Sensitivity of simulated hillslope subsurface flow to rainfall patterns,soil texture and land use

Knowledge of the generation of subsurface flow for hillslopes is important for controlling agricultural nonpoint nutrient losses. This study used a physically based hillslope hydrologic model HYDRUS‐2D to assess the sensitivity of simulated subsurface flow to the interactions between precipitation, soil texture and land use. Soil moisture data from 1 January 2013 to 23 August 2014 at two monitoring sites on a tea plantation hillslope were used to calibrate the van Genuchten–Mualem hydraulic parameters for this model. For six different textural classes (loamy sand, loam, silt, silt loam, clay loam and clay) and four land‐use types (tea garden, forest, grassland and bare soil), scenario‐based simulations were carried out for varied precipitation intensities (6.0, 15.0, 30.0, 45.0, 60.0 and 76.0 mm/day) and frequencies (time intervals of 1, 5, 10, 15, 20 and 25 days). Results indicated that the hillslope run‐off was dominated by subsurface flow, which was influenced by precipitation and antecedent moisture conditions. A threshold value of 0.18 m³/m³ of mean hillslope soil moisture was observed for the initiation of subsurface flow. High precipitation intensity (i.e. 75.0 mm/day) substantially increased subsurface flow for all soil textures. In addition, the sensitivity of the bare soil hillslope to rainfall patterns was more than two times higher than that of the vegetated (i.e. grassland, tea garden and forest) hillslope. These findings suggest that extreme precipitation events and land‐use change will increase the risks of subsurface flow on hillslopes. Therefore, optimal fertilizer application strategy and land‐use planning should be proposed for controlling the hillslope nonpoint nutrient losses.     

Particle packing and organization of the textural porosity in clay–silt–sand mixtures

The packing of elementary particles in soil largely determines the properties that depend on the textural soil pore space, but is studied little. The relations between packing and size and nature of soil particles were studied using fractions of clay, silt and sand, mixed when wet and then dried. Ternary mixtures (clay:silt:sand) were compared with binary mixtures (clay:silt, clay:sand). The pore space of the mixtures was studied using mercury porosimetry and scanning electron microscopy. In all the mixtures the textural pore space was divided into two compartments: (1) lacunar pores due to the presence of skeleton particles and to the shrinkage of the clay phase between these particles, and (2) the clay–fabric pores due to the packing of the clay. In the ternary mixtures, lacunar pores could be divided into two classes: (1) those due to sand particles within the clay–slit phase considered as a single phase, and (2) those due to silt particles within this same phase. For certain mixtures, lacunar pores, referred to as hidden lacunar pores, were not interconnected but were occluded. This occurred both for hidden pores caused by the presence of sand and occluded by the clay–slit phase, and for hidden pores caused by the presence of silt and occluded by the clay phase. The relations between these types of textural pores and the proportions of different size fractions in the mixtures provide guidelines for making optimum use of the particle-size characteristics of the soil to determine its properties.     

坡面尺度土壤特性的空间变异性

通过对20m长坡面土壤特性空间变异性的经典统计学分析,结果表明:(1)在同一土壤剖面内,各级粒径含量呈弱变异性,而有机质含量随土层深度的增大而逐渐降低,呈中等变异性;(2)土壤干容重的空间变异性较小,呈弱变异性,但土壤饱和导水率的空间变异性较大,呈中等变异性;(3)水分特征曲线具有一定的空间变异性,比水容量空间变异性较大,呈中等变异性。坡面土壤饱和导水率和干容重的等值线图表明,土壤饱和导水率的变化趋势并不仅仅取决于土壤干容重的相对大小,可能与有机质含量、黏粒含量以及根系分布情况等也有一定的关系。     

Evaluation of Different Predictor Models for Detailed Soil Particle-Size Distribution

An accurate mathematical representation of soil particle-size distribution (PSD) is required to estimate soil hydraulic properties or to compare texture measurements using different classification systems. However, many databases do not contain full PSD data, but instead contain only the clay, silt, and sand mass fractions. The objective of this study was to evaluate the abilities of four PSD models (the Skaggs model, the Fooladmand model, the modified Gray model GM (1,1), and the Fredlund model) to predict detailed PSD using limited soil textural data and to determine the effects of soil texture on the performance of the individual PSD model. The mean absolute error (MAE) and root mean square error (RMSE) were used to measure the goodness-of-fit of the models, and the Akaike''s information criterion (AIC) was used to compare the quality of model fits. The performance of all PSD models except the GM (1,1) improved with increasing clay content in soils. This result showed that the GM (1,1) was less dependent on soil texture. The Fredlund model was the best for describing the PSDs of all soil textures except in the sand textural class. However, the GM (1,1) showed better performance as the sand content increased. These results indicated that the Fredlund model showed the best performance and the least values of all evaluation criteria, and can be used using limited soil textural data for detailed PSD.     

Depositional seals in polyacrylamide-amended soils of varying clay mineralogy and texture

Purpose  

Depositional seals, formed when turbid waters infiltrate into soils, lead to a reduction in soil hydraulic conductivity (HC) and enhance runoff and soil erosion. Since clay size particles constitute a dominant proportion of depositional seals, soil texture and clay mineralogy play a significant role in determining the seal’s hydraulic characteristics. Presence of high molecular weight anionic polyacrylamide (PAM) in suspension flocculates fine sediments, and therefore, its application to the soil surface may modify the characteristics of the depositional seal. The impact of PAM on the latter is expected to be influenced by soil properties. The aim of this study was to elucidate the effects of PAM application on clay flocculation and the HC of depositional seals formed in four soils varying in texture (ranging from loamy sand to clay loam), and diverse proportions of clay mineral constituents (kaolinite, smectite, and vermiculite).     

A classification scheme for earthworm populations (Lumbricidae) in cultivated agricultural soils in Brandenburg,Germany

Earthworm activity is observed at long‐term monitoring sites as an indicator of soil function to assess changes resulting from environmental and management conditions. In order to assess changes, characteristic values of earthworm populations under different site conditions have to be known. Therefore, a classification scheme for site‐specific earthworm populations was developed for soil in agricultural use, taking interactions between earthworm populations and soil properties into account. Characteristics of sites grouped by means of a cluster analysis after principal‐component analysis served as a basis for the derivation of the classification scheme. Soil variables found to characterize site differences with respect to earthworm populations were the texture of the topsoil, the texture of the subsoil, and the soil organic‐matter (SOM) content. The textural classes of the topsoil were divided into five groups comprising sandy soils (Ss), silty sand soils (Su), slightly loamy sand soils (Sl2), medium to strongly loamy sand soils (Sl3/Sl4), and loam and clay soils. Soil organic matter was divided into grades of equal size in a range from <1%, 1%–2% up to >6%. The variables “earthworm abundance” and “earthworm species” were selected to represent earthworm populations and were divided into six groups ranging from very low to extremely high. Defined groups of earthworm populations showed a clear structure in relation to soil textural groups and the content of SOM. From this distribution, a classification scheme was derived as basis for prognostic values of site‐specific earthworm populations, thus enabling the interpretation of changes over time. For some soil textural groups, selected variables appeared to enable the derivations of expected earthworm densities and species composition outside the range of the given database, but for some soil textural groups, broader databases will be needed to specify these derivations.     

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

In soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter (SOM) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity (EC_a), reflectance in the visible and near‐infrared (vis‐NIR) parts of the electromagnetic spectrum and insertion force (IF). The measurements were made at 20 locations on each of two Swedish farms. At every location, sensor measurements were made at 1.5‐cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm‐specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression (PLSR) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis‐NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross‐validation (RMSECV) of 0.3–0.5% for SOM, about 6% for clay and silt and 10–11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including EC_a reduced the RMSECV on average for all particle size fractions by 5–10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis‐NIR sensor measurements with measurements of EC_a and or IF.     

Soil Catenas on pinedale and Bull Lake moraines, Willow Lake, wind river mountains, Wyoming

Soils in coarse-grained tills on Pinedale (about 20,000 years old) and Bull Lake (about 140,000 years old) moraines show significant variaton with slope position and age. Soils on the Pinedale moraine are mostly non-calcareous Typic Cryoborolls with A/Bw/C profiles that thicken considerably downslope. These soils are weakly developed due to their youth and to the unstable, steep (up to 24°), highly convexo-concave slopes. Soils on the Bull Lake moraine are mostly Argic Cryoborolls with A/Bt/C profiles. These soils are more strongly developed than soils at similar positions on the Pinedale moraine because the Bull Lake moraine is older and has less steep (up to 16°), more gently curved slopes. The soils on the Bull Lake moraine contain more clay, a higher proportion of pedogenic clay, more weathered stones, and more free sesquioxides. Erosion and dry conditions at the convex summit and shoulder sites on the Bull Lake moraine have resulted in thin soils with minimal argillic horizons; these soils are only slightly more strongly developed than soils at analogous sites on the much younger Pinedale moraine. In contrast, at the concave sites in downslope positions the soils on the Bull Lake moraine are much more strongly developed than analogous soils on the Pinedale moraine; slow deposition of weathered material, probably transported by creep, has acted concurrently with pedogenesis to form soils with thick, homogeneous argillic horizons. The relationship between hillslope curvature and soil properties on both moraines is shown by significant correlations between soil properties and the second derivative of the hillslope profile. This relationship is probably due primarily to the interaction of soil formation and soil creep.     

基于高时间分辨率农田土壤湿度观测的绿洲农业区土壤质地制图

Due to the almost homogeneous topography in low relief areas, it is usually difficult to make accurate predictions of soil properties using topographic covariates. In this study, we examined how time series of field soil moisture observations can be used to estimate soil texture in an oasis agricultural area with low relief in the semi-arid region of northwest China. Time series of field-observed soil moisture variations were recorded for 132 h beginning at the end of an irrigation event during which the surface soil was saturated. Spatial correlation between two time-adjacent soil moisture conditions was used to select the factors for fuzzy c-means clustering. In each of the ten generated clusters, soil texture of the soil sample with the maximum fuzzy membership value was taken as the cluster centroid. Finally, a linearly weighted average was used to predict soil texture from the centroids. The results showed that soil moisture increased with the increase of clay and silt contents, but decreased with the increase of sand content. The spatial patterns of soil moisture changed during the entire drying phase. We assumed that these changes were mainly caused by spatial heterogeneity of soil texture. A total of 64 independent samples were used to evaluate the prediction accuracy. The root mean square error (RMSE) values of clay, silt and sand were 1.63, 2.81 and 3.71, respectively. The mean relative error (RE) values were 9.57% for clay, 3.77% for silt and 12.83% for sand. It could be concluded that the method used in this study was effective for soil texture mapping in the low-relief oasis agricultural area and could be applicable in other similar irrigation agricultural areas used in this study.     

Determination of soil texture: Comparison of the sedimentation method and the laser‐diffraction analysis

Laser‐diffraction analysis (LDA) is a rapid automated method achieving highly resolved frequency distributions of particle sizes. Recently, LDA has come into use in environmental sciences. However, in the size range of silt and clay deviations from the particle‐size analysis with the standard pipette method, which is regarded as the reference method for soil‐texture classification, have been reported. Therefore, this study concentrates (1) on the verification of systematic relations between both methods using a series of soils of Lower Saxony (Germany) and (2) on the general applicability of the laser‐diffraction method to soil‐texture classification as well as (3) texture‐based estimates of air capacity, available field capacity, and permanent wilting point. The comparison of LDA with the pipette method demonstrated highly significant linear correlations in each of the particle‐size fractions from clay to coarse silt. The slope of regressions ranged from 0.4 with fine silt to 3.1 with clay. If the clay content derived from LDA was applied to texture classification, the resulting textural classes differed from the standard textural classes, except for purely sandy samples with a clay content of <5%. However, the linear‐regression model enabled an approach of the LDA‐based clay content to values produced with the standard pipette method. Using this transformation, a texture classification became practicable in many cases, but, despite of a high significance level between LDA and pipette method, still led to wrong textural classes in several cases. A comparison with regression models from other regions in Europe showed both similarities and discrepancies, even for similar substrates. Hence, the laser‐diffraction analysis cannot be used for the texture classification of soil samples without verification by the standard pipette method.     

Spatial variability of Southeastern U.S. Coastal Plain soil physical properties: Implications for site-specific management

Our objectives were to describe the field-scale horizontal and vertical spatial variability of soil physical properties and their relations to soil map units in typical southeastern USA coastal plain soils, and to identify the soil properties, or clusters of properties, that defined most of the variability within the field. The study was conducted on a 12-ha field in Kinston, NC. A 1:2400 scale soil survey had delineated three soil map units in the field: Norfolk loamy sand, Goldsboro loamy sand, and Lynchburg sandy loam. These are representative of millions of hectares of farmland in the Coastal Plain of the southeastern USA. Sixty soil cores were taken to ∼ 1-m depth, sectioned into five depth increments, and analyzed for: soil texture as percentage sand, silt, and clay; soil water content (SWC) at − 33 and − 1500 kPa; plant available water (PAW); saturated hydraulic conductivity (Ksat); bulk density (BD); and total porosity. A penetrometer was used to measure cone index (CI) at each sample location. Variography, two mixed-model analyses, and principal components analysis were conducted. Results indicated that soil physical properties could be divided into two categories. The first category described the majority of the within-field variability and included particle size distribution (soil texture), SWC, PAW, and CI. These characteristics showed horizontal spatial structure that was captured by soil map units and especially by the division between sandy loams and finer loam soils. The second class of variables included BD, total porosity, and Ksat. These properties were not spatially correlated in the field and were unrelated to soil map unit. These findings support the hypothesis that coastal plain soil map units that delineate boundaries between sandy loams versus finer loam soils may be useful for developing management zones for site-specific crop management.     

基于近红外光谱和正交信号-偏最小二乘法对土壤的分类

不同质地的土壤,由于蓄水能力和土壤颗粒大小的不同使得其光谱特性不同,这为采用近红外光谱技术对土壤质地进行判别分析提供了依据。该研究利用正交信号校正(OSC)方法可以获得与浓度有关的谱图信息这一优势,将其与偏最小二乘方法(PLS)结合,采用近红外光谱技术对不同质地的土壤:砂土、壤土、黏土进行判别分析。结果表明:建模样本的相关系数可达0.965,采用该模型对其余45个样本分别进行了预测,三种土壤预测样本的判别正确率分别为:93.3%,86.6%和86.6%。说明OSC方法可以提取谱图中的微弱的质地信息,实现土壤质地的快速鉴别分析。     

新疆伊犁新垦区土壤磁化率特征分析

对新疆伊犁新垦区几种不同土地利用方式下土壤磁化率参数及其与土壤有机质、粒度特征关系进行分析,结果表明:不同土地利用方式下土壤剖面的质量磁化率差异明显,杨树林地、农田林带边荒草地、杏林地与农田(几种油葵地)和干旱草场相比,土壤质量磁化率明显偏低;土壤质量磁化率与土壤有机质、粘粒、粉粒含量成负相关,不同土地利用方式相关系数差别较大,与土壤沙粒含量大多呈较好正相关.土壤频率磁化率与有机质、粘粒含量呈现较好正相关,个别达到极显著水平,与土壤粉粒含量呈不明显正相关;与土壤沙粒含量呈较好负相关.     

Effect of Continuous Rice–Wheat Rotation on Soil Properties from Four Agro‐ecosystems of Indian Punjab

In Indian Punjab, rice–wheat is a dominant cropping system in four agro‐ecosystems, namely undulating subregion (zone 1), Piedmont alluvial plains (zone 2), central alluvial plains (zone 3), and southwestern alluvial plains (zone 4), varying in rainfall and temperature. Static and temporal variabilities in soil physical and chemical properties prevail because of alluvial parent material, management/tillage operations, and duration of rice–wheat rotation. A detailed survey was undertaken to study the long‐term effect of rice–wheat rotation on soil physical (soil separates, bulk density, modulus of rupture, saturated and unsaturated hydraulic conductivities, soil water content, and suction relations) and chemical (organic carbon, pH, electrical conductivity) properties of different textured soils (sandy clay loam, loam, clay loam, and silty clay loam) in these four zones of Punjab. Soil samples (of 0‐ to 30‐cm depth) from 45 sites were collected during 2006 and were analyzed for physical and chemical properties. The results showed that sand content and pH increased whereas silt and organic carbon decreased significantly from zones 1 to 4. Compared to other textures, significantly greater organic carbon, modulus of rupture, and pH in silty clay loam; greater bulk density in clay loam, and greater saturated hydraulic conductivity in sandy clay loam were observed. Irrespective of zone and soil texture, in the subsurface soil, there was a hard pan at 15–22.5 cm deep, which had high soil bulk density, modulus of rupture, more silt and clay contents (by 3–5%) and less organic carbon and hydraulic conductivity than the surface (0–15 cm) layer. These properties deteriorated with fineness of the soil texture and less organic carbon content. Continuous rice–wheat cropping had a deleterious effect on many soil properties. Many of these soils would benefit from the addition of organic matter, and crop yields may also be affected by the distinct hardpan that exists between 15 and 22.5 cm deep.     

Estimation of soil texture from permanent wilting point measured with a chilled‐mirror dewpoint technique

Soil texture is an important factor governing a range of physical properties and processes in soil. The clay and fine fractions of soil are particularly important in controlling soil water retention, hydraulic properties, water flow and transport. Modern soil texture analysis techniques (x‐ray attenuation, laser diffraction and particle counting) are very laborious with expensive instrumentation. Chilled‐mirror dewpoint potentiameters allows for the rapid measurement of the permanent wilting point (PWP) of soil. As the PWP is strongly dictated by soil texture, we tested the applicability of PWP measured by a dewpoint potentiameter in predicting the clay, silt and sand content of humid tropical soils. The clay, silt, and sand content, organic matter and PWP were determined for 21 soils. Three regression models were developed to estimate the fine fractions and validated using independent soil data. While the first model showed reasonable accuracy (RMSE 16.4%; MAE 13.5%) in estimating the clay, incorporating the organic matter into the equation improved the predictions of the second model (RMSE 17.3%; MAE 10.9%). When used on all soil data, the accuracy of the third model in predicting the fine fraction was poor (RMSE 31.9%; MAE 24.5%). However, for soils with silt content greater than 30%, the model prediction was quite accurate (RMSE 7–12%; MAE 7–9%). The models were used to estimate the sand content and soil textures of soils, which proved relatively accurate. The dewpoint potentiometer can serve a dual purpose of rapidly estimating the PWP and the clay, fine fraction, and soil texture of soils in a cost efficient way.     

Evidence of subsurface preferential flow using soil hydrologic monitoring in the Shale Hills catchment

Characterization of preferential flow at multiple spatial and temporal scales is fundamental to the understanding of complex subsurface heterogeneity and catchment hydrology. Evidence of subsurface preferential flow and the conditions under which it occurs were investigated in the Shale Hills catchment, a humid forested region in central Pennsylvania, USA. Seven monitoring sites, plus five replicates, were established along a concave hillslope, a convex hillslope and a valley floor to monitor in situ the hydrology in various soil horizons and their interfaces at half‐minute intervals. Using the indicator of a lower horizon that responded to a rainstorm earlier than an upper horizon within the same soil profile, we investigated the subsurface preferential flow processes and their dynamics in each of the five soil series mapped in the catchment. Threshold behaviour, hydrophobicity impact, influence of soil thickness and topography were observed in the spatial and temporal variation of the subsurface preferential flow, which was initiated more readily under the conditions of more intense rain, drier initial soil, shallower soil, and steeper slope. Whereas preferential flow seemed common in this catchment, its frequency during the 15 storm events from 23 September 2006 to 1 January 2007 ranged from 0 to 73.3% for the 68 soil horizons monitored at the 12 stations, with an overall average frequency of 7.5% (i.e. ～5 horizons per storm event). This preferential flow was more frequent during the drier period than that during the wetter one. Variation was observed within the same soil series, even for those profiles adjacent to one another. This was due to the differences in hillslope position, slope gradient and orientation, the underlying bedrock fracture and orientation, or some combinations. Whereas different soil series help differentiate the processes and dynamics involved in the subsurface preferential flow, a combined consideration of soil types and landscape features is important to ensure proper use of the soil data for hydrological applications.     

The Role of Soil Properties in Pyrene Sorption and Desorption

Soil type will greatly affect the sorption and subsequent desorptionof hydrophobic contaminants. To gain a better understanding of theimpact of soil type on sorptive behavior, the sorption-desorption of pyrene (PYR) with three different soils was studied. The first soil originated from Colombia and is classified as silty sand with3.54% soil organic matter (SOM) and 18% clay materials (<2 microns). The New Mexico soil is a sandy lean clay comprisedof 8.4% SOM and 10% clay. The last soil originated fromOhio and is a silty sand with 1.84% SOM and 9.6% clay. Based on soil mineralogy and sorption-desorption isotherms,the Colombia soil had the greatest binding potential followedby the New Mexico and Ohio soils. The Freundlich model couldfit both the Colombia and New Mexico soils. For the Ohiosoil, a two-stage Freundlich model was required. For allthree soils, PYR desorption was slow and resistant, anddepicted an apparent hysteresis. The extent of sorption-desorption for each soil was attributed to its individual classification.For instance, the SOM present in the New Mexico soil (8.4%) enabled a relatively easy desorption in comparison to the other two soils. For the Ohio and Colombia soils, the interaction with the clay fractions rendered a stronger sorptive bond.     

基于地表昼夜温差的平原区土壤质地模糊聚类与预测制图

The use of landscape covariates to estimate soil properties is not suitable for the areas of low relief due to the high variability of soil properties in similar topographic and vegetation conditions.A new method was implemented to map regional soil texture (in terms of sand,silt and clay contents) by hypothesizing that the change in the land surface diurnal temperature difference (DTD) is related to soil texture in case of a relatively homogeneous rainfall input.To examine this hypothesis,the DTDs from moderate resolution imagine spectroradiometer (MODIS) during a selected time period,i.e.,after a heavy rainfall between autumn harvest and autumn sowing,were classified using fuzzy-c-means (FCM) clustering.Six classes were generated,and for each class,the sand (> 0.05 mm),silt (0.002-0.05 mm) and clay (< 0.002 mm) contents at the location of maximum membership value were considered as the typical values of that class.A weighted average model was then used to digitally map soil texture.The results showed that the predicted map quite accurately reflected the regional soil variation.A validation dataset produced estimates of error for the predicted maps of sand,silt and clay contents at root mean of squared error values of 8.4%,7.8% and 2.3%,respectively,which is satisfactory in a practical context.This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in plain areas using easily available data sources.     

Variability in topsoil texture and carbon content within soil map units and its implications in predicting soil water content for optimum workability

The ability to predict the timing of optimum soil workability depends on knowledge of the extent and structure of variability in main physical characteristics of the soil. Our objectives were to quantify the variability in texture and carbon content within soil map units in a small agriculture-dominated catchment in South-east Norway and to assess implications of variability in texture and carbon content on land management operations, using the predicted maximum water content for optimum workability as an example. Information from three different sources were used: a soil map (1:5000), a large sample grid (100 m spacing, 270 ha extent), and a small sample grid (10 m spacing, 2.25 ha extent). Readily available information on texture and organic matter content from the soil map was found to be of limited use for soil management due to broad textural classes together with deviations from the mapped main textural classes. There were significant differences in clay, silt and sand content between the different soil textural classes on the soil map. Statistical distributions within soil map units were generally either positively or negatively skewed and the coefficient of variation was intermediate, 15–50%. Most of the variation in both grids was spatially correlated. The large grid was dominated by a patchy structure, whilst the small grid showed a systematic trend with a gradual transition indicating fuzzy boundaries between map units in this catchment. The effective range for texture was 16 times larger in the large grid. Implications of variability in texture and carbon content on land management operations were assessed for the maximum water content for optimum workability (Wopt), predicted using pedotransfer functions. Wopt was usually in the same range as the water content at–100 kPa matric potential, indicating that considerable evaporation in addition to drainage is required for obtaining workable conditions in the field. The time required for obtaining the water content was estimated to about 5 days, which is longer than an average length of periods without precipitation in the area, median 3.7 days. Wopt predicted from the soil map deviated strongly from Wopt predicted from the sample grids. Comparing estimates of Wopt from the large grid with measurements in the small grid showed differences corresponding to ±2–3 days of evaporation.     

Usability of soil survey soil texture data for soil health indicator scoring

Soil textural information is an important component underlying other soil health indicators. Soil texture analysis is a common procedure, but it can be labor intensive and expensive. Soil texture data typically are available from the Soil Survey Geographic (SSURGO) database, which may be an option for determining soil health texture groups (SHTG). The SSURGO database provides soil texture information in the soil map unit (SMU) name, taxonomic class category (family), and detailed values (≤ 2 mm soil fraction) of percent sand, silt and clay by soil horizon. The objective of this study was to examine the possibility of using SSURGO data for SHTG at the 147-ha Cornell University Willsboro Research Farm in New York state as an alternative for soil texture data determined manually on collected soil core samples. Comparative results revealed that representative values for soil texture from the SSURGO database generally matched measured mean values for all SMUs.