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Switchgrass (Panicum virgatum L.) is a prominent bioenergy crop with robust resilience to environmental stresses. However, our knowledge regarding how precipitation changes affect switchgrass photosynthesis and its responses to light and CO2 remains limited. To address this knowledge gap, we conducted a field precipitation experiment with five different treatments, including -50%, -33%, 0%, +33%, and +50% of ambient precipitation. To determine the responses of leaf photosynthesis to CO2 concentration and light, we measured leaf net photosynthesis of switchgrass under different CO2 concentrations and light levels in 2020 and 2021 for each of the five precipitation treatments. We first evaluated four light and CO2 response models (i.e., rectangular hyperbola model, nonrectangular hyperbola model, exponential model, and the modified rectangular hyperbola model) using the measurements in the ambient precipitation treatment. Based on the fitting criteria, we selected the nonrectangular hyperbola model as the optimal model and applied it to all precipitation treatments, and estimated model parameters. Overall, the model fit field measurements well for the light and CO2 response curves. Precipitation change did not influence the maximum net photosynthetic rate (Pmax) but influenced other model parameters including quantum yield (α), convexity (θ), dark respiration (Rd), light compensation point (LCP), and saturated light point (LSP). Specifically, the mean Pmax of five precipitation treatments was 17.6 μmol CO2 m-2s-1, and the ambient treatment tended to have a higher Pmax. The +33% treatment had the highest α, and the ambient treatment had lower θ and LCP, higher Rd, and relatively lower LSP. Furthermore, precipitation significantly influenced all model parameters of CO2 response. The ambient treatment had the highest Pmax, largest α, and lowest θ, Rd, and CO2 compensation point LCP. Overall, this study improved our understanding of how switchgrass leaf photosynthesis responds to diverse environmental factors, providing valuable insights for accurately modeling switchgrass ecophysiology and productivity. 

Precipitation changes altered soil heterotrophic respiration, but the underlying microbial mechanisms remain rarely studied. This study conducted three-year switchgrass (Panicum virgatum L.) mesocosm experiment to investigate soil heterotrophic respiratory responses to altered precipitation. Five treatments were considered, including ambient precipitation (P0), two wet treatments (P+33 and P+50: 33% and 50% enhancement relative to P0), and two drought treatments (P-33 and P-50: 33% and 50% reduction relative to P0). The plant’s aboveground biomass (AGB), soil organic carbon (SOC), total nitrogen (TN), microbial biomass carbon (MBC), heterotrophic respiration (Rs), biomass-specific respiration (Rss: respiration per unit of microbial biomass as a reciprocal index of microbial growth efficiency), and extracellular enzymes activities (EEAs) were quantified in soil samples (0–15 cm). Despite significantly different soil moisture contents among treatments, results showed no impact of precipitation treatments on SOC and TN. Increasing precipitation had no effect, but decreasing precipitation significantly reduced plant AGB. Relative to P0, P+33 significantly increased Rs by more than 3-fold and caused no changes in MBC, leading to significantly higher Rss (P < 0.05). P+33 also significantly increased hydrolytic enzyme activities associated with labile carbon acquisition (Cacq) by 115%. The only significant effect of drought treatments was the decreased β-D-cellobiosidase (CBH) and peroxidase (PEO) under P-33. Nonparametric analyses corroborated the strong influences of moisture and CBH on the enhanced precipitation, which stimulated soil respiratory carbon loss, likely driven by both elevated hydrolase activities and reduced microbial growth efficiency. However, the less sensitive drought effects suggested potential microbial tolerance to water deficiency despite depressed plant growth. This study informs the likely decoupled impacts of microbes and plants on soil heterotrophic respiration under changing precipitation in the switchgrass mesocosm experiment. 

Switchgrass (SG) is considered a model bioenergy crop and a warm- season peren-nial grass (WSPG) that traditionally served as forage feedstock in the United States. To avoid the sole dependence on SG for bioenergy production, evaluation of other crops to diversify the pool of feedstock is needed. We conducted a 3- year field ex-periment evaluating eastern gamagrass (GG), another WSPG, as complementary feedstock to SG in one- and two- cut systems, with or without intercropping with crimson clover or hairy vetch, and under different nitrogen (N) application rates. Our results showed that GG generally produced lower biomass (by 29.5%), theoreti-cal ethanol potential (TEP, by 2.8%), and theoretical ethanol yield (TEY, by 32.9%) than corresponding SG under the same conditions. However, forage quality meas-ures, namely acid detergent fiber (ADF), crude protein (CP), and elements P, K, Ca, and Mg were significantly higher in GG than those in SG. Nitrogen fertilizer signifi-cantly enhanced biomass (by 1.54 Mg ha−1), lignin content (by 2.10 g kg−1), and TEY (787.12 L ha−1) in the WSPGs compared to unfertilized treatments. Intercropping with crimson clover or hairy vetch did not significantly increase biomass of the WSPGs, or TEP and TEY in unfertilized plots. This study demonstrated that GG can serve as a complementary crop to SG and could be used as a dual- purpose crop for bioenergy and forage feedstock in farmers' rotations. 

Incorporating microbial processes into soil biogeochemical models has received growing interest. However, determining the parameters that govern microbially driven biogeochemical processes typically requires case‐specific model calibration in various soil and ecosystem types. Here each case refers to an independent and individual experimental unit subjected to repeated measurements. Using the Microbial‐ENzyme Decomposition model, this study aimed to test whether a common set of microbially‐relevant parameters (i.e., generalized parameters) could be obtained across multiple cases based on a two‐year incubation experiment in which soil samples of four distinct soil series (i.e., Coland, Kesswick, Westmoreland, and Etowah) collected from forest and grassland were subjected to cellulose or no cellulose amendment. Results showed that a common set of parameters controlling microbial growth and maintenance as well as extracellular enzyme production and turnover could be generalized at the soil series level but not land cover type. This indicates that microbial model developments need to prioritize soil series type over plant functional types when implemented across various sites. This study also suggests that, in addition to heterotrophic respiration and microbial biomass data, extracellular enzyme data sets are needed to achieve reliable microbial‐relevant parameters for large‐scale soil model projections.

 

Abstract Extracellular glycosidases in soil, produced by microorganisms, act as major agents for decomposing labile soil organic carbon (e.g., cellulose). Soil extracellular glycosidases are significantly affected by nitrogen (N) fertilization but fertilization effects on spatial distributions of soil glycosidases have not been well addressed. Whether the effects of N fertilization vary with bioenergy crop species also remains unclear. Based on a 3-year fertilization experiment in Middle Tennessee, USA, a total of 288 soil samples in topsoil (0–15 cm) were collected from two 15 m 2 plots under three fertilization treatments in switchgrass (SG: Panicum virgatum L.) and gamagrass (GG: Tripsacum dactyloides L.) using a spatially explicit design. Four glycosidases, α-glucosidase ( AG ), β-glucosidase ( BG ), β-xylosidase ( BX ), cellobiohydrolase ( CBH ), and their sum associated with C acquisition ( C acq ) were quantified. The three fertilization treatments were no N input (NN), low N input (LN: 84 kg N ha −1  year −1 in urea) and high N input (HN: 168 kg N ha −1  year −1 in urea). The descriptive and geostatistical approaches were used to evaluate their central tendency and spatial heterogeneity. Results showed significant interactive effects of N fertilization and crop type on BX such that LN and HN significantly enhanced BX by 14% and 44% in SG, respectively. The significant effect of crop type was identified and glycosidase activities were 15–39% higher in GG than those in SG except AG . Within-plot variances of glycosidases appeared higher in SG than GG but little differed with N fertilization due to large plot-plot variation. Spatial patterns were generally more evident in LN or HN plots than NN plots for BG in SG and CBH in GG. This study suggested that N fertilization elevated central tendency and spatial heterogeneity of glycosidase activities in surficial soil horizons and these effects however varied with crop and enzyme types. Future studies need to focus on specific enzyme in certain bioenergy cropland soil when N fertilization effect is evaluated. 

Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (–8.2 ± 5.1% or –3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.