基于不同地表曲面模型预测土壤有机碳含量

Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of first-and second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30％ and 9.68％.The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.     

便携式X射线荧光光谱仪实验室异位检测法的实用性研究

采用分步取样法验证便携式X射线荧光光谱仪的实验室异位检测法的实用性,以ICP-AES法为基准,对土壤重金属Cr、Cu、As、Pb的含量进行测定,试验中选用70%土壤样品建立一元线性校准模型,30%土壤样品进行校准模型的验证。结果表明,便携式X射线荧光光谱仪实验室异位检测法的检出限分别为Cr 17.7 mg/kg,Cu 10.4 mg/kg,As 5.4 mg/kg,Pb 6.2 mg/kg,均低于国家土壤环境质量一级标准。对国内外标准物质进行多次重复测定,RPD在–8.9%~7.9%,说明仪器在异位测定重金属含量时具有较好的精密度与准确度。分析PXRF法与传统方法测定建立的拟合方程,其决定系数分别为0.817 3、0.787 0、0.673 3和0.722 1,表明PXRF室内异位法可用于土壤重金属Cr、Cu、As、Pb的快速测定;二次采样建立的验证模型R2分别为0.912 4、0.897 9、0.772 3、0.872 9,逐渐靠近理想模型,充分验证PXRF异位法的实用性和校准曲线的准确性,可适用于农田土壤Cr、Cu、As、Pb的快速测定、污染筛查,为土壤重金属速效测定提供有力依据;但仪器在异位测定As元素时准确性降低,建议异位测定时需进行数据校准。     

基于声学特性的西瓜糖度检测系统

该研究优化了由包裹橡皮的金属小球、压电式加速度传感器、电荷放大器、光电式触发电路、数据采集卡和计算机等组成的西瓜声学特性测试系统。由所获取的声波信号幅值谱计算出声透过率,采用TQ软件中SMLR(逐步多元线性回归)函数选取6个特征频率:752、869、1001、4556、322、3950Hz,由其对应的声透过率值建立了西瓜品质检测的多元线性回归模型。对47个西瓜样本的试验数据分析表明:将敲击点和接收点分别放置在西瓜自然生长状态的中部对侧可获得最佳的测定模型,模型的校正相关系数R、校正均方根误差RMSEC和预测均方根误差RMSEP分别是0.80753、0.646和0.655。实现了西瓜糖度检测目的,为声学无损检测西瓜成熟度提供了参考。     

2BFQ-6型油菜精量联合直播机气力式排种系统试验研究

为了对2BFQ-6型油菜精量联合直播机气力式排种系统进行研究,该文在气力式油菜精量排种检测系统研制基础上,以排种盘转速、风机转速及正压泄气孔直径为试验因素,进行了320次全因子组合试验,并运用多元非线性回归等数学方法进行试验数据分析处理。结果表明:各因素对系统排种效果影响程度主次为风机转速>排种盘转速>正压泄气孔直径,而风机转速及其与排种盘转速的交互作用分别是影响各行排种量及其一致性的首要因子;在一定范围内,随着风机转速的提高和排种盘转速的降低,系统排种性能趋于稳定;而当正压泄气孔直径在20和30mm时,系统具有较好的排种量一致性。所得出的气力式油菜精量排种系统的排种量方程、排种量一致性方程及90%排种率参数组合图,可为直播机参数匹配提供参考,也可对直播机的排种性能进行评估。     

基于近红外光谱的室温贮藏下鲜枣霉菌污染动力学模型

为了预测鲜枣常温贮藏的保鲜期,确保鲜枣的品质要求及食用安全,应用近红外光谱建立了室温贮藏下鲜枣内部霉菌菌落总数变化的动力学模型。通过对几种数据预处理方法的比较及特征波数的选择,实现了鲜枣霉菌菌落总数变化的近红外模型的优选。结果表明:经过多元散射校正处理的鲜枣近红外光谱,应用多元线性回归方法建立的霉菌菌落总数模型预测能力较好,校正集相关系数为0.920,均方根误差为1.503,预测集相关系数为0.889,均方根误差为1.514。同时,将近红外光谱模型应用于霉菌菌落总数随贮藏时间变化的零级反应动力学模型中,得到模型的相关系数为0.981。根据近红外光谱吸光度值与贮藏时间的线性关系,当霉菌菌落总数初始值小于等于10cfu/g时,预测出鲜枣在室温下的保鲜期一般为8d。研究表明,结合动力学模型的近红外光谱技术可以作为一种无损、快速检测方法来检测鲜枣霉菌菌落总数变化。     

基于地理权重回归模型的土壤有机质空间预测

准确了解土壤有机质的空间分布是合理施肥的重要前提,也是水土流失控制及保护环境的重要基础。利用113个土壤有机质样点数据,以海拔高度、土壤侵蚀强度、土地利用、比值植被指数、样点至河流的欧氏距离、亚铁矿物指数及坡度为参考因子,来尝试利用GWR(Geographically Weighted Regression)模型探索多重因素作用下的土壤有机质空间分布,并通过与普通线性回归(ordinary least squares,OLS)相比较,来了解GWR模型的精度,进而进行了土壤有机质的空间制图,并对其制图效果进行了评价。结果表明,与OLS模型相比,GWR预测模型它能显著降低AIC(Akaike Information Criterion)值,较大程度地提高模型的决定系数,并有效地减少模型的回归残差值。从制图的总体效果看,GWR模型的预测结果与实测值的吻合程度要优于OLS模型。文章还对利用GWR模型进行回归时的样点数量、因子筛选及因子定量化等方面进行了相应的讨论。     

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.     

盘刷式矮密栽培落地红枣集条装置研制

针对新疆落地红枣人工集条劳动强度大、机械化程度低的问题,研制一种与落地红枣捡拾机配套的红枣集条装置。重点设计避障系统和仿形机构等关键部件,确定盘刷、液压缸行程和仿形机构的工作参数,结合避障工况需求,设计扫盘避障液压系统并对液压元件进行计算选型。以盘刷转速、盘刷前倾角和前进速度为影响因素,集条率和破损率为评价指标,开展三因素三水平正交组合试验,结果表明：各因素对集条率影响显著性顺序为盘刷前倾角、前进速度、盘刷转速,各因素对破损率影响显著性顺序为盘刷转速、前进速度、盘刷前倾角;采用多元统计分析方法得出最优水平组合为前进速度08 m/s、盘刷转速200 r/min、盘刷前倾角15°,此时集条率为97.54%、破损率为1.11%;在最佳参数组合下进行田间验证试验,集条率为96.64%,破损率为1.13%,试验值与理论预测值相对误差分别为0.93%、1.77%,各项指标符合设计要求。     

基于CLUE-S模型的煤矿城市土地利用变化模拟

以淮南市为研究区,选择1985、1995、2005、2016年土地利用数据,在分析土地利用动态变化特征的基础上,利用CLUE-S模型模拟预测了未来土地利用格局。结果表明：1985—2016年,研究区耕地面积减少11.62%;建设用地和水体面积百分比分别增加7.98个百分点和4.29个百分点。2005—2016年是各地类变化最强烈的阶段,其综合土地利用动态度最大,为13.46%。建设用地变化速率最快,其土地利用动态度为5.19%。土地转移主要发生在耕地、水体和建设用地之间,以耕地向建设用地和水体的转换为主。耕地转为建设用地的面积达207.61 km²,新增水体集中分布在潘谢矿区。加入空间自相关性和土壤质量因子后,耕地和建设用地的Logistics回归效果显著改善,ROC分别增加0.201和0.133。年均降水量是影响耕地变化的主要驱动因子,与耕地分布概率呈负相关;而建设用地变化主要驱动因子为GDP。土地利用模拟的Kappa系数为0.74,CLUE-S模型在研究区域具有较好的模拟能力。运用CLUE-S模型预测了研究区2028、2034、2040年土地利用空间分布,未来...