施肥量对温室滴灌番茄干物质累积、产量及水肥利用的影响

为研究日光温室秋冬茬番茄生育时期干物质生产动态规律,探索高产高效的科学施肥模式,以标准冲施肥(含N量16%,含P_2O_5量5%,含K_2O量19%)为施肥种类,施肥量以N素为基准分为4个肥料处理水平,即F1(高肥)、F2(中肥)、F3(低肥)、F4(不追肥),分析了不同施肥量对日光温室番茄干物质累积、产量和水肥利用的影响。结果表明,番茄产量和水分利用效率(WUE)与施肥量呈"抛物线"关系,当N施量为290.6 kg/hm2(F2)时达到最高值,分别为67 776.93 kg/hm2和49.27 kg/m3,说明适量施肥可提高产量及水分利用效率。但肥料生产效率(PFP)在施肥量为(N)41.4~539.9 kg/hm2范围内呈现负指数降低趋势。植株干物质累积量表现出F2处理F1处理F3处理F4处理。果实干物质累积进程符合logistic函数轨迹,中肥处理比高肥、低肥处理提前进入快速累积期,使快速累积期持续时间比其他处理长1~8 d。全株总干物质转移量对果实贡献率为1.97%~6.96%,说明果实干物质高达90%以上依靠株体自身光合作用填充。因此,番茄结果期加强水肥的供给是必要的。     

基于三维Copula函数的沙颍河流域水文干旱频率分析

为进一步研究水文干旱特征及其演变规律,选取沙颍河流域2个水文站1951—2008年逐月径流资料,采用游程理论提取水文干旱特征变量,选取4种对称Archimedean Copula函数和对应的非对称Archimedean Copula函数拟合干旱特征变量的联合分布并计算联合重现期和同现重现期。经拟合检验,Frank Copula对干旱特征变量二维联合分布的拟合效果最优,M3 Copula函数对三维干旱变量的联合分布拟合效果最优。干旱特征的单变量重现期值大于二、三维变量的联合重现期,小于二、三维变量的同现重现期。因此,Copula函数能够较好地拟合沙颍河流域水文干旱特征变量间的联合分布。     

基于Translog生产函数的北京市西瓜最优种植规模研究

农业适度规模经营是未来农业的发展方向,本文以规模经济理论为基础,通过建立Translog生产函数模型和DEA包络分析法,最终得出以农户为经营主体的北京市西瓜最优种植规模为6670m2(10亩)。除此之外,研究还发现种植户年龄及文化程度对西瓜种植具有显著影响,但随着农村人口老龄化日趋严峻,培养新型职业农民可以衔接西瓜种植的"断层"。     

调亏灌溉下酿酒葡萄耗水特性及水分生产函数研究

为了确定酿酒葡萄的水分生产函数,以酿酒葡萄"梅鹿辄"为供试品种,采用滴灌的方式,以不同生育期土壤水分水平为试验因素,对酿酒葡萄不同生育期进行亏水处理,测定不同生育期土壤含水率、耗水量、产量及水分利用效率,研究了不同生育期、不同土壤水分状况对酿酒葡萄耗水量和产量的影响。结果表明,土壤水分对酿酒葡萄产量的影响规律为浆果膨大期最大,其次是开花期、着色成熟期、抽蔓期,萌芽期最小;通过对Jensen模型与Blank模型进行计算比较,发现在该试验中Jensen模型更为合理,且得出不同生育期水分生产函数的敏感指数为:浆果膨大期开花期着色成熟期抽蔓期萌芽期,与酿酒葡萄耗水规律一致;在萌芽期、抽蔓期及着色成熟期的土壤水分保持在田间持水率的60%~65%左右不会造成酿酒葡萄减产,而在浆果膨大期进行充分供水,既可获得高产,也使水分利用效率达到较高水平。     

冻融土壤Philip入渗模型参数的非线性传输函数模型研究

为满足季节性冻土地区越冬期间储水灌溉管理的需求,基于山西省汾河灌区季节性冻土的冬季大田土壤水分入渗试验,得到了120组Philip入渗模型参数实测样本,借助MATLAB软件,建立了以土壤温度、有机质质量分数、土壤含水率、土壤体积质量、物理性黏粒量为输入因子、以Philip入渗模型参数吸渗率和稳渗率为输出因子的多元非线性传输函数模型,并用实测资料对该模型进行了精度检验。结果表明,预测参数的相对误差均小于11%,预报精度在可接受范围之内。     

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km²), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m⁻² d⁻¹ and 2.7–6.1 g C m⁻² d⁻¹ for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.     

基于神经网络的多功能收割机发动机性能仿真及优化

随着全球经济和工业的快速发展,能源危机和环境保护问题越来越突出,传统内燃式发动机受到了巨大冲击,因此研究高效、节能的发动机显得尤为重要。为此,研究一种压缩比为10.6多功能收割机,并设计了基于MatLab仿真平台的GT-Power神经网络模型,利用神经网络训练和测试的数据自动进行仿真和储存,采用拉丁超立方采样算法设计试验,简化运算过程提高寻优效率。实验结果表明:神经网络模型转矩、比油耗和温度等参数模型预测误差很小、精度很高,可用于多功能收割机发动机的性能预测,使其各项指标参数最优化。     

极限函数与和函数的关系及其连续性

给出极限函数与和函数的定义．讨论了极限函数与和函数之间的关系，利用极限函数得到函数项级数收敛的一个必要条件，研究了极限函数与和函数的连续性，得到部分和函数列连续的一个充分条件．     

北京山区水源林结构与功能关系研究

为了准确把握北京山区水源林林分特征,掌握其结构与功能的关系,以北京山区的水源林16种森林类型作为研究对象,分析了林分的结构特征;采用因子分析方法对林分结构与水源涵养功能关系进行多因子分析研究,结果表明:在林分层次上,影响水源涵养功能的林分结构因子主要是林分树种组成、年龄、郁闭度、起源、层次、生物量以及土壤结构7个因子。研究结果可为北京山区水源林经营提供科学依据。