Simulating adaptive grazing management on soil organic carbon in the Southeast U.S.A. using MEMS 2.

Grazing lands play a significant role in global carbon (C) dynamics, holding substantial soil organic carbon (SOC) stocks. However, historical mismanagement (e.g., overgrazing and land-use change) has led to substantial SOC losses. Regenerative practices, such as adaptive multi-paddock (AMP) grazing, offer a promising avenue to improve soil health and help combat climate change by increasing SOC accrual, both in its particulate (POC) and mineral-associated (MAOC) organic C components. Because adaptive grazing patterns emerge from the combination of different levers such as frequency, intensity, and timing of grazing, studying AMP grazing management in experimental trials and representing it in models remains challenging. Existing ecosystem models lack the capacity to predict how different adaptive grazing levers affect SOC storage and its distribution between POC and MAOC and along the soil profile accurately. Therefore, they cannot adequately assist decision-makers in effectively optimizing adaptive practices based on SOC outcomes. Here, we address this critical gap by developing version 2.34 of the MEMS 2 model. This version advances the previous by incorporating perennial grass growth and grazing submodules to simulate grass green-up and dormancy, reserve organ dynamics, the influence of standing dead plant mass on new plant growth, grass and supplemental feed consumption by animals, and their feces and urine input to soil. Using data from grazing experiments in the southeastern United States and experimental SOC data from two conventional and three AMP grazing sites in Mississippi, we tested the capacity of MEMS 2.34 to simulate grass forage production, total SOC, POC, and MAOC dynamics to 1-m depth. Further, we manipulated grazing management levers, i.e., timing, intensity, and frequency, to do a sensitivity analysis of their effects on SOC dynamics in the long term. Our findings indicate that the model can represent bahiagrass forage production (BIAS = 9.51 g C m ^-2 , RRMSE = 0.27, RMSE = 65.57 g C m ^-2 , R ²  = 0.72) and accurately captured the dynamics of SOC fractions across sites and depths (0-15 cm: RRMSE = 0.05; 15-100 cm: RRMSE = 1.08-2.07), aligning with patterns observed in the measured data. The model best captured SOC and MAOC stocks across AMP sites in the 0-15 cm layer, while POC was best predicted at-depth. Otherwise, the model tended to overestimate SOC and MAOC below 15 cm, and POC in the topsoil. Our simulations indicate that grazing frequency and intensity were key levers for enhancing SOC stocks compared to the current management baseline, with decreasing grazing intensity yielding the highest SOC after 50 years (63.7-65.9 Mg C ha ^-1 ). By enhancing our understanding of the effects of adaptive grazing management on SOC pools in the southeastern U.S., MEMS 2.34 offers a valuable tool for researchers, producers, and policymakers to make AMP grazing management decisions based on potential SOC outcomes.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: M. Francesca Cotrufo discloses to be a cofounder of Cquester Analytics LLC. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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