Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity
Author(s) -
Chuang Liu,
Yi Liu,
Yanhong Lu,
Yulin Liao,
Jun Nie,
Xiaoliang Yuan,
Fang Chen
Publication year - 2019
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.6240
Subject(s) - biomass (ecology) , productivity , index (typography) , environmental science , leaf area index , chlorophyll , chlorophyll a , agronomy , agricultural engineering , horticulture , botany , biology , computer science , engineering , economics , macroeconomics , world wide web
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.
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