Few studies have attempted to isolate the various factors that may cause the observed increases in peak flows and erosion after high‐severity wildfires. This study evaluated the effects of burning by: (i) comparing soil water repellency, surface cover, and sediment yields from severely burned hillslopes, unburned hillslopes, and hillslopes where the surface cover was removed by raking; and (ii) conducting rainfall simulations to compare runoff, erosion, and surface sealing from two soils with varying ash cover. The fire‐enhanced soil water repellency was only stronger on the burned hillslopes than the unburned hillslopes in the first summer after burning. For the first 5 yr after burning, the mean sediment yield from the burned hillslopes was 32 Mg ha−1, whereas the unburned hillslopes generated almost no sediment. Sediment yields from the raked and burned hillslopes were indistinguishable when they had comparable surface cover, rainfall erosivity, and soil water repellency values. The rainfall simulations on ash‐covered plots generated only 21 to 49% as much runoff and 42 to 67% as much sediment as the plots with no ash cover. Soil thin sections showed that the bare plots rapidly developed a structural soil seal. Successive simulations quickly eroded the ash cover and increased runoff and sediment yields to the levels observed from the bare plots. The results indicate that: (i) post‐fire sediment yields were primarily due to the loss of surface cover rather than fire‐enhanced soil water repellency; (ii) surface cover is important because it inhibits soil sealing; and (iii) ash temporarily prevents soil sealing and reduces post‐fire runoff and sediment yields.

Mid-infrared (MIR) reflectance spectroscopy is commonly studied as a rapid and nondestructive method for predictive soil analysis under laboratory conditions. The first objective of this paper is to report an MIR spectral library based on 20,000+ soil samples collected from the United States. The second objective is to assess, using partial least squares regression (PLSR) and artificial neural networks (Ann), the performance of the library to predict 12 physical and chemical soil properties: organic carbon (OC), inorganic carbon (IC), total carbon (TC), total nitrogen (Tn), clay, silt, sand, Mehlich-3 extractable phosphorus (P), nH 4 OAc extractable potassium (k), cation exchange capacity (CeC), total sulfur (TS), and pH. The third objective is to investigate whether the use of auxiliary variables of master horizon (HZ), taxonomic order (TAXOn), and land use land cover (LULC) would improve MIR model performance. The results showed that OC, IC, TC, Tn and TS were predicted most satisfactorily with R 2 > 0.95 and RPD (ratio of performance to deviation) > 5.5. Soil CeC, pH, clay, silt, and sand were also predicted satisfactorily with R 2 > 0.75 and RPD > 2.0. P and k were predicted poorly, with R 2 < 0.4 and RPD < 1.4. The Ann models generally outperformed PLSR models, except for clay, silt and sand. Using auxiliary variables (HZ, TAXOn, and LULC) to develop stratified models generally improved model performance. The HZ-specific models showed the greatest improvements. Using an MIR spectral library for routine soil analysis would positively impact many modern applications where high spatial resolution, quantitative soil data are demanded.

No abstract

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).

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives The International Center for Tropical Agriculture (CIAT) believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. CIAT is committed to creating and sharing knowledge and information openly and globally. We do this through collaborative research as well as through the open sharing of our data, tools, and publications.

Phosphorus (P) is the second essential nutrient for plant growth but can become an ecological and economical concern in case of over-fertilization. Soil P dynamic is influenced by many parameters like soil physical-chemical properties and farming practices. A better understanding of the factors controlling its distribution is required to achieve best management of P in cropping systems. In Switzerland, the FRIBO network was launched in 1987 and consists of 250 sites covering a wide diversity of soils (Cambisols, Gleysols, Rendzinas, Lithosols, Luvisols, Fluvisols) and three different land uses (cropland, grassland and mountain pasture) across the Fribourg canton. A spatial investigation of the different P forms (total, organic and available) for the FRIBO network led to the following main conclusions:(i) The P status in agricultural soils was significantly different among the three land uses encountered, with the highest mean values of available P found in croplands, from 2.12 (CO 2 saturated water extraction) to 81.3 mg.kg −1 (acetate ammonium + EDTA extraction); whereas total P was more abundant in permanent grasslands (1186 mg.kg −1 ), followed by mountain pastures (1039 mg.kg −1 ) and croplands (935 mg.kg −1). This full characterization of the soil P status provides important data on P distribution related to soil properties and land use.(ii) Environmental variables such as altitude, slope, wetness index or plan curvature, derived from the digital elevation model (DEM) only explained a small part of the spatial variation of the different P forms (20 to 25%). Thus, the geostatistical analyses revealed that land use plays a significant role in soil P distribution. Improved predictions of the spatial distribution of P-related forms at landscape scales are needed and would require additional data points and variables such as parent material, soil types and terrain attributes.

No abstract

Core Ideas Topographic variation influenced soil nutrient distribution in a silvopasture system. High‐resolution digital maps of soil nutrients were generated. Terrain attributes identified topographic functional units as management zones. Level of soil nutrients in topographic functional units were different. Topography plays a crucial role in spatial distribution of nutrients in soils; however, studies to quantify topographic influence on soil nutrient distribution from a silvopasture system are mostly lacking. To address this question, a 4.3‐ha silvopasture site in northwest Arkansas was selected and a total of 51 topsoil (0–15 cm thickness) samples were collected and analyzed for primary (total N [TN], P, K), secondary (Ca, Mg, S), and micronutrients (Fe, Zn, Cu, Mn, B, Na). Topographic information was acquired from 12 terrain attributes derived from a 1‐m digital elevation model. The prediction model was based on random forest. Results showed TN, S, and P were best predicted, whereas Cu, Ca, and Mn had the lowest prediction performance. Levels of S, Ca, Zn, Fe, and TN increased with SAGA wetness index, valley depth, flow accumulation, and multi‐resolution valley bottom flatness index. Normalized height and slope height were positively related to Na but negatively to B and Cu distribution. Aspect had a positive influence on P and Mg concentrations. Based on terrain attributes, the study site could be divided into four topographic functional units (TFU), namely A, B, C, and D; TFU A had the highest nutrients present, whereas TFU B had the lowest P, K, Zn, Cu, Fe, and Ca but highest Na content. However, Mn, Mg, and B did not vary among TFUs. This study affirmed topographic influences on soil nutrient distribution, and the resulting continuous soil nutrient maps are useful for fine‐tuning production systems through optimum nutrient and pasture management.