Liu Chuang, Liu Yi, Lu Yanhong, Liao Yulin, Nie Jun, Yuan Xiaoliang, Chen Fang
Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden Chinese Academy of Sciences, Wuhan, Hubei, China.
University of Chinese Academy of Sciences, Beijing, China.
PeerJ. 2019 Jan 11;6:e6240. doi: 10.7717/peerj.6240. eCollection 2019.
Improving the accuracy of predicting plant productivity is a key element in planning nutrient management strategies to ensure a balance between nutrient supply and demand under climate change. A calculation based on intercepted photosynthetically active radiation is an effective and relatively reliable way to determine the climate impact on a crop above-ground biomass (AGB). This research shows that using variations in a chlorophyll content index (CCI) in a mathematical function could effectively obtain good statistical diagnostic results between simulated and observed crop biomass. In this study, the leaf CCI, which is used as a biochemical photosynthetic component and calibration parameter, increased simulation accuracy across the growing stages during 2016-2017. This calculation improves the accuracy of prediction and modelling of crops under specific agroecosystems, and it may also improve projections of AGB for a variety of other crops.
提高预测植物生产力的准确性是制定养分管理策略的关键要素，以确保在气候变化下养分供需平衡。基于截获的光合有效辐射进行计算是确定气候对作物地上生物量（AGB）影响的一种有效且相对可靠的方法。本研究表明，在数学函数中使用叶绿素含量指数（CCI）的变化能够有效地在模拟和观测的作物生物量之间获得良好的统计诊断结果。在本研究中，用作生化光合组分和校准参数的叶片CCI在2016 - 2017年整个生长阶段提高了模拟精度。这种计算提高了特定农业生态系统下作物预测和建模的准确性，并且还可能改善对多种其他作物AGB的预测。
