CONFRONTING ISOTOPE-ENABLED MODELS WITH DATA TO QUANTIFY AND PREDICT SOIL CARBON CHANGE IN DIVERSIFIED CROPPING SYSTEMS

Diversified cropping systems including extended rotations, cover crops, and perennials present an opportunity to reverse historical losses of soil organic carbon (SOC) and increase agroecosystem sustainability. However, benefits of these practices remain difficult to quantify and upscale due to empirical challenges in detecting SOC change. Carbon stable isotopes provide an underutilized tool for data-model integration. We propose to measure carbon stable isotopes in plants/soil from three long-term field experiments comparing a wide range of diversified and conventional cropping systems in the Midwestern US. We will modify code ofmechanistic ecosystem models to track isotope values of carbon pools in these experiments. Comparisons of measured and modeled carbon isotopes and pool sizes enable calibration, modification, and validation of models to test our assumptions of how diversified cropping systems alter SOC dynamics. Validated models will be used to quantify and predict sustainability impacts of adopting diversified cropping systems across multiple spatiotemporal scales. Results will be shared with stakeholders in collaboration with producer organizations and cooperative extension. Isotope-calibrated models will be freely shared to enable future use. Findings will enhance our mechanistic understanding of how diversified cropping systems alter SOC dynamics, and the implications of these practices for SOC sequestration and related sustainability gains.(1) Measure 13C of C inputs, SOC pools, and selected fluxes among cropping systems at three field experiments. These are prescribed inputs and response variables for driving and interpreting our mechanistic models. These data will be used to calibrate and validate model outputs.(2) Revise and/or develop new model code to track the 13C values of C pools and fluxes in ecosystem models, beginning with the Dynamic Land Ecosystem Model (DLEM), and also using the Agricultural Production System Simulator (APSIM) and Agro-IBIS as points of reference.(3) Compare measured data with model predictions using the field experiments during their actual period of existence and use calibrated models for prediction under near-future scenarios. In cases of disagreement, we will test alternative model parameterizations, structures, and mechanisms to achieve improved representations of 13C measurements and improve predictive capacity among models. Using the calibrated and validated models, we will predict possible gains in SOC among cropping systems over annual to decadal scales under representative management scenarios.(4) Synthesis and outreach: Products include manuscripts that describe and analyze impacts of diversified cropping systems on SOC dynamics from a mechanistic perspective; calibrated models that represent those dynamics under current model assumptions; and, application of the models for upscaling future SOC scenarios in our region. Our results will be communicated both to regional stakeholders (Iowa Learning Farms and the Practical Farmers of Iowa) and to the broader scientific community. For future use by the scientific community, model code will be made freely available to the scientific community.