In an era where the agricultural sector is grappling with the daunting impacts of climate change, a groundbreaking study offers new pathways to mitigate its environmental footprint through climate-smart agriculture. Utilizing a sophisticated ensemble of biogeochemical models, researchers have investigated the potential of innovative farming practices to sequester carbon in soil and curtail greenhouse gas emissions across two contrasting long-term agricultural research sites in the United States. This investigation illuminates the nuanced role of practices such as no-till farming, cover cropping, and residue retention in reshaping the future of agricultural sustainability.
The study, led by senior author Debjani Sihi from North Carolina State University, harnesses data accumulated over more than three decades from two distinct ecological regions: one situated in Michigan with its cooler, wetter climate and diverse crop rotations, and the other in Texas, characterized by warmer, drier conditions and different soil textures and farming systems. By integrating three distinct but complementary models—APSIM, Daycent, and RothC—into a model ensemble, the research transcends the limitations of individual approaches, providing a robust and comprehensive outlook on how agricultural management can influence carbon dynamics and greenhouse gas fluxes.
Central to the research is the concept of climate-smart agriculture (CSA), which encompasses practices aimed at increasing agricultural productivity while sequestering carbon and reducing emissions of gases such as nitrous oxide (N2O) and methane (CH4). Each of the models incorporated in the ensemble has unique structural architectures and parameterizations, enabling the team to capture a wide array of biological processes governed by climatic variables such as temperature fluctuations, precipitation patterns, and soil interactions. The convergence of these models allows for a more refined analysis of expected outcomes under various climate scenarios.
The research team simulated two contrasting future climate scenarios: a baseline scenario reflecting the historical climate data from the recent past three decades, and a high-emissions “worst-case” scenario projecting significant increases in greenhouse gas concentrations and associated climatic stressors. These scenarios provided a critical backdrop against which the projected efficacy of individual and combined CSA practices could be evaluated with an eye toward future adaptability and resilience.
Notably, the findings underscore that no-till farming combined with residue retention substantially enhances soil organic carbon (SOC) storage at both locations under the baseline emission scenario. The Michigan site, in particular, demonstrated increased SOC stocks when biochar amendments and residue retention practices were applied alongside no-till. Moreover, practices such as leguminous cover crops and reduced synthetic fertilizer applications were effective in curbing nitrous oxide emissions, an insight that aligns well with the models’ ability to simulate nitrogen cycling dynamics under variable agricultural management.
Conversely, the Texas site presented a somewhat different response. While most management practices led to enhanced SOC sequestration, greenhouse gas emissions were relatively unaffected, with the notable exception that the application of no-till practices alone had the potential to reverse net greenhouse gas emissions entirely under both baseline and high-emissions scenarios. This insight highlights the spatial variability in how climate-smart practices perform under distinct environmental and management contexts, emphasizing the need for localized adaptation strategies in agricultural policy and practice.
However, the study also delivers a sobering message: the effectiveness of climate-smart agricultural strategies diminishes under the high-emissions scenario. The intensified climatic stressors modeled in this scenario diminished the gains observed in soil carbon sequestration and in greenhouse gas mitigation. This attenuation of benefits underscores the complex interplay between management interventions and external environmental pressures, reinforcing the urgency of both mitigating emissions globally and adapting agricultural systems for climatic resilience.
The integrated model ensemble utilized in this study exemplifies a powerful methodological advancement. By synthesizing outputs from three well-established biogeochemical models, the researchers provide a nuanced understanding of potential future outcomes that accounts for uncertainties inherent in any single-model approach. This ensemble methodology facilitates identification of convergent trends while revealing discrepancies that can inform targeted improvements in model parameterization and experimental design.
According to Sihi, this model ensemble approach holds promise not only for advancing scientific understanding but also for informing policy interventions. The study paves the way for more extensive adoption and refinement of climate-smart agricultural practices at broader scales. However, the authors caution that expanded experiments across diverse geographic locations and agricultural systems are necessary to fully validate these findings and develop universally robust climate adaptation frameworks.
Adopting foundational practices such as no-till and cover cropping as base strategies, combined with residue retention, presents a compelling, multi-faceted approach to reducing net emissions and enhancing soil health. Yet the journey toward sustainable agriculture is far from complete. The study encourages the integration of real-world, on-farm data to calibrate and validate models further, alongside the inclusion of additional models with complementary strengths, to deepen the collective understanding of agroecosystem responses to climate perturbations.
The implications of this work resonate across multiple stakeholders—from farmers and agronomists to policymakers and scientists—highlighting the potential of data-driven, model-informed decision-making to revolutionize agriculture in the face of climate change. As agriculture seeks to balance productivity with environmental stewardship, model ensembles like the one developed in this study may become indispensable tools for designing resilient, sustainable farming systems in the decades ahead.
Published in the prestigious Agronomy Journal, this study reflects a pivotal step in the convergence of experimental agronomy, climate science, and modeling. With future research avenues clearly mapped, the continuous evolution of climate-smart agriculture is poised to play a pivotal role in the global response to climate change.
Subject of Research: Climate-smart agriculture practices for carbon sequestration and greenhouse gas emissions mitigation assessed through a model ensemble at two long-term U.S. agricultural research sites.
Article Title: Management alternatives for climate-smart agriculture at two long-term agricultural research sites in the U.S.: A model ensemble case study
News Publication Date: September 5, 2025
Web References: https://dx.doi.org/10.1002/agj2.70146
Image Credits: Photo courtesy of Kurt Stepnitz
Keywords: climate-smart agriculture, carbon sequestration, greenhouse gas emissions, no-till farming, cover crops, residue retention, model ensemble, APSIM, Daycent, RothC, soil organic carbon, nitrous oxide, methane, agricultural sustainability
