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Conservation agriculture (CA) has the potential to sustain soil productivity and benefit agroecosystems, yet it is not fully understood how yield responses of different cropping systems are affected by inherent soil characteristics, for example, texture and dynamic soil properties, such as aggregation, nutrients and erosion. In this study, we conducted a systematic review to compare crop yield from cropland with conventional management versus different CA practices, specifically reduced- or no-tillage, agroforestry, organic farming and cover crops. The data were first analysed for different climatic regions, soil textures and cash crop types. We then quantified how yield responses correlated with soil properties change under different CA practices. The results showed that CA practices were associated with an overall mean crop yield increase of 12%. This response was primarily driven by corn, which had a mean yield increase of almost 41% after CA implementation, whereas other cash crops did not have significant yield responses or showed slight decreases, as rotation with mixtures of multiple cash crops had a mean decrease of 6% when using CA. The increase in corn yield after CA may be related to the enhanced ability of that crop to absorb nutrient elements (e.g. nitrogen) and reduce nutrient leaching. Agroforestry increased crop yield by 66% and cover cropping increased yield by 11%, likely due to increases in soil water content and nutrient availability and decreases in erosion and surface runoff. However, other agricultural systems showed no significant increase after CA compared with conventional row cropping practices. Using CA practices had the greatest yield benefit in tropical climates and when farming in coarse-textured soils. In addition, legumes and grass-legume mixtures resulted in significant cash crop yield increases, possibly because legumes promoted the increase of soil nitrogen and depleted soil moisture less compared with other cover crops. The resul

Plant-parasitic pose a unique challenge to soybean producers despite crop rotation, variety resistance, and use of chemicals. Soybean nematodes cause an average 10-15% loss in yield with reductions being much higher in fields with high nematode population. Crop rotation can help however soils with low organic matter as is found in much of the Mid-Atlantic region can create challenges to managing nematode populations. With hybrid selection, this may eventually give way to heavy selection pressure that can create nematodes that can evolve resistance to cultivars over time. Chemical use of nematicides such as seed treatments can be costly and can negatively impact people and the microbiome. This project will focus on the impact of using a novel NSS (novel synthetic strigolactone) on nematode populations, soybean plant health, and yield in the Mid-Atlantic for the 2023 season. Trials will be conducted on three fields in the Delmarva area identified as having high nematode populations. Replicated treatments with and without NSS will be mapped and sampled for nematodes. Soil samples will be collected in treatment strips throughout the season to measure nematode populations and species variation. Additionally, yield data will be collected in test strips to determine the effect of NSS on yield.

Peatland drainage may degrade system resilience to high intensity, soil-consuming fires. Peat soil fires are unique in that they can smoulder vertically through the soil column, with a multitude of consequences including large carbon emissions, altered hydrology, and dramatic shifts in vegetation communities. In this work we developed and verified a new method to model peat burn depths with readily available water level and peat hydraulic property data at the Great Dismal Swamp National Wildlife Refuge (VA and NC, USA). To model peat burn depths across 11 sites in the Great Dismal Swamp National Wildlife Refuge we combined water table time series data and soil moisture release curves, developed at multiple depths, with moisture-to-ignition thresholds. A subset of the results from this empirical modelling approach of peat burn depth severity were compared against those made using a mechanistic model of soil moisture, HYDRUS 1-D. By comparing modelled burn depth potentials between these two approaches for a range of peats, we confirmed that our simpler, water table-based approach had similar performance to HYDRUS 1-D in drained and degraded peats, like those found in the Great Dismal Swamp National Wildlife Refuge. A comparative analysis of modelled burn depths across our study site found that water table position and peat water holding capacity were the key governing controls on burn depth potential. Our findings suggest that drainage weakens both short- and long-term controls on peat burn depths by reducing soil moisture and by decreasing peat water holding capacity. This new approach offers land managers with an additional tool for assessing risk while offering insight into the drivers of peatland wildfire severity.

Sorptivity is one of the most important parameters for the quantification of water infiltration into soils. proposed a specific formulation to derive sorptivity as a function of the soil water retention and hydraulic conductivity functions, as well as initial and final soil water contents. However, this formulation requires the integration of a function involving hydraulic diffusivity, which may be undefined or present numerical difficulties that cause numerical misestimations. In this study, we propose a mixed formulation that scales sorptivity and splits the integrals into two parts: the first term involves the scaled degree of saturation, while the second involves the scaled water pressure head. The new mixed formulation is shown to be robust and well-suited to any type of hydraulic function - even with infinite hydraulic diffusivity or positive air-entry water pressure heads - and any boundary condition, including infinite initial water pressure head, h -> -infinity. Lastly, we show the benefits of using the proposed formulation for modeling water into soil with analytical models that use sorptivity.

Vineyard soils often contain mineralogy that can confound predictions of plant-available potassium (K). Too little or too much K uptake into wine-grape (Vitis varieties) tissues can negatively affect fruit chemistry, making it important to have soil tests that accurately quantify plant-available K. Our goal was to determine the best soil sampling, processing, and extraction methods for predicting K availability in vineyard soils. We sampled soil and grapevine tissue in 39 vineyard blocks from 22 vineyards in Virginia, Maryland, and New Jersey. Plant tissue sampling included petioles at bloom and both petioles and leaf blades at veraison. Soil samples were collected from 0 to 10- and 0 to 38-cm depths. We tested three soil extraction methods-Mehlich 1, Mehlich 3, and sodium tetraphenylboron (NaTPB)-on both oven-dried and field-moist samples. The different sampling, processing, and extracting procedures produced distinct K concentrations, with shallow 0-10 cm samples and NaTPB extraction resulting in higher K concentrations than their counterparts. Upon drying, three soils fixed K and 24 samples released K. Whole leaf (petiole plus leaf blade) samples collected at veraison had the best relationships with most soil K concentrations. The best soil testing method for predicting tissue K concentration in whole leaves at veraison was Mehlich 1 extractions of field-moist soils from 0 to 38-cm depth. This sampling combination appears to be best suited for growers to use when assessing K concentrations in vineyard soils.

Sorptivity is one of the most important parameters for the quantification of water infiltration into soils. Parlange (1975) proposed a specific formulation to derive sorptivity as a function of the soil water retention and hydraulic conductivity functions, as well as initial and final soil water contents. However, this formulation requires the integration of a function involving hydraulic diffusivity, which may be undefined or present numerical difficulties that cause numerical misestimations. In this study, we propose a mixed formulation that scales sorptivity and splits the integrals into two parts: the first term involves the scaled degree of saturation, while the second involves the scaled water pressure head. The new mixed formulation is shown to be robust and well-suited to any type of hydraulic function – even with infinite hydraulic diffusivity or positive air-entry water pressure heads – and any boundary condition, including infinite initial water pressure head. Lastly, we show the benefits of using the proposed formulation for modeling water into soil with analytical models that use sorptivity.

Land application of manure, while beneficial to soil health and plant growth, can lead to an overabundance of nutrients and introduction of emerging contaminants into agricultural fields. Compared with surface application of manure, subsurface injection has been shown to reduce nutrients and antibiotics in surface runoff. However, less is known about the influence of subsurface injection on the transport and persistence of antibiotic-resistant microorganisms. We simulated rainfall to field plots at two sites (one in Virginia and one in Pennsylvania) 1 or 7 d after liquid dairy manure surface and subsurface application (56 Mg ha–1) and monitored the abundance of culturable antibiotic-resistant fecal coliform bacteria (ARFCB) in surface runoff and soils for 45 d. We performed these tests at both sites in spring 2018 and repeated the test at the Virginia site in fall 2019. Manure subsurface injection, compared with surface application, resulted in less ARFCB in surface runoff, and this reduction was greater at Day 1 after application compared with Day 7. The reductions of ARFCB in surface runoff because of manure subsurface injection were 2.5–593 times at the Virginia site in spring 2018 and fall 2019 and 4–5 times at the Pennsylvania site in spring 2018. The ARFCB were only detectable in the 0-to-5-cm soil depth within 14 d of manure surface application but remained detectable in the injection slits of manure subsurface-injected plots even at Day 45. This study demonstrated that subsurface injection can significantly reduce surface runoff of ARFCB from manure-applied fields.

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.

Peatland functions (e.g., carbon sequestration and flora diversity) are largely driven by soil moisture dynamics and thus dependent on interactions between hydrologic regimes and organic soil properties. Understanding these interactions is particularly important in drained peatlands, where drier conditions may alter soil properties with feedbacks to soil water retention and associated ecosystem functions. In this work, we focused on the Great Dismal Swamp (GDS) in Virginia, USA, a historically drained, temperate peatland with ongoing hydrologic restoration efforts. Two distinct soil layers varying in thickness exist at GDS: an upper layer with subangular blocky structure thought to be a result of past drainage, and a sapric lower layer with a massive structure more representative of an undisturbed state. To understand the occurrence and consequences of these distinct layers, we used continuous water table data and analysed soil physical and hydraulic properties to characterize soil profiles at 16 locations. We found significant differences between layer properties, where upper layers had lower fibre and organic matter contents and higher bulk densities. Further, moisture release curves demonstrated lower water retention in upper layers compared with lower layers and key differences in pore structure, with upper layers having higher macroporosity. Upper layers varied in thickness across sampling locations (similar to 0.30 to 1.0 m) with a transition to lower soil layers typically occurring at depths below contemporary water level observations, suggesting that the upper layer may be a result of historical drainage and deeper water table conditions. Yet, upper layers with more frequent saturation exhibited higher water retention and lower macroporosity compared with drier upper layers, thus indicating potential recovery following re-wetting efforts. These findings highlight how past drainage influences soil properties and water retention, with important implications fo

Modeling infiltration in water-repellent soils is difficult, as the underlying processes remain poorly quantified. However, recent work has adapted the Beerkan Soil Transfer Parameter (BEST) algorithm to include an exponential correction term for characterizing these types of soils. The original BEST-WR (WR = Water Repellent) method used a two-term approximate expansion of the Haverkamp quasi-exact implicit model. However, the BEST-WR method can have considerable inaccuracy, particularly as the time of infiltration and the soil water repellency increase. Here, we extended the BEST-WR model by adapting a three-term approximation of the Haverkamp quasi-exact implicit model to water-repellent soils. We then tested the new method using analytical data. For highly water-repellent soils, the proposed method had better performance when estimating soil sorptivity (S) and soil saturated conductivity (Ks), with respective errors of less than 1.5 % and 8 %, compared to relative errors of more than 10 % and 30 % with the two-term BEST-WR method. We also tested both approaches with experimental data. The two methods provided similar estimates for hydraulic parameters, with linear correlations between methods of R2 = 0.84 for S and R2 = 0.88 for Ks. Initial infiltration was not well modeled by either the two-term or three-term model for 33 tests, thus revealing limitations in the applied exponential model that we used to account for soil repellency. Nonetheless, the proposed three-term expression provided better fits than the two-term model for most of the infiltration runs, meaning that this new approach is more robust when modeling infiltration processes in water-repellent soils.