Matthew R. Levi

Cover crop utilization is an important strategy for improving row crop sustainability and plays a critical role in managing plant–soil nutrient relations needed to maintain cotton (Gossypium hirsutum L.) yields in the southeastern United States. Most cover crops are annual species, but perennial cover crops, sometimes called “living mulches” (LMs), can provide year‐round benefits. The effects of LM on nitrogen and carbon dynamics have been explored in corn systems. However, information on these dynamics and effects on other nutrients are limited in cotton systems. This project quantified the effect of a white clover LM on nutrient cycling of macro‐ and micronutrients (excluding N) in a cotton production system in the Georgia Piedmont. This included soil, plant, and pore water samples collected at important cotton reproductive states for 2 site‐years with different soil fertility. Additional samples were taken between cotton growing seasons to evaluate soil–clover interactions. Nutrient responses in the system (specifically K) were connected to soil test levels for nutrients at the start of reproduction. Only when the soil had above optimum K levels (2021), the LM luxury consumed excess K from the soil. Cotton leaves had the greatest fluctuations in K concentrations when LM was present from pinhead to cutout when soil K was below optimum. In winter, the soil had the greatest K concentration in treatments with the most clover relative to bare ground. Using optimal LM coverage can prevent excess K from potentially being lost from the system while not affecting cotton leaf K nutrition during reproductive growth.

Precision soil sampling on larger grid sizes (≥2.0 ha) is a common practice to reduce the number of soil samples and associated sampling costs. A study was conducted to evaluate the influence of different grid sizes on the depiction of spatial nutrient variability and their influence on the accuracy of variable-rate fertilizer application and total application costs. Soil sampling was conducted in nine agricultural fields using grid sizes of 0.4, 1.0, 2.0, 3.0, and 4.0 ha, and the resulting variable-rate prescription maps for lime, P, and K were spatially analyzed and compared with a reference map (generated from high-density soil sampling; approximately 2.5 samples per hectare) to determine the amount of under-, on-target, and over-application that would occur within each field. An economic analysis was conducted including the soil sampling costs, soil analysis costs, and nutrient costs to determine the effect of grid size on total application costs. Soil sampling on a 0.4 ha grid size had the best performance in depicting the spatial variability of soil pH, P, and K within the fields, and exhibited the highest application accuracy for the variable-rate prescription maps. The general trend was that the application accuracy decreased with an increase in grid size, with the potential for the under- and over-application of nutrients significantly increasing at the larger grid sizes of ≥2.0 ha. The total application cost varied among the fields as it was largely influenced by the amount of under- and over-application associated with each grid size. In most fields, the total application costs for a 0.4 ha grid size were lower or comparable to other grid sizes. In some fields, the larger grid sizes exhibited lower application costs but at the expense of reduced application accuracy. Overall, the results suggest that the smaller grid sizes of ≤1.0 ha are optimal for soil sampling in agricultural fields to ensure accurate and cost-effective variable-rate applications for site-specific nutrient management.

Palmer amaranth (Amaranthus palmeri S. Watson) is the most problematic weed of cotton cropping systems in the Southeast US. Heavy reliance on herbicides has selected for resistance to multiple herbicide mechanisms of action. Effective management of this weed may require the integration of cultural practices that limit germination, establishment, and growth. Cover crops have been promoted as a cultural practice that targets these processes. We conducted a two-year study in Georgia, USA, to measure the effects of two annual cover crops (cereal rye [Secale cereale L.] and crimson clover (Trifolium incarnatum L.), a perennial living mulch (Durana® white clover, (Trifolium repens L.), and a bare ground control on A. palmeri population dynamics. The study was conducted in the absence of herbicides. Growth stages were integrated into a basic demographic model to evaluate differences in population trajectories. Cereal rye treatments suppressed weed seedling recruitment (seedlings seed-1) by 19.2 and 13 times, and living mulch 12 and 25 times more than the bare ground control. Low recruitment was correlated positively with low light transmission (above canopy PAR/below cover crop PAR) at the soil surface. Low recruitment rates were also negatively correlated with high survival rates. Greater survival rates and reduced adult plant densities resulted in greater biomass (g plant-1) and fecundity (seeds plant-1) in cereal rye and living mulch treatments in both years. The annual rate of population change (seeds seed-1) was equivalent across all treatments in the first year but was greater in the living mulch treatment in the second year. Our results highlight the potential of annual cover crops and living mulches for suppressing A. palmeri seedling recruitment and would be valuable tools as part of an integrated weed management strategy.

Precision agriculture is a reality in agriculture and is playing a key role as the industry comes to terms with the environment, market forces, quality requirements, traceability, vehicle guidance ...

Soil moisture conditions are represented in fire danger rating systems mainly through simple drought indices based on meteorological variables, even though better sources of soil moisture information are increasingly available. This review summarises a growing body of evidence indicating that greater use of in situ, remotely sensed, and modelled soil moisture information in fire danger rating systems could lead to better estimates of dynamic live and dead herbaceous fuel loads, more accurate live and dead fuel moisture predictions, earlier warning of wildfire danger, and better forecasts of wildfire occurrence and size. Potential uses of soil moisture information in existing wildfire danger rating systems include (1) as a supplement or replacement for drought indices, (2) for live and (3) dead fuel moisture modelling, (4) for estimating herbaceous fuel curing, and (5) for estimating fuel loads. We identify key remaining research questions and note the logistical challenge of convincing wildfire professionals of the importance of soil moisture compared with more familiar wildfire danger metrics. While obstacles remain, the path forward is clear. Soil moisture information can and should be used to improve fire danger rating systems and contribute to more effective fire management for the protection of communities and ecosystems worldwide.

Tidal wetlands, widely considered the most extensive reservoir of soil organic carbon (SOC), can benefit from remote sensing studies enabling spatiotemporal estimation and mapping of SOC stock. We found that a majority of the remote-sensing-based SOC mapping efforts have been focused on upland ecosystems, not on tidal wetlands. We present a comprehensive review detailing the types of remote sensing models and methods used, standard input variables, results, and limitations for the handful of studies on tidal wetland SOC. Based on that synthesis, we pose several unexplored research questions and methods that are critical for moving tidal wetland SOC science forward. Among these, the applicability of machine learning and deep learning models for predicting surface SOC and the modeling requirements for SOC in subsurface soils (soils without a remote sensing signal, i.e., a soil depth greater than 5 cm) are the most important. We did not find any remote sensing study aimed at modeling subsurface SOC in tidal wetlands. Since tidal wetlands store a significant amount of SOC at greater depths, we hypothesized that surface SOC could be an important covariable along with other biophysical and climate variables for predicting subsurface SOC. Preliminary results using field data from tidal wetlands in the southeastern United States and machine learning model output from mangrove ecosystems in India revealed a strong nonlinear but significant relationship (r² = 0.68 and 0.20, respectively, p < 2.2 × 10⁻¹⁶ for both) between surface and subsurface SOC at different depths. We investigated the applicability of the Soil Survey Geographic Database (SSURGO) for tidal wetlands by comparing the data with SOC data from the Smithsonian’s Coastal Blue Carbon Network collected during the same decade and found that the SSURGO data consistently over-reported SOC stock in tidal wetlands. We concluded that a novel machine learning framework that utilizes remote sensing data and derived products, the standard covariables reported in the limited literature, and more importantly, other new and potentially informative covariables specific to tidal wetlands such as tidal inundation frequency and height, vegetation species, and soil algal biomass could improve remote-sensing-based tidal wetland SOC studies.

Cover crops can improve soil health by increasing soil organic matter, soil porosity, permeability, and crop yield. Yet, land planted to cover crops are often limited by economic constraints. Perennial living mulch (LM) cover crops may provide better benefits to soil health because they are actively growing throughout the year and self‐regenerate without reseeding. The objective of this study was to compare the impact of a white clover (Trifolium repens L.) LM vs. annual cover crops on soil health traits. Treatments were established on a Cecil sandy loam soil in the fall of 2014 and annual cover crop treatments re‐established each fall of the following 3 yr. White clover re‐established in the LM without reseeding. Corn (Zea mays L.) was planted into the treatments in the spring of each year. Soils were sampled at the V4/V5, V12, and R5 stages of corn development and analyzed for chemical traits. Surface soil characteristics were measured after corn harvest in 2018. Soils in the LM system had lower lime buffering capacity and greater pH, base saturation, cation exchange capacity (CEC), Ca, K₂O, Mg, P₂O₅, and total organic C concentrations than other treatments. Soil NH₃ and NO₃ had seasonal fluctuations associated with mineral N fertilizer and were lower in the LM treatment. After 3 yr, the soil bulk density was lower and porosity, water infiltration, and labile C were greater in surface soils from the LM treatment than in the surface soils of the other treatments. Use of a perennial LM cover crops expedited soil health regeneration compared to other treatments.