Conservation agriculture and associated soil health practices potentially enhance soil resilience by improving the soil's physical, chemical, and biological properties and processes. This study assessed the impact of tillage (conventional tillage [CT] and no‐till [NT]); crop rotation: 2‐year corn ( Zea mays L.)–soybean ( Glycine max L.), 3‐year corn–soybean–oat ( Avena sativa L.), and 4‐year corn–soybean–oat–wheat ( Triticum aestivum L.)]; cover crops (cover crop [CC] and no cover crop [NC]); and drainage (tile drainage [TD] and without drainage [ND]) on soil organic matter (SOM), bulk density, wet aggregate stability (WAS), and field‐saturated hydraulic conductivity ( K _fs ). Soil samples were collected over 2 years apart from five depths (0–10, 10–20, 20–30, 30–60, and 60–90 cm) and analyzed for SOM, bulk density, and WAS. In‐situ infiltration tests were conducted in each plot to determine field‐saturated hydraulic conductivity. This study showed that NT practice significantly increased SOM by 5.4%, WAS by 7.7%, and bulk density by 6.7% within 0–10 cm soil profile but decreased K _fs by 47.6% compared to CT through increased bulk density in the topsoil. NT increased SOM for every soil depth and, similarly, increased WAS for every depth but only statistically significant increases occurred at 0–10 cm and 60–90 cm. Further, higher crop diversity decreased bulk density and increased WAS. Nine years of CC and six years of tile drainage practices had no significant effect on SOM, bulk density, WAS, and K _fs .

Winter rye (Secale cereale L.) is an important cover crop (CC) in the northern Great Plains (NGP), yet concerns over its establishment under the variable weather conditions of this region are an important limitation for its widespread adoption. This study evaluated the impacts of no‐till corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation with winter rye CC established in 2017 on (a) water quality (nitrate‐N [NO₃^––N], ammonia‐N [NH₄⁺–N], and total nitrogen [TN]) and (b) soil health parameters at the 0‐to‐15‐cm depth. Data showed that rye CC biomass was 251 kg ha^–1 in 2018, 1,213 kg ha^–1 in 2019, and 147 kg ha^–1 in 2020, coinciding with contrasting growing degree days for rye CC (i.e., 1,458, 2,042, and 794, respectively), as a consequence of variable weather conditions. Water quality was not affected for the periods when rye growth was <300 kg ha^–1. In the season when rye CC had greater biomass (1,213 kg ha^–1), significant reductions in leached NO₃^––N (19–20%) and TN (8.5–16%) concentrations were observed due to greater N uptake by rye CC (18.8 kg N ha^–1). Rye CC showed significantly (p ≤ .05) higher microbially active carbon ( ∼13%) and water‐extractable organic N(∼11%) than the control treatment. Nonsignificant impacts on soil health indicators due to rye CC showed that the study duration (3 yr) may not be sufficient to see the beneficial impacts of CCs on soils. However, significant reductions in leached NO₃^––N and TN concentrations for one (2019) out of three study years suggest that rye with optimal growth has the potential of reducing N leaching and enhancing soil health for the NGP region.

Artificial rain, a technology primarily used for drought relief, has recently been used for combating regional air pollution. However, there are limited available measurement data to confirm the effectiveness of this control practice. In this study, we summarize control theories and indirect but relevant observations/findings, including air pollutant reduction after natural rain events and roadside sprinkling. A brief review of artificial rain basics is also provided. Our work shows that artificial rain appears to be a promising management strategy for air pollution control. However, field measurements are needed to further assess the cost-effectiveness of the practice, as well as the other benefits or challenges it may create.

Highlights Changes to soil properties and precipitation scenarios significantly affect the water balance in agro-hydrology. SPAW model is sensitive to simulated runoff and infiltration, but it has limitations in responding to soil compaction and organic matter change. Increasing organic matter (1% to 5%) did not significantly affect runoff or infiltration in silty and sandy loam soil. Low precipitation generates significantly lower runoff (%) and higher infiltration. Abstract. Agricultural practices can change soil properties and the amount of runoff generated from a landscape. Modeling results could be significantly different than expected if the web soil survey or other commonly used remote sensing applications are used as model inputs without site verification. This study assessed the applicability and sensitivity of the Soil-Plant-Air-Water (SPAW) Model for simulating the runoff (%) and infiltration (%) components of the water balance for various soil physical properties, cover crop, and weather variables. Soil profiles in 135 combinations were developed with three soil classes (sandy loam, silt loam, and clay), five organic matter levels (1%, 2%, 3%, 4%, and 5%), three levels of compaction (low, medium, and high), and three topsoil layer thicknesses (7.6 cm, 11.4 cm, and 15 cm). Also, three cover crop treatments were simulated by modifying surface cover and evapotranspiration during the non-growing season. Finally, two precipitation regimes were considered (Iowa City, IA, as high precipitation and Brookings, SD, as low precipitation) to simulate runoff and infiltration. In total, 810 scenarios were run, resulting in over 300 million data points. This study confirmed that soil texture, bulk density, and topsoil thickness significantly (p<0.01) influence runoff generation and infiltration percentage based on the water balance criterion. Interestingly, the SPAW model had no significant response on runoff (%) and infiltration (%) to organic matter levels changing from 1% to 5%. This simulation demonstrates that runoff estimations can be significantly influenced by soil properties that can change due to agricultural conservation practices (ACPs) or, conversely, by compaction events. Inputs to models must account for these changes rather than relying only on historical or remote sensing inputs. Keywords: Agricultural conservation practices, Conservation agriculture, Field hydrology, Infiltration, Runoff, SPAW.