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Simulating greenhouse gas budgets of four California cropping systems under conventional and alternative management

Gryze, Steven De, Wolf, Adam, Kaffka, Stephen R., Mitchell, Jeff, Rolston, Dennis E., Temple, Steven R., Lee, Juhwan, Six, Johan
Ecological applications 2010 v.20 no.7 pp. 1805-1819
Carthamus tinctorius, Helianthus annuus, Monte Carlo method, beans, carbon dioxide, conservation tillage, corn, cotton, crop yield, cropping systems, crops, ecosystems, greenhouse gas emissions, greenhouse gases, management systems, methane, models, nitrous oxide, pollution control, prediction, risk, soil heterogeneity, tomatoes, uncertainty, variance, weather, wheat, California
Despite the importance of agriculture in California's Central Valley, the potential of alternative management practices to reduce soil greenhouse gas (GHG) emissions has been poorly studied in California. This study aims at (1) calibrating and validating DAYCENT, an ecosystem model, for conventional and alternative cropping systems in California's Central Valley, (2) estimating CO₂, N₂O, and CH₄ soil fluxes from these systems, and (3) quantifying the uncertainty around model predictions induced by variability in the input data. The alternative practices considered were cover cropping, organic practices, and conservation tillage. These practices were compared with conventional agricultural management. The crops considered were beans, corn, cotton, safflower, sunflower, tomato, and wheat. Four field sites, for which at least five years of measured data were available, were used to calibrate and validate the DAYCENT model. The model was able to predict 86–94% of the measured variation in crop yields and 69–87% of the measured variation in soil organic carbon (SOC) contents. A Monte Carlo analysis showed that the predicted variability of SOC contents, crop yields, and N₂O fluxes was generally smaller than the measured variability of these parameters, in particular for N₂O fluxes. Conservation tillage had the smallest potential to reduce GHG emissions among the alternative practices evaluated, with a significant reduction of the net soil GHG fluxes in two of the three sites of 336 ± 47 and 550 ± 123 kg CO₂‐eq·ha⁻¹·yr⁻¹ (mean ± SE). Cover cropping had a larger potential, with net soil GHG flux reductions of 752 ± 10, 1072 ± 272, and 2201 ± 82 kg CO₂‐eq·ha⁻¹·yr⁻¹. Organic practices had the greatest potential for soil GHG flux reduction, with 4577 ± 272 kg CO₂‐eq·ha⁻¹·yr⁻¹. Annual differences in weather or management conditions contributed more to the variance in annual GHG emissions than soil variability did. We concluded that the DAYCENT model was successful at predicting GHG emissions of different alternative management systems in California, but that a sound error analysis must accompany the predictions to understand the risks and potentials of GHG mitigation through adoption of alternative practices.