Testing the performance of spatial interpolation techniques for mapping soil properties

In this paper, we implement and compare the accuracy of ordinary kriging, lognormal ordinary kriging, inverse distance weighting (IDW) and splines for interpolating seasonally stable soil properties (pH, electric conductivity and organic matter) that have been demonstrated to affect yield production.The choice of the exponent value for IDW and splines as well as the number of the closest neighbours to include was decided from the root mean squared error (RMSE) statistic, obtained from a cross-validation procedure. Experimental variograms were fitted with the exponential, spherical, Gaussian and linear models using weighted least squares. The model with the smallest residual sum of squares (RSS) was further interrogated to find the number of neighbours that returned the best cross-validation result.Overall, all of the methods gave similar RMSE values. On this experimental field, ordinary kriging performed best for pH in the topsoil and lognormal ordinary kriging gave the best results when applied to electrical conductivity in the topsoil. IDW interpolated subsoil pH with the greatest accuracy and splines surpassed kriging and IDW for interpolating organic matter.In all uses of IDW, the power of one was the best choice, which may due to the low skewness of the soil properties interpolated. In all cases, a value of three was found to be the best power for splines. Lognormal kriging performed well when the dataset had a coefficient of skewness larger than one. No other summary statistics offered insight into the choice of the interpolation procedure or its parameters. We conclude that many parameters would be better identified from the RMSE statistic obtained from cross-validation after an exhaustive testing.     

Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis

In this study, the body measurements (BMs) of Holstein cows were determined using digital image analysis (IA) and these were used to estimate the live weight (LW) of each cow. For this purpose, an image capture arrangement was established in a dairy cattle farm. BMs including wither height (WH), hip height (HH), body length (BL), hip width (HW), plus the LWs of cows were first determined manually, by direct measurement. Then the digital photos of cows were taken from different directions synchronously and analyzed by IA software to calculate WH, HH, BL and HW of each cow. After comparing the BMs obtained by IA with the manual measurements, the accuracy was determined as 97.72% for WH, 98.00% for HH, 97.89% for BL and 95.25% for HW. The LW estimation using BMs was then performed by the aid of the regression equations, and the correlation coefficient between the estimated and real (manual) LW values obtained by weighing was calculated as 0.9787, which indicates the IA method is appropriate for LW estimation of Holstein cows.     

Measurements of cadmium in soil extracts using multi-variate data analysis and electrochemical sensors

There is a growing awareness of potential health risks due to exposure to heavy metals. One source of uptake is via agriculture, when heavy metals in the soil are taken up by the crop. The metal cadmium holds a special position, since it is considered to be a health risk, even at the low concentrations observed in our food supply, furthermore, it is ranked as eight on the top 20 hazardous substances list. Two measurement systems are described based on stripping voltammetry for analysis of cadmium. One is based on a three metal direct probe system (TMDPS) with three working electrodes (platinum, gold and rhodium), combined with a polishing unit, the other is an automatic flow through system, using one working electrode of gold, also equipped with a polishing unit. A number of different soils were extracted with an ammonium-lactate solution and analyzed with the systems, and the data obtained were subjected to multi-variate data analysis (MVDA). Using modeling based on partial least square (PLS), concentrations of cadmium in the soil extracts could be predicted for the TMDPS in the concentration area 0.5–10 μg/l with a root mean square error of prediction (RMSEP) of 0.8 μg/l and a relative predicted deviation (RPV) of 2.0. One sample could be analyzed in 4 min. It was also shown that by using different PLS models, the concentration of the elements copper, aluminum, lead and iron could be predicted. The possibilities of using the technique for field use were also evaluated by studies of mixtures of different soils in 0.1 M HNO₃ solution, the time for an analysis was, however, rather large, around 20 min.     

黄土高原退耕地复垦对土壤理化性状及空间变异特征的影响

【目的】明确复垦措施对黄土高原退耕地土壤理化性质的影响,并揭示其空间变异特征,为评价退耕地复垦对当地生态环境的影响提供帮助,并为相关政府决策提供参考。【方法】在实地调查和取样分析的基础上,使用经典统计学和地统计学相结合的方法,以山西省广灵县退耕6年后复垦的典型坡耕地为对象,对比研究了复垦前(2007-04)和复垦2年后（2009-05）表层(0～15 cm)土壤理化性质的差异及空间变异特征。【结果】与复垦前相比,复垦2年后表层土壤有机质、全氮、全磷以及碱解氮、速效磷和速效钾含量分别降低了38%,11%,15%,24%,81%和8%,而体积质量则降低了7%;研究区土壤养分属中等变异,而土壤体积质量属弱变异;复垦前土壤性质的合理采样数依次为有机质>速效磷>碱解氮>速效钾>全氮>全磷>体积质量,复垦后略有变化;土壤养分和体积质量的变化主要是随机性因素（人为活动）作用的结果,且复垦措施可使土壤理化性质的空间相关性减弱并朝均一化方向发展,从而明显影响土壤养分和体积质量的空间分布特征。【结论】退耕地复垦可使土壤理化性状迅速恶化,使其空间相关性减弱和朝均一化方向发展,因此应予以避免。     

黄土高原退耕地复垦对土壤理化性状及空间变异特征的影响

【目的】明确复垦措施对黄土高原退耕地土壤理化性质的影响,并揭示其空间变异特征,为评价退耕地复垦对当地生态环境的影响提供帮助,并为相关政府决策提供参考。【方法】在实地调查和取样分析的基础上,使用经典统计学和地统计学相结合的方法,以山西省广灵县退耕6年后复垦的典型坡耕地为对象,对比研究了复垦前(2007-04)和复垦2年后(2009-05)表层(0~15 cm)土壤理化性质的差异及空间变异特征。【结果】与复垦前相比,复垦2年后表层土壤有机质、全氮、全磷以及碱解氮、速效磷和速效钾含量分别降低了38%,11%,15%,24%,81%和8%,而体积质量则降低了7%;研究区土壤养分属中等变异,而土壤体积质量属弱变异;复垦前土壤性质的合理采样数依次为有机质>速效磷>碱解氮>速效钾>全氮>全磷>体积质量,复垦后略有变化;土壤养分和体积质量的变化主要是随机性因素(人为活动)作用的结果,且复垦措施可使土壤理化性质的空间相关性减弱并朝均一化方向发展,从而明显影响土壤养分和体积质量的空间分布特征。【结论】退耕地复垦可使土壤理化性状迅速恶化,使其空间相关性减弱和朝均一化方向发展,因此应予以避免。     

Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia

The Russian wheat aphid, Diuraphis noxia, is an important pest of winter wheat, Triticum aestivum, and barley, Hordeum vulgare that has caused an annual economic loss estimated at over 1 billion dollars since it first appeared in the United States. The objective of this study was to determine the potential of combining multispectral imagery with spatial pattern recognition to identify and spatially differentiate D. noxia infestations in wheat fields. Multispectral images were acquired using an MS3100-CIR multispectral camera. D. noxia, drought, and agronomic conditions were identified as major causes for stresses found in wheat fields. Seven spatial metrics were computed for each stress factor. The analysis of spatial metrics quantitatively differentiated the three types of stress found within wheat fields. Detection and differentiation of wheat field stress may help in mapping stress and may have implications for site-specific monitoring systems to identify D. noxia infestations and help to target pesticide applications.     

主成分分析与聚类分析在土壤分类中的应用

为了更好地保护上海古树,对古树生长点的土壤进行调查与测试,并进行了指标间相关分析、主成分分析和聚类分析。结果表明:古银杏、古香樟的生长与土壤的容重、有效N、速效P和脲酶呈显著相关,由主成分分析划分的5个主成分可全面概括17项土壤肥力指标;采用聚类分析将古树生长样点划为4种肥力类型,并对各土壤类型提出了相应的改良措施。     

Classification of sugar beet and volunteer potato reflection spectra with a neural network and statistical discriminant analysis to select discriminative wavelengths

The objectives of this study were to determine the reflectance properties of volunteer potato and sugar beet and to assess the potential of separating sugar beet and volunteer potato at different fields and in different years, using spectral reflectance characteristics. With the ImspectorMobile, vegetation reflection spectra were successfully repeatedly gathered in two fields, on seven days in 2 years that resulted in 11 datasets. Both in the visible and in the near-infrared reflection region, combinations of wavelengths were responsible for discrimination between sugar beet and volunteer potato plants. Two feature selection methods, discriminant analysis (DA) and neural network (NN), succeeded in selecting sets of discriminative wavebands, both for the range of 450-900 and 900-1650 nm. First, 10 optimal wavebands were selected for each of the 11 available datasets individually. Second, by calculating the discriminative power of each selected waveband, 10 fixed wavebands were selected for all 11 datasets analyses. Third, 3 fixed wavebands were determined for all 11 datasets. These three wavebands were chosen because these had been selected by both DA and NN and were for sensor 1: 450, 765, and 855 nm and for sensor 2: 900, 1440, and 1530 nm. With the resulting three sets of wavebands, classifications were performed with a DA, a neural network with 1 hidden neuron (NN1) and a neural network with two hidden neurons (NN2). The maximum classification performance was obtained with the near-infrared sensor coupled to the NN2 method with an optimal adapted set of 10 wavebands, where the percentages were 100 ± 0.1 and 1 ± 1.3% for true negative (TN) classified volunteer potato plants and false negative (FN) classified sugar beet plants respectively. In general the NN2 method gave the best classification results, followed by DA and finally the NN1 method. When the optimal adapted waveband sets were generalized to a set of 10 fixed wavebands, the classification results were still at a reasonable level of a performance at 87% TN and 1% FN for the NN2 classification method. However, when a further reduction and generalization was made to 3 fixed wavebands, the classification results were poor with a minimum performance of 69% TN and 3% FN for the NN2 classification method. So, these results indicate that for the best classification results it is required that the sensor and classification system adapt to the specific field situation, to optimally discriminate between volunteer potato and sugar beet pixel spectra.     

土壤理化性质在不同强度采伐干扰下的响应及其评价

对南方丘陵山区在不同强度人为干扰[弱度择伐(13.0%)、中度择伐(29.1%)、强度择伐(45.8%)、极强度择伐(67.1%)和皆伐]10 a后的土壤理化性质进行分析。结果表明,经过10 a的恢复,弱度和中度择伐样地土壤结构稳定性、水分、孔隙状况和养分含量得到恢复,并有所改善,而强度择伐、极强度择伐和皆伐迹地土壤各项指标仍未恢复,其破坏程度均随采伐强度的增大而加剧。经主成分分析得出全K、速效K、速效P、有机质、全N、非毛管孔隙度、土壤密度、最小持水量、最大持水量、总孔隙度10项指标,能较好地反映林地土壤理化性质的变化状况,不同强度采伐对林地土壤理化性质的干扰程度依次为皆伐作业>极强度择伐>强度择伐>中度择伐>弱度择伐。     

还田棉秆腐解与土壤理化性质对氮肥施用的响应特征

[目的]研究棉花秸秆还田配施不同氮肥处理对棉秆腐解及土壤理化性质的影响.[方法]以干旱区典型农业土壤-灰漠土为研究对象,采用随机区组设计,以不添加秸秆与氮肥(CK)为对照,设置无氮(0 kg/hm2)、低氮(N1:112.5 kg/hm2、N2:225 kg/hm2)、中氮(N3:450 kg/hm2)、高氮(N4:7...     

黄土高原不同地貌类型区农田土壤有机碳与其他理化属性的关系

【目的】以黄土高原不同地貌气候类型区农田土壤为研究对象,研究农田土壤有机碳与其他理化属性的关系,探讨影响有机碳的土壤因子,为区域农田土壤固碳影响因素及固碳现状评估提供理论依据。【方法】采用多专题数据图综合分析方法,考虑土地利用、种植制度、地形地貌、土壤类型等将黄土高原地区划分为5个地貌类型区,在每个地貌类型区选取一个典型县,结合"联合单元布点法"进行样点布设,运用逐步线性回归模型、回归树模型和相关性分析研究不同地貌类型区农田土壤有机碳与其他理化属性的关系。【结果】1)不同地貌类型区农田土壤有机碳与pH、砂粒含量呈显著负相关关系,与粉粒含量、全氮、全钾呈显著正相关关系,该相关性受气候和管理措施影响较小;与体积质量、黏粒含量呈负相关,与全磷、有效磷、速效钾呈正相关,该相关性受气候和管理措施影响较大。农田土壤有机碳与黏粒含量的相关关系和黏粒含量有关,当黏粒含量为20%~25%,有机碳与黏粒的相关性较高;当黏粒含量大于30%时,有机碳与黏粒呈负相关趋势。2)区域尺度上,全氮、体积质量、全磷是影响农田土壤有机碳含量的重要因子;在各个地貌类型区中,全氮是影响农田土壤有机碳含量的重要因子。关中平原区、渭北高塬区、丘陵沟壑区、银川平原区、浅山丘陵区有机碳处于最高水平时的全氮阈值分别为1.39,0.95,1.33,1.38,1.64g/kg,有机碳处于低级水平的全氮阈值分别为0.97,0.59,0.86,0.76,1.06g/kg。【结论】黄土高原不同地貌类型区农田土壤有机碳与其他理化属性的相关性存在较大差异,且受气候和管理措施影响的程度不同。整体来看,全氮是影响黄土高原全区及各县域农田土壤有机碳的重要因子。     

Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis

Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal infection levels in rice (Oryza sativa L.) panicles. Four infection levels in rice panicles were used in the study: no infection condition, light and moderate infection caused by rice glume blight disease, and serious infection caused by rice false smut disease. Hyperspectral reflectance of rice panicles was measured through the wavelength range from 350 to 2500 nm with a portable spectroradiometer in the laboratory. The spectral response characteristics of rice panicles were analyzed, and principal component analysis (PCA) was performed to obtain the principal components (PCs) derived from different spectra processing methods, namely raw, inverse logarithmic, first, and second derivative reflectance. A learning vector quantization (LVQ) neural network classifier was employed to classify healthy, light, moderate, and serious infection levels. Classification accuracy was evaluated using overall accuracy and Kappa coefficient. The overall accuracies of LVQ with PCA derived from the raw, inverse logarithmic, first, and second derivative reflectance spectra for the validation dataset were 91.6%, 86.4%, 95.5%, and 100% respectively, and the corresponding Kappa coefficients were 0.887, 0.818, 0.939 and 1. Our results indicated that it is possible to discriminate different fungal infection levels of rice panicles under laboratory conditions using hyperspectral remote sensing data.     

集中连片特困区旅游扶贫效率及旅游经济强度空间特征分析——基于Super-SBM模型和社会网络分析法

旅游扶贫是脱贫攻坚的有效途径之一，旅游扶贫效率是衡量旅游扶贫工作的核心指标，旅游经济强度是促进旅游发展的重要因素，二者彼此关联性对于开展旅游扶贫工作具有重要指导意义。以湖北大别山区16个县（区）为研究对象，运用Super-SBM模型，测度2011至2017年各县（区）旅游扶贫效率，并通过修正传统引力模型，构建各县（区）旅游强度关系矩阵，基于社会网络分析法，对16个县（区）2017年旅游经济强度空间特征进行可视化分析。结果表明：湖北大别山区旅游扶贫综合效率处于中等偏上水平(均值0.776)，整体呈现下降趋势；县（区）之间旅游联系强度较弱，具有明显的不均衡性，网络密度呈现“大分散，小聚集”的空间格局，核心区与边缘区之间连接密度较低，联系不够紧密，旅游经济联系地域空间格局分为东南，西北和中部三个主要派系。旅游扶贫效率与旅游经济强度呈现负相关性，边缘区旅游扶贫效率远比核心区旅游扶贫效率高，具体呈现高效率高强度、高效率低强度、低效率低强度、低效率高强度四种模式。提高旅游扶贫效率，优化旅游扶贫和旅游发展之间内生关系是当务之急，从改进旅游扶贫手段，优化扶贫管理模式；科学把握旅游规模，有效配置旅游资源；加强旅游经济联系，提升旅游扶贫效率等方面提出建议。     

常规方法对新疆地方土壤元素空间分析及重金属风险评价

结合数理统计学和地统计学方法对博斯腾湖流域绿洲表土17种元素进行分析,探讨其含量、空间变异性、空间分布特征,并用地累积指数法和潜在生态危害指数分析其污染状况。结果表明:17种元素平均含量顺序为CaAlFeMnVZnCrNiCuPbAsCoSbMoTlCdHg。其中常量元素Al、Ca、Fe含量远远超过新疆土壤背景值,除重金属元素Co、Mo含量低于背景值外,其余12种重金属元素存在不同程度的超标,Cd、Hg超标率高达77%、62%。表土元素的空间变异特征显示,Fe、Mn、V、Cr、Co、Zn、As、Tl受结构性因素影响较大,其空间自相关性良好;Cu、Mo、Cd、Sb、Pb、Hg在较小范围内的空间自相关性良好,说明整体结构性特征依然存在,部分区域随机因素(农田、交通、工业生产)影响较大,影响因素复杂。Fe是地壳中稳定元素,受区域地质的影响。Al变程较小,除了受成土母质影响,还受随机因素影响。Ca是活动性元素,主要与风化淋溶、迁移、沉积等因素有关。空间分布结果显示大多数重金属在县城周边、交通干线以及工厂附近的区域含量高,As在西北部和东南部含量高,既有自然来源,也有人为来源。利用内梅罗污染指数法计算13种重金属污染指数,结果表明Co无污染,Mn、Cu、Tl、Pb的污染程度在警戒线内。V、Zn、Cr、Ni、As、Cd、Sb、Hg的内梅罗污染指数在1~2之间,属于轻微污染程度,其中Cd、Hg的污染指数值接近1.5,轻微污染程度稍高于其余重金属;单因子潜在生态风险结果显示Cd、Hg具有轻微生态风险,综合生态风险指数结果显示27%的样点存在轻微生态风险,73%的样点存在中等风险,主要是Cd、Hg的风险值较高,应该引起有关部门的重视。     

呼伦贝尔草原2000—2010年土地覆盖变化的遥感监测与分析

分析2000—2010年呼伦贝尔草原土地覆盖空间分布格局、时空变化趋势,揭示生态工程实施效果,为草原的合理开发利用提供科学依据,研究方法:基于2000、2005和2010年的3期Landsat 4/5TM遥感影像,采用最大似然法进行监督分类,得到各时期的土地覆盖分布图,通过制作不同时期不同地类间的转移矩阵,定量分析不同时期不同地类之间的相互转换结果。研究结果表明:1)草地年均减少面积为3 063.03hm2,与未利用土地、耕地和湿地发生剧烈转换;水域年减少面积为3 960.98hm2,与湿地和未利用土地发生剧烈转换;2)耕地、建设用地和未利用土地有不同程度增加;3)林地和湿地变化相对稳定。2000—2010年,由于气候、农业开发、建设用地占用等原因,造成呼伦贝尔草原水域和草地大面积减少,转换为耕地、建设用地和未利用土地总体趋势显著,总体而言呼伦贝尔草原水域、草地的生态保护形势严峻。     

利用地形、土壤和作物信息辅助提高东北漫岗地数字高程模型精度的新方法

【目的】SRTM DEM是可免费访问公开可用的数字高程模型,但是当前SRTM DEM的垂直精度不能满足精细农业对地形数据的需求,提高其垂直精度,为精准农业等领域提供数据基础。【方法】以黑龙江省海伦东兴农机合作社为研究区,采集实际地面高程数据,获取SPOT-6、Sentinel-2A遥感影像和SRTM DEM。提取归一化湿度指数（NDMI）、归一化植被指数（NDVI）、土壤亮度（TCB）、潜在太阳辐射（PSR）等变量分析地形对其影响关系。利用极限学习机（ELM）和反向传播神经网络（BPNN）提高SRTM DEM水平空间分辨率和垂直精度。使用实际地面高程点进行精度验证,与基于无人机和光学立体像对（ZY-3）生成的DEM进行对比。【结果】SRTM、NDVI、NDMI、TCB与改进后高程的灰色关联度在90%以上,是改进SRTM DEM的重要辅助信息。在整个研究区,BPNN方法的RMSE_P为0.98,R²_P为0.98,ELM的RMSE_P为1.00,R²_P为0.90。在平坦区,BPNN方法的RMSE_P为0.84,ELM的RMSE_P为1.00;在起伏区,BPNN方法的RMSE_P为0.99,ELM的RMSE_P为0.94。该方法获得的DEM的垂直精度高于ZY-3光学立体像对生成的 DEM的垂直精度,为提高SRTM的水平空间分辨率和垂直精度提供了新思路。【结论】引入SRTM、NDVI、NDMI、TCB辅助信息有利于提高SRTM DEM的空间分辨率和垂直精度,获得高精度的DEM。BPNN方法获得的数字高程模型的精度整体上高于ELM方法, BPNN方法更加适用于平坦区高精度DEM的获取,ELM方法更加适用于起伏区。     

Discrimination between organically and conventionally grown winter wheat farm pair samples using the copper chloride crystallisation method in combination with computerised image analysis

Organic and conventional winter wheat farm pair grain samples were tested with the copper chloride crystallisation method and submitted to computerised image analyses followed by pattern recognition and classification with multivariate statistical tools.Appropriate discriminant analyses (DA) models were established. Depending on the analysed region of interest up to 100% of “unknown” samples could be correctly predicted using the DA models.     

基于能值与生命周期评价耦合模型的农业系统生态效率评估——以北京市郊区为例

结合能值分析和生命周期评价模型,从期望产出效率和非期望产出效率两个角度构建生态效率计量方法,评价京郊3种典型农作物生产系统的可持续性,为北京市农业内部结构优化提供科学的决策依据。研究表明:玉米、蔬菜和桃的能值投入产出比分别是6.61×10~4、1.47×10~5、1.92×10~5sej·J~(-1),玉米的期望产出效率最高,蔬菜其次,桃最低;玉米、蔬菜和桃生产系统的非期望产出效率分别是1.29×10~6、1.60×10~5和2.59×10~5,玉米的非期望产出效率最高,桃其次,蔬菜最低;玉米的生态效率最高,为2.95×10~(-4),蔬菜其次,为7.42×10~(-6),桃最低,为7.05×10~(-6);以蔬菜为例进行敏感性分析表明,投入要素中对生态效率敏感性最大的是电能(用于农田灌溉),氮肥次之,再次是有机堆肥和农药。调整种植业结构不仅要考虑农产品的产量及经济收益,还需要考虑生态效率。有机肥替代化肥能够显著提高农作物生产系统的生态效率,但要注意有机肥中重金属含量对人体健康的影响。总之,发展节水型粮食作物种植、加强农业节水和提高有机肥比例是改善北京市农田生态系统生态效率的重要措施。     

叶锈菌与‘TcLr19’小麦互作体系中PR1基因的克隆及分析

利用RT-PCR和cDNA末端快速扩增技术(RACE),从被小麦叶锈菌诱导的抗叶锈病近等基因系材料TcLr19中获得1个病程相关蛋白1(Pathogenesis-related proteins 1,PR1)基因,暂命名为TcLr19PR1。该基因长度为810bp,包含495bp ORF区,编码164个氨基酸,基因产物具有植物防御体系中病程相关蛋白SCP保守结构域,与多个植物病程相关蛋白1基因具有较高同源性。利用半定量分析表明,TcLr19PR1基因受叶锈菌诱导后表达量变化明显,非亲和组合表达高于亲和组合。Southern杂交验证说明TcLr19PR1在小麦基因组中为低拷贝。利用‘中国春’缺体-四体系成功将该基因定位在小麦7D染色体上,为进一步明确叶锈菌与‘TcLr19’小麦互作体系中病程相关蛋白1基因的抗叶锈相关性奠定了基础。