基于多年产量数据的精准农业管理分区提取与尺度效应评价

 本研究利用带有差分全球定位系统（DGPS）接收机和产量监测传感器的联合收割机获取的4年产量数据进行精准农业管理分区的提取研究。对经过一系列处理后的4年的产量数据进行栅格平均运算，得到分辨率为4 m的综合产量图。分别采用尺度为12、20、28、36、44、52、60 m 的正方形窗口对分类后的综合产量图进行众数过滤，并从方差减少率、差异显著性、空间破碎化、空间一致性4个角度进行了尺度效应评价。结果表明，分类后众数过滤法有效地去除了由随机变异造成的孤立像元或碎片，保留了实际的产量变异，增加了管理分区的有效面积，提高了管理分区的连续性。分区结果可以直接作为精准农业目标产量分区图，用于作物种植前或产中适时肥料推荐管理决策。     

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data—enhanced by satellite remote sensing and open-source policies—provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and map-overlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment.     

A comparison of different algorithms for the delineation of management zones

One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to: (1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 × 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a non-parametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (2005–2006, 2006–2007 and 2007–2008) were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approach.     

Management zones delineation using fuzzy clustering techniques in grapevines

Precision viticulture aims at managing vineyards at a sub-field scale according to the real needs of each part of the field. The current study focused on delineating management zones using fuzzy clustering techniques and developing a simplified approach for the comparison of zone maps. The study was carried out in a 1.0 ha commercial vineyard in Central Greece during 2009 and 2010. Variation of soil properties across the field was initially measured by means of electrical conductivity, soil depth and topography. To estimate grapevine canopy properties, NDVI was measured at different stages during the vine growth cycle. Yield and grape composition (must sugar content and total acidity) mapping was carried out at harvest. Soil properties, yield and grape composition parameters showed high spatial variability. All measured data were transformed on a 48-cell grid (10 × 20 m) and maps of two management zones were produced using the MZA software. Pixel-by-pixel comparison between maps of electrical conductivity, elevation, slope, soil depth and NDVI with yield and grape composition maps, set as reference parameters, allowed for the calculation of the degree of agreement, i.e. the percentage of pixels belonging to the same zone. The degree of agreement was used to select the best-suited parameters for final management zones delineation. For the year 2009 soil depth, early and mid season NDVI were used for yield-based management zones while for quality-based management zones ECa, early and mid season NDVI were utilized. For the year 2010 ECa, elevation and NDVI acquired during flowering and veraison were used for the delineation of yield-based management zones while for quality-based management zones ECa and NDVI acquired during flowering and harvest were utilized. Results presented here could be the basis for simple management zone delineation and subsequent improved vineyard management.     

A Data Generator for Evaluating Spatial Issues in Precision Agriculture

With the rapid rise in site-specific data collection, many research efforts have been directed towards finding optimal sampling and analysis procedures. However, the absence of widely available high quality precision agriculture data sets makes it difficult to compare results from separate experiments and to assess the optimality and applicability of procedures. To provide a tool for spatial data experimentation, we have developed a spatial data generator that allows users to produce data layers with given spatial properties and a response variable (e.g. crop yield) dependent upon user specified functions. Differences in response functions within fields can be simulated by assigning different models to regions in coordinate-(x and y) or feature space (multidimensional space of attributes that may have an influence on response). Noise, either unexplained variance or sensor error, can be added to all spatial layers. Sampling and interpolation error is modeled by sampling a continuous data layer and interpolating values at unsampled locations. The program has been successfully tested for up to 15000 grid points, 10 features and 5 models. As an illustration of the potential uses of generated data, the effect of sampling density and kriging interpolation on neural network prediction of crop yield was assessed. Yield prediction accuracy was highly related (correlation coefficient 0.98) to the accuracy of the interpolated layers indicating that unless data are sampled at very high densities relative to their geostatistical properties, one should not attempt to build highly accurate regression functions using interpolated data. By allowing users to generate large amounts of data with controlled complexity and features, the spatial data generator should facilitate the development of improved sampling and analysis procedures for spatial data.     

How to define the optimal grid size to map high resolution spatial data?

The development and the release of sensors capable of providing data with high spatial resolution (>?4 000 points ha^?1) in agriculture raises new questions as to how to represent this spatial information. The objective of this study was to propose a methodology to help define the optimal grid size to map high resolution data in agriculture. The geostatistical method finds the grid size which maximizes the sum of two components: (i) the proportion of nugget variance that is removed, and (ii) the proportion of sill variance that remains in the data. The optimum grid size was found to be dependent on the resolution of the available information and the spatial structure of the raw data. Experiments on simulated datasets with varying data resolution (from 500 to 2 000 pts.ha^?1) and spatial structure (range of variogram between 10 and 45 m) showed that the proposed methodology was able to define varying optimal grid sizes (from 5 to 12 m). The proposed geostatistical approach was then applied on a real dataset of total soluble solids/sugar content of table grape so that the optimal mapping grid size could be found. Once it was defined, two interpolation methods: simple averaging over blocks and block kriging, were applied to mapping the data. Results show that both methods help depict the within-field variability in the data. While the averaging procedure is easier to automate, the block kriging approach provides users with a level of uncertainty in the aggregated data. Both mapping approaches significantly impacted the within-field spatial structure: (i) the small-scale variations were ten times lower than in the raw data, and (ii) the signal-to-noise ratio of the aggregated data with the optimal grid was twice as high as that of the raw data. As the proposed geostatistical methodology is a first attempt to define the optimal grid size to map high resolution spatial data, areas for future development applications are also proposed.     

An integrated approach to site-specific management zone delineation

Dividing fields into a few relatively homogeneous management zones (MZs) is a practical and cost-effective approach to precision agriculture. There are three basic approaches to MZ delineation using soil and/or landscape properties, yield information, and both sources of information. The objective of this study is to propose an integrated approach to delineating site-specific MZ using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS) and evaluate it against two other approaches using only soil and landscape information (ROSE) or clustering multiple year yield maps (CMYYM). The study was carried out on two no-till corn-soybean rotation fields in eastern Illinois, USA. Two years of nitrogen (N) rate experiments were conducted in Field B to evaluate the delineated MZs for site-specific N management. It was found that in general the ROSE approach was least effective in accounting for crop yield variability (8.0%–9.8%), while the CMYYM approach was least effective in accounting for soil and landscape (8.9%–38.1%), and soil nutrient and pH variability (9.4%–14.5%). The integrated ROSE-YSTTS approach was reasonably effective in accounting for the three sources of variability (38.6%–48.9%, 16.1%–17.3% and 13.2%–18.7% for soil and landscape, nutrient and pH, and yield variability, respectively), being either the best or second best approach. It was also found that the ROSE-YSTTS approach was effective in defining zones with high, medium and low economically optimum N rates. It is concluded that the integrated ROSE-YSTTS approach combining soil, landscape and yield spatial-temporal variability information can overcome the weaknesses of approaches using only soil, landscape or yield information, and is more robust for MZ delineation. It also has the potential for site-specific N management for improved economic returns. More studies are needed to further evaluate their appropriateness for precision N and crop management.     

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.     

Processing of Yield Map Data

Yield maps reflect systematic and random sources of yield variation as well as numerous errors caused by the harvest and mapping procedures used. A general framework for processing of multi-year yield map data was developed. Steps included (1) raw data screening, (2) standardization, (3) interpolation, (4) classification of multi-year yield maps, (5) post-classification spatial filtering to create spatially contiguous yield classes, and (6) statistical evaluation of classification results. The techniques developed allow more objective mapping of yield zones, which are an important data layer in algorithms for prescribing variable rates of production inputs.     

Optimised Spatial Sampling Scheme for Soil Electriclal Conductivity Based on Variance Quad-Tree （VQT） Method

The acquisition of precise soil data representative of the entire survey area,is a critical issue for many treatments such as irrigation or fertilization in precision agriculture.The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity(ECb)in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm,the variance quad-tree(VQT)method.Soil ECb data were collected from the field at 20m interval in a regular grid scheme.The smooth contour map of the whole field was obtained by ordinary kriging interpolation,VQT algorithm was then used to split the smooth contour map into strata of different number desired,the sampling locations can be selected within each stratum in subsequent sampling.The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy.The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large.Thus the sampling efficiency can be improved,hence facilitate an assessment methodology that can be applied in a rapid,practical and cost-effective manner.     

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.     

Adaptation of regional digital soil mapping for precision agriculture

In the initial phase of a national project to map clay, sand and soil organic matter (SOM) content in arable topsoil in Sweden, a study area in south-west Sweden comprising about 100 000 ha of arable land was assessed. Models were created for texture, SOM and two estimated variables for lime requirement determination (target pH and buffering capacity), using a data mining method (multivariate adaptive regression splines). Two existing reference soil datasets were used: a grid dataset and a dataset created for individual farms. The predictor data were of three types: airborne gamma-ray spectrometry data, digital elevation from airborne laser scanning, and legacy data on Quaternary geology. Validations were designed to suit applicability assessments of prediction maps for precision agriculture. The predictor data proved applicable for regional mapping of topsoil texture at 50 × 50 m² spatial resolution (root mean square error: clay = 6.5 %; sand = 13.2 %). A novel modelling strategy, ‘Farm Interactive’, in which soil analysis data for individual farms were added to the regional data, and given extra weight, improved the map locally. SOM models were less satisfactory. Variable-rate application files for liming created from derived digital soil maps and locally interpolated soil data were compared with ‘ground truth’ maps created by proximal sensors on one test farm. The Farm Interactive methodology generated the best predictions and was deemed suitable for adaptation of regional digital soil maps for precision agricultural purposes.     

利用3D Surfer实现田间土壤信息的三维可视化

为了清晰地分析田间土壤养分空间变异,直观的展示农田养分的空间分布,为农田实施精准施肥管理及控制农业生产面源污染提供依据,利用3D Surfer软件中的5种插值算法实现对规则网格数据和散乱数据的插值处理,通过体成像功能来制作横向切片,实现对土壤不同深度硝态氮数据的三维可视化。选择绘制了0~200 cm土壤不同层面NO3--N含量的切片图来直观、清晰地反映太原市清徐县粮蔬轮作灌溉区域田块土壤NO3--N的空间变异性。     

An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture

Precision agriculture (PA) technologies allow us to assess field variability and support site-specific (SSP) application of inputs. The joint application of PA and organic farming practices might be synergetic. The objective of this 3-year study was to propose a multivariate statistical and geostatistical approach, to evaluate the effects of SSP nitrogen (N) fertilization on durum wheat in transition to organic farming. Soil parameters were measured to assess soil fertility level before the SSP fertilization on wheat, which was carried out by management zones in the third year. Radiometric measurements were performed with a hyperspectral spectroradiometer and N-uptake at anthesis and grain yield were determined. The expected values and 95 % confidence intervals of the soil parameters, N-uptake and yield data were estimated with polygon kriging for each management zone. Reflectance data were reduced through principal component analysis and the retained principal components were submitted to factorial co-kriging analysis to estimate orthogonal scale-dependent factors. Comparisons between N-uptake and yield and between the retained regionalized factors (F1) and yield were performed. The spatial pattern of F1 at shorter scales was mostly reproduced in the N-uptake map, suggesting the predictive capacity of hyperspectral data for crop N-status. Within-cluster variance for yield was reduced, quite probably as a combined effect of meteorological pattern and management. The preliminary results seem to be promising in the perspective of PA. Moreover, an inverse relationship between grain yield and crop N-status was observed.     

Estimation of yield zones using aerial images and yield data from a few tracks of a combine harvester

Yield maps derived from yield mapping systems are often erroneous not only due to limitations in measuring the yield precisely but due to insufficient consideration of the requirements of yield mapping systems in practice as well. Aerial images of cultivated crop fields at an advanced growth stage frequently provide a spatial pattern similar to that of yield maps. Therefore, the possibility of generating a yield map using aerial images and measured yield data of a few tracks was examined for a period of 2 years in two fields grown with cereals. Yield zones based on Visible Atmospherically Resistant Index (VARI) values were compared with yield zones based on measured yield data of the whole field. About half of the grid cells of a field were allocated to the same yield zones irrespective of the mode of yield determination. Using the Kruskal–Wallis test, the data sub-sets of measured yield within the yield zones based on the VARI values differed significantly for all tested yield zones. As a result, the approach was successful in the case of these experimental sites.     

Soil Test,Aerial Image and Yield Data as Inputs for Site-specific Fertility and Hybrid Management Under Maize

Several potential sources of information exist to support precision management of crop inputs. This study evaluated soil test data, bare-soil remote sensing imagery and yield monitor information for their potential contributions to precision management of maize (Zea mays L.). Data were collected from five farmer-managed fields in Central New York in 1999, 2000, and 2001. Geostatistical techniques were used to analyze the spatial structure of soil fertility (pH, P, K, NO₃ and organic matter content) and yield variables (yield, hybrid response and N fertilization response), while remote sensing imagery was processed using principal component analysis. Geographic information system (GIS) spatial data processing and correlation analyses were used to evaluate relationships in the data. Organic matter content, pH, P, and K were highly consistent over time and showed high to moderate levels of spatial autocorrelation, suggesting that grid soil sampling at 2.5–5.5ha scale may be used as a basis for defining fertility management zones. Soil nitrate levels were strongly influenced by seasonal weather conditions and showed low potential for site-specific N management. Aerial image data were correlated to soil organic matter content and in some cases to yield, mainly through the effect of drainage patterns. Aerial image data were not well correlated with soil fertility indicators, and therefore were not useful for defining fertility management zones. Yield response to hybrid selection and nitrogen fertilization rates were highly variable among years, and showed little justification for site-specific management. In conclusion, we recommend grid-based management of lime, P, and K, but no justification existed within our limited study area for site-specific N or hybrid management.     

Optimization of corn plant population according to management zones in Southern Brazil

Precision agriculture relies on site-specific interventions determined by the spatial variability of factors driving plant growth. The main objective of this study was to assess the efficiency of variable-rate seeding of corn (Zea mays L.) with delineated management zones. This study involved two experiments carried out in Não-Me-Toque, Rio Grande do Sul, Brazil. For the first experiment, carried out in 2009/2010, management zones were delineated by the farmer’s knowledge of the crop field. The field was split into low (LZ), medium (MZ) and high (HZ) crop performance zones. In the second experiment, carried out in 2010/2011, management zones were delineated by overlaying standardized yield data from nine crop seasons (seven of soybean and two of corn). The experiment was carried out with a randomized block design with three management zones and five corn seeding rates ranging from 50 000 to 90 000 seeds per ha^?1. The soil was a Rhodic Hapludox with a subtropical climate. Optimization of the corn plant population within the field increased grain yield compared to the reference plant population (70 000 plants ha^?1). Yield increases in the LZ, due to corn plant population reduction in relation to the target population, were 1.20 and 1.90 Mg ha^?1 for first and second experiments, respectively. This resulted in economic gains of 19.8 and 28.7 %, respectively. Yield increases in the HZ were 0.89 and 0.94 Mg ha^?1, respectively, and were due to an increase in plant population in relation to the target population. This resulted in economic gains of 5.6 and 6.6 % for the first and second experiments, respectively. In the MZ, the adjustment of the target plant population was not necessary. Optimizing corn population according to management zones is a promising tool for precision agriculture in Southern Brazil.     

Comparison of QuickBird Satellite Imagery and Airborne Imagery for Mapping Grain Sorghum Yield Patterns

Timely and accurate information on crop conditions obtained during the growing season is of vital importance for crop management. High spatial resolution satellite imagery has the potential for mapping crop growth variability and identifying problem areas within fields. The objectives of this study were to use QuickBird satellite imagery for mapping plant growth and yield patterns within grain sorghum fields as compared with airborne multispectral image data. A QuickBird 2.8-m four-band image covering a cropping area in south Texas, USA was acquired in the 2003 growing season. Airborne three-band imagery with submeter resolution was also collected from two grain sorghum fields within the satellite scene. Yield monitor data collected from the two fields were resampled to match the resolutions of the airborne imagery and the satellite imagery. The airborne imagery was related to yield at original submeter, 2.8 and 8.4 m resolutions and the QuickBird imagery was related to yield at 2.8 and 8.4 m resolutions. The extracted QuickBird images for the two fields were then classified into multiple zones using unsupervised classification and mean yields among the zones were compared. Results showed that grain yield was significantly related to both types of image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery at the 2.8 and 8.4 m resolutions. Moreover, the unsupervised classification maps effectively differentiated grain production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for determining plant growth and yield patterns for within-field crop management.     

A synthesis of multi-disciplinary research in precision agriculture: site-specific management zones in the semi-arid western Great Plains of the USA

Researchers from Colorado State University, in collaboration with scientists from the United States Department of Agriculture (USDA), initiated a long-term multi-disciplinary study in precision agriculture in 1997. Site-specific management zones (SSMZ) were investigated as a means of improving nitrogen management in irrigated maize cropping systems. The objective was to develop precise nutrient management strategies for semi-arid irrigated cropping systems. This study was conducted in five fields in northeastern Colorado, USA. Two techniques for delineating management zones were developed and compared: SSMZ and yield-based management zones (YBMZ). Nitrogen uptake and grain yield differences among SSMZs were compared as were soil properties. Both management zone techniques were used to divide fields into smaller units that were different with regard to productivity potential (e.g., high zones had high productivity potential while low zones had low productivity potential). Economic analysis was also performed. Based on grain yield productivity, the SSMZs performed better than the YBMZ technique in most cases. Grain yield and N uptake between the low and high productivity management zones were statistically different for most site-years and N fertilizer rates (p < 0.05). Soil properties helped to explain the productivity potential of the management zones. The low SSMZ was markedly different from the high SSMZ based on bulk density, organic carbon, sand, silt, porosity and soil moisture. Net returns ranged from 188 to 679 USD ha^?1. In two out of three site-years the variable yield goal strategy resulted in the largest net returns. In this study, the SSMZ approach delineates areas of different productivity accurately across the agricultural fields. The SSMZs are different with regard to soil properties as well as grain yield and N uptake. Site-specific management zones are an inexpensive and pragmatic approach to precise N management in irrigated maize.     

基于Surfer软件的田间信息制图与分析

为了直观清晰地表达田间信息的空间分布状况,为农田的定位施肥等农田精细管理提供依据,利用Surfer8.0软件中提供的克立格插值法处理农田信息数据,分别绘制在不同采样方案下土壤速效磷的等值线分布图,并对其进行了定性和定量分析,确定了合理的采样方案。又分别绘制了碱解氮、速效磷、速效钾与小麦产量的三维线框图,该图直观、清晰地反映了碱解氮、速效磷、速效钾与小麦产量的空间变异性分布情况以及它们之间存在的正相关关系。