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Switchgrass (Panicum virgatum L.) production for biofuel has the potential to produce reasonable yields on lands not suited for conventional agriculture. We assessed nine switchgrass cultivars representing lowland and upland ecotypes grown for 11 years at a site in the upper Midwest USA for belowground differences in soil carbon and nitrogen stocks, soil organic matter fractions, and standing root biomass to 1 m depth. We also compared potential nitrogen mineralization and carbon substrate use through community‐level physiological profiling in surface soils (0–10 cm depth). Average yields and standing root biomass differed among cultivars and between ecotypes, but we found no significant cultivar‐related impacts on soil carbon and nitrogen stocks, on the distribution of particulate and mineral‐associated soil organic matter fractions, nor on potential nitrogen mineralization or microbial community‐level physiological profiles. That these traits did not differ among cultivars suggests that soil carbon and nitrogen gains under switchgrass are likely to be robust with respect to cultivar differences, and to this point not much affected by breeding efforts. 

Changes in land surface albedo can alter ecosystem energy balance and potentially influence climate. We examined the albedo of six bioenergy cropping systems in southwest Michigan USA: monocultures of energy sorghum (Sorghum bicolor), switchgrass (Panicum virgatumL.), and giant miscanthus (Miscanthus×giganteus), and polycultures of native grasses, early successional vegetation, and restored prairie. Direct field measurements of surface albedo (α_s) from May 2018 through December 2020 at half‐hourly intervals in each system quantified the magnitudes and seasonal differences in albedo (∆_α) and albedo‐induced radiative forcing (RF_∆α). We used a nearby forest as a historical native cover type to estimate reference albedo and RF_∆αchange upon original land use conversion, and a continuous no‐till maize (Zea mays L.) system as a contemporary reference to estimate change upon conversion from annual row crops. Annually,α_sdiffered significantly (p < 0.05) among crops in the order: early successional (0.288 ± 0.012SE) >> miscanthus (0.271 ± 0.009) ≈ energy sorghum (0.270 ± 0.010) ≥ switchgrass (0.265 ± 0.009) ≈ restored prairie (0.264 ± 0.012) > native grasses (0.259 ± 0.010) > maize (0.247 ± 0.010). Reference forest had the lowest annualα_s(0.134 ± 0.003). Albedo differences among crops during the growing season were also statistically significant, with growing seasonα_sin perennial crops and energy sorghum on average ~20% higher (0.206 ± 0.003) than in no‐till maize (0.184 ± 0.002). Average non‐growing season (NGS)α_s(0.370 ± 0.020) was much higher than growing seasonα_s(0.203 ± 0.003) but these NGS differences were not significant. Overall, the original conversion of reference forest and maize landscapes to perennials provided a cooling effect on the local climate (RF_αMAIZE: −3.83 ± 1.00 W m⁻²; RF_αFOREST: −16.75 ± 3.01 W m⁻²). Significant differences among cropping systems suggest an additional management intervention for maximizing the positive climate benefit of bioenergy crops, with cellulosic crops on average ~9.1% more reflective than no‐till maize, which itself was about twice as reflective as the reference forest.

 

Our knowledge of microbial processes—who is responsible for what, the rates at which they occur, and the substrates consumed and products produced—is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process‐level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well‐calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait‐based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

 

‘Marginal lands’ are low productivity sites abandoned from agriculture for reasons such as low or high soil water content, challenging topography, or nutrient deficiency. To avoid competition with crop production, cellulosic bioenergy crops have been proposed for cultivation on marginal lands, however on these sites they may be more strongly affected by environmental stresses such as low soil water content. In this study we used rainout shelters to induce low soil moisture on marginal lands and determine the effect of soil water stress on switchgrass growth and the subsequent production of bioethanol. Five marginal land sites that span a latitudinal gradient in Michigan and Wisconsin were planted to switchgrass in 2013 and during the 2018–2021 growing seasons were exposed to reduced precipitation under rainout shelters in comparison to ambient precipitation. The effect of reduced precipitation was related to the environmental conditions at each site and biofuel production metrics (switchgrass biomass yields and composition and ethanol production). During the first year (2018), the rainout shelters were designed with 60% rain exclusion, which did not affect biomass yields compared to ambient conditions at any of the field sites, but decreased switchgrass fermentability at the Wisconsin Central–Hancock site. In subsequent years, the shelters were redesigned to fully exclude rainfall, which led to reduced biomass yields and inhibited fermentation for three sites. When switchgrass was grown in soils with large reductions in moisture and increases in temperature, the potential for biofuel production was significantly reduced, exposing some of the challenges associated with producing biofuels from lignocellulosic biomass grown under drought conditions.

 

Despite the extensive application of the Soil and Water Assessment Tool (SWAT) for water quality modeling, its ability to simulate soil inorganic nitrogen (SIN) dynamics in agricultural landscapes has not been directly verified. Here, we improved and evaluated the SWAT–Carbon (SWAT-C) model for simulating long-term (1984–2020) dynamics of SIN for 40 cropping system treatments in the U.S. Midwest. We added one new nitrification and two new denitrification algorithms to the default SWAT version, resulting in six combinations of nitrification and denitrification options with varying performance in simulating SIN. The combination of the existing nitrification method in SWAT and the second newly added denitrification method performed the best, achieving R, NSE, PBIAS, and RMSE of 0.63, 0.29, −4.7 %, and 16.0 kg N ha−1, respectively. This represents a significant improvement compared to the existing methods. In general, the revised SWAT-C model's performance was comparable to or better than other agroecosystem models tested in previous studies for assessing the availability of SIN for plant growth in different cropping systems. Sensitivity analysis showed that parameters controlling soil organic matter decomposition, nitrification, and denitrification were most sensitive for SIN simulation. Using SWAT-C for improved prediction of plant-available SIN is expected to better inform agroecosystem management decisions to ensure crop productivity while minimizing the negative environmental impacts caused by fertilizer application. 

Abstract Granular temporal and spatial scale observations of conservation practices are essential for identifying changes in the production systems that improve soil health and water quality and inform long-term agricultural research and adaptive policy development. In this study, we demonstrate an innovative use of farmer practice survey data and what can be uniquely known from a detailed survey that targets specific farm groups with a regional focus over multiple consecutive years. Using three years of survey data ( n = 3914 respondents), we describe prevailing crop rotation, tillage, and cover crop practice use in four Midwestern US states. Like national metrics, the results confirm dominant practices across the landscape, including corn-soybean rotation, little use of continuous no-till, and the limited use of cover crops. Our detailed regional survey further reveals differences by state for no-till and cover crop adoption rates that were not captured in federal datasets. For example, 66% of sampled acreage in the Midwest has corn and soybean rotation, with Illinois having the highest rate (72%) and Michigan the lowest (41%). In 2018, 20% of the corn acreage and 38% of the soybean acreage were in no-till, and 13% of the corn acres and 9% of the soybean acres were planted with a cover crop. Cover crop adoption rates fluctuate from year to year. Results demonstrate the value of a farmer survey at state scales over multiple years in complementing federal statistics and monitoring state and yearly differences in practice adoption. Agricultural policies and industry heavily depend on accurate and timely information that reflects spatial and temporal dynamics. We recommend building an agricultural information exchange and workforce that integrates diverse data sources with complementary strengths to provide a greater understanding of agricultural management practices that provide baseline data for prevailing practices. 

Leaf photosynthesis of perennial grasses usually decreases markedly from early to late summer, even when the canopy remains green and environmental conditions are favorable for photosynthesis. Understanding the physiological basis of this photosynthetic decline reveals the potential for yield improvement. We tested the association of seasonal photosynthetic decline in switchgrass ( Panicum virgatum L.) with water availability by comparing plants experiencing ambient rainfall with plants in a rainfall exclusion experiment in Michigan, USA. For switchgrass exposed to ambient rainfall, daily net CO 2 assimilation ( A n e t ' ) declined from 0.9 mol CO 2 m -2 day -1 in early summer to 0.43 mol CO 2 m -2 day -1 in late summer (53% reduction; P<0.0001). Under rainfall exclusion shelters, soil water content was 73% lower and A n e t ' was 12% and 26% lower in July and September, respectively, compared to those of the rainfed plants. Despite these differences, the seasonal photosynthetic decline was similar in the season-long rainfall exclusion compared to the rainfed plants; A n e t ' in switchgrass under the shelters declined from 0.85 mol CO 2 m -2 day -1 in early summer to 0.39 mol CO 2 m -2 day -1 (54% reduction; P<0.0001) in late summer. These results suggest that while water deficit limited A n e t ' late in the season, abundant late-season rainfalls were not enough to restore A n e t ' in the rainfed plants to early-summer values suggesting water deficit was not the sole driver of the decline. Alongside change in photosynthesis, starch in the rhizomes increased 4-fold (P<0.0001) and stabilized when leaf photosynthesis reached constant low values. Additionally, water limitation under shelters had no negative effects on the timing of rhizome starch accumulation, and rhizome starch content increased ~ 6-fold. These results showed that rhizomes also affect leaf photosynthesis during the growing season. Towards the end of the growing season, when vegetative growth is completed and rhizome reserves are filled, diminishing rhizome sink activity likely explained the observed photosynthetic declines in plants under both ambient and reduced water availability.