用多变量灰色预测模型模拟预测参考作物蒸散量的研究

当观测资料的数据量少而又存在多个相互影响或关联的变量时,常用的灰色预测模型GM（1,1）不能全面考虑多个变量。为此,采用自适应MGM（1,n）模型—多变量灰色预测模型,较好地解决了这一问题。针对一些地区气象数据较少甚至缺失的情况,以内蒙古正蓝旗的气象资料用Penman-Monteith计算的参考作物蒸散量（ET0）为研究对象,运用灰色系统理论建立MGM（1,3）模型,模拟预测参考作物蒸散量变化规律,并与GM（1,1）模型和BP神经网络模型比较,结果表明MGM（1,3）模型有较好的预测效果。     

近28a河南主要农业气象灾害及其影响

利用河南1978-2005年农作物受灾面积资料和新中国成立以来的产量资料，分析了河南主要农业气象灾害的变化特征和对农业生产的影响，结果表明，28a来河南农作物受灾面积呈上升趋势，其阶段性变化为20世纪70年代后期至80年代前期较少，80年代中后期至21世纪初明显增多，最近几年又明显减少。气候条件对农业生产的影响以50年代最有利，80年代次之，70年代较差，60年代最差。     

安徽省旱涝灾害及其对农作物产量影响

通过对建国以来安徽省多种农业气象资料分析,采用Z指数方法建立安徽省旱涝灾害气候判别指标,分析安徽省旱涝灾害发生规律。结果表明：1961—2000年安徽省有13年偏涝、13年偏旱;分区域看,淮北旱多于涝,沿淮、江淮、江南旱涝相当,沿江、大别山区涝多于旱。通过水稻（一季稻）、小麦典型旱涝年灾损率与发育期间气象条件、旱涝程度的对比统计分析,建立了分区水稻、小麦旱涝灾害损失评估模型和指标。春季涝渍灾害是影响安徽省冬小麦产量的主要灾害,其对冬小麦产量的危害程度远大于干旱,尤以4—5月发生的涝渍影响最严重,极重涝渍灾害的减产损失可达4成以上。同时重点研究了春季渍害对冬小麦产量的影响,提出改进的涝渍强度指标Qw,并进一步综合分析作物的敏感性和区域脆弱性对灾损率的影响。对1961—2000年冬小麦灾损率进行的敏感性和脆弱性订正表明,订正后拟合误差平均值和差异变率都明显降低,灾损评估精度得到提高。     

Macrozoobenthic community of Fuxian Lake, the deepest lake of southwest China

Located in the Yunnan–Guizhou Plateau in southwest China, Fuxian Lake covers an area of 211 km², with maximum depth of 155 m. It is known to have a unique fauna, including 14 described endemic species. In order to describe the zoobenthic community of the lake more completely, the present study was conducted from August 2002 to August 2003. Altogether 62 benthic taxa, including 22 oligochaetes, 21 molluscs and 18 insects were identified, of which the dominant taxa belonged to Potamothrix, Procladius and Paraprososthenia. The standing stocks of benthos were much higher in the littoral (824 ind/m² in density, 3.72 g/m² in biomass) than in the profundal region (23 ind/m² in density, 0.10 g/m² in biomass). Species richness was greatest in summer and standing stocks were larger in spring and summer than in other seasons. Analyses of functional feeding groups indicated that collector-gatherers and scrapers were predominant in entire lake. Stepwise multiple regression analysis demonstrated that the water depth is the most important factor affecting the distribution of macrozoobenthos.     

A review on statistical models for identifying climate contributions to crop yields

Statistical models using historical data on crop yields and weather to calibrate relatively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale (e.g. global scale, national scale, provincial scale and county scale). Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.     

Statistical characterization of frost zones: Case of tea freeze damage in the Kenyan highlands

Frequent frost occurrences in the Kenyan highlands have had devastating effects on agricultural productivity. With inadequate management systems to mitigate the impacts, farmers have often had to bear the burden of losses resulting from frost damage. While agriculture in Kenya remains dependent on weather and climate, the agricultural economy of Kenya continues to suffer, underscoring the need for building local knowledge as basis for development of early warning systems. The current paper attempts to delineate frost zones by statistically characterizing them based on known risk factors related to topography (elevation, convexity, aspect, upslope flow length) and Land Surface Temperature (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS). Through binary logistic regression, a logistic regression model was developed utilizing observation data (frost occurrence and non-occurrence) as a binary dependent variable to estimate the probability of frost occurrence. Assuming a 0.5 probability cut-off threshold between frost occurrence and non-occurrence, an overall accuracy of 81% with area under Receiver Operating Characteristics (ROC) Curve of 0.88 was obtained. No evidence of lack of model fit was detected. This model outperforms the currently operational model that utilizes MODIS LST alone to detect frost zones in the Kenyan tea plantations. It provides an improved method for effective delineation of frost zones by incorporating local topographic characteristics.     

Regionalization for large area crop estimates by remote sensing

The crop estimates by remote sensing, developing quickly in recent decades, is a up-to-date technique. Regionalization for large area crop estimates by remote sensing, a special applied regionalization, is the foundation of crop estimates in a large area by remote sensing. According to the actual demands of wheat yield estimation by remote sensing and wheat agroclimatic demarcation of China, this paper first puts forward some principles upheld in this regionalization and analyses its main bases. Secondly, it works out the classificatory schemes about the optimum temporal for estimating wheat yield by remote sensing, information sources of space remote sensing and landuse structure in China. Finally, According to the regionalization indices, this study divides the wheat plantable region of China into 14 regions of crop yield estimates and 31 subregions of crop yield estimates.     

气候变化对我国南方双季稻发育和产量的影响

基于WOFOST作物模式,结合气候模式输出的气候情景资料,模拟研究了未来100a(2001-2100年)气候变化对我国南方双季稻发育和产量的影响。结果表明：未来气候情景下,我国南方大部分地区双季稻(早稻、晚稻)的生长期会有所缩短;产量可能会有所下降,但下降的幅度不是很大,其中早稻受气候变化的影响较大。     

影响我国主要粮食作物产量的气象因子研究

通过收集前人主要粮食作物气象产量预报的研究成果，将所得到的资料按作物进行分类，并进行归一化处理，使用系统聚类分析方法，以预报方程中的影响因子为指标，分区找出各区主要粮食作物产量的主、次要影响因子和影响时期，为大范围作物产量预报提供了科学依据。     

黄土高原作物产量及水土流失地形分异模拟

退耕坡地是近年来黄土高原整治生态环境和控制水土流失的有效措施。基于WIN_YIELD 软件, 以延安燕沟流域为例,利用2005 年延安站的逐日气象数据和燕沟流域地貌、土壤及土地利用等资料, 模拟分析了不同地形高程、坡度和坡向条件下不同作物产量及水土流失的分异特征。结果表明： 在黄土高原, 地形坡度是影响作物产量、径流和泥沙的重要因素, 地形高程和坡向的影响普遍较微弱; 地形坡度越大, 作物产量越低, 高粱和玉米在坡度为25^o 时的产量模拟值分别较0^o 时下降15.44%和14.32%, 大豆、绿豆和马铃薯的下降幅度依次为5.26%、4.67%和3.84%; 产生径流和泥沙随地形坡度的增大而增大, 在坡度20^o左右存在一个高值区间; 不同作物的水土保持效益由高到低依次为大豆、绿豆(豆类)、马铃薯、玉米和高粱; 坡度小于5^o的坡耕地可以不进行梯田改造, 坡度大于15^o 的坡耕地应及早实现退耕还林还草。