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      An enhanced chlorophyll estimation model with a canopy structural trait in maize crops: Use of multi-spectral UAV images and machine learning algorithm

      , , ,
      Ecological Informatics
      Elsevier BV

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          Random Forests

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              Photosynthesis and nitrogen relationships in leaves of C3 plants

              The photosynthetic capacity of leaves is related to the nitrogen content primarily bacause the proteins of the Calvin cycle and thylakoids represent the majority of leaf nitrogen. To a first approximation, thylakoid nitrogen is proportional to the chlorophyll content (50 mol thylakoid N mol-1 Chl). Within species there are strong linear relationships between nitrogen and both RuBP carboxylase and chlorophyll. With increasing nitrogen per unit leaf area, the proportion of total leaf nitrogen in the thylakoids remains the same while the proportion in soluble protein increases. In many species, growth under lower irradiance greatly increases the partitioning of nitrogen into chlorophyll and the thylakoids, while the electron transport capacity per unit of chlorophyll declines. If growth irradiance influences the relationship between photosynthetic capacity and nitrogen content, predicting nitrogen distribution between leaves in a canopy becomes more complicated. When both photosynthetic capacity and leaf nitrogen content are expressed on the basis of leaf area, considerable variation in the photosynthetic capacity for a given leaf nitrogen content is found between species. The variation reflects different strategies of nitrogen partitioning, the electron transport capacity per unit of chlorophyll and the specific activity of RuBP carboxylase. Survival in certain environments clearly does not require maximising photosynthetic capacity for a given leaf nitrogen content. Species that flourish in the shade partition relatively more nitrogen into the thylakoids, although this is associated with lower photosynthetic capacity per unit of nitrogen.
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                Author and article information

                Journal
                Ecological Informatics
                Ecological Informatics
                Elsevier BV
                15749541
                November 2024
                November 2024
                : 83
                : 102811
                Article
                10.1016/j.ecoinf.2024.102811
                e47463ba-b0bf-4c00-a74b-79a3382de5f4
                © 2024

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://www.elsevier.com/legal/tdmrep-license

                http://creativecommons.org/licenses/by-nc/4.0/

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