Proceedings - Soil Science Society of America最新文献

Climate events, such as drought and rainfall, can lead to a cycle of drying and wetting that may cause changes in soil structure, leading to deteriorations in the health of saline soils. However, little is known about the extent and behavior of soil structure degradation under the combined influences of salinity and drying–wetting (D-W) cycles. Thus, we systematically investigated the effects of salinity (0, 5, 30, and 100 g/L, labeled as CK, T5, T30, and T100) and D-W cycles on soil structure by determining soil volume, shrinkage, and swelling potentials along with soil pore character obtained from soil shrinkage characteristics curve, intending to explore how D-W cycles and salinity affect soil structure. The results showed that soil deformation behaviors in saline and non-saline soils varied with the number of D-W cycles. Irreversible deformation of the soil was observed during continual D-W cycles. The soil volume increased by 3.75%–15.73% after three D-W cycles. In vertical direction, the maximum expansion magnitude for each treatment was reached with the value of 29.03%, 23.42%, 34.23%, and 35.87% in CK, T5, T30, and T100, respectively. The magnitudes of shrinkage and expansion were equal in the horizontal direction since the soil samples consistently returned to their original dimensions. Furthermore, the decrease was observed in the micropores and capillary pores affected by salinity, with values of 50%, 58.6%, and 70.4% in CK, T5, T30, and T100, respectively. However, the D-W cycles primarily affected large pores. High salinity levels enhanced swelling potential and inhabit shrinkage potential, prolonging the water processes required for the soil structure to achieve stability. The results of this study underscore the necessity of understanding the hysteresis of soil volume change and elucidate the mechanisms of soil structure deterioration driven by salinity and D-W cycles. These findings provide a valuable reference for healthier soil management.

Closed depressions are common landscape features across glaciated landscapes. Erosion and runoff from depression hillslopes may result in phosphorus (P) accumulation near the bottom of the depression, with this “legacy P” potentially at risk of loss to surface waters when drained via tile drainage. We assessed spatial patterns of soil P within a tile-drained watershed in northeastern Indiana as a function of landscape position and agricultural management practices. Paired soil samples (depression bottom vs. hillslope contributing area) were collected from agricultural (n = 14) depressions at four depths (0–60 cm). Water-extractable phosphorus (WEP), Mehlich-3 extracted phosphorus (M3-P), total phosphorus (TP), Hedley P fractions, and other physical and chemical characteristics were determined. To assess the risk of P loss, P desorption from surface soils (0–5 cm) was quantified using flow-through experiments. Results showed that WEP, M3-P, and TP were 2–10 times greater in the depression bottom compared to hillslopes across all depths. Long-term management practices such as P application history and tillage influenced the magnitude of soil P concentration, degree of P saturation, and vertical stratification of soil P. Flow-through experiments highlighted that the risk of P loss was highly dependent on M3-P concentration for both hillslope and depression soils. Findings therefore indicate that closed depressions may act as hotspots for P cycling and loss in tile-drained watersheds. Including low-lying depressional areas as part of a routine soil sampling strategy combined with variable rate P application could lessen P accumulation in depressions and reduce P loading to surface waters.

High levels of soil water repellency (SWR) can hinder water infiltration and increase surface soil erosion risk and runoff. Although SWR occurs naturally in many areas, it is often patchy and does not impede water movement. However, fire can increase the connectedness and extent of SWR leading to topsoil loss, nutrient limitations, increased root water stress, and ultimately slower ecosystem recovery. This study examines the naturally hydrophobic soils of Oregon's Deschutes National Forest following the 2020 Green Ridge Fire to (1) examine the relationship between SWR and management-relevant burn severity classes, (2) quantify an appropriate spatial scale over which to evaluate SWR properties, and (3) determine which environmental factors drive patterns in SWR. We found that the top 1–3 cm of soil became less hydrophobic after fire, while the profile to about 10 cm became more uniformly hydrophobic. This could indicate that surface soil is more prone to post-fire erosion in burned areas. However, predicting SWR is still challenging. While burn severity and environmental metrics did statistically account for some variation in SWR following fire, the range of SWR spatial autocorrelation was at best a few meters. Due to this fine-scale variation, future work should focus on determining an efficient post-fire soil evaluation protocol with adequate density and scale of sampling while also incorporating the influence of environmental factors to inform management decisions.

Poultry litter (PL) is an excellent source of micro- and macronutrients. However, surface applications result in greater nutrient runoff and nitrogen loss via ammonia volatilization. Subsurface banding PL is a promising technology for combating these challenges, but scant information exists on proper soil sampling techniques and management recommendations for subsurface-applied PL. Therefore, objectives were to quantify the nutrient status based on sampling depth (0–15 cm and 15–30 cm) and schema (systematic [0, 3, 7, and 10 cm from PL bands] and composite) to develop subsurface PL recommendations per system (annual cropping and perennial pasture). Soil samples were collected during PL application (subsurface and surface) and 1, 6, 12, 18, and 24 months thereafter. On average, total N, Mehlich-3 extractable P, and Mehlich-3 extractable K were 15%, 96%, and 72% greater, respectively, for subsurface compared to surface applications. Further, Mehlich-3 and water-soluble P at the 0- to 15-cm depth were 4–5 and 2–3 times greater in soils receiving subsurface PL in perennial pasture and row crop systems, respectively, compared to surface applications, likely owing to lesser nutrient losses to the air, soil, and water under subsurface PL systems. Compared to surface applications, subsurface PL increased (p < 0.05) N, P, and K crop removal by 75%, 70%, and 72%, respectively, and resulted in 80% and 78% yield increases and N-use efficiency, respectively. Consequently, subsurface PL conserved greater N, P, and K at the 0- to 15-cm depth, thus increasing nutrient-use efficiency in row crop systems and improving water quality in sensitive watersheds.

Crop N decision support tools are typically based on either empirical relationships that lack mechanistic underpinnings or simulation models that are too complex to use on farms with limited input data. We developed an N mineralization model for corn that lies between these endpoints; it includes a mechanistic model structure reflecting microbial and texture controls on N mineralization but requires just a few simple inputs: soil texture soil C and N concentration and cover crop N content and carbon to nitgrogen ratio (C/N). We evaluated a previous version of the model with an independent dataset to determine the accuracy in predictions of unfertilized corn (Zea mays L.) yield across a wider range of soil texture, cover crop, and growing season precipitation conditions. We tested three assumptions used in the original model: (1) soil C/N is equal to 10, (2) yield does not need to be adjusted for growing season precipitation, and (3) sand content controls humification efficiency (ε). The best new model used measured values for soil C/N, had a summertime precipitation adjustment, and included both sand and clay content as predictors of ε (root mean square error [RMSE] = 1.43 Mg ha⁻¹; r² = 0.69). In the new model, clay has a stronger influence than sand on ε, corresponding to lower predicted mineralization rates on fine-textured soils. The new model had a reasonable validation fit (RMSE = 1.71 Mg ha⁻¹; r² = 0.56) using an independent dataset. Our results indicate the new model is an improvement over the previous version because it predicts unfertilized corn yield for a wider range of conditions.

The soil health concept has evolved over the past several decades, recognizing that the response of dynamic soil properties to management is dependent on site-specific factors. The Soil Health Assessment Protocol and Evaluation (SHAPE) tool provides scores and benchmark values by forming soil peer groups based on site-specific, climate-edaphic characteristics. Data for model development were compiled from the Cornell Soil Health Laboratory and the Kellogg Soil Survey Laboratory databases. The SHAPEv1.0 interpretation curves produce scores between 0% and 100% for measured laboratory values that reflect the quantile within a population conditional cumulative distribution function along with measures of uncertainty. The original SHAPE tool was developed for soil organic carbon and has been expanded to include two wet aggregate stability methods, permanganate oxidizable organic carbon, autoclaved citrate extractable protein, and 4-day microbial respiration. In addition, SHAPE provides site-specific benchmark values at user-defined percentiles within a given soil peer group. The difference between the actual measured value and the selected benchmark value represents the soil health opportunity gap. Case studies in Missouri and Texas show that the SHAPE scoring curves are sensitive to land-use and management practices across multiple soil types and provide a regionally relevant interpretation of key soil health indicators.

Intercropping kura clover (Trifolium ambiguum) (KC) with prairie cordgrass (Spartina pectinata) (PCG) has great potential for biofuel feedstock on marginal lands. This study evaluated the impacts of 10-year PCG-KC intercropping and PCG monoculture fertilized with different nitrogen (N) rates of granular urea (five treatments: PCG-KC, PCG-0N, PCG-75N, PCG-150N, and PCG-225N) on soil biogeochemical properties: (i) in the surface soil (0- to 10-cm depth) at three different sampling times during the crop growing season: spring (April, pre-emergence), summer (June, active growth), and fall (November, post-harvest); and (ii) at different soil depths (0–5, 5–15, 15–30, 30–45, and 45–60 cm) (only total carbon (C) and N) in fall 2021. All soil biogeochemical parameters were higher during summer as compared to spring and/or fall, except urease activity, ammonium-N, microbial biomass C and N, and fluorescein diacetate (FDA). On average over the sampling times, PCG-KC had significantly higher β-glucosidase activity and hot-water extractable organic N than PCG-0N; but no significant difference between PCG-KC and N-fertilized PCG. Cold-water extractable organic N was significantly lower than the highest N rate, but not significantly different from PCG-0N and lower N rate treatments. Urease activity under PCG-KC treatment was double that of PCG-0N and PCG-75N; FDA was higher in PCG-KC than all monocultures. No treatment effect was found on soil total C and N, except that they decreased with depth. Overall, intercropping PCG-KC showed some benefits in terms of promoting soil biogeochemical properties during crop growth periods, having lower residual reactive N in the soil, and maintaining biomass yield and quality on marginal lands.

Soil fertility diagnosis often omits subsoil measurements, impacting precision. Our objective was to compare the vertical distribution of nitrate and sulfate in agricultural Mollisols. Both anions were measured in 34 Mollisols of the Pampean region (Argentina) sampled to 160-cm depth at 20 cm intervals. Nitrate exhibited a continuous downward trajectory, with maximum values at 0–20 cm (12.7 mg N kg⁻¹) and minimum values at 140–160 cm (3.3 mg N kg⁻¹). Sulfate displayed a sinuous pattern, with a minimum at 60–80 cm (3 mg S kg⁻¹). The 60–160/0–160 cm concentration ratio was 42% for nitrate and 60% for sulfate, indicating greater topsoil stratification for nitrate. Predicting deep-layer nitrate concentrations from topsoil data was more accurate than for sulfate. This poses a challenge for assessing soil S bioavailability, as subsoil sulfates go undetected in conventional sampling.