运用马尔科夫链方法预测陕西苹果花期冻害年型

依据陕西29个苹果基地县1971～2007年4月极端最低气温资料,将陕西果区苹果花期冻害年型划分为严重冻害(NTd≤-2℃≥10),中等冻害(NTd≤-2℃<10 & NTd≤0℃≥15),轻度冻害(NTd≤-2℃<10 & NTd≤0℃<15 & NTd≤2℃≥19),和正常年型(NTd≤-2℃<10 & NTd≤0...     

遗传算法优化神经网络的坡面入渗产流模型

引入遗传算法优化BP神经网络权重和阈值的方法建立黄土坡面产流入渗模型.模型以雨强、降雨历时、表层40 cm土壤前期含水量、坡度值为输入项,径流量、入渗量为输出项,用实测资料对网络进行模拟和预测.模拟结果平均误差6.32%和1.93%,预测结果平均误差为5.71%和1.92%.并与传统BP神经网络模型和定雨强Philip...     

河西走廊中部近53年降水变化及未来趋势预测

利用位于河西走廊中部的6个气象站1957-2009年的降水资料,分析近53年降水量年际年代际变化、季节变化、空间变化、各月变化.在分析降水量历史演变的基础上,利用均生函数方法建立了河西走廊中部降水量的预测模型.结果表明:近53年该地区降水量以4.75 mm/10 a的速度在递增,20世纪80-90年代前期降水量出现下降...     

基于信息熵的张掖市土地利用结构分析及其灰色预测

基于Shannon信息熵理论,分析了2003-2007年张掖市土地利用结构信息熵动态变化,对影响信息熵值大小的土地利用类型做了初步探讨,并运用灰色系统理论的GM(1,1)模型,对2008-2015年该市信息熵进行预测.结果表明:①影响张掖市土地利用结构信息熵动态演变的主要土地利用类型有牧草地、其他农用地、居民点及工矿用...     

鄂尔多斯市工业用水变化趋势和需水量预测研究

随着工业的快速发展,工业需水量与日俱增,工业需水量的预测已成为城市供水和水资源规划极其重要的部分.以鄂尔多斯市为研究对象,对工业用水变化规律和影响因素进行了分析,确定其未来的需水定额.采用定额法和增长比率法对鄂尔多斯市2010、2015和2020年工业需水量进行了预测研究,结果比较符合鄂尔多斯市的发展实际,对水资源决策...     

立木生物量方程的优度评价和精度分析

以建立东北落叶松和南方马尾松立木生物量模型为例,对模型的优度评价和精度分析进行专题研究。首先,在综合分析各种常用模型评价指标的基础上,提出6项基本评价指标,并通过对2个树种的生物量模型进行评价,证明是可行和有效的;然后,基于线性回归估计的基本假设及其置信区间计算公式,提出适合立木生物量模型预估的条件均值和单一预估值置信区间的估计方法,并确定2个树种生物量模型的置信区间;最后,通过蒙特卡罗模拟进行随机再抽样试验,结果表明采用检验样本进行适用性检验的做法不可取,应该利用全部样本(不分建模样本和检验样本)来建立模型,它能充分利用样本信息,使模型的预估误差最小。     

石羊河流域水资源对武威市经济发展模式的影响

[目的]通过分析甘肃省武威市水资源消费量与产业结构之间的关系,为制定干旱区城市发展策略提供参考。[方法]利用IPAT模型的演化方程(IGT方程)以及优化建模软件LINGO 10.0。[结果]在1998—2004年无约束的水资源供给条件下,武威市经济增长严重依赖于水资源消费量。2005年开始实施限水措施后,其经济增长对水源消耗依赖程度开始大幅下降,2008年以后处于一种较为稳定的下降状态。通过水资源利用情景模拟,得出最有利于减缓水资源消费量的途径。武威市2015年的水资源消费量为1.67×109 m3,地区生产总值为4.40×1010元。[结论]在加速经济增长,合理调整产业结构,加强节水的弱控水条件下,最有利于实现武威市经济与水资源消费稳步发展。     

中国东北地区中长期畜禽粪尿资源与污染潜势估算

规模化畜牧业、农牧分离等引起了一系列问题，尤其是饲养方式改变后，畜禽粪尿养分的循环和处理最值得关注。该文利用统计资料和文献数据，估算了东北三省畜禽粪尿产生量及其中的氮磷养分和COD含量，在此基础上评价和预测了2002～2020年畜禽粪尿资源及其环境风险。结果表明，2003年辽宁省、吉林省、黑龙江省畜禽粪尿耕地承载量分别为24、20、11 t/hm²，耕地畜禽粪尿承载量在空间上分布极不平衡。2003年，辽宁省、吉林省、黑龙江省禽粪尿排泄物进入水体的COD数量占畜禽粪尿、工业、生活排放COD总量的52％、65％、40％。预测表明，2010年、2020年畜禽养殖业对东北三省环境污染的风险将进一步扩大。因此，需要制定相应的政策法规来控制畜禽粪尿污染。     

Hierarchical classification of environmental factors and agricultural practices affecting soil fauna under cropping systems using Bt maize

The population dynamics of soil organisms under agricultural field conditions are influenced by many factors, such as pedology and climate, but also farming practices such as crop type, tillage and the use of pesticides. To assess the real effects of farming practices on soil organisms it is necessary to rank the influence of all of these parameters. Bt maize (Zea mays L.), as a crop recently introduced into farming practices, is a genetically modified maize with the Cry1Ab gene which produces a protein toxic to specific lepidopteran insect pests. To assess the effects of Bt maize on non-target soil organisms, we conducted research at a field site in Foulum (Denmark) with a loamy sand soil containing 6.4% organic matter. The study focused on populations of springtails (Collembola) and earthworms (Oligochaeta) from samples taken at the beginning and at the end of the maize crop-growing season during 2 consecutive years. Farming practices, soil parameters, the biological structure of soil communities, and the type and age of the crop at the time of sampling, were used as attributes to predict the total abundance of springtails and biomass of earthworms in general and the abundance or biomass for specific functional groups (epigeic, endogeic and anecic groups for earthworms, and eu-, eu to hemi-, hemi-, hemi to epi- and epiedaphic groups for Collembola). Predictive models were built with data mining tools, such as regression trees that predict the value of a dependent variable from a set of independent variables. Regression trees were constructed with the data mining system M5′. The models were evaluated by qualitative and quantitative measures of performance and two models were selected for further interpretation: anecic worms and hemi-epiedaphic Collembola. The anecic worms (r²=0.83) showed preferences for less clay and more silt soil with medium pH but were not influenced directly by farming practices. The biomass of earthworms was greater in early autumn than in spring or late autumn. Biomass of hemi-epiedaphic Collembola (r²=0.59) increased at the end of the maize growing season, while higher organic matter content and pH tended to increase their biomass in spring. Greater abundance of Collembola was also noted in early autumn if the crop was non-Bt maize. The models assessed by this research did not find any effects of the Bt maize cropping system on functional groups of soil fauna.     

Organic Carbon and Total Iron Effect on Soil Vis-SWNIR Spectraand Quantification of Their Contents Using PLS R Models

ABSTRACT

Many factors could influence simultaneously soil spectra. We aimed to study the single effect of organic carbon and total iron in soil visible and short-wave near-infrared spectra and to quantify their contents. Two datasets of soil mixture samples were prepared by mixing, in various fractions, an organic carbon-rich material with a total iron-rich material and then with a total iron-poor material. For these two datasets, contents in organic carbon are quite similar but contents in total iron are significantly different. Results show that samples of the same dataset have the same overall spectral shape. Organic carbon has a decreasing effect that affects the whole spectral range without showing any specific absorption peaks. By contrast, total iron has specific absorption peaks. Spectra of the second dataset characterized by soil mixtures with higher total iron contents were more compact within the spectral bands 400–440 and 920–950 nm. Besides, continuum removal enables to exaggerate absorption peaks of wavelengths linked to total iron content. Partial Least Squares Regression (PLS R) models of both total organic carbon and total iron assign high coefficients to the wavelengths that are considered relevant and conversely low coefficients to those that are considered irrelevant. Both organic carbon content and total iron content were well predicted. For these models, coefficients of determination were superior to 0.9 and RMSE was closed to zero. The global models calibrated on all the samples demonstrated that PLS R was able to integrate sample heterogeneity.