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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. 

A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that DRS can produce accurate and precise estimates of SOC across landscapes, whether DRS can detect subtle management induced changes in SOC at a given site has not been resolved. Here, we leverage archived soil samples from seven long-term research trials in the U.S. to test this question using mid infrared (MIR) spectroscopy coupled with the USDA-NRCS Kellogg Soil Survey Laboratory MIR spectral library. Overall, MIR-based estimates of SOC%, with samples scanned on a secondary instrument, were excellent with the root mean square error ranging from 0.10 to 0.33% across the seven sites. In all but two instances, the same statistically significant (p < 0.10) management effect was found using both the lab-based SOC% and MIR estimated SOC% data. Despite some additional uncertainty, primarily in the form of bias, these results suggest that large existing MIR spectral libraries can be operationalized in other laboratories for successful carbon monitoring. 

Terrestrial ecosystems are increasingly enriched with resources such as atmospheric CO 2 that limit ecosystem processes. The consequences for ecosystem carbon cycling depend on the feedbacks from other limiting resources and plant community change, which remain poorly understood for soil CO 2 efflux, J CO2 , a primary carbon flux from the biosphere to the atmosphere. We applied a unique CO 2 enrichment gradient (250 to 500 µL L −1 ) for eight years to grassland plant communities on soils from different landscape positions. We identified the trajectory of J CO2 responses and feedbacks from other resources, plant diversity [effective species richness, exp(H)], and community change (plant species turnover). We found linear increases in J CO2 on an alluvial sandy loam and a lowland clay soil, and an asymptotic increase on an upland silty clay soil. Structural equation modeling identified CO 2 as the dominant limitation on J CO2 on the clay soil. In contrast with theory predicting limitation from a single limiting factor, the linear J CO2 response on the sandy loam was reinforced by positive feedbacks from aboveground net primary productivity and exp(H), while the asymptotic J CO2 response on the silty clay arose from a net negative feedback among exp(H), species turnover, and soil water potential. These findings support a multiple resource limitation view of the effects of global change drivers on grassland ecosystem carbon cycling and highlight a crucial role for positive or negative feedbacks between limiting resources and plant community structure. Incorporating these feedbacks will improve models of terrestrial carbon sequestration and ecosystem services.