Natural Resources Conservation Service

Abstract Spatial climate data sets of 1971–2000 mean monthly precipitation and minimum and maximum temperature were developed for the conterminous United States. These 30‐arcsec (∼800‐m) grids are the official spatial climate data sets of the U.S. Department of Agriculture. The PRISM (Parameter‐elevation Relationships on Independent Slopes Model) interpolation method was used to develop data sets that reflected, as closely as possible, the current state of knowledge of spatial climate patterns in the United States. PRISM calculates a climate–elevation regression for each digital elevation model (DEM) grid cell, and stations entering the regression are assigned weights based primarily on the physiographic similarity of the station to the grid cell. Factors considered are location, elevation, coastal proximity, topographic facet orientation, vertical atmospheric layer, topographic position, and orographic effectiveness of the terrain. Surface stations used in the analysis numbered nearly 13 000 for precipitation and 10 000 for temperature. Station data were spatially quality controlled, and short‐period‐of‐record averages adjusted to better reflect the 1971–2000 period. PRISM interpolation uncertainties were estimated with cross‐validation (C‐V) mean absolute error (MAE) and the 70% prediction interval of the climate–elevation regression function. The two measures were not well correlated at the point level, but were similar when averaged over large regions. The PRISM data set was compared with the WorldClim and Daymet spatial climate data sets. The comparison demonstrated that using a relatively dense station data set and the physiographically sensitive PRISM interpolation process resulted in substantially improved climate grids over those of WorldClim and Daymet. The improvement varied, however, depending on the complexity of the region. Mountainous and coastal areas of the western United States, characterized by sparse data coverage, large elevation gradients, rain shadows, inversions, cold air drainage, and coastal effects, showed the greatest improvement. The PRISM data set benefited from a peer review procedure that incorporated local knowledge and data into the development process. Copyright © 2008 Royal Meteorological Society

The ‘4 per mille Soils for Food Security and Climate’ was launched at the COP21 with an aspiration to increase global soil organic matter stocks by 4 per 1000 (or 0.4 %) per year as a compensation for the global emissions of greenhouse gases by anthropogenic sources. This paper surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia). We asked whether the 4 per mille initiative is feasible for the region. The outcomes highlight region specific efforts and scopes for soil carbon sequestration. Reported soil C sequestration rates globally show that under best management practices, 4 per mille or even higher sequestration rates can be accomplished. High C sequestration rates (up to 10 per mille) can be achieved for soils with low initial SOC stock (topsoil less than 30 t C ha− 1), and at the first twenty years after implementation of best management practices. In addition, areas which have reached equilibrium will not be able to further increase their sequestration. We found that most studies on SOC sequestration only consider topsoil (up to 0.3 m depth), as it is considered to be most affected by management techniques. The 4 per mille number was based on a blanket calculation of the whole global soil profile C stock, however the potential to increase SOC is mostly on managed agricultural lands. If we consider 4 per mille in the top 1m of global agricultural soils, SOC sequestration is between 2-3 Gt C year− 1, which effectively offset 20–35% of global anthropogenic greenhouse gas emissions. As a strategy for climate change mitigation, soil carbon sequestration buys time over the next ten to twenty years while other effective sequestration and low carbon technologies become viable. The challenge for cropping farmers is to find disruptive technologies that will further improve soil condition and deliver increased soil carbon. Progress in 4 per mille requires collaboration and communication between scientists, farmers, policy makers, and marketeers.

Abstract Soil‐water potential and hydraulic conductivity relationships with soil‐water content are needed for many plant and soil‐water studies. Measurement of these relationships is costly, difficult, and often impractical. For many purposes, general estimates based on more readily available information such as soil texture are sufficient. Recent studies have developed statistical correlations between soil texture and selected soil potentials using a large data base, and also between selected soil textures and hydraulic conductivity. The objective of this study was to extend these results by providing mathematical equations for continuous estimates over broad ranges of soil texture, water potentials, and hydraulic conductivities. Results from the recent statistical analyses were used to calculate water potentials for a wide range of soil textures, then these were fit by multivariate analyses to provide continuous potential estimates for all inclusive textures. Similarly, equations were developed for unsaturated hydraulic conductivities for all inclusive textures. While the developed equations only represent a statistical estimate and only the textural influence, they provide quite useful estimates for many usual soil‐water cases. The equations provide excellent computational efficiency for model applications and the textures can be used as calibration parameters where field or laboratory soil water characteristic data are available. Predicted values were successfully compared with several independent measurements of soil‐water potential.

The size and shape of soil particles reflect the formation history of the grains. In turn, the macroscale behavior of the soil mass results from particle level interactions which are affected by particle shape. Sphericity, roundness, and smoothness characterize different scales associated with particle shape. New experimental data and results from published studies are gathered into two databases to explore the effects of particle shape on packing density and on the small-to-large strain mechanical properties of sandy soils. In agreement with previous studies, these data confirm that increased angularity or eccentricity produces an increase in emax and emin. Furthermore, the data show that increasing particle irregularity causes a decrease in stiffness yet heightened sensitivity to the state of stress; an increase in compressibility under zero-lateral strain loading; an increase in the critical state friction angle ϕcs; and an increase in the intercept Γ of the critical state line (there is a weak effect on the slope λ). Therefore, particle shape emerges as a significant soil index property that needs to be properly characterized and documented, particularly in clean sands and gravels. The systematic assessment of particle shape will lead to a better understanding of sand behavior.

Abstract The C stored in soils is nearly three times that in the aboveground biomass and approximately double that in the atmosphere. Reliable estimates have been difficult to obtain due to a lack of global data on kinds of soils and the amount of C in each soil. With new data bases, our study is able to provide more reliable data than previous estimates. Globally, 1576 Pg of C is stored in soils, with ≈ 506 Pg (32%) of this in soils of the tropics. It is also estimated that ≈ 40% of the C in soils of the tropics is in forest soils. Other studies have shown that deforestation can result in 20 to 50% loss of this stored C, largely through erosion.

Abstract Most researchers agree that soil cover methods offer the most useful approach to measuring nitrous oxide (N 2 O) exchange in the field, but there is little uniformity in the design of covers used by them. The cover design least subject to the potential errors associated with this approach is a vented enclosure with either closed‐loop air circulation or no forced air circulation. Although the soil N 2 O concentration gradient beneath these covers decreases with time as the gas accumulates, this problem can be overcome by a proposed change in the equation for computing flux from the increase in head‐space N 2 O concentration. Over a broad range of sampling conditions, N 2 O fluxes computed from the new equation were independent of the time allowed for N 2 O accumulation. Other equations were developed that predict optimum soil cover vent dimensions for effectively transmitting ambient pressure fluctuations to the enclosed space while minimizing loss of the accumulating N 2 O by diffusion to the outside. Soil covers with vent dimensions dictated by the equations had N 2 O accumulation rates significantly higher than covers with 60% smaller vent diameters but not significantly different than covers with 60% larger vent diameters.

Abstract The probable effect of the increasing global atmospheric CO 2 concentration on agricultural yields was evaluated. More than 430 observations of the yield of 37 species grown with CO 2 enrichment were extracted from more than 70 reports published during the past 64 years. Most of the studies were performed in greenhouses or growth chambers. Open fields might respond less than greenhouses or growth chambers to increased CO 2 because nutrient levels in general world‐wide agriculture are lower than those in the indoor studies, or open fields might respond more because light levels are generally higher outside. The data also were dominated by high value crops, but results should be applicable to the three‐fourths of the world agriculture represented by the C 3 crops and possibly to the remaining C 4 crops as well. Keeping these limitations of the data in mind, the analysis showed that yields probably will increase by 33% (with a 99.9% confidence interval from 24 to 43%) with a doubling of atmospheric CO 2 concentration.

Abstract A simple method of estimating changes in biologically active soil carbon (C) could help evaluate soil quality impacts of alternative management practices. Most reports of permanganate for active C determination use highly concentrated solutions (0.333 M) that are difficult to work with and tend to react with a large fraction of soil C that is not well distinguished from total organic C. We report on a highly simplified method in which dilute, slightly alkaline KMnO 4 reacts with the most readily oxidizable (active) forms of soil C, converting Mn(VII) to Mn(II), and proportionally lowering absorbance of 550 nm light. The amount of soil C that reacted increased with concentration of KMnO4 used (0.01 to 0.1 M), degree of soil drying (moist fresh soil to air-dried for 24 hour) and time of shaking (1–15 minutes). Shaking of air-diy soil in a 0.02 M KMnO 4 solution for 2 minutes produced consistent and management-sensitive results, both in the laboratory and with a field kit that used a hand-held colorimeter. Addition of 0.1 M. CaCl 2 to the permanganate reagent enhanced settling of the soil after shaking, eliminating the need for centrifugaron in the field kit. Results from the laboratory and field-kit protocols were nearly identical (R 2 = 0.98), as were those from an inter-laboratory sample exchange (R 2 = 0.91). The active soil C measured by the new procedure was more sensitive to management effects than total organic C, and more closely related to biologically mediated soil properties, such as respiration, microbial biomass and aggregation, than several other measures of soil organic C.

Forty‐four records of reservoir trap efficiency and the factors affecting trap efficiency are analyzed. The capacity‐inflow (C/I) ratio is found to offer a much closer correlation with trap efficiency than the capacity‐watershed (C/W) ratio heretofore widely used. It appears likely from the cases studied that accurate timing of venting or sluicing operations to intercept gravity underflows can treble or quadruple the amount of sediment discharged from a reservoir. Desilting basins, because of their shape and method of operation, may have trap efficiencies above 90 pct even with very low C/I ratios. Semi‐dry reservoirs with high C/I ratios, like John Martin Reservoir, may have trap efficiencies as low as 60 pct. Truly “dry” reservoirs, such as those in the Miami Conservancy District, probably have trap efficiencies in the 10 to 40 pct range, depending upon C/I ratio

In this Focus article, the authors ask a seemingly simple question: Are harmful algal blooms (HABs) becoming the greatest inland water quality threat to public health and aquatic ecosystems? When HAB events require restrictions on fisheries, recreation, and drinking water uses of inland water bodies significant economic consequences result. Unfortunately, the magnitude, frequency, and duration of HABs in inland waters are poorly understood across spatiotemporal scales and differentially engaged among states, tribes, and territories. Harmful algal bloom impacts are not as predictable as those from conventional chemical contaminants, for which water quality assessment and management programs were primarily developed, because interactions among multiple natural and anthropogenic factors determine the likelihood and severity to which a HAB will occur in a specific water body. These forcing factors can also affect toxin production. Beyond site-specific water quality degradation caused directly by HABs, the presence of HAB toxins can negatively influence routine surface water quality monitoring, assessment, and management practices. Harmful algal blooms present significant challenges for achieving water quality protection and restoration goals when these toxins confound interpretation of monitoring results and environmental quality standards implementation efforts for other chemicals and stressors. Whether HABs presently represent the greatest threat to inland water quality is debatable, though in inland waters of developed countries they typically cause more severe acute impacts to environmental quality than conventional chemical contamination events. The authors identify several timely research needs. Environmental toxicology, environmental chemistry, and risk-assessment expertise must interface with ecologists, engineers, and public health practitioners to engage the complexities of HAB assessment and management, to address the forcing factors for HAB formation, and to reduce the threats posed to inland surface water quality.

Increased demand and advanced techniques could lead to more refined mapping and management of soils.

(1969). The Concept of Environmental Education. Environmental Education: Vol. 1, No. 1, pp. 30-31.

Abstract In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ∼20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within ±8 cm of the measured values, with most estimates falling much closer.

Abstract A procedure for obtaining iron which is both convenient and reliable is described. The procedure involves shaking the samples overnight in a citrate‐dithionite buffer. This is followed by a colorimetric iron determination using orthophenanthroline. The use of an automatic pipette, a flocculating agent, and noncritical quantities of dry reagents facilitates the determination. The results show increased effectiveness and reliability over Kilmer's porcedure for high‐iron Oxisols and Ultisols from Puerto Rico.

Abstract Adsorption of phosphorus by soils from dilute solutions showed a closer agreement with the Langmuir isotherm than with the Freundlich isotherm. Constants calculated from the Langmuir isotherm and interpretations based upon the meaning of these constants permit a sound theoretical approach to some of the problems of phosphorus retention in soils. The adsorption maximum claculated from the Langmuir isotherm was closely correlated with the surface area of soils as measured by ethylene glycol retention. The correlation coefficients and regression equations were r = 0.98 and y = 0.276x + 3.47 for 10 alkaline soils, and r = 0.96 and y = 0.641x + 5.73 for 12 acid soils, where y = mg. P per 100 g. soil and x = mg. ethylene glycol retained per g. of soil. For a given surface area, i.e., 30 mg. glycol per g. soil, the acid soils held 2.17 times as much phosphorus as the alkaline soils. The average values of a second constant, k, derived from the slope and intercept values, were 0.92 and 4.39 for the alkaline and acid soils, respectively. As the value of this constant increases, the bonding energy of the soil for phosphorus increases. Thus, the acid soils retained more phosphorus per unit of surface area and also held the phosphorus with a greater bonding energy than the alkaline soils

Pyrolysis is the anaerobic thermal conversion of biomass for energy production. It offers an option of returning carbon and nutrients to the soil while producing energy. The Ultisols in the southeastern United States have inherently low soil organic carbon and fertility, and may benefit from the addition of biochar from pyrolysis. Our objectives were to evaluate the effect of peanut hull and pine chip biochars on soil nutrients, corn ( Zea mays L.) nutrient status and yield in a Kandiudult for two growing seasons (2006 and 2007). Experiments for each biochar source were conducted as completely randomized designs with the biochar applied at 0, 11, and 22 Mg ha −1 with and without N fertilizer. Nitrogen in the peanut hull biochar (209 kg ha −1 at 11 Mg ha −1 rate) was not available during the study based on corn tissue concentrations. The peanut hull biochar linearly increased Mehlich I K, Ca, and Mg in the surface soil (0–15 cm). The increased available K was reflected in the plant tissue analysis at corn stage R1 in 2006, but not in 2007. Pine chip biochar decreased soil pH, but had no effect on other nutrients except Mehlich I Ca. In the peanut hull biochar experiment, grain yields decreased at the 22 Mg ha −1 rate in the fertilized treatments. In the pine chip biochar experiment, grain yields decreased linearly with application rate in 2006, but this did not persist in 2007. Overall yield responses to biochar were smaller than expected based on previous studies.

Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent >10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources.

A geographical information system (GIS) or expert knowledge‐based fuzzy soil inference scheme (soil‐land inference model, SoLIM) is described. The scheme consists of three major components: (i) a model employing a similarity representation of soils, (ii) a set of inference techniques for deriving the similarity representation, and (iii) use of the similarity representation. The similarity representation allows the soil landscape to be considered as a continuum, and thereby overcomes the generalization of soils in conventional soil mapping. The set of inference techniques is based on the soil factor equation and the soil–landscape model. The soil–landscape concept contends that if one knows the relationships between each soil and its environment for an area, then one is able to infer what soil might be at each location on the landscape by assessing the environmental conditions at that point. Under the SoLIM, soil environmental conditions over an area are characterized using GIS or remote sensing techniques. The relationships between soils and their formative environmental conditions are extracted from local soil experts or from field observations using a set of artificial intelligence techniques. The characterized environmental conditions are then combined with the extracted relationships to derive a similarity representation of soils over an area. It is demonstrated through two case studies that the SoLIM for soil survey has many advantages over the conventional soil survey approach. Soil information products derived through the SoLIM are of high quality in terms of both level of spatial detail and degree of attribute accuracy. In addition, the scheme shows promise for improving the efficiency of soil survey and subsequent updates through reducing time and costs of conducting a survey. However, the degree of success of the SoLIM highly depends on the availability and quality of environmental data, and the quality of knowledge on soil–environmental relationships over the study area.

ABSTRACT: This paper describes the application of a river basin scale hydrologic model (described in Part I) to Richland and Chambers Creeks watershed (RC watershed) in upper Trinity River basin in Texas. The inputs to the model were accumulated from hydro‐graphic and geographic databases and maps using a raster‐based GIS. Available weather data from 12 weather stations in and around the watershed and stream flow data from two USGS stream gauge station for the period 1965 to 1984 were used in the flow calibration and validation. Sediment calibration was carried out for the period 1988 through 1994 using the 1994 sediment survey data from the Richland‐Chambers lake. Sediment validation was conducted on a subwatershed (Mill Creek watershed) situated on Chambers Creek of the RC watershed. The model was evaluated by well established statistical and visual methods and was found to explain at least 84 percent and 65 percent of the variability in the observed stream flow data for the calibration and validation periods, respectively. In addition, the model predicted the accumulated sediment load within 2 percent and 9 percent from the observed data for the RC watershed and Mill Creek watershed, respectively.

Simple albedo measurement may prove useful for sensing surface soil water content and as a research tool in the study of evaporation of water from soil. Intensive concurrent measurements of the albedo and soil water content of a drying bare soil indicate that albedo, normalized for sun zenith angle effects, is a linear function of the soil water content of a very thin surface layer (less than 0.2 cm thick) over a sizeable volumetric water content range (0.00 to 0.18 for an Avondale loam). Albedo is also well correlated with the average soil water content of greater soil thicknesses. Measurements to a depth of 10 cm indicate that the relation is relatively independent of season.