Soil organic matter (SOM) has been known to hold water and be an important factor in contributing to the available water-holding capacity (AWHC). Recently, however, there have been overestimates of this amount. The objective of this research was to reevaluate the relative contribution of SOM to AWHC as influenced by soil physical properties (particle size, texture, and bulk density) and mineralogy using the National Cooperative Soil Survey (NCSS) Soil Characterization Database and also to elucidate on the theoretical capacity of SOM to hold water. Silt content had the greatest correlation with AWHC (r = 0.56). AWHC increased with decreasing soil bulk density (r =-0.34), but the relationship was highly variable depending on SOM and soil texture. Soil organic matter was weakly correlated with AWHC for samples between 0% and 8% SOM (r = 0.27) but moderately correlated (r = 0.62) for all samples (0% to 100% SOM). The increase of AWHC was more pronounced for sandy soils than for silty clay loam and silt loam soils. For soils with clay contents greater than 40%, the correlation varied by minerology class: mixed (r = 0.24), smectitic (r = 0.08), and kaolinitic (r = 0.49). In general, a 1% increase in SOM content increased AWHC, on average, up to 1.5% times its weight, depending on soil texture and clay mineralogy. These values were consistent with the theoretical calculations that showed that the potential AWHC increase (on a volumetric basis) from a unit increase in SOM (% weight) is about 1.5% to 1.7% for the 0% to 8% SOM range. This equates to 10,800 L of water for each additional 1% increase in SOM (up to 8% SOM) for a layer thickness of 15 cm covering 0.4 ha area (an acre furrow slice).

Development of more efficient (rapid) and cost-effective methodologies are needed in soil survey to meet the demand for quantitative data in digital soil mapping and updates. The objective of this study was to pilot the application of mid-infrared (MIR)-diffuse reflectance spectroscopy (DRS) coupled with partial least squares regression (PLSR) in a soil survey field office where soil samples are processed, MIR spectra are acquired, and predictions are obtained using calibration models developed and validated from the Kellogg Soil Survey Laboratory spectral library. Mid-infrared models were built for total C, organic C, CaCO 3 equivalent, total clay, cation exchange capacity, 1500 kPa water, and pH in water and CaCl 2 for Mollisols of the central United States. Validation results (from the MIR library) using Lin's concordance correlation (r c) of measured versus predicted values showed that most properties predicted very well (r c = 0.967-0.996), whereas models for total clay in B horizons and 1500 kPa water in B horizons predicted fairly well (r c = 0.844-0.955). Models for pH predicted the least well (r c = 0.750-0.921). The MIR-DRS coupled with PLSR was successful in predicting soil properties for completely independent samples that were collected, processed, and MIR scanned in a soil survey field office. Predicted results using r c ranged from 0.697 to 0.992, with pH in water having the lowest r c and CaCO 3 having the highest r c. All properties except pH had an acceptable level of accuracy for use in soil survey and a marginal level of acceptable accuracy for total clay. Direct calibration transfer was feasible.

Successful hydrological model predictions depend on appropriate framing of scale and the spatial-temporal accuracy of input parameters describing soil hydraulic properties. Saturated soil hydraulic conductivity (K sat ) is one of the most important properties influencing water movement through soil under saturated conditions. It is also one of the most expensive to measure and is highly variable. The objectives of this research were (i) to assess the ability of Amoozemeters, wells, piezometers, and flumes to accurately represent K sat at a small catchment scale and (ii) to extrapolate K sat to a larger watershed based on available soil data and soil landscape models for simulating streamflow using the Distributed Hydrological Soil Vegetation Model. The mean K sat between Amoozemeters, wells, and flumes varied from 2.4 to 4.9 ´ 10 −7 m s −1 , and differences were not significant. Mixed trends in mean K sat for slope positions and soil series were observed. The strongest significant and consistent trend in mean K sat was observed for soil depth. The mean K sat decreased exponentially with depth, from 6.51 ´ 10 6 m s −1 for upper horizons to 2.37 ´ 10 −7 m s −1 for bottom horizons. Recognizing the significantly decreasing trend of K sat with soil depth and the lack of consistent trends between soils and slope positions for small catchments, K sat values were extrapolated from the small catchments occurring in Dillon Creek to another large watershed (Hall Creek) based on soil similarity and distribution. The Nash-Sutcliffe model overall efficiency of 0.52 indicated a good performance in simulating streamflows without model calibration. Combining K sat measurement methods in small catchments with an understanding of soil landscapes and soil distribution relationships allowed successful upscaling of localized soil hydraulic properties for streamflow predictions to larger watersheds.

This report addresses the development of dryland oilseed crops to provide feedstock for production of biofuels in semi-arid portions of the northwestern USA. Bioenergy feedstocks derived from Brassica oilseed crops have been considered for production of hydrotreated renewable jet fuel, but crop growth and yields in the northwestern region are limited by a lack of plant available water. Based on a review of the scientific literature, several areas were identified where research could be directed to provide improvements. The current agronomic limitations for oilseed production are mainly due to seedling establishment under extreme heat, dry seedbeds at optimum planting times, survival under extreme cold, and interspecific competition with weeds. To improve emergence and stand establishment, future work should focus on developing soil management and seeding techniques that optimize plant available water, reduce heat stress, and provide a competitive advantage against weeds that are customized for specific crops, soil types, and soil and environmental conditions. Spring and winter cultivars are needed that offer increased seedling vigor, drought resistance, and cold tolerance.

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