Department of Plant Sciences, University of California, One Shields Avenue, Davis, California 95616, USA.
Ecol Appl. 2010 Oct;20(7):1805-19. doi: 10.1890/09-0772.1.
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 x ha(-1) x 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 x ha(-1) x yr(-1). Organic practices had the greatest potential for soil GHG flux reduction, with 4577 +/- 272 kg CO2-eq x ha(-1) x 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.
尽管农业在加利福尼亚中央谷地具有重要地位，但替代管理实践在减少土壤温室气体（GHG）排放方面的潜力在加利福尼亚州尚未得到充分研究。本研究旨在：（1）为加利福尼亚中央谷地的常规和替代种植系统校准和验证生态系统模型 DAYCENT；（2）估算这些系统的 CO2、N2O 和 CH4 土壤通量；（3）量化输入数据变异性引起的模型预测不确定性。所考虑的替代做法包括覆盖作物、有机做法和保护性耕作。这些做法与常规农业管理进行了比较。考虑的作物有豆类、玉米、棉花、红花、向日葵、西红柿和小麦。使用了四个至少有五年实测数据的野外站点来校准和验证 DAYCENT 模型。该模型能够预测 86-94%的作物产量实测变化和 69-87%的土壤有机碳（SOC）含量实测变化。蒙特卡罗分析表明，SOC 含量、作物产量和 N2O 通量的预测变异性通常小于这些参数的实测变异性，特别是对于 N2O 通量。在评估的替代做法中，保护性耕作减少 GHG 排放的潜力最小，在三个地点中的两个地点，净土壤 GHG 通量减少了 336 +/- 47 和 550 +/- 123 kg CO2-eq x ha(-1) x yr(-1)（平均值 +/- SE）。覆盖作物的潜力更大，净土壤 GHG 通量减少了 752 +/- 10、1072 +/- 272 和 2201 +/- 82 kg CO2-eq x ha(-1) x yr(-1)。有机做法减少土壤 GHG 通量的潜力最大，为 4577 +/- 272 kg CO2-eq x ha(-1) x yr(-1)。天气或管理条件的年度差异对年度 GHG 排放的变异性的贡献大于土壤变异性。我们得出结论，DAYCENT 模型成功地预测了加利福尼亚不同替代管理系统的 GHG 排放，但必须进行可靠的误差分析以了解通过采用替代实践减少 GHG 的风险和潜力。
