利用有机质和pH值估算中国西北和东北退耕还林土壤容重

Regression models for predicting soil bulk density (BD) have usually been related to organic matter content,but it remains unknown whether soil acidity modifies this relationship,particularly for afforested/reforested soils.We measured soil BD along with organic matter content and pH in an afforested/reforested area in Northwest and Northeast China.Using these measurements,we parameterized and validated three BD models:the Adams equation,and exponential and radical models.Model validation showed that the Adams equation failed to predict the BD of the afforested/reforested soils,producing a large overestimation.Incorporation of soil pH into the Adams equation significantly improved its performance.The exponential and radical models parameterized by the measured data simulated soil BD quite well,particularly when soil pH was incorporated.However,incorporation of soil texture variables into these models did not improve model performance compared with the pH-modified models.This led to the conclusion that the Adams equation,exponential,and radical models with pH modification are applicable to afforested/reforested soils with various acidities.     

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.     

基于逻辑回归算法的复杂背景棉田冠层图像自适应阈值分割

棉田冠层覆盖度是监测棉田棉花长势的重要指标,针对棉田复杂环境中冠层图像难以准确分割的问题,该文提出了一种基于逻辑回归算法的复杂背景棉田冠层图像自适应阈值分割方法。首先将棉田冠层图像像素分成叶片冠层和地表背景2类,在HSV颜色空间中分别提取两类像素的H通道值,在RGB颜色空间中分别提取绿色占比值(G/(G+R+B))作为颜色特征;再利用逻辑回归算法确定出各颜色特征的分割阈值,通过H通道分割阈值实现图像的初次分割;再对初次分割结果中的低亮像素使用逻辑回归算法计算出的超绿特征阈值进行低亮像素分割,同时采用绿色占比分割阈值对图像高亮像素及低亮像素分割结果整体实现二次分割,最后采用形态学滤波方法对分割结果进行优化。为评价该分割方法,利用从新疆棉花产区采集到的320幅棉田冠层图像进行试验。结果表明,该方法可在棉田复杂自然背景下,有效分割出棉田冠层区域,平均相对目标面积误差率仅为5.46%,总体平均匹配率达到93.07%;优于超绿特征OTSU分割方法(平均相对目标面积误差率11.78%,总体平均匹配率76.43%)、四分量分割方法(平均相对目标面积误差率24.11%,总体平均匹配率71.67%)、显著性分割方法(平均相对目标面积误差率36.92%,总体平均匹配率66.92%)。该方法的平均处理时间为4.63 s,相对于超绿特征OTSU法(3.84 s)和四分量分割法(2.56 s),耗时多一些,但与显著性分割法(6.25 s)对比,花费时间要少。研究结果可为棉田自然复杂环境下机器视觉技术监测棉花覆盖度提供一种有效途径。     

Selecting cotton yield response function to estimate profit-maximizing potassium fertilization rates for cotton production in Tennessee

Recent research has shown that stochastic yield response functions fit data better than deterministic yield response functions; however, little research exists comparing profit-maximizing fertilizer rates for upland cotton (Gossypium hirsutum L.) using stochastic and deterministic yield response functions. The objective of this research was to determine the most appropriate yield response functional form from which to determine the profit-maximizing potassium (K) rate for cotton in Tennessee. Data were used from a nine-year K fertilizer experiment in Jackson, Tennessee. Quadratic, quadratic-plus-plateau, linear response plateau, and linear response stochastic plateau response functions were estimated to determine the yield response function that best fit the data. The linear response stochastic plateau function was the most suitable yield response function for the data. The profit-maximizing K rate ranged from 65.2 to 74.3 kg K ha^?1. Misspecification of the yield response function recommended over-application of K fertilizer and decreased the producer's short-term profits.     

Soil moisture index improves models of forage production in central Iran

This study is focused on the relative importance of bioclimatic variables and soil moisture index (SMI) for predicting forage production in Central Iran rangelands. We used a dataset of 93 forage production collected from 16 rangeland sites during 8 years in Isfahan province. Climatic and topographic variables were obtained using a digital elevation model and geographical information system. SMI is derived using a triangle space concept between the land surface temperature and normalized difference vegetation index from MODIS satellite data over Isfahan province. Using Nonparametric Multiplicative Regression model, we produced two distinct models to predict forage production, the first model with climatic and topographic variables (reduced model) and the second model with climatic, topographic and SMI variables (complete model). The inclusion of SMI significantly improved the quality of forage production predictions within the study area. The forage production is predicted to vary from 41.4 to 474 kg ha^?1 in first model and 20.5 to 485 kg ha^?1 in second model. We anticipated that using SMI would improve standing crop estimates relative to using bioclimatic variables. The cross-validated R² (×R²) increased from 57% to 63% when SMI is added to NPMR model.     

Evaluation of the spatial pattern of surface soil water content of a karst hillslope in Southwest China using a state-space approach

This study aims to evaluate the effects of soil physicochemical properties and environmental factors on the spatial patterns of surface soil water content (SWC) based on the state-space approach and linear regression analysis. For this purpose, based on a grid sampling scheme (10 m × 10 m) applied to a 90 m × 120 m plot located on a karst hillslope of Southwest China, the SWC at 0–16 cm depth was measured 3 times across 130 sampling points, and soil texture, bulk density (BD), saturated hydraulic conductivity (K_s), organic carbon (SOC), and rock fragment content as well as site elevation (SE) were also measured at these locations. Results showed that the distribution pattern of SWC could be more successfully predicted by the first-order state-space models (R² = 67.5–99.9% and RMSE = 0.01–0.14) than the classic linear regression models (R² = 10.8–79.3% and RMSE = 0.11–0.24). The input combination containing silt content (Silt), K_s, and SOC produced the best state-space model, explaining 99.9% of the variation in SWC. And Silt was identified as the first-order controlling factor that explained 98.7% of the variation. In contrast, the best linear regression model using all of the variables only explained 79.3% of variation.     

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。     

基于近地高光谱的土壤氯离子含量估测

[目的]建立土壤氯离子与高光谱波段的多元线性回归模型,获取盐渍化信息,为盐分的高精度提取提供更有效的方法,为农业生态环境重建工作提供科学依据。[方法]以山东省垦利县作为研究区,于2014年10月5—7日野外采集93个土壤样本,利用ASD高光谱仪野外采集土样高光谱数据并进行预处理,然后采用多元回归和主成分分析方法建立估测氯离子含量的高光谱模型,以快速估测氯离子含量。[结果]氯离子在近红外749,830,987,1301,1 432,1 486nm较为敏感,在土壤光谱分析的基础上,得到室内风干土壤氯离子含量预测最优模型,模型均通过T检验和F检验,能较好地预测土壤氯离子含量。[结论]研究区土壤各组分盐离子中,阳离子以钠离子为主,阴离子以氯离子为主,该模型使间接得到土壤盐分含量具有较好可行性。     

基于低空无人机遥感技术的油菜机械直播苗期株数识别

植株数量识别是油菜机械直播效果和质量评估的关键.该文针对油菜机械直播田间植株数量检测中人工统计耗时、费工、效率低下的现实,通过自主搭建的低空无人机遥感平台采集油菜机械直播区域的遥感影像,基于超高分辨率(0.18 cm/pixel)遥感影像计算的颜色植被指数进行油菜目标识别及其形态特征信息提取.结合田间调查数据,采用逐步回归分析方法,建立了机械直播油菜在苗期的株数与遥感特征信息之间的关系.结果显示,油菜目标的株数与其外接矩形的长宽比、像素分布密度和周长栅格数具有较好的线性关系,回归模型的决定系数R2为0.803,并通过显著性检验,其标准估计误差为0.699.模型检验结果显示,观测值与预测值之间的R2为0.809,均方根误差RMSE为0.728.研究结果表明,利用集成超高分辨率传感器的低空无人机遥感平台,通过计算颜色植被指数并分析油菜目标数量与形态特征的相关性,能有效识别油菜机械直播的出苗株数,可为后续油菜机械直播效果的快速、准确评估提供技术支持.     

春季土壤墒情变化与气象条件相关性分析

从农业气象情报服务需求出发,根据近3年土壤墒情实时变化资料和定期测墒数据,分析研究与土壤墒情变化高相关的气象因子。结果表明,通过现有观测资料,可以定量评估局地土壤墒情变化,为预测春旱趋势和程度提供可靠的依据。