基于CI指数的黑龙江省作物生长关键期干旱变化

利用1961—2008年黑龙江省逐日气象观测资料,计算综合气象干旱指数（CI）,分析黑龙江省作物生长关键期（5—8月）气象干旱频率和气象干旱强度变化。结果表明：黑龙江省作物生长关键期干旱频率1960年最大为79%,1980年最小为58%,干旱频发中心位置变化不大,主要出现在松嫩平原西南部,但集中区向北转移。黑龙江省作物生长关键期干旱强度1960年为最强,其次为2000年,干旱强度最大区域基本稳定在松嫩平原西南部和三江平原中东部。2000年以来,作物生长关键期干旱频发且强度加大,北部渐频、渐强,原来较少发生干旱的三江平原干旱也时有发生且强度较大。     

豫东地区早期社会复杂化进程的农业经济基础——以河南淮阳平粮台遗址为例

新石器时代晚期开始的社会复杂化进程是探索中华文明起源的关键所在。作为社会变革的经济基础,这一时期的农业发展状况自然成为众多相关研究中不可或缺的一环。豫东地区的淮阳平粮台遗址因其中轴对称的城市布局、成熟完备的排水系统,被认为开创了中国城市规划的先河,在中华文明起源的研究中占据着特殊的地位。本研究通过对2016~2019年平粮台遗址发掘中系统获取的植物遗存进行鉴定和分析,揭示了豫东地区早期复杂社会形成到早期国家阶段的生业经济基础。平粮台遗址龙山文化时期的农业结构以粟黍为主,兼有少量水稻和大豆,这一作物结构在商周时期也未发生显著的变化。直到战国秦汉时期,粟仍然是当地最主要的农作物,同时小麦所占比重大幅提升,重要性仅次于粟。区域性的比较分析显示,这一农业模式在豫东及其邻近地区具有较高的一致性,旱作农业是当地早期社会复杂化进程的经济基础。

     

DISTRIBUTIONS AND VARIATIONS OF SURFACE OZONE IN CHANGSHU, YANGTZE DELTA REGION*

Through one and half year continuous in-situ measurements,the distributions and variations of surface ozone and its precursors at a typical mixed agricultural and metropolitan area-Changshu,Yangtze Delta region,were studied.The preliminary analysis on the concentration levels and variations of surface ozone indicated the obvious seasonal and diurnal cycles during the experiment.The hourly averaged concentrations of surface ozone were high,in about 17% of total valid hours the surface ozone concentration exceeded 50 ppb,and in 22 days the hourly averaged ozone concentration was greater than 100 ppb.There were about 40% of the days in that the daily maximum 8-hour ozone concentration was greater than 50 ppb.The days with daily maximum 8-hour ozone concentration greater than 80 ppb were about 33 days that accounted for about 8% of the observational days.The variations of 5-day moving averaged ozone concentrations depended both on the weather conditions and on the changes of ozone in background atmosphere.Photochemical process had the significant impacts on ozone productions.     

基于GIS的主要农作物病虫害气象等级预报系统研究

为了将农作物病虫害气象等级预报能力扩展到更大空间尺度,根据相邻和相近农作物种植区域的一致性,进行农作物病虫害预报模型区域化应用和拓展。建立了气象等级划分标准,在Oracle农业气象数据库和地理空间数据库的支持下,采用Visual Basic.NET和GIS组件,设计并实现了基于地理空间信息的主要农作物病虫害气象等级预报系统。该系统可对北方草原蝗虫、东北玉米螟、江南稻飞虱、黄淮棉铃虫、黄淮小麦条锈病、江淮江汉小麦赤霉病和西南地区水稻稻瘟病7大类主要作物病虫害发生发展气象等级进行实时预报,取得了较好效果。     

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue,France

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.     

Changes in climatic variability and maize yield inNortheast China

1IntroductionTheconventionalassessmentSonthepossibleimPactsofclilnateAngehaveahrpothesisofkeepingclimaticvariabilityinaccordtviththatofpresentclimateduetolackofavailableinformationchangesinclimaticvariabiiltyFixedtemperaturechangeandfixedprecipitationadjustingfactorsassumedorderivedffomGCMs'shoulahonareaddedtotheobserveddailytemperatUrormulhpliedwithhiStoricaldailypreeipitahonrespechvelyinthesestUdiest11.Alti1oughthereareanUInerofapparentdeficienciesinGCMs,includinglowertemporalandspatia…     

Estimations of climate change impacts on crop production in the Republic of Moldova

Statistical methods for assessing crop sensitivity and vulnerability to climate change in Moldova were demonstrated and the following procedures were discussed: (1) projections of likely agroclimatic change; (2) assessments of crop sensitivity to climate change; and (3) assessments of the impact of climate change on crops. In order to predict the future agroclimate, key meteorological variables were transformed statistically to correspond to changes in plants' heat and water supply characteristics. Sensitivity of crop production was examined for corn and winter wheat. By combining the agroclimatic changes with crop response, possible impacts have been estimated and form a basis for possible adaptation strategies. It was shown that regional climate change can result in elevated aridity of Moldova's territory, especially during periods of crop growth. Cultivation of cereal crops in new agroclimatic conditions without adaptation measures will negatively affect yields, especially of winter wheat, whose yield decrease may be 18–39% by 2020s and 22–50% by 2050s. Corn yields may increase by 0–3% and 1–6%, respectively. As an example of adaptation, it is shown that the use of an increased number of late hybrids results in a 25–35% increase in corn yields. This revised version was published online in July 2006 with corrections to the Cover Date.