气候变化条件下木瓜秀粉蚧在中国的潜在适生区预测

为明确木瓜秀粉蚧Paracoccus marginatus Williams et Granara de Willink 1922在中国的潜在适生区,基于中国820个气象站点的气象数据和未来气候变化数据,结合木瓜秀粉蚧的生物学特性,利用CLIMEX模型对该虫在中国当前气候以及未来气候条件下的潜在适生区进行预测。结果显示,木瓜秀粉蚧在中国的潜在适生区主要在南方,适生区面积占全国陆地总面积的9.07%。高适生区主要包括海南省、广东省南部、广西壮族自治区南部以及福建省和云南省小部分地区。在未来气候条件下,2050年木瓜秀粉蚧的潜在适生区将进一步扩大,适生区面积所占比例将增加至15.46%,且有向北移动的趋势。表明木瓜秀粉蚧对中国南方地区的农业潜在威胁巨大,建议将该虫增补为检疫对象,并加强检疫监管和种群动态监测。     

一种溶质运移数学模型的应用研究

土壤中溶质运移的过程、规律和机理是土壤溶质运移研究的主要内容。本文的目的是对模拟非饱和介质中水分和溶质迁移的二维数值模型SWMS－2D进行研究和验证。以硝酸盐形式存在的溶解氮是地下水中最常见的污染物，将硝态氮视为土壤溶质，考虑其根系吸收、生物固持、吸附、解吸、硝化和反硝化等作用，土壤表面即上边界条件用大气边界条件描述，下边界条件定为第一类边界条件，在田间条件下对模型作了检验。     

塑料连栋温室黄瓜生长温度数学模型的研究

为提高塑料连栋温室黄瓜的栽培水平,以能量平衡理论为分析平台,将温室中的空气温度拟合于栽培环境模型中,运用MATLAB程序模拟,并以试验验证模型。结果表明,该模型物理意义明确,便于数值模拟,且模拟结果与试验吻合好。就温室内5月份空气温度模拟而言,回归直线拟合度高,R2=0．8999,绝对误差在±0．85℃之内,平均相对误差9．1％。对回归方母=ax＋b,a与b的标准误差Sa=0．0352〈a／2,Sb=0．1053〈b／2。     

内蒙古锡林郭勒盟典型草原固碳量及固碳潜力估算

为了评价不同放牧强度对草原固碳量及固碳潜力的影响,本研究采用系统动力学建模方法耦合CASA光合利用率模型、Shiyomi放牧模型、Raich土壤呼吸模型等模型,建立了基于系统动力学库-流思路的碳循环模型,该模型包含3个子系统、4个碳库。结果表明：1998至2015年,在内蒙古锡林郭勒盟的温度降低、降水量增加的背景下,净初级生产力呈现升高的趋势,典型草原土壤固碳量呈现下降趋势;放牧强度在3羊·公顷^-1下净生态系统初级生产力最低,固碳潜力最大,分别为-16.2 gC·m^-2和24.84 TgC。因此,建议内蒙古锡林郭勒盟典型草原西部（阿巴嘎旗、那仁宝力格站）的放牧强度不宜超过1.5羊·公顷^-1;东部（多伦县、东乌珠穆沁、西乌珠穆沁、锡林浩特站）不宜超过4.5羊·公顷^-1。     

Vertical Distribution of Arthropod Interactions Within Turfgrass

Arthropod predators are abundant in turfgrass systems, and they play an important role in managing pests. Understanding the vertical distribution of predation is critical to developing cultural strategies that enhance and conserve predatory services. However, little is known on how the predation is vertically distributed within the turfgrass canopy. Thus, the objective of this study was to determine the vertical distribution of predation within the turfgrass canopy. Clay models were used to emulate the general appearance of Noctuidae caterpillars, to estimate the predatory activity. The choice and no-choice experiments were conducted by placing clay models at 2.54, 5.08, and 7.62 cm from the thatch surface and denoted as lower, intermediate, and upper levels, respectively, within turfgrass canopy. The predator-mediated impressions, paired mark, scratch, deep cut mark, deep distortion, prick, dent, stacked surface impression, scooped mark, granulation, and U-shaped mark, were identified on clay models. The incidence and severity of impressions were significantly greater on clay models placed at the lower canopy level than on those placed at the intermediate and upper canopy levels in the choice and no-choice experiments (P < 0.05). Thus, predators are more likely to find their prey at the soil level. This information can be used to refine management strategies, such as mowing height and insecticide use for effectively managing soil-borne and foliar-feeding arthropod pests and beneficial arthropods.     

基于无人机数字图像与高光谱数据融合的小麦全蚀病等级的快速分类技术

小麦全蚀病是检疫性的土传病害,对小麦生产危害极大,对其发生的监测是治理的根本。遥感技术可实时、宏观地监测病害发生发展,尤其是将光谱信息与高分辨率数字图像进行融合,可直观、精准地对病害识别和分类。本文基于计算机视觉技术,通过光谱数据与高分辨率数字图像结合的方法,对小麦全蚀病等级进行快速分类。首先,通过ASD非成像光谱仪获取小麦全蚀病的光谱信息,提取全蚀病特征光谱,建立光谱比。其次,利用无人机获取的实时田间数码图像,对其颜色特征进行重量化。最后,利用基于支持向量机的决策树分类对图像视场中的不同全蚀病等级进行分类。结果表明,4个全蚀病等级的分类精度均大于86%(Kappa0.81),平均运算时间小于30s。通过与实地调查的小麦全蚀病的白穗率等级做比对,验证分类结果的准确性,结果表明该方法基本可以实现对小麦全蚀病等级的实时监测。     

中国六省土地沙漠化敏感性时空格局与趋势分析

以我国新疆维吾尔自治区、青海省、内蒙古自治区、甘肃省、宁夏回族自治区和陕西省六省区为研究区,分析了1990年、2000年、2005年、2010年4个时期的土地沙漠化敏感性空间格局分布和时间动态变化特征,探讨变化成因并采用CA-Markov耦合模型对土地沙漠化敏感性格局变化趋势进行预测。结果表明:中国六省四期土地沙漠化敏感性格局相似,各级敏感区按分布面积大小排序为:轻度敏感区域 > 中度敏感区域 > 高度敏感区域 > 不敏感区域 > 极敏感区域。极敏感区域主要分布在土壤质地为流动沙地的沙漠区域,不敏感区域主要分布在高山、湖泊附近;人口增多、人为活动强度增加的区域敏感性程度增高;建立治沙工程的区域敏感性降低。对2020年土地沙漠化敏感性格局预测结果显示,相比2010年,极敏感区域在原有的基础上向外围扩张了7 120.04 km²,增幅为4.63%。本文通过分析中国六省土地沙漠化敏感性时空格局与趋势,为中国土地沙漠化防治分区策略制定和划分沙漠化扩展屏障区提供科学依据。     

樟子松人工林生长与收获预估模型的研究

以可变密度全林分预估模型理论为指导,探讨建立了樟子松人工林地位级指数和林分密度指数经验方程,结合林分密度动态预估模型,建立了樟子松人工林断面积预估模型和蓄积量预估模型。经检验模型预测值与实测值误差为3.64%,模型精度96.37%。     

党参种子的离散元仿真参数标定与试验验证

为设计及优化党参机械化播种设备,提供基础仿真参数,本文对甘肃省种植的两种党参种子(渭党1号和甘党2号)通过试验法确定其基本物理参数(形状尺寸、质量密度、体积密度、千粒重、含水率)和接触力学参数(恢复系数、静摩擦系数);通过斜面滚动试验结合EDEM仿真,预测党参种子与ABS塑料板间的滚动摩擦系数;通过调整党参种子模型间的...     

Prediction of irrigation return flows through a hierarchical modeling approach

The quantity of water available for irrigation is getting scarce in many countries and it assumes great importance for assured crop production, especially in view of the erratic behavior of the monsoon. Thus, there is a pressing need to improve the water efficiency of irrigation systems. One-way of improving the efficiency of the irrigation system is reusing the return flow from the irrigation system. This task requires quantification of return flow, which still remains as a grey area in irrigation water management. The estimation of return flow from the irrigation system is usually obtained using thumb rules depending upon the site-specific conditions like command area conditions and soil properties. In this paper, a hierarchical modeling technique, namely, regression tree is developed for return flow estimation. Regression tree is built through binary recursive partitioning. The effective rainfall, inflow, consumptive water demand, and percolation loss are taken as predictor variables and return flow is treated as the target variable. The applicability of the hierarchical model is demonstrated through a case study of Periyar-Vaigai Irrigation System in Tamil Nadu, India. The model performance shows a good match between the simulated and the field measured return flow values. Results of statistical analysis indicated that the correlation coefficients are high for both single as well as double crop seasons.