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Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity
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 relia...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330949/ https://www.ncbi.nlm.nih.gov/pubmed/30648006 http://dx.doi.org/10.7717/peerj.6240 |
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author | Liu, Chuang Liu, Yi Lu, Yanhong Liao, Yulin Nie, Jun Yuan, Xiaoliang Chen, Fang |
author_facet | Liu, Chuang Liu, Yi Lu, Yanhong Liao, Yulin Nie, Jun Yuan, Xiaoliang Chen, Fang |
author_sort | Liu, Chuang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6330949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63309492019-01-15 Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity Liu, Chuang Liu, Yi Lu, Yanhong Liao, Yulin Nie, Jun Yuan, Xiaoliang Chen, Fang PeerJ Agricultural Science 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. PeerJ Inc. 2019-01-11 /pmc/articles/PMC6330949/ /pubmed/30648006 http://dx.doi.org/10.7717/peerj.6240 Text en © 2019 Liu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Agricultural Science Liu, Chuang Liu, Yi Lu, Yanhong Liao, Yulin Nie, Jun Yuan, Xiaoliang Chen, Fang Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity |
title | Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity |
title_full | Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity |
title_fullStr | Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity |
title_full_unstemmed | Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity |
title_short | Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity |
title_sort | use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity |
topic | Agricultural Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330949/ https://www.ncbi.nlm.nih.gov/pubmed/30648006 http://dx.doi.org/10.7717/peerj.6240 |
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