摘要翻译：
许多人仍然面临饥饿，全球粮食短缺仍然是一个紧迫的问题。与此同时，全球变暖依然严峻。因此，我们提出了一种基于模拟的优化方法来提高作物产量和减少农业系统的温室气体排放。利用反硝化-分解(DNDC)模型对作物产量和碳/氮循环进行了模拟和验证。选取一组DNDC模型的经验方程，并在gPROMS中实现，以求肥料用量的最优解。试验结果表明，当尿素用量为72.42kgN/ha时，优化后的结构可使作物增产18%。同时，系统的温室气体排放量减少了10%。结果表明，在农业系统中进行肥料优化规划和使用的必要性。
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英文标题：
《Process simulation and optimization of agro-systems by DNDC model》
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作者：
Jinyue Cui
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Many people are still facing hunger and the global food shortages is still an urgent problem. Meanwhile, global warming is still severe. Therefore, we propose a simulation-based optimization approach for improving crop yield and reducing the greenhouse gas emissions (GHG) of agriculture system. We simulated and verified the crop yield and carbon/nitrogen cycle with Denitrification-Decomposition (DNDC) model. A set of empirical equations of DNDC model were selected and implemented in gPROMS for obtaining the optimal solution of fertilizer usage. A case study shows that the optimized framework improves crop yield by 18%, when 72.42kg N/ha urea was used. Meanwhile the GHG emission of the system was reduced by 10%. The results show the necessity of optimal planning and usage of fertilizer in agriculture system.
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PDF链接：
https://arxiv.org/pdf/1912.07782