P. L. Mask

SOIL CARBON RELATIONSHIPS WITH TERRAIN ATTRIBUTES, ELECTRICAL CONDUCTIVITY, AND A SOIL SURVEY IN A COASTAL PLAIN LANDSCAPE

Soil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha−1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha−2) while regression was the worst (MSE = 20.65 Mg2 ha−2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ≤ 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased SOC variance (about 30%). The study suggests that terrain attributes and EC surveys can be used to differentiate zones of variable SOC content, which may be used as bench marks to evaluate field-level impact of management practices on C sequestration.

USING SITE-SPECIFIC SUBSOILING TO MINIMIZE DRAFT AND OPTIMIZE CORN YIELDS

Subsoiling is often required to alleviate soil compaction; however, deep tillage can be expensive and time-consuming. If this tillage operation is conducted deeper than the compacted soil layer, energy is wasted. However, if this tillage operation is conducted shallower than the compacted soil layer, energy is again wasted, and plant roots may be prevented from penetrating the compacted layer. Technologies are now available that allow subsoiling to be conducted at the specific depth of the compacted layer, which would conserve natural resources without sacrificing crop yields. An experiment was conducted over four years in a field located in southern Alabama to evaluate whether the concept of site-specific subsoiling (tilling just deep enough to eliminate the hardpan layer) would reduce tillage draft and energy requirements and/or reduce crop yields. Average corn (Zea mays L.) yields over this four-year period showed that site-specific subsoiling produced yields equivalent to those produced by the uniform deep subsoiling treatment while reducing draft forces, drawbar power, and fuel use.

Using Remote Sensing Data to Evaluate Surface Soil Properties in Alabama Ultisols

Evaluation of surface soil properties via remote sensing could facilitate soil survey mapping, erosion prediction, and allocation of agrochemicals for precision management. The objective of this study was to evaluate the relationship between soil spectral signature and surface soil properties in conventionally managed row crop systems. High-resolution remote sensing data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0 to 1 cm) were collected from 161 sampling points for gravimetric soil water content, soil organic carbon, particle size distribution, and citrate dithionite extractable iron content. Surface roughness and crusting were also measured during sampling. Two methods of analysis were evaluated: (1)multiple linear regression using common spectral band ratios and (2)partial least-squares regression. Our data show that thermal infrared spectra are highly, linearly related to soil organic carbon, sand and clay content. Soil organic carbon content was the most difficult to quantify in these highly weathered systems, where soil organic carbon was generally <1.2%. Estimates of sand and clay content were best using partial least-squares regression at the Valley site, explaining 42 to 59% of the variability. In the Coastal Plain, sandy surfaces prone to crusting limited estimates of sand and clay content via partial least-squares and regression with common band ratios. Estimates of iron oxide content were a function of mineralogy and best accomplished using specific band ratios, with regression explaining 36 to 65% of the variability at the Valley and Coastal Plain sites, respectively.

Crop residue effects on electrical conductivity of Tennessee Valley soils

Electrical conductivity (ECa) measurements taken with soil-contact sensors are commonly used to evaluate field-scale soil variability. Several studies have evaluated soil property effects on ECa, however, relatively few studies have evaluated the impacts of crop residues on these measurements. Crop residues may impact ECa readings, thus affecting the ability of ECa to depict spatial patterns of static soil properties. Residue effects on ECa measurements were evaluated in the intensively cropped Tennessee Valley region of Alabama where the adoption of conservation tillage practices that utilize cover crops is increasing. Ten transects (3.1 m wide by 73.2 m long) were established within a 0.22 ha (30.5 m×73.2 m) no-till field in a corn (Zea mays L.)-wheat (Triticum aestivum L.)-soybean (Glycine max L.) rotation. Soils classified in fine-loamy, siliceous, semiactive, thermic Typic Paleudults. Geo-referenced ECa measurements (at 0–30 and 30–90 cm depths) collected with a coulter mounted direct-contact sensor were taken with 1) crop residue (3789±955 kg ha−1) in place, 2) residue removed from five randomly selected transects, and 3) residue incorporated through shallow disking (5 to ∼10 cm). Measurements were taken at operating speeds of 2.2 and 4.4 m s−1. Sand and clay content and gravimetric water content (θg) were highly correlated with ECa, and regression equations using measured soil properties were developed that explained between 56% and 86% of the ECa variability. Difference in operating speeds had minimal effects on ECa measurements. Significant differences were observed between ECa values for residue remaining vs residue removed, but differences were small (∼0.5 mS m−1). When compared within two groups stratified by clay content (< or > 200 g kg−1), larger differences in ECa(0–30 cm) values for residue removed vs residue remaining were observed. Minimal differences in geostatistical parameters were observed. Overall, for the residue quantities and soils evaluated in this study, residue had minimal impacts on ECa values.

Soil Sampling Techniques For Alabama Grain Fields

Characterizing the spatial variability of nutrients facilitates precision soil sampling. Questions exist regarding the best technique for directed soil sampling based on a priori knowledge of soil and crop patterns. The objective of this study was to evaluate zone delineation techniques for Alabama grain fields to determine which method best minimized the soil test variability. Site one (25.8 ha) and site three (20.0 ha) were located in the Tennessee Valley region, and site two (24.2 ha) was located in the Coastal Plain region of Alabama. Tennessee Valley soils ranged from well drained Rhodic and Typic Paleudults to somewhat poorly drained Aquic Paleudults and Fluventic Dystrudepts. Coastal Plain soils ranged from coarse-loamy Rhodic Kandiudults to loamy Arenic Kandiudults. Soils were sampled by grid soil sampling methods (grid sizes of 0.40 ha and 1 ha) consisting of: (1) twenty composited cores collected randomly throughout each grid (grid-cell sampling) and, (2) six composited cores collected randomly from a ∼3×3m area at the center of each grid (grid-point sampling). Zones were established from (1) an Order 1 Soil Survey, (2) corn (Zea mays L.) yield maps, and (3) airborne remote sensing images. All soil properties were moderately to strongly spatially dependent as per semivariogram analyses. Differences in grid-point and grid-cell soil test values suggested grid-point sampling does not accurately represent grid values. Zones created by soil survey, yield data, and remote sensing images displayed lower coefficient of variations (%CV) for soil test values than overall field values, suggesting these techniques group soil test variability. However, few differences were observed between the three zone delineation techniques. Results suggest directed sampling using zone delineation techniques outlined in this paper would result in more efficient soil sampling for these Alabama grain fields.

Evaluating Corn Nitrogen Variability via Remote-Sensed Data

Abstract Transformations and losses of nitrogen (N) throughout the growing season can be costly. Methods in place to improve N management and to facilitate split N applications during the growing season can be time consuming and logistically difficult. Remote sensing (RS) may be a method to rapidly assess temporal changes in crop N status and to promote more efficient N management. This study was designed to evaluate the ability of three different RS platforms to predict N variability in corn (Zea mays L.) leaves during vegetative and early reproductive growth stages. Plots (15 × 15 m) were established in the Coastal Plain (CP) and in the Appalachian Plateau (AP) physiographic regions each spring from 2000 to 2002 in a completely randomized design. Treatments consisted of four N rates (0, 56, 112, and 168 kg N ha−1) applied as ammonium nitrate (NH4NO3) replicated four times. Spectral measurements were acquired via spectroradiometer (λ = 350–1050 nm), Airborne Terrestrial Applications Sensor (ATLAS) (λ = 400–12,500 nm), and the IKONOS satellite (λ = 450–900 nm). Spectroradiometer data were collected on a biweekly basis from V4 through R1. Due to the nature of satellite and aircraft acquisitions, these data were acquired per availability. Chlorophyll meter (SPAD) and tissue N were collected as ancillary data, along with each RS acquisition. Results showed vegetation indices derived from hand-held spectroradiometer measurements as early as V6–V8 were linearly related to yield and tissue N concentration. The ATLAS data were correlated with tissue N at the AP site during the V6 stage (r 2 = 0.66), but no significant relationships were observed at the CP site. No significant relationships were observed between plant N and IKONOS imagery. By using a combination of the greenness vegetation index and the normalized difference vegetation index, RS data acquired via ATLAS and the spectroradiometer could be used to evaluate tissue N variability and to estimate corn yield variability given ideal growing conditions.

Site-Specific Subsoiling Benefits for Cotton Production

The negative impacts of soil compaction on crop yields can often be alleviated by subsoiling. However, this subsoiling operation is often conducted at unnecessarily deep depths where it wastes energy and disturbs surface residue necessary for erosion control and improved soil quality. A corn (Zea mays L.)-cotton (Gossypium hirsutum L.) rotation experiment was conducted over four years on a Coastal Plain soil with a hardpan in east-central Alabama to evaluate the potential for site-specific subsoiling (tilling just deep enough to eliminate the hardpan layer) to improve crop yields while conserving energy. Seed cotton yield showed benefits of subsoiling compared to the no-subsoiling treatment. Site-specific subsoiling produced yields equivalent to deep subsoiling while not excessively disturbing surface soil and residues. Significant reductions in draft force and drawbar power were found for site-specific subsoiling as compared to uniform deep subsoiling. Producers in the Coastal Plains who can determine the depth of their root-impeding layer and can provide site-specific subsoiling to loosen compacted soil profiles should have comparable yields and reduced energy requirements as those producers implementing uniform deep subsoiling.