Methods to interpolate soil categorical variables from profile observations: Lessons from Iran

The paper compares semi-automated interpolation methods to produce soil-class maps from profile observations and by using multiple auxiliary predictors such as terrain parameters, remote sensing indices and similar. The Soil Profile Database of Iran, consisting of 4250 profiles, was used to test different soil-class interpolators. The target variables were soil texture classes and World Reference Base soil groups. The predictors were 6 terrain parameters, 11 MODIS EVI images and 17 physiographic regions (polygon map) of Iran. Four techniques were considered: (a) supervised classification using maximum likelihoods; (b) multinominal logistic regression; (c) regression-kriging on memberships; and (d) classification of taxonomic distances. The predictive capabilities were assessed using a control subset of 30% profiles and the kappa statistics as criterion. Supervised classification and multinominal logistic regression can lead to poor results if soil-classes overlap in the feature space, or if the correlation between the soil-classes and predictors is low. The two other methods have better predictive capabilities, although both are computationally more demanding. For both mapping of texture classes and soil types, the best prediction was achieved using regression-kriging of indicators/memberships (κ = 45%, κ = 54%). In all cases kappa was smaller than 60%, which can be explained by the preferential sampling plan, the poor definition of soil-classes and the high variability of soils. Steps to improve interpolation of soil-class data, by taking into account the fuzziness of classes directly on the field are further discussed.     

Empirical analysis and prediction of nitrate loading and crop yield for corn-soybean rotations

Nitrate nitrogen losses through subsurface drainage and crop yield are determined by multiple climatic and management variables. The combined and interactive effects of these variables, however, are poorly understood. Our objective is to predict crop yield, nitrate concentration, drainage volume, and nitrate loss in subsurface drainage from a corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) rotation as a function of rainfall amount, soybean yield for the year before the corn-soybean sequence being evaluated, N source, N rate, and timing of N application in northeastern Iowa, U.S.A. Ten years of data (1994-2003) from a long-term study near Nashua, Iowa were used to develop multivariate polynomial regression equations describing these variables. The regression equations described over 87, 85, 94, 76, and 95% of variation in soybean yield, corn yield, subsurface drainage, nitrate concentration, and nitrate loss in subsurface drainage, respectively. A two-year rotation under average soil, average climatic conditions, and 125 kg N/ha application was predicted to loose 29, 37, 36, and 30 kg N/ha in subsurface drainage for early-spring swine manure, fall-applied swine manure, early-spring UAN fertilizer, and late-spring split UAN fertilizer (urea ammonium nitrate), respectively. Predicted corn yields were 10.0 and 9.7 Mg/ha for the swine manure and UAN sources applied at 125 kg N/ha. Timing of application (i.e., fall or spring) did not significantly affect corn yield. These results confirm other research suggesting that manure application can result in less nitrate leaching than UAN (e.g., 29 vs. 36 kg N/ha), and that spring application reduces nitrate leaching compared to fall application (e.g., 29 vs. 37 kg N/ha). The regression equations improve our understanding of nitrate leaching; offer a simple method to quantify potential N losses from Midwestern corn-soybean rotations under the climate, soil, and management conditions of the Nashua field experiment; and are a step toward development of easy to use N management tools.     

数字图像和逐步回归客观评定冷却猪肉肉色

为开发准确、快速的猪肉肉色质量客观评定方法,研究了数字图像处理和和逐步回归模型对冷却猪肉肉色客观评定分级的效果.对宰后冷却24 h的猪删体,切开第3～4肋骨间背最长肌,发色60 min,数码相机获取数字图像处理后提取断面肉色参数(L *、a *、b *、Chroma、Hue angle).提取的80头猪胴体背最长肌肉色参数经逐步回归建立了肉色评定模型.结果表明,数字图像处理后提取肉色参数建立的逐步回归模型评定冷却猪肉肉色分值的效果优于BP人工神经网络模型;若以|评定肉色分值-感官肉色分值|≤0.3为评定正确判断标准,前者评定正确率为78.8%,后者为60.4%; 前者与本试验评定正确率最高的单个感官评定人员相比(78.2%),差异不显著(P>0.05).因此,数字图像处理可有效地对冷却猪肉肉色进行客观评定.     

基于支持向量机回归的营养液调控模型研究

针对目前设施栽培中营养液动态调配精确度低的问题,提出一种基于支持向量机回归(Support vector machine regression,SVR)的营养液调控模型.首先,通过设计嵌套试验采集了13个温度、50组不同Knop营养液(A:99％Ca(N03)2·4H20、B:98％KN03、C:99％KH2P04、D...     

基于ANSYS的U29型钢可缩性支架建模及网格划分分析

利用ANSYS合理建立模型及划分网格,将直接影响计算结果的精度,也是充分发挥计算软件数值模拟能力解决实际问题的关键。通过对ANSYS模型建立和网格划分模块的系统分析,结合巷道支护经验,找出了对U29型钢可缩性支架进行模拟计算的方法。为精确计算截面比较复杂、结构比较庞大的可缩性金属支架在各种载荷作用下的结构反应奠定了基础,并指出ANSYS建模和网格划分的一些应用技巧。     

基于粗糙集理论的岩质边坡稳定性评价

针对岩质边坡的稳定性评价问题,应用粗糙集理论对既有岩质边坡的失稳实例进行分析,采用遗传算法进行约简,抽取评价知识,建立了规则库。采用不精确推理和默认推理方法开发了岩质边坡稳定性评价专家系统,提出了待评对象与规则不完全匹配情况下的近似推理方法,结合工程实例对该系统进行了测试和验证,并与模糊回归方法进行的比较。     

中华鳖的消化道指数和血液生化指标

主要测定了中华鳖的消化道指数及血液中几种重要酶的活性,并将胃、肠、胰重与体重进行回归分析。结果表明:消化道的重量随体重同步变化,回归分析表明二者的幂回归相关系数最高。中华鳖的乳酸脱氢酶、肌酸激酶、碱性磷酸酶、谷-草转氨酶和谷-丙转氨酶的活性分别是人上限值的3.4、11.2、4.8、6.6和2.4倍,且谷-草转氨酶、谷-丙转氨酶和碱性磷酸酶的活性两两正相关。     

Potential chemical time bombs in the schelde estuary

The Schelde estuary is heavily polluted with many different (micro)pollutants. This results in high concentrations in the abiotic and biotic compartments of the Schelde estuary and in various effects. the present day suboxic and anoxic conditions in the upper estuary are probably responsible for the high distribution coefficients for some trace metals in the lower Schelde estuary (Western Schelde). a future reduction in discharges with a high biological oxygen demand will therefore not only result in higher oxygen concentrations, but probably also in lower distribution coefficients for trace metals in the Western Schelde. Simulations show that lower trace metal distribution coefficients will probably result in higher concentrations of dissolved (bioavailable) trace metals, even with substantially reduced discharge rates, due to the desorption of trace metals from resuspended bottom sediments.     

玉溪市林分植被覆盖变化及驱动力分析

林分植被覆盖在很大程度上能够反映林分生态系统的健康与否,其时空变化是林分生态系统发展方向的判断依据。对玉溪市2010—2019年林分植被覆盖的时空格局变化进行了分析,并利用单因素和多元回归分析方法分别建立回归模型探索了林分植被覆盖改善与退化的驱动因素。结果表明:林分植被覆盖整体呈向好趋势,各驱动因素与斜率均不是简单线性关系;道路和居民区与林分植被覆盖改善速度呈负相关、与退化速度呈正相关;DEM、坡度、大型水体、公益林管护与林分植被覆盖改善速度呈正相关、与退化速度呈负相关;坡向与林分植被覆盖改善速度呈正相关,地形起伏度与林分植被覆盖退化速度呈负相关。林分植被覆盖改善与退化的驱动力不尽相同,影响机制也存在差异,这为更好制定生态保护相关政策提供了有力的科学依据。     

稻纵卷叶螟发生程度及发生期预测模型研究

对余庆县1982~2004年稻纵卷叶螟(Cnaphalocis medinalisGuenee)测报资料的筛选分析结果为,第三代幼虫发生程度预测回归模型^y=0.1802 0.0024x1 0.0050x2 0.0185x3±Sy/12=0.6924,第四代幼虫发生程度预测回归模型^y=0.2853 0.0005x1 0.0084x2 0.0112x3±Sy/12=0.6334;第三代低龄幼虫盛发期预测回归模型^y=13.3830 0.6823x±Sy/x=2.61,第四代低龄幼虫盛发期预测回归模型^y=3.1293 0.8776x±Sy/x=2.951。利用其回归模型对稻纵卷叶螟幼虫发生程度和低龄幼虫盛发期进行预测,其方法简便,结果较为准确。