scholarly journals Improved light and temperature responses for light use efficiency based GPP models

2013 ◽  
Vol 10 (5) ◽  
pp. 8919-8947 ◽  
Author(s):  
I. McCallum ◽  
O. Franklin ◽  
E. Moltchanova ◽  
L. Merbold ◽  
C. Schmullius ◽  
...  
Keyword(s):  
Cross Validation ◽  
Model Performance ◽  
Light Response ◽  
Temperature Acclimation ◽  
Light Use Efficiency ◽  
Diagnostic Models ◽  
Non Linear ◽  
Use Efficiency ◽  
Big Leaf Model ◽  
Leaf Model

Abstract. Gross primary production (GPP) is the process by which carbon enters ecosystems. Diagnostic models, based on the theory of light use efficiency (LUE) have emerged as one method to estimate ecosystem GPP. However, problems have been noted particularly when applying global results at regional levels. We hypothesize that accounting for non-linear light response and temperature acclimation of daily GPP in boreal regions will improve model performance. To test this hypothesis, we have chosen four diagnostic models for comparison, namely: an LUE model (linear in its light response) both with and without temperature acclimation and an LUE model and a big leaf model both with temperature acclimation and non-linear in their light response. All models include environmental modifiers for temperature and vapour pressure deficit (VPD). Initially, all models were calibrated against four eddy covariance sites within Russia for the years 2002–2004, for a total of 10 site years. Model evaluation was performed via 10-out cross-validation. This study presents a methodology for comparing diagnostic modeling approaches. Cross validation clearly demonstrates the improvement in model performance that temperature acclimation makes in modeling GPP at strongly temperature controlled sites in Russia. Additionally, the inclusion of a non-linear light response function is shown to further improve performance. Furthermore we demonstrate the parameterization of the big leaf model, incorporating environmental modifiers for temperature and VPD.

scholarly journals Improved light and temperature responses for light-use-efficiency-based GPP models

2013 ◽  
Vol 10 (10) ◽  
pp. 6577-6590 ◽  
Author(s):  
I. McCallum ◽  
O. Franklin ◽  
E. Moltchanova ◽  
L. Merbold ◽  
C. Schmullius ◽  
...  
Keyword(s):  
Cross Validation ◽  
Gross Primary Production ◽  
Model Performance ◽  
Light Response ◽  
Temperature Acclimation ◽  
Light Use Efficiency ◽  
Cold Temperatures ◽  
Non Linear ◽  
Use Efficiency ◽  
Temperature Controlled

Abstract. Gross primary production (GPP) is the process by which carbon enters ecosystems. Models based on the theory of light use efficiency (LUE) have emerged as an efficient method to estimate ecosystem GPP. However, problems have been noted when applying global parameterizations to biome-level applications. In particular, model–data comparisons of GPP have shown that models (including LUE models) have difficulty matching estimated GPP. This is significant as errors in simulated GPP may propagate through models (e.g. Earth system models). Clearly, unique biome-level characteristics must be accounted for if model accuracy is to be improved. We hypothesize that in boreal regions (which are strongly temperature controlled), accounting for temperature acclimation and non-linear light response of daily GPP will improve model performance. To test this hypothesis, we have chosen four diagnostic models for comparison, namely an LUE model (linear in its light response) both with and without temperature acclimation and an LUE model and a big leaf model both with temperature acclimation and non-linear in their light response. All models include environmental modifiers for temperature and vapour pressure deficit (VPD). Initially, all models were calibrated against five eddy covariance (EC) sites within Russia for the years 2002–2005, for a total of 17 site years. Model evaluation was performed via 10-out cross-validation. Cross-validation clearly demonstrates the improvement in model performance that temperature acclimation makes in modelling GPP at strongly temperature-controlled sites in Russia. These results would indicate that inclusion of temperature acclimation in models on sites experiencing cold temperatures is imperative. Additionally, the inclusion of a non-linear light response function is shown to further improve performance, particularly in less temperature-controlled sites.

Estimating photosynthetic light-use efficiency using the photochemical reflectance index: variations among species

2004 ◽  
Vol 31 (3) ◽  
pp. 255 ◽  
Author(s):  
Jianmin Guo ◽  
Craig M. Trotter
Keyword(s):  
Light Response ◽  
Species Group ◽  
Photosynthetic Parameters ◽  
Co2 Uptake ◽  
Light Use Efficiency ◽  
Photochemical Reflectance Index ◽  
Response Data ◽  
Use Efficiency ◽  
Species Specific ◽  
The Relationship

Recent studies have shown that the photochemical reflectance index (PRI), derived from narrow waveband reflectance at 531 and 570 nm, can be used as a remote measure of photosynthetic light-use efficiency (LUE). However, uncertainty remains as to the consistency of the relationship between PRI and LUE across species. In this study we examined the relationship between the PRI and various photosynthetic parameters for a group of species with varying photosynthetic capacity. At constant irradiance, for the species group as a whole, the PRI was well correlated with LUE (r2=0.58) and with several other photosynthetic parameters, but best correlated with the ratio of carotenoids to chlorophylls contents (Caro / Chl). Despite the interspecific trends observed, determination of light response functions for the PRI in relation to photosynthetic parameters revealed that species-specific relationships were clearly stronger. For example, r2>0.90 for species-level PRI / LUE relationships. Also, the species-specific light-response data show that the magnitude of the PRI can be related to the magnitude of the saturated irradiance and the rate of CO2 uptake. As demonstrated here, a light response function provides a simple yet precise approach for characterising the relationship between the PRI and photosynthetic parameters, which should assist with improved evaluation of the usefulness of the PRI as a generalised measure of LUE.

scholarly journals Monte Carlo based calibration and uncertainty analysis of a coupled plant growth and hydrological model

2013 ◽  
Vol 10 (12) ◽  
pp. 19509-19540 ◽  
Author(s):  
T. Houska ◽  
S. Multsch ◽  
P. Kraft ◽  
H.-G. Frede ◽  
L. Breuer
Keyword(s):  
Monte Carlo ◽  
Plant Growth ◽  
Cross Validation ◽  
Model Performance ◽  
Coupled Model ◽  
Radiation Use Efficiency ◽  
Retention Curve ◽  
Modelling Framework ◽  
Use Efficiency ◽  
Radiation Use

Abstract. Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures, e.g. by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow a more detailed analysis of the dynamic behaviour of the soil–plant interface. We used the Python programming language to couple two of such high process oriented independent models and to calibrate both models simultaneously. The Catchment Modelling Framework (CMF) simulated soil hydrology based on the Richards equation and the van-Genuchten–Mualem retention curve. CMF was coupled with the Plant growth Modelling Framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation. The Monte Carlo based Generalised Likelihood Uncertainty Estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions to it. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 106 model runs randomly drawn from an equally distributed parameter space. Three objective functions were used to evaluate the model performance, i.e. coefficient of determination (R2), bias and model efficiency according to Nash Sutcliffe (NSE). The model was applied to three sites with different management in Muencheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matters of roots, storages, stems and leaves. Best parameter sets resulted in NSE of 0.57 for the simulation of soil moisture across all three sites. The shape parameter of the retention curve n was highly constrained whilst other parameters of the retention curve showed a large equifinality. The root and storage dry matter observations were predicted with a NSE of 0.94, a low bias of −58.2 kg ha−1 and a high R2 of 0.98. Dry matters of stem and leaves were predicted with less, but still high accuracy (NSE = 0.79, bias = 221.7 kg ha−1, R2 = 0.87). We attribute this slightly poorer model performance to missing leaf senescence which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use-efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need of including agricultural management options in the coupled model.

scholarly journals Quality control stress test for deep learning-based diagnostic model in digital pathology

2021 ◽  
Author(s):  
Birgid Schömig-Markiefka ◽  
Alexey Pryalukhin ◽  
Wolfgang Hulla ◽  
Andrey Bychkov ◽  
Junya Fukuoka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Detection ◽  
Stress Testing ◽  
Computational Analysis ◽  
Stress Test ◽  
Digital Pathology ◽  
Model Performance ◽  
Diagnostic Model ◽  
Model Accuracy ◽  
Diagnostic Models

AbstractDigital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

Enhanced light interception and light use efficiency explain overyielding in young tree communities

2021 ◽  
Author(s):  
Laura J. Williams ◽  
Ethan E. Butler ◽  
Jeannine Cavender‐Bares ◽  
Artur Stefanski ◽  
Karen E. Rice ◽  
...  
Keyword(s):  
Light Interception ◽  
Light Use Efficiency ◽  
Young Tree ◽  
Tree Communities ◽  
Use Efficiency
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