Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site‐specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (EC_a) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and EC_a. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.     

Variograms of Ancillary Data to Aid Sampling for Soil Surveys

To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograms of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site. Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograms showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.     

多时段空间剩余协克立格模型基本理论及其在水文领域中的应用

本文将随机水文学理论和地质数学理论有机地结合在一起，提出了用时空随机函数观点去分析水文数据的多时段空间剩余协克立格模型。该模型仅利用有限站的多变量水文时间序列数据即可建立起反映每一时段多变量时空相关特征的变差函数模型，从而能够在局部范围内对多个水文变量进行最优估计或外推预测。     

Limited Spatial Transferability of the Relationships Between Kriging Variance and Soil Sampling Spacing in Some Grasslands of Ireland: Implications for Sampling Design

Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems:i) different population variograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.     

基于时空变异的地下水模拟参数插值研究

由于地下水的储藏与运行特性及实际条件的限制,在对其进行监测时往往只能得到有限的监测结果,因此需要借助已知点信息来对地下水污染物运移参数的空间分布特性进行估计,着重阐述了浅层地下水环境评价指标的时空变异性,通过实验模拟水质指标在浅层地下水非均质环境中的运移,分析了示踪剂氯离子迁移过程中浓度变化的各向异性,进行了空间点去除率为33%和50%条件下的时空协同克里格估值;结果表明,在其他时刻只有参估点时间信息而无待估点时间信息,无论待估点的多寡,时空协同克立格估计效果相比普通克立格基本相当,证明了建立在地下水环境评价变量随机性空间统计分析基础之上的协同克里格最优估计方法切合实际,具有一定的实用价值。     

典型黄土地貌高程空间变异性研究

黄土高原地形与地貌自南向北有序变化,构成其独特的地理景观。为了研究不同黄土地貌之间高程的空间变异特征,选择7个典型黄土地貌样区,以其30 m分辨率DEM数据为数据源,使用变异函数模型特征参数定量地反映样区地貌的个体特征与空间分布特征。研究表明:4 000 m为最能突出高程空间变异规律的窗口,并在此尺度下分析得出不同样区高程空间分布规律:地貌越复杂、破碎,高程空间自相关性范围越小;高程的空间变化幅度随地貌的发育不断减小;高程具有较强的空间相关性,其变化为各向异性。5个指标按样区顺序从淳化向北到神木,按大—小—大或小—大—小的顺序变化,与黄土高原由南到北的地形复杂程度变化相吻合。     

A risk-qualified approach to calculate locally varying herbicide application rates

Weed competition can decrease crop yield and profit. Herbicides are applied to reduce weed populations, minimize crop loss and maximize profit. Traditional practice is to apply herbicides at a uniform rate over an entire field. Complete knowledge of the weed distribution and appropriate instrumentation on the spraying equipment would allow the farm manager to apply the 'correct' locally varying herbicide application rate. The locally variable rate would be greater in areas of high weed density and less where there are few weeds. A locally varying treatment would have both economic and environmental advantages. A major challenge facing farm managers is the unavoidable uncertainty in the spatial distribution of weeds in any particular field. This uncertainty in weed distribution influences the optimal locally varying herbicide rate. A mathematical model is presented to calculate the optimal herbicide application rate using geostatistical models of uncertainty in weed density combined with principles from decision making. Weed data from a 34-ha field near Saskatoon, Saskatchewan, Canada, illustrate the application of these tools. Weed control was achieved with a significant reduction in total herbicide use.     

Regional pattern of the mobile water fraction in soils as determined by disc infiltrometer experiments

In order to characterize the partitioning of solutes into the mobile and the immobile fraction of pore water at the soil surface, field measurements of infiltration in unsaturated loamy sands were carried out using a disc infiltrometer in a region of about 10 km² at the Lüneburger Heide, Germany. The aims of these measurements were to obtain the mobile water fraction and the hydraulic properties of the soil, to relate them to other measured soil properties like the soil texture, and to assess their spatial variability in the region. From the infiltration results we obtained the hydraulic conductivity K₀ and the sorptivity S₀ using a three-dimensional infiltration analysis and the two-term infiltration equation, respectively. The mobile water fraction θ_m/θ of the soil was estimated from the infiltration results of a second disc infiltrometer filled with bromide solution. The estimated values of K₀ and S₀ were in good agreement with those reported in the literature as typical for loamy sands. The average value of θ_m/θ was calculated to be 71 % under the experimental conditions. Neither the horizontal spatial distribution of water and bromide concentration beneath the disc infiltrometer nor the change of the infiltrometer during the experiment had a notable influence on the experimental results. The spatial variability of the data was analyzed by means of variograms. All variograms were spatially structured and indicated a high small scale variation of the variables. In conclusion, the technique applied is suitable for the estimation of the hydraulic and solute transport properties of soils, especially at larger scales.     

基于最大似然法与矩法的黄土高原小流域土壤碳氮空间变异分析

以黄土高原寺底沟小流域为研究对象,根据不同土地利用方式采集46个样点的土壤样品,通过地统计方法对土壤有机碳和全氮的空间变异特征进行了分析。采用受限最大似然法(REML)和矩法(MOM)两种方法分别对变异函数进行了估计,通过交叉检验选择克里金预测效果较好的变异函数进行地统计插值。(1)与矩法(MOM)相比,在多数情况下受限最大似然法(REML)估计的变异函数进行克里金插值更加准确。(2)土层深度对土壤全氮空间变异影响较小,对土壤有机碳影响较大,表层土壤有机碳含量及变异程度明显高于下层土壤。(3)土地利用方式对土壤有机碳和全氮的空间分布有重要影响,灌木林和天然草地土壤有机碳和全氮水平最高,弃耕地其次,梯田、果园、人工草地最低,表明退耕还林对提高土壤碳氮水平有重要贡献。     

Erfassung der Variabilität von Bodenmerkmalen in einem Blindversuch auf Fahlerde mittels Methoden der räumlichen Statistik

Variability of soil parameters in a uniformity trial on a Luvisol evaluated by means of spatial statistics The relationships between soil parameters and indicators of soil genesis were investigated in a uniformity trial on Luvisol by means of classical and spatial statistics. The following properties were investigated in the topsoil (0–30 cm) and subsoil (31–40 cm): content of total carbon (C_t), content of clay plus fine silt (FAT), concentrations of available potassium (K) and phosphorus (P), and pH values. The depth loamy Bt horizon and microrelief were considered as indicators of soil genesis. Smoothing of the raw data by means of block kriging eliminated the small scale variability of all parameters. In this way, the rank correlation coefficients between soil parameters increased by factor 2 and were to 0.52 for C_t–FAT; 0.69 for C_t–K, and 0.55 for C_t–P in the top soil. The maps for smoothed data allowed a much better visual analysis of spatial distribution of investigated properties compared to the raw data. The significant correlations as well as the similarity of maps and variograms suggested that the spatial variability of FAT, C_t, K, and P in the top soil was determined by the same factors. The depth of the loamy Bt horizon and the relief were the most important factors defining the distribution of the investigated soil parameters. The rank correlation coefficients of the smoothed and detrended relief with the soil parameters amount to –0.63, –0.77, –0.71, and –0.57 for FAT, C_t, K, and P, respectively.