Scaling the spatial distribution of photosynthesis from leaf to canopy in a plant growth chamber

Boonen, Christiaan; Samson, Roeland; Janssens, Karl; Pien, Helga; Lemeur, Raoul; Berckmans, Daniël

doi:10.1016/s0304-3800(02)00171-0

Cited by 23 publications

(11 citation statements)

References 17 publications

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“…As far as diffuse irradiance is concerned, we substitute k b (θ) with k d in equation 6. The extinction and reflectance coefficients for the planophile representation as well as the variables and values used for the multilayer models are presented in the appendix.…”

Section: Multilayer Canopy Modelsmentioning

confidence: 99%

Assessing the Impact of Canopy Structure Simplification in Common Multilayer Models on Irradiance Absorption Estimates of Measured and Virtually Created Fagus sylvatica (L.) Stands

et al. 2009

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Abstract:Multilayer canopy representations are the most common structural stand representations due to their simplicity. Implementation of recent advances in technology has allowed scientists to simulate geometrically explicit forest canopies. The effect of simplified representations of tree architecture (i.e., multilayer representations) of four Fagus sylvatica (L.) stands, each with different LAI, on the light absorption estimates was assessed in comparison with explicit 3D geometrical stands. The absorbed photosynthetic radiation at stand level was calculated. Subsequently, each geometrically explicit 3D stand was compared with three multilayer models representing horizontal, uniform, and planophile leaf angle distributions. The 3D stands were created either by in situ measured trees or by modelled trees generated with the AMAP plant growth software. The Physically Based Ray Tracer (PBRT) algorithm was used to simulate the irradiance absorbance of the detailed 3D architecture stands, while for the three multilayer representations, the probability of light interception was simulated by applying the Beer-Lambert's law. The irradiance inside the canopies was characterized as direct, diffuse and scattered irradiance. The irradiance absorbance of the stands was computed during OPEN ACCESSRemote Sens. 2009, 1 1010 eight angular sun configurations ranging from 10° (near nadir) up to 80° sun zenith angles. Furthermore, a leaf stratification (the number and angular distribution of leaves per LAI layer inside a canopy) analysis between the 3D stands and the multilayer representations was performed, indicating the amount of irradiance each leaf is absorbing along with the percentage of sunny and shadow leaves inside the canopy. The results reveal that a multilayer representation of a stand, using a multilayer modelling approach, greatly overestimated the absorbed irradiance in an open canopy, while it provided a better approximation in the case of a closed canopy. Moreover, the actual stratification of leaves differed significantly between a multilayer representation and a 3D architecture canopy of the same LAI. The deviations in irradiance absorbance were caused by canopy structure, clumping and positioning of leaves. Although it was found that the use of canopy simplifications for modelling purposes in closed canopies is demonstrated as a valid option, special care should be taken when considering forest stands irradiance simulation for sparse canopies and particularly on higher sun zenith angles where the surrounding trees strongly affect the absorbed irradiance and results can highly deviate from the multilayer assumptions.

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Section: Multilayer Canopy Modelsmentioning

confidence: 99%

Assessing the Impact of Canopy Structure Simplification in Common Multilayer Models on Irradiance Absorption Estimates of Measured and Virtually Created Fagus sylvatica (L.) Stands

et al. 2009

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“…In addition, mean temperatures will probably increase due to climate change, a trend already detected in the last years in Spain (Del Río, S., Penas, A., Fraile, R., 2004), so an increase of transparency is expected also. This rise in maximum temperatures may cause physiologic effects in trees, having a negative impact on primary processes as photosynthesis and causing the increase of respiration rates (Boonen, C., Samson, R., Janssens, K., Pien, H., Lemeur, R., Berckmans, D., 2002).…”

Section: Climate Change Scenariosmentioning

confidence: 99%

Influence of Climatic Variables on Crown Condition in Pine Forests of Northern Spain

Sanz-Ros

2017

Managing Forest Ecosystems: The Challenge of Climate Change

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“…Similarly, researchers have included photosynthesis in models aimed at optimizing C0 2 enrichment for tomato crops (Aikman, 1996). Photosynthesis Model 2: This model has also been used in (Boonen, et al, 2002). This quantity can be scaled by LAI to provide the equivalent amount of photosynthesis per unit soil area.…”

Section: ^ = ±{(K V + Kit 0 -T G ) + K P {T P -T G ) + K S {T S -T Gmentioning

confidence: 99%

“…The spatial distribution of plant photosynthesis from leaf to canopy has been studied (Boonen, 2002 contributed to the greenhouse air by photosynthesis is potentially significant, although probably not as significant as incorrectly modeling the effects of humidity. The above equations do not contribute much to the overall C0 2 concentration of the greenhouse in practice but they do predict what an increased level of C0 2 will have on photosynthesis.…”

Section: ^ = ±{(K V + Kit 0 -T G ) + K P {T P -T G ) + K S {T S -T Gmentioning

confidence: 99%

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Cybernetic Approach to Modeling Greenhouse Dynamics

Glackin¹,

Siddique²

2008

Journal of Intelligent Systems

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More-accurate control, resulting in increased yield, higher quality, and minimizing costs to the grower remain the main driving forces of greenhouse climate research.Studies using techniques such as computational fluid dynamics, plant sensor measurements, and tracer gas analysis of greenhouses are now widespread. Feedback processes such as mass and energy transfers between the crop and its environment are the focus of this paper. Of the panoply of possible strategies for modeling greenhouse climate that could have been used, this study investigates greenhouse climate modeling in terms of biological cybernetics. The approach here is simply to represent the most important components of the greenhouse climate with a view to developing an accurate neural controller, the production of which involves system identification to produce first a neural reference model. For the neural model to have flexibility in predicting the dynamics of abrupt weather changes and the complex feedback processes between the crop and its environment requires good training data. In this paper, different greenhouse control strategies are reviewed, and a biological cybernetic model is constructed. The model is then used to train a neural network, with the training results presented. Cybernetic Approach to ModelingGreenhouse Dynamics system identification approach. Alternatively, quasi-static models consider the greenhouse as being comprised of two distinct parts, namely a crop model and a greenhouse model (Schmidt, 1996). The two sub-models vary in a turn-based way during simulation with the speed at which each part can react to such stimulus as adverse changes in weather. In essence, these models are assuming that the exchange processes for temperature, water vapor and C0 2 between the crop and the greenhouse environment are minimal, and that these processes are slow. Plant processes, for example photosynthesis exhibit almost instantaneous response to stimulus and as such can cause serious errors with these models. Similarly, problems occur when solar radiation levels fluctuate due to scattered clouds (Tap et al., 1993).Ultimately, such significant errors affect the seasonal performance of the crop. A more effective strategy is to treat the crop as a biological process to be controlled by monitoring the crop's reaction to climate. This is what is termed the 'speaking plant concept' (Udink ten Cate et al., 1978), and this has been successfully implemented to good effect in controlling humidity (de Jong & Stranghellini, 1996, Schmidt, 1996.This approach ideally entails the use of sensors to monitor the crop's progress.However, progress can be made toward this ideal by modeling the feedback exchanges between the crop and its environment.In this study, a biological cybernetic framework of the model is provided by a set of coupled linear differential equations. The framework introduces numerous variables that enable many other processes to be included, such as plant dynamics and meteorological data. The development of the model and the use of a set...

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Scaling the spatial distribution of photosynthesis from leaf to canopy in a plant growth chamber

Cited by 23 publications

References 17 publications

Assessing the Impact of Canopy Structure Simplification in Common Multilayer Models on Irradiance Absorption Estimates of Measured and Virtually Created Fagus sylvatica (L.) Stands

Assessing the Impact of Canopy Structure Simplification in Common Multilayer Models on Irradiance Absorption Estimates of Measured and Virtually Created Fagus sylvatica (L.) Stands

Influence of Climatic Variables on Crown Condition in Pine Forests of Northern Spain

Cybernetic Approach to Modeling Greenhouse Dynamics

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