Skye Wills

A holistic strategy for adaptive land management

Adaptive management is widely applied to natural resources management (Holling 1973; Walters and Holling 1990). Adaptive management can be generally defined as an iterative decision-making process that incorporates formulation of management objectives, actions designed to address these objectives, monitoring of results, and repeated adaptation of management until desired results are achieved (Brown and MacLeod 1996; Savory and Butterfield 1999). However, adaptive management is often criticized because very few projects ever complete more than one cycle, resulting in little adaptation and little knowledge gain (Lee 1999; Walters 2007). One significant criticism is that adaptive management is often used as a justification for undertaking actions with uncertain outcomes or as a surrogate for the development of specific, measurable indicators and monitoring programs (Lee 1999; Ruhl 2007). In this paper, we argue for a more holistic and systematic approach to adaptive management. We define holistic adaptive land management (HALM) as a refinement of adaptive management that requires (1) a process-based understanding of ecosystem dynamics and ecological mechanisms, (2) a willingness and ability to identify and consider all possible management alternatives, (3) rigorous monitoring of management effects, and (4) constant adaptation of management based on monitoring data and associated observations. Thus, HALM requires both…

Overview of the U.S. Rapid Carbon Assessment Project: Sampling Design, Initial Summary and Uncertainty Estimates

The Rapid Carbon Assessment (RaCA) project was undertaken by the Soil Science Division of the Natural Resource Conservation Service (NRCS) to capture baseline soil carbon stocks across the conterminous US (CONUS). A multi-level hierarchical design was used to ensure that samples were distributed across regions, soils and land use/land cover classes (LULC). Within those strata, sites were selected at random locations where five pedons were described and sampled at 0–5 cm and by genetic horizon from 5 to 100 cm. A total of 6,148 sites, 32,084 pedons and 144,833 samples were described. Bulk density was calculated for samples from the upper 50 cm and predicted for deeper samples using pedon and horizon information in a regression tree developed with random forests. Soil organic carbon (SOC) concentration was predicted for each sample using processed Visible-Near Infrared spectra and a random forest model. Pedon SOC stocks were calculated by fixed depth to 100 cm. Expected variance was introduced into the stock calculations using analytical and modeling prediction errors (e.g., SOC concentration and bulk density measurements) and the stratified sampling design was partitioned using a hierarchical Bayesian modeling approach. Pedons were averaged by site. The mean of all RaCA site SOC stocks to 100 cm was 321.1, with a median of 173.3 and range of 2 to over 5,000 Mg ha−1. Geometric means of soil groups and LULC classes were used to extrapolate results to all assessed areas. Further work is needed to properly weight averages by areal extent and assess the cause of higher than expected site SOC stock values.

Mineralizable Soil Organic Carbon Dynamics in Corn-Soybean Rotations in Glaciated Derived Landscapes of Northern Indiana

The concerns about climate change have increased interest in understanding differences in soil carbon pools and availability. The objective of this study was to assess total Soil Organic Carbon (SOC) and mineralizable SOC (Cmin) dynamics and spatial distribution as controlled by slope position, in glaciated northern Indiana. We collected 210 soil samples from the 0 to 25 cm surface layer along 10-point transects along a soil catena. Total SOC was determined by dry combustion and Cmin by incubation. The spatial distribution of total SOC followed patterns related to soil wetness. Overall, the depression areas stored between 50 and 141 Mg C ha−1 or between 50 and 68 % more total SOC when compared to the drier areas. After 28 days of incubation (Cmin), depressions released 1.2 Mg C ha−1, which was significantly more than the drier areas at 0.8 Mg ha−1. These differences indicate the potential of wetter areas, to store C if converted to C accruing management practices. The mean daily rate of C-CO2 evolved decreased exponentially during the first 28 days from 1.5 to 0.2 μg g−1 h−1. The management of these targeted areas can potentially increase soil C stock in arable lands and assist managers in developing systems that will sequester soil carbon.

Reevaluating the effects of soil organic matter and other properties on available water-holding capacity using the National Cooperative Soil Survey Characterization Database

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).

Human Land-Use and Soil Change

Soil change refers to the alteration of soil and soil properties over time in one location, as opposed to soil variability across space. Although soils change through natural processes (pedogenesis), this chapter focuses on human-caused soil change. Soil change can occur with human land use and management over long or short time periods and small or large scales. While change can be negative or positive, often soil change is observed when short-term or narrow goals overshadow soil’s other ecosystem services. Assessing soil change depends upon the ecosystem services and soil functions being evaluated. The interaction of soil properties with the type and intensity of management and disturbance determines the changes that will be observed. Many soils have been changed in their chemical, physical, or biological properties through agricultural activities, including cultivation, tillage, weeding, terracing, subsoiling, deep plowing, manure and fertilizer addition, liming, draining, and irrigation. Tillage of cropland disrupts aggregates and decreases soil organic carbon content, which can lead to decreased infiltration, increased erosion, and reduced biological function. Improved agricultural management systems can increase soil functions including crop productivity and sustainability. Forest management is most intensive during harvesting and seedling establishment. Most active management in forests causes disturbance of the soil surface, which may include loss of forest floor organic materials, increases in bulk density , and increased risk of erosion. In grazing lands, pasture management often includes periods of biological, chemical, and physical disturbance in addition to the grazing management imposed on rangelands. Grazing animals have both direct and indirect impacts on soil change. Hoof action can lead to the disturbance of biological crusts and other surface features impairing the soil’s physical, biological, and hydrological functions. There are clear feedbacks between vegetative systems and soil properties; when vegetation is altered because of grazing or other disturbances, soil property changes often follow. Some soils are very sensitive to management and disturbance and can undergo rapid change; for example, cropping led to massive gully formation in the southeastern USA, exposure of acid sulfate soils leads to irreversible changes in soil mineralogy, and thawing of cold soils has created thermokarst features. These soil changes alter soil properties and functions and may impact soil ecosystem services far into the future.

Digital Summaries of Pedon Descriptions

Soil scientists have been describing and analyzing pedons for over a hundred years. In the USA, a small portion of this data has been captured in the National Soil Information System (NASIS). While NASIS serves as a data repository, its analytical capabilities are limited, and the data are underutilized. In order to facilitate the analysis of soil horizon data in NASIS, we have used R to develop R Markdown (Rmd) reports. These Rmd reports are designed to provide numerical and graphical summaries of soil horizon data used for soil survey activities, such as the development of Official Series Descriptions and soil map unit components.

Comparative Analysis of Saturated Hydraulic Conductivity ( K sat ) Derived from Image Analysis of Soil Thin Sections, Pedotransfer Functions, and Field-Measured Methods

Saturated hydraulic conductivity (K sat) is an important soil parameter that governs water movement through horizons, pedons, and soil landscapes. K sat is infamous for its spatial and temporal variability, which contributes to the difficulty and considerable expense in measuring or otherwise quantifying it. Consequently, predictive methods such as pedotransfer functions (PTFs) that use physical soil properties, such as texture and bulk density, have been developed to derive K sat values. Soil texture and structure are key factors influencing K sat because of their direct relationship to pore size distribution. Quantitatively defining the combined effects of texture and structure on pore size distribution in a PTF is a difficult task. The objectives of this research were to: (i) estimate K sat based on pore characteristics derived from soil thin sections via image analysis; and (ii) compare the resultant values with field-measured K sat and with K sat estimated by a PTF using soil texture and bulk density parameters. We digitally scanned 39 thin sections from 11 pedons of soils derived from loess over till and/or over weathered sandstone. Soil voids were classified based on their size and shape. K sat was measured in the field using a Compact Constant-head Permeameter (Amoozemeter) and estimated using a Rosetta PTF. Simple and multiple linear regression (MLR) analyses were used to relate pore indexes and soil physical properties with measured and estimated K sat. The mean measured K sat was 0.74 cm h−1, whereas the PTF-estimated K sat from Rosetta and MLR were 0.36 cm h−1 and 0.49 cm h−1, respectively. The addition of pore characteristics into the model improved K sat predictions compared to predictions using Rosetta alone. The estimated K sat based on the model with added pore characteristics was better correlated with field-measured K sat (r = 0.82) than that based on Rosetta (r = 0.62). The addition of pore characteristics can improve K sat predictions. However, thin section void analysis from additional parent materials is needed.