黑土质量演变初探IV——吉林省公主岭地区土壤肥力指标空间变异与评价

采用地理信息系统（GIS）和地统计学相结合的方法研究了吉林省公主岭地区土壤表层（0～20cm）pH、有机质、速效磷、速效钾、粘粒这5种土壤肥力指标的空间变异性。结果表明：土壤pH、有机质、速效钾、粘粒的理论模型均为球状模型；速效磷的理论模型为纯块金效应模型。粘粒、速效钾、有机质、pH的Ｃ0/（C0＋C）变化范围是37．27％～49．66％，均表现出中等的空间相关性，说明其空间变异是由随机性因素和结构性因素共同作用的结果。根据目前的评价标准，该地区土壤pH和有机质主要分布在2～3级；速效磷和速效钾主要分布在2～4级；而粘粒则分布在3～4级，说明该地区肥力指标基本上处于中等级别。     

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.     

Evaluation of vegetation indices and apparent soil electrical conductivity for site-specific vineyard management in Chile

Spatial variability of Chilean vineyards, in terms of yield and quality, is high, which fully justifies site-specific management, particularly differential harvest. In this study, the most common zoning tools (NDVI and ECa measurements) were evaluated and compared. Comparisons also included a calibrated GVI. Two contrasting large field experiments (pruning, irrigation, and N fertilization treatments) were established in vineyards to (1) evaluate two vegetation indices: (i) a non-calibrated airplane-based NDVI and (ii) calibrated satellite-based GVI and to (2) evaluate the ECa measurements. The GVI was also assessed at the commercial level, in different vineyards and valleys. The GVI was more sensitive in discriminating grape yields and quality while the NDVI failed to adequately sense vigor patterns and fruit quality in the more homogeneous site. Thus, a calibrated GVI can be recommended as a better tool than NDVI for defining management zones as well as making spatial and temporal comparisons among fields and seasons. In general, ECa explained few differences in the alluvial soil properties and did not predict differences in plant vigor as measured by either vegetation indices, therefore ECa by itself was not a good estimator of the most commonly measured soil properties for establishing management zones in these fields with low variability in terms of EC and other soil characteristics.     

Spatial variability in grape yield and quality influenced by soil and crop nutrition characteristics

Knowledge of spatial variability of soil fertility and plant nutrition is critical for planning and implementing site-specific vineyard management. To better understand the key drivers behind vineyard variability, yield mapping from 2002 to 2005 and 2007 (the monitor broke down in 2006) was used to identify zones of different productive potential in a Pinot Noir field located in Raimat (Lleida, Spain). Simultaneously, the vineyard field was sampled in 2002, 2003 and 2007, applying three different schemes (depending on the number of target vines in different grape yield zones). The sampling carried out in 2002, which involved different soil, topographic and crop properties (mineral contents in petiole), made it possible to evaluate the influence of these parameters on the grape yield variability. The zones of lowest yield coincided with locations in which the nutritional status of the crop exhibited the lowest values, particularly with respect to petiole contents of calcium and manganese. Sampling systems adopted in 2003 and 2007 (grape quality and soil attributes) confirmed the inverse spatial correlation between grape yield and some grape quality parameters and, more importantly, showed that the percentage of soil carbonates had a great influence on grape quality probably due to the reduced availability of manganese in calcareous soils. Site-specific vineyard management could therefore be considered using two different strategies: variable-rate application of foliar fertilizers to increase the yield in areas with low production and also foliar or soil fertilizers to improve the quality specifications in some areas.     

Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture

The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (EC_a) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the EC_a in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the EC_a patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the EC_a and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the EC_a data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.     

Site-specific management zones based on the Rasch model and geostatistical techniques

Delineation of management zones (MZ), i.e. areas within the field which represent subfield regions of similar production potential, is the first stage to implement site-specific management. During the last years different algorithms have been proposed to define MZ, with different results. In this work, the use of an objective method, the formulation of the Rasch model, which synthesizes data with different units into a uniform analytical framework, is considered to get representative measures of soil fertility potential which could be used to delimit MZ.To illustrate the method, a case study was conducted in a experimental field using five soil properties: clay, sand and silt content, and deep (ECd) and shallow (ECs) soil apparent electrical conductivity (approximately 0-90 and 0-30 cm depths, respectively). Two main results were obtained after applying this method: (1) a classification of all locations according to the soil fertility potential, which was the value of the Rasch measure and (2) the influence on the soil fertility of each individual soil property, being ECs the most influential and silt content the less influential property.Later, from the measures of soil fertility potential at sampled points, estimates were carried out using the ordinary kriging technique. Consequently, kriged estimates were utilized to map soil fertility potential and MZ were delimited using an equal-size classification method, which practically coincided with the MZ determined by a unsupervised classification.It is also shown the possibility of using probability maps to delimit MZ or provide information for hazard assessment of soil fertility in a field.     

Mapping soil and pasture variability with an electromagnetic induction sensor

The general objective of this study was to test a non-contact electromagnetic induction probe to evaluate the soil and pasture variability in a precision agriculture project. Assessment of the variability of soil and vegetation in a permanent pasture is the basis for management of variable rate fertilization, which is the main instrument used by farmers for improvements in permanent pasture in Alentejo, Portugal. The traditional process of sampling and evaluation of the soil is very demanding in terms of time, labour and reagents, and can derail a project of precision agriculture. This paper describes the major steps followed by the authors to simplify the methodology of soil evaluation in a permanent pasture based on measuring the apparent soil electrical conductivity. Tests were carried out in a parcel of approximately 6 ha, which was subdivided into 28 m × 28 m squares. The soil samples and the evaluation of apparent electrical conductivity were geo-referenced with a global positioning system. The geospatial data were processed by ArcGIS software and the statistical analysis resulted in significant correlation coefficient values between apparent electrical conductivity and altitude, soil pH and pasture dry matter yield.     

Using low-cost geophysical survey to map soil properties and delineate management zones on grazed permanent pastures

Usually, soils utilised for livestock production have similar high spatial variability as those for agricultural or forest use. As a consequence, it is necessary to determine the spatial patterns of the main soil properties as the first stage to implement site-specific management. However, this has to be performed using an inexpensive technique because the profitability in these types of farm are very low, so owners need a cheap, effective, and reliable method to know which zones have similar production potential. Using soil apparent electrical conductivity (ECa) measurements, obtained with a contact sensor at many locations, as the basis to perform a directed soil sampling, 10 samples were taken at two depths (0–0.25 m and 0.25–0.50 m) in a 2.3 ha field in Évora (southern Portugal). Firstly, relationships between ECa and many soil properties were analysed using regression analysis. Six soil properties (clay, silt, fine sand, soil moisture content, pH, and cation exchange capacity) were significantly correlated with ECa. Consequently, spatial distributions of these variables were visualised using map algebra techniques. Later, a fuzzy clustering algorithm was utilised to delineate management zones, resulting in two subfields to be managed separately. Finally, a principal component analysis was conducted to analyse the influence of the soil properties and elevation on the soil variability. It was determined that elevation and clay were the most important contributing properties. Therefore, these can be regarded as key latent variables in this soil. Results showed that low-cost data based on ECa surveys can be used to implement site-specific management in soils with permanent pastures, such as those in the montado or dehesa ecosystems, in the southwest of the Iberian Peninsula.

     

基于模糊聚类分析的田间精确管理分区研究

【目的】以海涂围垦区盐碱土为研究对象，将从SPOT遥感影像提取出的NDVI数据和盐碱地土壤生产力的主要限制因子盐分数据及部分养分数据作为变量进行精确农业管理分区研究。【方法】模糊c均值聚类方法被用来进行分类分区，并引入了模糊聚类指数和归一化分类熵两种分区效果评价指标，对分区结果进行比较和评价。【结果】本研究区，最佳的分区数目为3个。对处于每一子区内土样的化学特性和实测棉花产量数据进行方差分析，发现其均值在所定义的每个管理分区内都存在着统计意义上的显著差异性，其中子区3具有最高的肥力水平和作物产量，而子区1最低。【结论】利用所选取的变量，模糊c均值聚类算法可以较好地进行管理分区划分。分区结果不但可以指导采样，而且可用于实施变量投入和精确施肥推荐，为样区土壤管理提供科学的决策依据。     

Nutrient Management Zones for Citrus Based on Variation in Soil Properties and Tree Performance

Site-specific soil management can improve profitability and environmental protection of citrus groves having large spatial variation in soil and tree characteristics. The objectives of this study were to identify soil factors causing tree performance decline in a variable citrus grove, and to develop soil-specific management zones based on easily measured soil/tree parameters for variable rate applications of appropriate soil amendments. Selected soil properties at six profile depths (0–1.5 m), water table depth, ground conductivity, leaf chlorophyll index, leaf nutrients and normalized difference vegetation index were compared at 50 control points in a highly variable 45-ha citrus grove. Regression analysis indicated that 90% of spatial variation in tree growth, assessed by NDVI, was explained by average soil profile properties of organic matter, color, near-infrared reflectance, soil solution electrical conductivity, ground conductivity and water table depth. Regression results also showed that soil samples at the surface only (0–150 mm) explained 78% of NDVI variability with NIR and DTPA-extractable Fe. Excessive available copper in low soil organic matter areas of the grove apparently induced Fe deficiency, causing chlorotic foliage disorders and stunted tree growth. The semivariograms of selected variables showed a strong spatial dependence with large ranges (varied from 230 m to 255 m). This grove can be divided into different management zones on the basis of easily measured NDVI and/or soil organic matter for variable rate application of dolomite and chelated iron to improve tree performance.     

Delineating productivity zones in a citrus grove using citrus production,tree growth and temporally stable soil data

The productivity of a citrus grove with variation in tree growth was mapped to delineate zones of productivity based on several indicator properties. These properties were fruit yield, ultrasonically measured tree canopy volume, normalized difference vegetation index (NDVI), elevation and apparent electrical conductivity (EC_a). The spatial patterns of soil series, soil color and EC_a, and their correspondence with the variation in yield emphasized the importance of variation in the soil in differentiating the productivity of the grove. Citrus fruit yield was positively correlated with canopy volume, NDVI and EC_a, and yield was negatively correlated with elevation. Although all the properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. Tree canopy volume was used to classify the citrus grove into five productivity zones termed as ‘very poor’, ‘poor’, ‘medium’, ‘good’ and ‘very good’ zones. The study showed that productivity of citrus groves can be mapped using various attributes that directly or indirectly affect citrus production. The productivity zones identified could be used successfully to plan soil sampling and characterize soil variation in new fields.     

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.     

Spatial variation in tree characteristics and yield in a pear orchard

We examined the spatial structure of fruit yield, tree size, vigor, and soil properties for an established pear orchard using Moran’s I, geographically weighted regression (GWR) and variogram analysis to determine potential scales of the factors affecting spatial variation. The spatial structure differed somewhat between the tree-based measurements (yield, size and vigor) and the soil properties. Yield, trunk cross-sectional area (TCSA) and normalized difference vegetation index (NDVI, used as a surrogate for vigor) were strongly spatially clustered as indicated by the global Moran’s I for these measurements. The autocorrelation between trees (determined by applying a localized Moran’s I) was greater in some areas than others, suggesting possible management by zones. The variogram ranges for TCSA and yield were 30–45 m, respectively, but large nugget variances indicated considerable variability from tree to tree. The variogram ranges of NDVI varied from about 14–27 m. The soil properties copper, iron, organic matter and total exchange capacity (TEC) were spatially structured, with longer variogram ranges than those of the tree characteristics: 31–95 m. Boron, pH and zinc were not spatially correlated. The GWR analyses supported the results from the other analyses indicating that assumptions of strict stationarity might be violated, so regression models fitted to the entire dataset might not be fitted optimally to spatial clusters of the data.     

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.     

伊犁绢蒿荒漠草地土壤养分和植被指数的空间异质性研究

[目的]土壤养分与植被分布格局之间的关系.[方法]应用地统计学方法,研究伊犁绢蒿(Seriphidium transiliense)荒漠草地土壤养分与植被指数的空间异质性.[结果](1)绢蒿荒漠土壤碱解氮、速效钾含量空间自相关性较差,而有机质、全磷、速效磷、全钾、全氮、植被指数(NDVI)自相关性较好.(2)通过半方差分析各项目都能用很好的模型来拟合,有机质符合线形模型,全钾、全磷、全氮、速效磷、碱解氮、速效钾和植被指数均符合高斯模型.(3)土壤养分元素和植被指数的变程在23.58～110 m,相差比较大.[结论]通过普通克立格插值,绘制了植被指数与土壤有机质和土壤养分的等值线分布图.对绢蒿蒿类荒漠草地土壤养分与植被指数空间异质性分析有助于分析植被指数的空间异质化程度与土壤元素伊犁(OM,N、P、K)空间异质化的相关性.     

东南地区土壤养分的空间变异性与取样策略I．土壤属性与大量元素

在我国东南6省(市)选择代表性区域(自然村),采用ASI和地统计学等方法对耕作土壤的pH值、有机质、有效氮、磷和钾的空间变异性与合理取样数量进行了研究。结果表明,①pH值和有机质是相对稳定的土壤属性,在整个研究区域其变异较小,近似于正态分布;施肥元素的变异则较大,表现出明显的负偏峰分布特征。②尽管某些土壤属性或大量元素的总体变异性相似,但在各研究区域,其实际半方差结构并不相同。③对于明显偏峰分布的土壤养分来说,建议采用最适分配法确定合理取样数量。纯随机合理取样数量可在一定程度作为参考。     

采样密度对土壤有机质空间变异解析的影响

确定合理的采样密度以便更有效地揭示土壤属性的空间变异,是提高土壤质量评价工作准确性的前提。本文以合肥市北部地区为例,从5 207个土壤采样点(采样密度为1个/km2)中重复20次随机抽取不同采样密度的六个样本子集(对应采样密度分别是0.8个/km2、0.56个/km2、0.39个/km2、0.28个/km2、0.19个/km2、0.13个/km2),采用地理信息系统(GIS)技术和地统计学方法,研究采样密度对土壤有机质(SOM)空间变异解析的影响。结果表明:上述不同采样密度下,有机质含量的均值差异不显著,各样本对总体均具有较强的代表性。土壤有机质表现出中等的空间相关性,采样密度为0.28个/km2时探测到的SOM含量变异结构中结构性组分比例最高。采样密度小于0.28个/km2时,则局部细节信息被过滤,不能准确充分地表现其空间变异特征。在研究区的这种自然地理条件和土地利用方式下,若评价目的主要针对土壤肥力质量,揭示土壤有机质空间变异的最佳采样密度应为0.28个/km2。若是针对区域土壤碳汇潜力,则大约22 km2布置一个采样点即可获得预期的效果。     

Airborne Videography to Identify Spatial Plant Growth Variability for Grain Sorghum

Much research has focused on the use of intensive grid soil sampling and yield monitors to identify within-field spatial variability in precision farming. This paper reports on the use of airborne videography to identify spatial plant growth patterns for grain sorghum. Color-infrared (CIR) digital video images were acquired from two grain sorghum fields in south Texas several times during the 1995 and 1996 growing seasons. The video images were registered, and classified into several zones of homogeneous spectral response using an unsupervised classification procedure. Ground truthing was performed upon a limited number of sites within each zone to determine plant density, plant height, leaf area index, biomass, and grain yield. Results from both years showed that the digital video imagery identified within-field plant growth variability and that classification maps effectively differentiated grain production levels and growth conditions within the two fields. A temporal comparison of the images and classification maps indicated that plant growth patterns differed somewhat between the two successive growing seasons, though areas exhibiting consistently high or low yield were identified within each field.     

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.