scholarly journals Measured and modeled interactive effects of potassium deficiency and water deficit on gross primary productivity and light‐use efficiency in E ucalyptus grandis plantations

2015 ◽  
Vol 21 (5) ◽  
pp. 2022-2039 ◽  
Author(s):  
Mathias Christina ◽  
Guerric Le Maire ◽  
Patricia Battie‐Laclau ◽  
Yann Nouvellon ◽  
Jean‐Pierre Bouillet ◽  
...  
Keyword(s):  
Water Deficit ◽  
Primary Productivity ◽  
Potassium Deficiency ◽  
Gross Primary Productivity ◽  
Interactive Effects ◽  
Light Use Efficiency ◽  
Use Efficiency

scholarly journals Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna

2017 ◽  
Vol 14 (1) ◽  
pp. 111-129 ◽  
Author(s):  
Caitlin E. Moore ◽  
Jason Beringer ◽  
Bradley Evans ◽  
Lindsay B. Hutley ◽  
Nigel J. Tapper
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Seasonal Pattern ◽  
Gross Primary Productivity ◽  
Perennial Grasses ◽  
Light Use Efficiency ◽  
Tropical Savanna ◽  
Flux Tower ◽  
Use Efficiency

Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological dynamics that vary spatially and temporally with climate. Australian savannas comprise a complex variety of life forms and phenologies, from evergreen trees to annual/perennial grasses, producing a boom–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured the temporal dynamics of the understory (grass) and overstory (trees) phenology and were correlated well with tower GPP for understory (r2 =  0.65 to 0.72) but less so for the overstory (r2 =  0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 =  0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.

scholarly journals Environment-sensitivity functions for gross primary productivity in light use efficiency models

2022 ◽  
Vol 312 ◽  
pp. 108708
Author(s):  
Shanning Bao ◽  
Thomas Wutzler ◽  
Sujan Koirala ◽  
Matthias Cuntz ◽  
Andreas Ibrom ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Light Use Efficiency ◽  
Sensitivity Functions ◽  
Use Efficiency

Comparison of the Photochemical Reflectance Index and Solar-induced Fluorescence for Estimating Gross Primary Productivity

2020 ◽  
Author(s):  
Qian Zhang ◽  
Jinghua Chen
Keyword(s):  
Linear Model ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Light Use Efficiency ◽  
Photochemical Reflectance Index ◽  
Maize Field ◽  
Number Of Factors ◽  
Use Efficiency ◽  
Better Than ◽  
Angle Observation

<p>Photochemical reflectance index (PRI) as a proxy for light use efficiency (LUE) has the potential to improve the estimates of vegetation gross primary productivity (GPP) using LUE model. Solar-induced fluorescence (SIF) has increasingly been shown to be a promising approach for directly estimating GPP. However, a number of factors including the view-geometry and environmental variables, which may disassociate PRI and SIF products from photosynthesis, are important for the estimation of GPP, but rarely investigated. In this study, we observed multi-angle SIF and PRI in a maize field during the 2018 growing season, and compared the PRI-based LUE model and SIF-based linear model in estimating GPP. Our results showed that the averaged PRI and SIF using the multi-angle observations performed better than the single angle observed PRI and SIF in estimating LUE and, GPP respectively. We also found that the seasonal GPP dynamics were better captured by the SIF-based linear model (R<sup>2</sup>=0.50) than the PRI-based LUE model (R<sup>2</sup>=0.45), while the PRI-based LUE model performed better in estimating the diurnal variations of GPP (R<sup>2</sup>=0.71). Random forest analysis demonstrated that PAR and RH were of the most importance in the estimation of diurnal GPP variations using the SIF-based and the PRI-based models, respectively. The PRI-based LUE model performed better than the SIF-based model under most environmental conditions, while SIF should be a preference under clear days (Q>2). Thus, our study confirmed the importance of multi-angle observation of SIF and PRI in estimating GPP and LUE, and suggested that the environmental effects should be considered for accurately estimating GPP using SIF and PRI.</p>

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