Agro-C: A biogeophysical model for simulating the carbon budget of agroecosystems

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• 作者：Yao Huang ; Yongqiang Yu ; Wen Zhang ; Wenjuan Sun ; Shiliang Liu ; Jun Jiang ; Jinshui Wu ; Wantai Yu ; YuWang ; Zhaofang Yang
• 关键词：Agro-C model ; Agroecosystem ; Net primary production ; Soil organic carbon ; Net CO₂ exchange ; Carbon budget
• 刊名：Agricultural and Forest Meteorology
• 出版年：2009
• 出版时间：4 January 2009
• 年：2009
• 卷：149
• 期：1
• 页码：106-129
• 全文大小：1960 K

文摘

We developed a biogeophysical model called Agro-C to assess the regional carbon budgets of agroecosystems on a large scale. The model consists of two submodels, Crop-C and Soil-C. Crop-C simulates crop photosynthesis, autotrophic respiration and net primary production (NPP). Soil-C simulates soil heterotrophic respiration via the decomposition of both input organic carbon and soil organic carbon (SOC). Changes in SOC are determined by balancing the loss of soil carbon with the sequestration of input organic carbon. The model also simulates the net ecosystem exchange (NEE) between cropland and the atmosphere. Agro-C was validated against independent datasets from the literature, multi-year agricultural statistics, and field measurements in China. These datasets covered areas with a range of climates, soils, crop rotations and agricultural practices. Model validation showed that Crop-C simulated NPP quite well for rice, wheat and maize, and reasonably well for cotton, while it did not simulate NPP well for soybean and rapeseed. Changes in SOC at different sites could be properly simulated, although there was a systematic negative bias. Observed seasonal fluctuations of NEE in a winter wheat–maize rotation system were well captured by Agro-C, but a discrepancy existed at a daily timescale. These systematic biases and errors may result from imperfect inputs, insufficient knowledge and inappropriate simplifications during model development. We conclude that the Agro-C can simulate crop NPP and changes in SOC under various conditions of climate, soil, and agricultural practices in general, making it possible to extrapolate the model to a wider domain. Agro-C also has the potential for modeling net CO₂ exchange between cropland and the atmosphere. Further improvements of Agro-C should focus on the enhancement of its descriptive power for key processes, as well as on parameterization, re-calibration and validation using spatiotemporal datasets across a wider domain so that it can be more applicable to broader regions.