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Abstract Switchgrass (Panicum virgatumL.) 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 CO₂remains 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 CO₂concentration and light, we measured leaf net photosynthesis of switchgrass under different CO₂concentrations and light levels in 2020 and 2021 for each of the five precipitation treatments. We first evaluated four light and CO₂response 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 CO₂response curves. Precipitation change did not influence the maximum net photosynthetic rate (P_max) but influenced other model parameters including quantum yield (α), convexity (θ), dark respiration (R_d), light compensation point (LCP), and saturated light point (LSP). Specifically, the meanP_maxof five precipitation treatments was 17.6 μmol CO₂m⁻² s⁻¹, and the ambient treatment tended to have a higherP_max. The +33% treatment had the highestα, and the ambient treatment had lowerθandLCP, higherRd, and relatively lowerLSP. Furthermore, precipitation significantly influenced all model parameters of CO₂response. The ambient treatment had the highestP_max, largestα, and lowestθ,R_d, and CO₂compensation pointLCP. 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. 

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. 

Abstract Background Precipitation plays an important role in crop production and soil greenhouse gas emissions. However, how crop yield and soil nitrous oxide (N 2 O) emission respond to precipitation change, particularly with different background precipitations (dry, normal, and wet years), has not been well investigated. In this study, we examined the impacts of precipitation changes on corn yield and soil N 2 O emission using a long-term (1981–2020, 40 years) climate dataset as well as seven manipulated precipitation treatments with different background precipitations using the DeNitrification-DeComposition (DNDC) model. Results Results showed large variations of corn yield and precipitation but small variation of soil N 2 O emission among 40 years. Both corn yield and soil N 2 O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N 2 O emission to precipitation manipulations. Corn yield showed a positive linear response to precipitation manipulations in the dry year, but no response to increases in precipitation in the normal year, and a trend of decrease in the wet year. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N 2 O emission mostly responded linearly to precipitation manipulations. Decreases in precipitation in the dry year reduced more soil N 2 O emission than those in the normal and wet years, while increases in precipitation increased more soil N 2 O emission in the normal and wet years than in the dry year. Conclusions This study revealed different response patterns of corn yield and soil N 2 O emission to precipitation and highlights that mitigation strategy for soil N 2 O emission reduction should consider different background climate conditions. 

Abstract Yield improvement in cotton could be accelerated through selection for functional yield drivers such as interception of cumulative photosynthetically active radiation (∑IPAR), radiation use efficiency (RUE), and harvest index (HI). However, information on the extent to which these traits vary in cotton in the southeastern United States is limited. It was hypothesized that functional yield drivers would vary significantly within a diverse cotton collection. This study was conducted in Tifton and Athens, GA, and included a total of 4 site‐years. Lint yield, total biomass production, ∑IPAR, RUE, and HI were all affected by genotype. Biomass was more strongly correlated with RUE than ∑IPAR. Even among the highest yielding genotypes, values for functional yield drivers (biomass and harvest index) differed significantly, indicating that high yields could be achieved by differentially manipulating these underlying traits. However, when considered for all genotypes, only HI exhibited a significant positive correlation with yield. Boll production and intra‐boll yield components were also affected by genotype. When considered across upland genotypes, lint per boll, lint per seed, and lint percent were strongly associated with HI and lint yield, whereas boll mass and seed number per boll were not. We conclude that the genotypes evaluated in the current study achieve high lint production per boll and lint yields by manipulating different yield drivers. However, lint yield was primarily maximized through an increase in HI due to increases in boll production and within‐boll distribution of biomass to fiber, not due to increases in total biomass production or boll size.