Radiation-Use Efficiency Under Different Climatic Conditions

Bhattacharya, A.

doi:10.1016/b978-0-12-816209-5.00002-7

Cited by 8 publications

(4 citation statements)

References 438 publications

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“…At leaf scale, LUE can be measured as the proportion of energy used in the photochemical quenching to that directed through nonphotochemical quenching and chlorophyll fluorescence (Coops et al, 2010; Hilker et al, 2008). At the ecosystem scale, however, LUE can be further affected by nonbiochemical factors, such as the canopy structure, vegetation phenology, and constraints by climatic factors (Bhattacharya, 2019; Hilker et al, 2008), making ecosystem LUE more variable and challenging to be studied. Ecosystem LUE was described as a biome‐specific constant downregulated by scalars of air temperature and vapor pressure deficit (VPD) in many remote sensing‐based photosynthesis estimates (e.g., Running et al, 2004; Zhao & Running, 2010).…”

Section: Introductionmentioning

confidence: 99%

Global Patterns and Climate Controls of Terrestrial Ecosystem Light Use Efficiency

Tang

Wang

et al. 2020

JGR Biogeosciences

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Light use efficiency (LUE) is a critical parameter, on which we rely to quantify the vegetation's capability to harvest solar energy and thus the photosynthesis carbon uptake. However, our understanding on its magnitude, global pattern, and climatic drivers remains uncertain. In this study, we investigated global mean annual LUE during 1980-2013 based on state-of-the-art estimates from scaled-up eddy covariance measurements (data-driven models) and 10 process-based models used in Global Carbon Project. We found comparable global LUE estimates from data-driven models (0.90 ± 0.25 g C MJ −1) and process-based models (0.73 ± 0.22 g C MJ −1), with individual model estimates ranging from 0.49 to 0.98 g C MJ −1. While both types of models agreed on highest LUE over the tropical evergreen forests, discrepancies remained over middle and high latitudes of the Northern Hemisphere, which probably resulted from different response of LUE to climatic factors. Spatial analyses revealed that LUE variations were more driven by temperature and precipitation than by solar radiation. While the response of LUE to precipitation was almost always positive globally, the response of LUE to temperature was only positive in part of the nontropical regions. Process-based models showed that precipitation strongly regulated the response of LUE to temperature, which shifted from negative to positive when mean annual precipitation was larger than 1,000 mm yr −1. Our results provide converging global LUE estimates and highlight the need to take fully account of interactive effects of climatic factors in regulating LUE and thus the response of photosynthesis to climate change.

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Section: Introductionmentioning

confidence: 99%

Global Patterns and Climate Controls of Terrestrial Ecosystem Light Use Efficiency

Tang

Wang

et al. 2020

JGR Biogeosciences

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“…However, there is limited information on the relationship between multispectral VIs and IPAR f . The amount of light intercepted by a cotton canopy is highly influenced by canopy structure and architectural traits such as leaf area index ( Brodrick et al., 2013 ; Bhattacharya, 2019 ). Multiple studies have reported the association between cotton leaf area index (LAI) and multispectral VIs ( Hatfield et al., 1984 ; Zhao et al., 2007b ; Gutierrez et al., 2012 ), and insights on the relationship between IPAR f and VIs can be drawn from these studies.…”

Section: Discussionmentioning

confidence: 99%

Estimating yield-contributing physiological parameters of cotton using UAV-based imagery

Pokhrel,

Virk,

Snider

et al. 2023

Front. Plant Sci.

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Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPARf), RUE, and biomass throughout the season, 2) to estimate lint yield using the cotton fiber index (CFI), and 3) to determine the potential use of biomass and lint yield models for estimating cotton HI. An experiment was conducted during the 2021 and 2022 growing seasons in Tifton, Georgia, USA in randomized complete block design with five different nitrogen treatments. Different nitrogen treatments were applied to generate substantial variability in canopy development and yield. UAV imagery was collected bi-weekly along with light interception and biomass measurements throughout the season, and 20 different vegetation indices (VIs) were computed from the imagery. Generalized linear regression was performed to develop models using VIs and growing degree days (GDDs). The IPARf models had R2 values ranging from 0.66 to 0.90, and models based on RVI and RECI explained the highest variation (93%) in IPARf during cross-validation. Similarly, cotton above-ground biomass was best estimated by models from MSAVI and OSAVI. Estimation of RUE using actual biomass measurement and RVI-based IPARf model was able to explain 84% of variation in RUE. CFI from UAV-based RGB imagery had strong relationship (R2 = 0.69) with machine harvested lint yield. The estimated HI from CFI-based lint yield and MSAVI-based biomass models was able to explain 40 to 49% of variation in measured HI for the 2022 growing season. The models developed to estimate the yield-contributing physiological parameters in cotton showed low to strong performance, with IPARf and above-ground biomass having greater prediction accuracy. Future studies on accurate estimation of lint yield is suggested for precise cotton HI prediction. This study is the first attempt of its kind and the results can be used to expand and improve research on predicting functional yield drivers of cotton.

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“…A number of published references considered the PAR levels that crops process as an input to model the behavior of mixed plantations: coconut/corn (Dauzat and Eroy, 1997), maize/peanut (Awal et al, 2006), wheat/cotton (Zhang et al, 2008), maize/soybean (Liu et al, 2018). Research in the field of plant physiology and ecology has included the value of the incident PAR as a key variable for modeling the photosynthetic behavior of plants in response to sunlight (Bhattacharya, 2019;Campillo et al, 2012;Jajoo et al, 2014;Ogburn and Edwards, 2010), for selecting photosynthetically efficient cultivars (Peng et al, 2000), for analyzing the metabolic behavior of aquatic species (O'Gorman et al, 2012) and, in some examples, plant growth and flowering (Bredmose and Costes, 2017).…”

Section: Introductionmentioning

confidence: 99%

Validation and calibration of models to estimate photosynthetically active radiation considering different time scales and sky conditions

Blas

García-Rodríguez

García

et al. 2022

Advances in Space Research

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Radiation-Use Efficiency Under Different Climatic Conditions

Cited by 8 publications

References 438 publications

Global Patterns and Climate Controls of Terrestrial Ecosystem Light Use Efficiency

Global Patterns and Climate Controls of Terrestrial Ecosystem Light Use Efficiency

Estimating yield-contributing physiological parameters of cotton using UAV-based imagery

Validation and calibration of models to estimate photosynthetically active radiation considering different time scales and sky conditions

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