂岩的新生代剥露具有重要影响。     

基于空间插值分析的指标空间化及吉林省玉米种植区划研究

以吉林省为例,构建玉米种植区划指标体系,并利用空间插值技术和辅助信息对降水、土壤和玉米单产等指标的空间模拟进行重点研究。其中,将多元回归+残差内插相结合对气象站点观测的降水数据进行了空间模拟;将相关性较高的变量作为协因子,并结合土壤类型数据,利用Cokriging插值,获取了土壤属性空间模拟数据;利用耕地质量空间分布数据对玉米统计单产数据进行修正得到玉米单产空间模拟数据。然后,开展吉林省玉米种植适宜性评价和区划研究,将吉林省分为高度适宜区、中度适宜区、低度适宜区和不适宜区4个等级;并结合农业综合分区,得到13个不同的玉米种植区域。     

基于作物空间分配模型的东北三省春玉米时空分布特征

利用1980-2010 年东北三省分县玉米播种面积与产量统计、耕地分布、农业灌溉分布以及作物适宜性分布等多源数据,结合基于交叉信息熵原理的作物空间分配模型（Spatial Production Allocation Model,SPAM）,在5'×5'的像元尺度模拟了春玉米种植面积与产量的时空分布,并重点分析了两者在纬向、经向,以及高程上的时空变化规律。结果显示：（1）玉米种植面积在2000 年前向北扩展至北纬44°~48°间,2000 年后在中南部出现大规模发展（北纬42°~44°）,并进一步向东扩展至东经123°~127°间,同时还表现为向低海拔（高程100 m以下）和较高海拔（高程200~350 m）扩展的态势;（2）单产在纬向上的增加区主要集中在北纬42°~48°,经向上的单产增加则相对均匀,高程上单产提升区主要集中在海拔350 m以下。（3）像元内玉米种植比例整体上由中低种植比例为主逐步演变为中高比例占据主体,并且中高种植比例像元对应的玉米单产水平整体上较高,反映了市场经济驱动下的玉米种植集聚化和规模化的发展趋势。     

中国东北耕地物候期对气候变化的响应(英文)

We investigated the responses of cropland phenophases to changes of agricultural thermal conditions in Northeast China using the SPOT-VGT Normalized Difference Vegetation Index (NDVI) ten-day-composed time-series data, observed crop phenophases and the climate data collected from 1990 to 2010. First, the phenological parameters, such as the dates of onset-of-growth, peak-of-growth and end-of-growth as well as the length of the growing season, were extracted from the smoothed NVDI time-series dataset and showed an obvious correlation with the observed crop phenophases, including the stages of seedling, heading, maturity and the length of the growth period. Secondly, the spatio-temporal trends of the major thermal conditions (the first date of ≥10℃, the first frost date, the length of the temperature-allowing growth period and the accumulated temperature (AT) of ≥10℃) in Northeast China were illustrated and analyzed over the past 20 years. Thirdly, we focused on the responses of cropland phenophases to the thermal conditions changes. The results showed that the onset-of-growth date had an obvious positive correlation with the first date of ≥10℃ (P < 0.01), especially in the northern part of the Songnen Plain, the eastern part of the Sanjiang Plain and the middle and eastern parts of Jilin Province. For the extracted length of growing season and the observed growth period, notable correlations were found in almost same regions (P < 0.05). However, there was no obvious correlation between the end-of-growth date and the first frost date in the study area. Opposite correlations were observed between the length of the growing season and the AT of ≥10℃. In the northern part of the Songnen Plain, the eastern part of the Sanjiang Plain and the middle part of Jilin and Liaoning Provinces, the positive correlation coefficients were higher than the critical value of 0.05, whereas the negative correlation coefficients reached a level of 0.55 (P < 0.05) in the middle and southern parts of Heilongjiang Province and some parts of the Sanjiang Plain. This finding indicated that the crop growth periods were shortened because of the elevated temperature; in contrast, the extended growth period usually meant a crop transformation from early- or middle-maturing varieties into middle or late ones.     

东北三省耕地物候期对热量资源变化的响应

选取1998-2010 年期间的SPOT/VGT NDVI逐旬时间序列数据、物候观测数据和气候数据,利用函数拟合方法、GIS空间分析和统计方法,分析了东北三省不同耕地物候期对热量资源变化的响应关系。首先,基于拟合数据提取了耕地物候期特征(包括生长季开始期、峰值期、结束期与生长季长度等),验证结果显示与不同作物的观测物候期存在明显对应关系,能较好地反映作物物候期特征的宏观时空分异。其次,分析了1991-2009 年期间区内主要农业热量资源(≥ 10 oC初日,初霜日,温度生长期天数和≥ 10 oC积温) 的时空变化特征。最后,重点分析了1998-2009年期间耕地物候期特征对热量资源变化的响应。结果表明,生长季开始期与≥10 oC初日在松嫩平原北部、三江平原东部和吉林省中东部的正相关系显著(P < 0.01);生长季长度与温度生长期天数亦在上述区域内表现为显著正相关关系(P < 0.05);然而,生长季结束期与初霜日的相关关系整体上较不明显;此外,生长季长度与≥ 10 oC积温的相关关系存在两种相反的情形,在松嫩平原西北部、三江平原、吉林省中部和辽宁省中部的负相关关系显著(P < 0.05),而黑龙江省中南部和三江平原部分区域,两者相关系数达到0.55 (P < 0.05),分别反映了作物生长发育周期的缩短态势和中早熟作物被中晚熟作物替代的趋势。     

近30年中国农作物种植结构时空变化分析

综合运用时序变化趋势、空间集聚分析等方法,从种植结构类型和种植比例变化趋势分析了1980年以来中国县域种植结构的时空特征。结果表明：① 近30年来中国前10位的种植结构类型有16种,2002年后多元种植结构逐步替代单一型种植结构。粮食作物占优的单一种植结构类型呈逐年递减趋势,其中1980年全国82.7%的县级农业种植结构是水稻、小麦、玉米及其组合种植类型,2002年后的果蔬类型增加改变了种植结构格局。② 全国种植县中有47%的水稻、61%的小麦和29.6%的玉米的种植比例显著减少,其他作物呈现增加趋势。粮食作物由以水稻为主的格局调整为水稻、小麦和玉米共存格局,其中玉米种植面积比例在空间上变化最为显著,在中国形成北东—西南向的“玉米减少带”。种植结构调整热点的城市地区,城市化对种植结构变化影响显著,水果和蔬菜类种植比例在城市化地区快速增加。③ 种植结构变化趋势在1300个县形成空间集聚效应,水稻的高高聚集占全国县数的2.86%、小麦占5.64%、玉米占6.11%、大豆为4.53%、麻类为1.62%、棉花占7.77%、蔬菜占8.24%、薯类占12%、水果占10%、糖料占1.41%、油料占9.35%,主要分布于中国东北、新疆和沿海的城市化地区。     

江汉平原稻虾共作技术时空扩散的过程及特征

农业技术创新的外溢性导致新技术在地理空间发生扩散，影响区域农业农村经济社会发展和农民福祉。近年来，稻虾共作种养结合的新技术模式在我国长江中下游地区迅速兴起并扩张，科学厘清该新技术的时空扩散过程、特征及其驱动因素具有重要意义。本研究以稻虾共作技术发源地—江汉平原为区域，基于时间序列Landsat影像，引入CART分类算法和简单非迭代分割算法，优化改进面向对象的水体季相差异算法，提取2013—2019年江汉平原稻虾共作空间分布；在此基础上，引入应用空间平滑和引力模型方法，从区域扩张、空间聚集和区域交互等角度探究江汉平原稻虾共作技术扩散的时空过程与特征。结果表明，江汉平原稻虾共作技术在时间上呈现出先缓慢后加速的非线性扩散，在空间上表现为由点至面、由单源至多源的扩张特征。技术传播表现出显著的聚集性和方向性特征，高密度的稻虾农田集中于江汉平原中部地区，传播重心逐渐由中部向东南部迁移。区域内部可划分为以潜江为代表的波动性增长和以监利为代表的持续性增长。县区之间的空间交互作用以倍数形式快速上升，体现为以潜江和监利作为主导的对邻近地区的技术辐射影响。稻虾技术时空扩散受到高经济收益、政府支持和邻近效应的...     

近30年中国水稻种植区域与产量时空变化分析

通过综合80年代初以来的农作物面积与产量统计、耕地分布、农业灌溉分布以及作物生长适宜性分布等多源数据,利用基于交叉信息熵原理的作物空间分配模型(Spatial Production Allocation Model,SPAM),获得了我国10km像元尺度的水稻分布信息。在此基础上,重点分析了80年代初以来水稻种植面积与产量的时空变化特征。总体来看,在全国水稻种植区域内发生变化的地区中有超过50%的地区水稻种植面积出现缩减态势,但仍有近70%的地区水稻产量在增加。空间变化来看,种植面积缩减主要发生在东南沿海的广东、福建和浙江等省,而增加主要出现在东北地区的吉林和黑龙江等省,我国水稻种植重心因此向东北方向迁移约230km,产量重心向东北迁移约320km。同时,研究还发现我国水稻种植面积变化对产量增减具有重要影响,其中产量增加表现为面积与非面积因素的共同作用,数据显示种植面积扩展对水稻增产的平均贡献率约54.5%,而在产量减少的区域,面积缩减对减产的贡献率高达80%以上。