Search

Abstract Preferential flow reduces water residence times and allows rapid transport of pollutants such as organic contaminants. Thus, preferential flow is considered to reduce the influence of soil matrix-solute interactions during solute transport. While this claim may be true when rainfall directly follows solute application, forcing rapid chemical and physical disequilibrium, it has been perpetuated as a general feature of solute transport—regardless of the magnitude preferential flow. A small number of studies have alternatively shown that preferential transport of strongly sorbing solutes is reduced when solutes have time to diffuse and equilibrate within the soil matrix. Here we expand this inference by allowing solute sorption equilibrium to occur and exploring how physiochemical properties affect solute transport across a vast range of preferential flow. We applied deuterium-labeled rainfall to field plots containing manure spiked with eight common antibiotics with a range of affinity for the soil after 7 days of equilibration with the soil matrix and quantified preferential flow and solute transport using 48 soil pore water samplers spread along a hillslope. Based on > 700 measurements, our data showed that solute transport to lysimeters was similar—regardless of antibiotic affinity for soil—when preferential flow represented less than 15% of the total water flow. When preferential flow exceeded 15%, however, concentrations were higher for compounds with relatively low affinity for soil. We provide evidence that (1) bypassing water flow can select for compounds that are more easily released from the soil matrix, and (2) this phenomenon becomes more evident as the magnitude of preferential flow increases. We argue that considering the natural spectrum preferential flow as an explanatory variable to gauge the influence of soil matrix-solute interactions may improve parsimonious transport models.

Abstract Obtaining water quality samples from surface runoff is essential for understanding erosion and pollutant transport processes. Existing water sampling devices are expensive, require installation of extensive infrastructure or power supplies, or have limited ability to collect flow‐weighted samples. We created an inexpensive, nonpowered, flow‐weighted water sampling device that can be incorporated into runoff quantification systems like the Upwelling Bernoulli Tube (UBeTube). Our device, called the Holey Sampler, consists of a standpipe with holes drilled at specific heights. Water enters the standpipe in proportion to the water flow rate through the UBeTube and is routed to an external collection bottle. We built and tested two versions of the Holey Sampler that sampled water at an approximate ratio of 1:250 of the runoff rate. The Standard Opening Holey Sampler (Standard‐OHS) configuration was made with 1.6‐mm‐diam. holes, whereas the Miniature Opening Holey Sampler (Mini‐OHS) configuration was designed to prevent oversampling at low outflows but required drill bit of smaller diameter (0.8‐mm). Laboratory and numerical experiments demonstrated that both configurations obtained accurate flow‐weighted runoff samples when incorporated into the UBeTube. Flow rates in the Mini‐OHS were better correlated with runoff rates under constant‐flow (R2 = .992 vs. .965 for the Standard‐OHS) and variable‐flow conditions (R2 = .996 vs. .954 for the Standard‐OHS). Further modification of the Holey Sampler could also allow flow‐weighted sampling of other runoff quantification devices like flumes and weirs.

Greenhouse gas (GHG) emissions from crop residue management have been studied extensively, yet the effects of harvesting more than one crop residue in a rotation have not been reported. Here, we measured the short-term changes in methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) emissions in response to residue removal from continuous corn (Zea mays L.) (CC) and corn–wheat (Triticum aestivum L.)–soybean (Glycine max L. Merr.) (CWS) rotations in the Mid-Atlantic USA. A first experiment retained five corn stover rates (0, 3.33, 6.66, 10, and 20 Mg ha−1) in a continuous corn (CC) in Blacksburg, VA, in 2016 and 2017. Two other experiments, initiated during the wheat and corn phases of the CWS rotation in New Kent, VA, utilized a factorial combination of retained corn (0, 3.33, 6.66, and 10.0 Mg ha−1) and wheat residue (0, 1, 2, and 3 Mg ha−1). Soybean residue was not varied. Different crop retention rates did not affect CO2 fluxes in any of the field studies. In Blacksburg, retaining 5 Mg ha−1 stover or more increased CH4 and N2O emissions by ~25%. Maximum CH4 and N2O fluxes (4.16 and 5.94 mg m−2 day−1) occurred with 200% (20 Mg ha−1) retention. Two cycles of stover management in Blacksburg, and one cycle of corn or wheat residue management in New Kent did not affect GHG fluxes. This study is the first to investigate the effects of crop residue on GHG emissions in a multi-crop system in humid temperate zones. Longer-term studies are warranted to understand crop residue management effects on GHG emissions in these systems.

The dataset presented here supports the research paper entitled “A calculator to quantify cover crop effects on soil health and productivity”. Soil health (sometimes used synonymously with soil quality) is a concept that describes soil as a living system to sustain plants, animals, and human. Soil physical, chemical, and biological properties, along with their interactions, are required to quantify soil health. The use of cover crops in agricultural rotations may enhance soil health, yet there has been little progress in understanding how external factors such as climate, soil type, and agronomic practices affect soil and cash crop responses. In response, this dataset compiles measurements from 281 studies and provides an analysis of field-measured changes in 38 soil health indicators due to cover crop usage. Environmental and background indicators were also compiled to assess how climatic and management practices affect soil and cash crop responses to cover crops, with specific categories including climate type (tropical, arid, temperate, and continental), soil texture (coarse, medium, and fine), cover crop type (legume, grass, multi-species mixture, and other), and cash crop type (corn, soybean, wheat, vegetable, corn-soybean rotation, corn-soybean-wheat rotation, and other). An unbalanced analysis of variation was used to determine the hierarchy of most to least important factors that affected responsiveness of each soil health indicator. Based on the hierarchy structure, a soil health calculator was then developed to quantify the response of 13 parameters – erosion, runoff, weed suppression, soil aggregate stability, leaching, infiltration, microbial biomass carbon, soil bulk density, soil organic carbon, soil nitrogen, microbial biomass nitrogen, cash crop yield, and saturated hydraulic conductivity – to cover crops. The presented data in the calculator report the mean change in parameter values based on all combinations of climate, soil texture, cover crop type, and cash crop type. Keywords: Soil health, Soil quality, Cover crop, Conservation management, Agriculture

Abstract Containerized crop production can enhance plant health and ensure environmental sustainability, yet proper management requires improved understanding of water fluxes and storage within soilless substrates. Numerical simulation tools developed to simulate water movement in porous media, such as HYDRUS‐3D, may help to quantify effective hydraulic properties of soilless substrates but have not yet been tested in this capacity. Therefore, this study had three main objectives: (a) to assess the accuracy of HYDRUS‐3D for simulating water flow through peat‐ and bark‐based soilless substrates by comparing measured and modeled drainage and water storage; (b) to determine sensitivity of model outputs to individual hydraulic parameters; and (c) to compare model parameterization using three laboratory characterization methods (instantaneous profile sorption, instantaneous profile desorption, and evaporation) vs. inverse modeling with HYDRUS. The results showed that the modeled water contents and drainage timing and amounts were most sensitive to saturated volumetric water content (θs) and least sensitive to saturated hydraulic conductivity (Ks). With regard to parameterization methods, the inverse modeling approach provided the most accurate water balance simulations for both substrates, followed by the sorption method. These two methods estimated lower peak water contents and greater drainage compared with simulations parameterized using desorption and evaporation measurements. Overall, the study results showed that the Richards equation, as calculated using HYDRUS, can provide accurate simulations of water flux through containers when properly calibrated, though sorption‐derived parameters may suffice when model optimization is impractical.

The need for water conservation continues to increase as global freshwater resources dwindle. Turfgrass mangers are adapting to these concerns by implementing new tools to reduce water consumption. Time-domain reflectometer (TDR) soil moisture sensors can decrease water usage when scheduling irrigation, but nonuniformity across unsampled locations creates irrigation inefficiencies. Remote sensing data have been used to estimate soil moisture stress in turfgrass systems through the normalized difference vegetation index (NDVI). However, numerous stressors other than moisture constraints impact NDVI values. The water band index (WBI) is an alternative index that uses narrowband, near-infrared light reflectance to estimate moisture limitations within the plant canopy. The green-to-red ratio index (GRI) is a vegetation index that has been proposed as a cheaper alternative to WBI as it can be measured using digital values of visible light instead of relying on more costly hyperspectral reflectance measurements. A replicated 2 × 3 factorial experimental design was used to repeatedly measure turf canopy reflectance and soil moisture over time as soils dried. Pots of ‘007’ creeping bentgrass (CBG) and ‘Latitude 36’ hybrid bermudagrass (HBG) were grown on three soil textures: United States Golf Association (USGA) 90:10 sand, loam, and clay. Reflectance data were collected hourly between 07:00 and 19:00 using a hyperspectral radiometer and volumetric water content (VWC) data were collected continuously using an embedded soil moisture sensor from soil saturation until complete turf necrosis by drought stress. The WBI had the strongest relationship to VWC (r = 0.62) compared to GRI (r = 0.56) and NDVI (r = 0.47). The WBI and GRI identified significant moisture stress approximately 28 h earlier than NDVI (p = 0.0010). Those metrics also predicted moisture stress prior to fifty percent visual estimation of wilt (p = 0.0317), with lead times of 12 h (WBI) and 9 h (GRI). By contrast, NDVI provided 2 h of prediction time. Nonlinear regression analysis showed that WBI and GRI can be useful for predicting moisture stress of CBG and HBG grown on three different soil textures in a controlled environment.

Dual-permeability models simulate flow and transport within soils characterized by preferential (macro) and matrix (micro) pore domains, with each exhibiting distinct hydraulic properties. The lack of suitable methods for determining appropriate and physically based model parameters remains a major challenge to applying these models. Here, we present a method that characterizes dual-permeability model parameters using experimental results of saturated flows from water and a non-Newtonian fluid. We present two submodels that solve for the effective pore sizes of micropores and macropores, with macropores represented either with cylindrical (for biological pores) or planar (for shrinkage cracks and fissures) pore geometries. The model also determines the percentage contribution () of the representative macro- and micropores to water flow. We applied the model to experimental soil samples complemented with capillary tubes simulating the macropores and showed its ability to derive the bimodal pore size distributions in dual-domain soils using only two fluids. As such, we present this method of characterization of dual structures for improved modeling of nonuniform preferential flow and transport in macroporous soils.

Soil respiration measurements are widely used to quantify carbon fluxes and ascertain soil biological properties related to soil microbial ecology and soil health, yet current methods to measure soil respiration either require expensive equipment or use discrete spot measurements that may have limited accuracy, and neglect underlying response dynamics. To overcome these drawbacks, we developed an inexpensive setup for measuring CO2 called the soil microbial activity assessment contraption (SMAAC). We then compared the SMAAC with a commercial infrared gas analyzer (IRGA) unit by analyzing a soil that had been subjected to two different management practices: grass buffer vs. row crop cultivation with tillage. These comparisons were done using three configurations that detected (1) in situ soil respiration, (2) CO2 burst tests, and (3) substrate induced respiration (SIR), a measure of active microbial biomass. The SMAAC provided consistent readings with the commercial IRGA unit for all three configurations tested, showing that the SMAAC can perform well as an inexpensive yet accurate tool for measuring soil respiration and microbial activity.

Macropores induce preferential flow in many soils, creating the need for parsimonious solutions to describe nonequilibrium infiltration and wetting processes. This study applied the Green–Ampt infiltration model within a dual-domain framework to distinguish water movement through the soil matrix vs. through macropores. Using a nondimensional parameter set to generalize the results, the developed equations enabled estimates of infiltration and depth of wetting due to preferential flow during constant-intensity rain. The analysis revealed that infiltration partitioning varies with time, with flow regimes changing at time of ponding of the matrix and again at time of ponding in the macropores. The results also showed that the fraction of infiltration due to preferential flow increases as a function of rainfall and relative volume of the macropore domain. Conversely, macropore volume has an inverse relationship with wetting depth: all other factors being equal, infiltration due to preferential flow becomes proportionally greater than matrix infiltration as macropore volume decreases. Finally, the proposed infiltration and wetting equations were compared with numerical simulations of the Richards equation for dual-permeability soils. The analytical solutions closely approximated the numerical results, with root mean square deviation values ≤0.15 and simulated wetting depths within 35% of one another, even as modeled times to ponding varied by 5 to 80%. Altogether, the theoretical framework developed in this study provides new insight into preferential flow dynamics during rainfall events.