Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Chuang, Liu, Yi, Lu, Yanhong, Liao, Yulin, Nie, Jun, Yuan, Xiaoliang, Chen, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
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
_version_ 1783387061183578112
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
work_keys_str_mv AT liuchuang useofaleafchlorophyllcontentindextoimprovethepredictionofabovegroundbiomassandproductivity
AT liuyi useofaleafchlorophyllcontentindextoimprovethepredictionofabovegroundbiomassandproductivity
AT luyanhong useofaleafchlorophyllcontentindextoimprovethepredictionofabovegroundbiomassandproductivity
AT liaoyulin useofaleafchlorophyllcontentindextoimprovethepredictionofabovegroundbiomassandproductivity
AT niejun useofaleafchlorophyllcontentindextoimprovethepredictionofabovegroundbiomassandproductivity
AT yuanxiaoliang useofaleafchlorophyllcontentindextoimprovethepredictionofabovegroundbiomassandproductivity
AT chenfang useofaleafchlorophyllcontentindextoimprovethepredictionofabovegroundbiomassandproductivity