Determining nugget:sill ratios of standardized variograms from aerial photographs to krige sparse soil data

Maps of kriged soil properties for precision agriculture are often based on a variogram estimated from too few data because the costs of sampling and analysis are often prohibitive. If the variogram has been computed by the usual method of moments, it is likely to be unstable when there are fewer than 100 data. The scale of variation in soil properties should be investigated prior to sampling by computing a variogram from ancillary data, such as an aerial photograph of the bare soil. If the sampling interval suggested by this is large in relation to the size of the field there will be too few data to estimate a reliable variogram for kriging. Standardized variograms from aerial photographs can be used with standardized soil data that are sparse, provided the data are spatially structured and the nugget:sill ratio is similar to that of a reliable variogram of the property. The problem remains of how to set this ratio in the absence of an accurate variogram. Several methods of estimating the nugget:sill ratio for selected soil properties are proposed and evaluated. Standardized variograms with nugget:sill ratios set by these methods are more similar to those computed from intensive soil data than are variograms computed from sparse soil data. The results of cross-validation and mapping show that the standardized variograms provide more accurate estimates, and preserve the main patterns of variation better than those computed from sparse data.     

Geostatistical analysis of fruit yield and detachment force in coffee

The aim of this study was to use geostatistical analysis to evaluate the spatial variation in the detachment force of coffee fruit and coffee yield by variograms and kriging for precision agriculture. This study was conducted at Brej?o farm, Três Pontas, Minas Gerais, Brazil. The detachment force of green and mature coffee fruit was measured with a prototype dynamometer and georeferenced. The yield data were obtained from manual harvesting and were georeferenced. The data were evaluated by variograms estimated by residual maximum likelihood (REML), which provided a satisfactory approach for modeling all the variables with a small sample size. Spherical and exponential models were fitted, the first provided the better fit to mature fruit detachment force and the latter provided the better fit to coffee yield and green fruit detachment force. They were used to describe the structure and magnitude of spatial variation in the variables studied. Kriged estimates were obtained with the best fitting variogram models and mapped. The statistical and geostatistical analyses enabled us to characterize the spatial variation of the detachment force of green and mature coffee fruit and coffee yield and to visualize the spatial relations among these variables. The precision agriculture techniques used in this paper to collect, map and analyze the variables studied will help coffee farmers to manage their fields. Maps of coffee yield will enable farmers to apply nutrients site-specifically and manage harvesting either manually or mechanically. In addition, maps of detachment force of coffee fruit can enable farmers to harvest coffee selectively by choosing the appropriate places and the right time to start. This will improve the quality of the final product and also increase profits.     

Exploring the Spatial Relationships Between Some Soil Properties and Wheat Yields in Two Soil Types

A field study was conducted to quantify spatial soil variability and to analyze correlations among soil properties at different spatial scales. Soil samples from 0 to 30 cm depth were collected from two adjacent fields in the southwestern Beauce Plain (France) which consisted of Haplic Calcisols and Rendzic Leptosols. Factorial kriging analysis (FKA) was used to describe the co-regionalization of nine soil properties. A linear model of co-regionalization including a nugget effect, and two spherical models were fitted to the experimental data direct and cross-variograms of the topsoil layer properties which were previously estimated. Co-kriged regionalized factors, related to short and long-range variation, were then mapped to characterize soil variation across the two fields. The potential value of ancillary sampled variables, such as yield data, to provide information on soil properties was tested. The relation between yield and measured soil properties appeared to be weak in general. However, the structures of the variation in yield appeared to be relatively stable for two years and showed similar patterns as the co-kriged soil factors. This suggests that information on the scale of variation of soil properties can be derived from yield maps, which can also be used as a guide to suitable sampling interval for soil properties and as a basis for managing fields in a precise way.     

不同样本数量下土壤属性空间预测比较

【目的】通过不同样本数量情形下土壤属性空间预测精度的对比,探讨样本数对预测精度的影响。【方法】以土壤有机质、土壤质量含水量、土壤速效钾和土壤有效锰4个土壤属性作为研究对象,采用矩量法（MoM）和有限最大似然法（REML）两种变异函数计算方法,对比分析在不同样本数情形下各土壤属性空间预测精度。【结果】（1）不论是利用MoM法还是REML法计算变异函数,样本点数从50增加到70,各土壤变量预测精度都有了明显提高;样本数从70逐步增加到150过程中,预测精度没有明显提高;（2）在不同样本数情形下,REML法相对于MoM法的预测精度的提高率具有明显的变化,而且在有些情况下,REML法的预测精度比MoM低,而且对于不同土壤变量,表现结果也不相同。【结论】样本数对土壤空间预测准确性具有显著影响,在本研究区域尺度下,当样本数小于70个时,无论用哪种变异函数计算方法进行预测,预测结果的可信度都比较低。     

Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense datasets.     

Relationship Between Spectral Data from an Aerial Image and Soil Organic Matter and Phosphorus Levels

Early ventures into site-specific management involved fertilizer management decisions based on soil chemical properties characterized by some form of grid sampling. This is both labor and capital intensive and practitioners quickly began investigating other methods to get a measure of spatial variability. Aerial photographs, which were mainly used to evaluate and assess crop status, allow for the collection of whole-field data at relatively low cost. Our objective is to determine what relationships exist between aerial spectral data and intensive grid soil test results and whether this information can be used to improve future soil sampling strategies. Soil-test organic matter (OM) and Bray-1 P concentrations were measured on soil samples taken using an alternating 12.2- by 24.4-m grid in late March 1994 from a quarter section under center pivot irrigation. Spectral data were collected in the spring of 1996 prior to planting using a multispectral network of digital cameras. Correlations of brightness values from the blue, green, and NIR bands with both OM and Bray-1 P were significant, but relatively low. Normality tests revealed that brightness values for the spectral data sets were generally evenly distributed while those of the soil test OM and Bray-1 P were positively skewed. Many of the very high soil-test data values were due to past management. When those values were removed from the database, greater correlations between spectral data and soil test data were obtained. These results substantiated that aerial imagery can be used to improve sampling strategies, but it must be used in conjunction with existing knowledge and past management histories.     

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Accurate characterization of soil properties across a field can be difficult, especially when compounded with the diverse landscapes used for pastureland. Indirect methods of data collection have the advantage of being rapid, noninvasive, and dense; they may improve mapping accuracy of selected soil parameters. The objective of this study was to determine if the use of soil electrical conductivity (EC) as a covariate improved mapping accuracy of five soil variables across four sampling schemes and two sampling densities in a central Iowa, USA pasture. In this study, cokriging methods were compared to kriging methods for the measured soil properties of soil pH, available P and K, organic matter and moisture. Maps resulting from cokriging each of the soil variables with soil EC exhibited more local detail than the kriged maps of each soil variable. A small, but inconsistent, improvement occurred in kriging variance and prediction accuracy of non-sampled sites when cokriging was implemented. The improvement was generally greater for soil variables more highly correlated with soil EC. This work indicates that cokriging of EC with less densely and invasively collected soil parameters of P, K, pH, organic matter (OM) and moisture does not consistently and substantially improve the characterization accuracy of pasture soil variability.     

Estimating Average and Proportional Variograms of Soil Properties and Their Potential Use in Precision Agriculture

Precision Agriculture requires a method of gathering information about the spatial variability of soil that reduces the need for expensive and intensive sampling. This can be achieved through the use of what we term average and proportional variograms. A literature search has enabled the gathering of variograms for multiple soil properties, allowing comparison of the magnitude of variability and the construction of averages. For soil properties that display proportionality between their mean squared and variance, the variogram can be predicted from a mean value. These average and proportional variograms are potentially beneficial to implementers of Precision Agriculture as they can be used to plan optimal soil sampling and management schemes. It was found that if wishing to implement site-specific management to a resolution of 20×20 m then grid soil sampling will generally have to be performed at 20–30 m intervals depending on the attribute of interest. A decision-support chart for differential soil management based on a variogram's comparative magnitude to the average is presented. Further work needs to be done on increasing the data base these results are based on and refining the proportional variogram parameters to site-specificity.     

地统计学在湿地土壤养分空间异质性研究中的应用

在地统计学的区域化变量理论和变异函数的基础上建立了一套对湿地土壤养分的空间异质性进行定量化的较系统且完整研究的方法,通过向海湿地的案例研究证实了该方法在湿地土壤养分空间异质性研究中具有较强的操作性和准确性。     

The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared reflection (MIR) from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a cost-effective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.     

Exploitation of spatial information in high resolution digital imagery to map leaf area index

Crop responses to management practices and the environment, as quantified by leaf area index (LAI), provide decision-making criteria for the delineation of crop management zones. The objective of this work was to investigate whether spatial correlations inferred from remotely sensed imagery can be used to interpolate and map LAI using a relatively small number of ground-based LAI measurements. Airborne imagery was recorded with the Airborne Imaging Spectrometer for Applications (AISA) radiometer over a 3.2 ha corn field. Spectral vegetation indexes (SVI) were derived from the image and aggregated to cells of 2 × 2 m², 4 × 4 m², and 8 × 8 m² resolution. The residual maximum likelihood method was used to estimate the LAI variogram parameters. A generalized least squares regression was used to relate ground truth LAI data and collocated image pixels. This regression result was then used to convert variograms from the imagery to LAI units as well as to interpolate and map LAI. The decrease in resolution by merging pixels led to an increase in the value of the r ² and to a decrease in root mean-squared error (RMSE) values. The accuracy of kriged estimates from the variogram of the measured LAI and that from the image derived variograms was estimated by cross-validation. There was no difference in the accuracy of the estimates using either variograms from measured LAI values or from those of converted SVIs. Maps of LAI from ground-based measurements made by kriging the data with image-derived variogram parameters were similar to those obtained by with kriging with the variogram of measured LAI. Similar coarse spatial trends of high, medium and low LAI were evident for both maps. Variogram parameters from ground-based measurements of LAI compared favorably with those derived from remotely sensed imagery and could be used to provide reasonable results for the interpolation of LAI measurements.     

Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains of the USA

A four-year study was conducted from 2000 to 2004 at eight field sites in Montana, North Dakota and western Minnesota. Five of these sites were in North Dakota, two were in Montana and one was in Minnesota. The sites were diverse in their cropping systems. The objectives of the study were to (1) evaluate data from aerial photographs, satellite images, topographic maps, soil electrical conductivity (EC_a) sensors and several years of yield to delineate field zones to represent residual soil nitrate and (2) determine whether the use of data from several such sources or from a single source is better to delineate nitrogen management zones by a weighted method of classification. Despite differences in climate and cropping, there were similarities in the effectiveness of delineation tools for developing meaningful residual soil nitrate zones. Topographic information was usually weighted the most because it produced zones that were more correlated to actual soil residual nitrate than any other source of data at all locations. The soil EC_a sensor created better correlated zones at Minot, Williston and Oakes than at most eastern sites. Yield data for an individual year were sometimes useful, but a yield frequency map that combined several years of standardized yield data was more useful. Satellite imagery was better than aerial photographs at most locations. Topography, satellite imagery, yield frequency maps and soil EC_a are useful data for delineating nutrient management zones across the region. Use of two or more sources of data resulted in zones with a stronger correlation with soil nitrate.     

基于无人机RGB影像估测田间小麦穗数

单位面积穗数是小麦产量构成的重要因素,利用图像信息处理技术快速、准确地估测田间小麦穗数,可以为小麦长势监测和产量估测提供直接依据.利用无人机路径规划和控制系统(fragmentation monitoring and analysis with aerial photography,FragMAP)获取标准统一、高分辨...     

Spatial variability of soil properties and yield of a grazed alfalfa pasture in Brazil

Knowledge of the spatial variability of soil properties and of forage yield is needed for informed use of soil inputs such as variable rate technology (VRT) for lime and fertilizers. The objective of this research was to map and evaluate the spatial variability of soil properties, yield, lime and fertilizer needs and economic return of an alfalfa pasture. The study was conducted in a 5.3 ha irrigated alfalfa pasture in Sao Carlos, SP, Brazil that was directly grazed and intensively managed in a 270-paddock rotational system. Alfalfa shoot dry matter yield was evaluated before grazing. Soil samples were collected at 0–0.2 m depth, and each sample represented a group of 2 or 3 paddocks. Apparent soil electrical conductivity (ECa) was measured with a contact sensor. The cost of producing 1 ha of alfalfa was estimated from the amount of lime and fertilizer needed and was then used to estimate the total cost of production for the dairy system. The alfalfa dry matter yield was used to simulate the pasture stocking rate, milk yield, gross revenue and net profit. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms with VESPER software, the soil fertility information and economic return were modeled with SPRING software. The results showed that geostatistics and GIS were effective tools for revealing soil and pasture spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application maps. Spatial variation in forage and spatial estimates of stocking and milk yield are adequate pasture management tools. Spatial analyses of needs, forage availability and economic return are management tools for avoiding economic problems, as well as potential environmental problems, caused by unbalanced nutrient supplies and over- or under-grazing.     

Spatial Analysis of Soil Fertility Parameters

Databases identifying spatial distributions of soil properties are needed to implement site-specific management practices. This study examined spatial patterns for nine soil chemical properties in two adjacent fields, one in a corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation with inorganic fertilizer and the other in a 5-yr corn-soybean-corn-oat (Avena sativa L.)-meadow rotation with organic nutrient sources. We established sampling grids in both fields and collected soil cores to a depth of 30 cm. Soil properties with strong spatial correlations (low nugget variance/total variance ratio) and the maximum distance to which those properties were correlated (range) differed for the two fields. Soil pH, exchangeable Ca, total organic C, and total N were strongly correlated and had range values greater than 182 m in the conventional field. Bray P and exchangeable Mg were strongly correlated with range values of less than 100 m within the other. Low nugget/total variance ratios and small range values for P and Mg suggest patchy distributions, probably from long-term animal manure and municipal sludge application. Since most variance was structural in the organic field, placing sampling points closer together would improve data precision. In contrast, a relatively coarse sampling grid with fewer sampling points spaced further apart appears adequate for the conventional field. To develop accurate sampling strategies for precision agriculture, long-term field management histories should be documented since the practices appear to affect both the properties that are strongly correlated and the range to which the correlation exists.     

棉粕对盐碱化土壤改良及棉花产量的影响

【目的】研究棉粕对盐碱化土壤改良及棉花产量的影响,提高作物产量提供支持。【方法】以新疆现在主要栽培作物棉花为试验材料,在石河子大学农学院试验站进行田间控制试验,设计在不同的盐化和碱化土壤施用棉粕,取样不同生育期土壤调查土壤中的理化性质,结合棉花产量,分析棉粕改良盐碱化土壤。【结果】施加棉粕处理,可有效降低土壤容重、pH、EC,改善土壤理化性质,其中表层中度盐化土壤容重下降最高为16.1%,轻度以及重度碱化土壤pH下降明显,盐化土壤EC下降比碱化土壤下降明显,土壤阳离子出现不同程度的下降,棉花产量有效提高。【结论】棉粕对盐碱土改良效果明显,添加棉粕改良能够降低盐碱化土壤容重、pH、EC、土壤交换性阳离子,可有效提高土壤理化性质,对盐化土壤和碱化土壤的效果不同,棉粕对盐化土壤的改良措施在于降低土壤EC,对于碱化土壤的改良措施为降低土壤pH。盐碱地土壤pH、EC过高是抑制棉花产量的主要原因,添加棉粕能够缓解盐碱胁迫下对棉花的伤害, 提高棉花产量。     

Within and between-field spatial variation in soil phosphorus in permanent grassland

Soil phosphorus (P) concentrations above certain critical thresholds are a problem in many areas leading to its transport into surface and ground waters. Site-specific nutrient applications and the development of nutrient management plans for farms would help to optimize nutrient applications, meet crop requirements and take into consideration current soil nutrient status. In Northern Ireland, high concentrations of soil P are common, whereas low concentrations of soil potassium (K) and sulphur (S) have been reported in many silage fields. This study used grid and transect soil sampling to measure within- and between-field spatial variation in soil Olsen-P status across a 50-ha permanent grassland site used for silage production. Soil phosphorus indices ranged from Index 1 to Index 4 within single fields. The spatial patterns of soil P across fields suggested that there was scope for site-specific P fertilizer applications, with variable quantities of P being applied to different fields and within individual fields. Site-specific nutrient management has the potential to reduce excess P applications in some areas and avoid deficiencies in others, thereby minimizing environmental problems and optimizing yield.     

Data fusion techniques for delineation of site-specific management zones in a field in UK

Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields.     

Three dimensional spatial distribution modeling of soil texture under agricultural systems using a sequence indicator simulation algorithm

Understanding the distribution of alluvial soil textures on a large scale is crucial for agricultural and environmental management. In our study an indicator variogram and a sequence indicator simulation (SIS) algorithm were used to analyze and simulate the spatial distribution of soil textures based on observations of 139 soil profiles in a 15 km² region in the Huabei alluvial plain in China. The nugget-to-sill ratio value (SH) of the indicator variograms for all textures in a vertical direction (Z) was equal to 1. This suggests that spatial auto-correlation dominates in the direction of sedimentary deposition with 0.05 m sampling intervals. In contrast, SH ratios from 0.48 to 0.81 show that the soil textures have a degree of randomness in the horizontal direction (X, Y) where the sampling distance was about 300 m. Using the indicator variograms in 3 directions (X, Y and Z) as outlined above, a 3D SIS algorithm was used to simulate textures. Finally, the simulation results were evaluated by the reproduction of a histogram, variogram and the mean absolute error (MAE) of prediction. The mean absolute percentage error (MAPE) of the histogram reproduction showed that the main textures (sand, sandy loam and clay) were described well, whereas the less prevalent textures were underestimated. The MAPE of the indicator variograms reproduction were reasonable although some deviation existed as less prevalent textures in the vertical direction. The mean absolute error (MAE) of the SIS prediction was 0.47. This result is considered acceptable for a category variable because of the stochastic nature of soil textures in a horizontal direction, and hence may provide useful data for other agricultural research.