ited on sandy or on loamy sediments. Therefore, it is important to study not only the extent of surface spatial Analysis and interpretation of spatial variability of soils is a keyvariability, but also the distribution of subsurface and stone in site-specific farming. Soil survey maps may have up to 0.41ha inclusions of dissimilar soils within a mapping unit. The objectives deep soil horizons. of this study were to determine the degree of spatial variability of soil Among the various soil physical properties, K s and physical properties and variance structure, and to model the sampling related measures are reported to have the highest statisinterval of alluvial floodplain soils. Soil profiles (n ϭ 209) from 18 tical variability (Biggar and Nielsen, 1976). Bouma parallel transects were sampled with a mean separation distance of (1973) stressed the need for more studies on field vari-79.4 m. Each profile was classified into surface, subsurface, and deep ability of K s and soil water retention curves. Stockton horizons. Structural analysis of soil bulk density (b), sand, clay, satuand Warrick (1971) indicated that variability in K s is rated hydraulic conductivity (K s), volumetric water content (v) at both a function of soil depth and position in the landseven pressure potentials (⌿ a) (Ϫ1, Ϫ10, Ϫ33, Ϫ67, Ϫ100, Ϫ500, and scape, as well as experimental errors in measuring K s. Ϫ1500 kPa) were modeled for the three horizons. Variance of soil Cameron (1978) sampled clay loam soils at six depths physical properties varied from as low as 0.01% (b) to as high as 1542% (K s). The LSD test indicated significant (P Ͻ 0.05) differences from five grid-sampled locations in a 225-m 2 plot. He in sand, clay, b , K s , and v at various ⌿ a. Geostatistical analyses used the desorption method to determine soil water illustrated that the spatially dependent stochastic component was preretention curves at pressure heads ranging from Ϫ10 to dominant over the nugget effect. Structured semivariogram functions Ϫ500 kPa to calculate K s. He found no consistent trend of each variable were used in generating fine-scale kriged contour across sampling depths in pressure head values from maps. Overall autocorrelation, Moran's I, indicated a 400-m sampling Ϫ10 to Ϫ500 kPa, but the shape and magnitude of the range would be adequate for detection of spatial structure of sand, average water retention curve differed among locations. silt, clay, and a 100-m sampling range for soil hydraulic properties He further reported that the coefficient of variation of and b. The magnitude and spatial patterns soil physical property soil water content ranged from 4.3 to 13% in the surface variability have implications for variable rate applications and design layer and from 2.4 to 6.5% in the deeper layers. In a of soil sampling strategies in alluvial floodplain soils. study of spatial variability in soil hydraulic properties, Vieira et al. (1981) used variogram, kriging, and cokriging techniques to determine the magnitude of spatial J. Iqbal, Dep. of Agricultu...
Identifying the vulnerability of subsoils to compaction damage is an increasingly important issue both in the planning and execution of farming operations and in planning environmental protection measures. Ideally, subsoil vulnerability to compaction should be assessed by direct measurement of soil bearing capacity but currently no direct practical tests are available. Similarly, soil mechanics principles are not suitably far enough advanced to allow extrapolation of likely compaction damage from experimental sites to situations in general. This paper, therefore, proposes a simple classification system for subsoil vulnerability to compaction based for field use on local soil and wetness data at the time of critical trafficking, and, at European level, on related soil and climatic information. Soil data are readily available 'in Country' or from the European Soil Database and climatic data are stored in the agrometeorological database of the MARS Project. The vulnerability to compaction is assessed using a two-stage process. First, the inherent susceptibility of the soil to compaction is estimated on the basis of the relatively stable soil properties of texture and packing density. Second, the susceptibility class is then converted into a vulnerability class through consideration of the likely soil moisture status at the time of critical loadings. For use at local level, adjustments are suggested to take account of possible differences in the support strength of the topsoil and specific subsoil structural conditions. The vulnerability classes proposed are based on profile pit observations, on a wide range of soils examined mainly in intensively farmed areas where large-scale field equipment is employed. A map of soil susceptibility to compaction in Europe has been produced, as the first stage in developing a more rigorous quantitative approach to assessing overall vulnerability than has been possible hitherto.
The success of precision agriculture (PA) depends strongly upon an efficient and accurate method for in-field soil property determination. This information is critical for farmers to calculate the proper amount of inputs for best crop performance and least environmental effect. Grid sampling, as a traditional way to explore in-field soil variation, is no longer considered appropriate since it is labor intensive, time consuming and lacks spatial exhaustiveness. Remote sensing (RS) provides a new tool for PA information gathering and has advantages of low cost, rapidity, and relatively high spatial resolution. Great progress has been made in utilizing RS for in-field soil property determination. In this article, recent publications on the subject of RS of soil properties in PA are reviewed. It was found that a large array of agriculturally-important soil properties (including textures, organic and inorganic carbon content, macro-and micro-nutrients, moisture content, cation exchange capacity, electrical conductivity, pH, and iron) were quantified with RS successfully to the various extents. The applications varied from laboratoryanalysis of soil samples with a bench-top spectrometer to field-scale soil mapping with satellite hyper-spectral imagery. The visible and near-infrared regions are most commonly used to infer soil properties, with the ultraviolet, mid-infrared, and thermal-infrared regions have been used occasionally. In terms of data analysis, MLR, PCR, and PLSR are three techniques most widely used. Limitations and possibilities of using RS for agricultural soil property characterization were also identified in this article.
in OM, N, and P on both terraced and steep cultivated hillslopes. Selective removal of finer particles by water Topographical land features shape the spatial variability of soils erosion caused a linear decrease in clay content of 0.02% and crop yields, especially in dryland cotton (Gossypium hirsutum L.). The objectives of this study were to (i) quantify the relationships m Ϫ1 , and a corresponding increase in silt content of between cotton lint yields vs. derived topographical attributes in com-0.04% m Ϫ1 downslope on the steep cultivated hillslope. bination with measured soil physical properties, and (ii) quantify the Kravchenko et al. (2000) reported higher crop yield relationships between measured soil physical properties and derived at lower slope locations, and a wide range of yield values topographical attributes. The dominant soil of the study area was on moderate and higher slopes during moderate to dry classified as Vaiden soil series (very-fine, smectitic, thermic Aquic weather conditions; however, low yield values were Dystruderts). More than 4500 elevation point data were recorded in measured on lower slope locations during the wet seaa 42-ha field using a real-time kinematic-global positioning system son. Kravchenko et al. (2000) also examined the effects (RTK-GPS) used in a geographic information system (GIS) to derive of derived topographic and hydrologic derived indices topographic (slope, curvature and aspect) and hydrologic attributes on variability in soil properties and crop yield. They (wetness index, flow direction, flow length, flow accumulation, and sediment transport index). Surface (0-17 cm) sand, clay, saturated reported crop yield had a significant negative correlahydraulic conductivity (K s), bulk density (b), water content at seven tion with elevation, slope and curvature. Sinai et al. equilibrium pressure levels ranging from Ϫ0.01 to Ϫ1.5 MPa, and (1981) calculated a soil surface curvature factor from 2-yr cotton lint yield data were measured from sites selected based on the elevation of neighboring points in a grid-sampled classified normalized difference vegetation index (NDVI). Stepwise field. The factor was positive in concave positions in linear regression indicated that cotton lint yield variability was exthe landscape, negative in convex positions, and highly plained by soil properties (65% in 2001 and 58% in 2002), and topocorrelated with soil water content. The redistribution of graphic and hydrologic attributes (40 and 21%), as well as their comsoil water downslope, both at the surface and subsurface bined effects (82 and 72%). Elevation, flow direction, sediment (throughflow), gave soil properties downslope indicatransport index, percentage sand content, and volumetric water contive of soil water conditions and moved solute laterally. tent (v) at Ϫ0.001 MPa pressure explained most of the lint yield variation. Overall, statistical analysis indicated that higher elevation This process could be beneficial in terms of higher yield areas generally yielded lower (r ϭ Ϫ0.50, P...
A classification of structural condition in surface soils is proposed, based on the volumes of two categories of pore size, termed air capacity (pores greater than 60 urn diameter) and available water (pores of 60 to 0.2 urn diameter.Relationships of pore volumes to particle size class, organic carbon content and soil water regime are examined. Soil structural conditions are mainly affected by water regime and organic carbon and, apart from the extremes of sandy or clayey textures, less influenced by particle size distribution. IntroductionTHE complex relationships between soil structure, climate, land use and more permanent soil properties have been studied by researchers and advisers for many decades in most agriculturally advanced countries. Interest is stimulated from time to time by unusually bad seasons, economic conditions and the adoption of new husbandry techniques.
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