Canopy Photosynthesis Model Research Articles

Maximizing the Radiation Use Efficiency by Matching the Leaf Area and Leaf Nitrogen Vertical Distributions in a Maize Canopy: A Simulation Study.

The radiation use efficiency (RUE) is one of the most important functional traits determining crop productivity. The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence. However, previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth, rarely considering the optimization of the leaf area distribution, especially for C4 crops. The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy. To test this hypothesis, various virtual maize canopies were generated by combining the leaf inclination angle, vertical leaf area distribution, and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model. We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE. The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios, particularly in dense canopies. This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.

Spatial Estimation of Daily Growth Biomass in Paddy Rice Field Using Canopy Photosynthesis Model Based on Ground and UAV Observations

Precision farming, a labor-saving and highly productive form of management, is gaining popularity as the number of farmers declines in comparison to the increasing global food demand. However, it requires more efficient crop phenology observation and growth monitoring. One measure is the leaf area index (LAI), which is essential for estimating biomass and yield, but its validation requires destructive field measurements. Thus, using ground and UAV observation data, this study developed a method for indirect LAI estimation based on relative light intensity under a rice canopy. Daily relative light intensity was observed under the canopy at several points in paddy fields, and a weekly plant survey was conducted to measure the plant length, above-ground biomass, and LAI. Furthermore, images from ground-based and UAV-based cameras were acquired to generate NDVI and the canopy height (CH), respectively. Using the canopy photosynthetic model derived from the Beer–Lambert law, the daily biomass was estimated by applying the weekly estimated LAI using CH and the observed light intensity data as input. The results demonstrate the possibility of quantitatively estimating the daily growth biomass of rice plants, including spatial variation. The near-real-time estimation method for rice biomass by integrating observation data at fields with numerical models can be applied to the management of major crops.

Disentangling the Heterosis in Biomass Production and Radiation Use Efficiency in Maize: A Phytomer-Based 3D Modelling Approach

Maize (Zea mays L.) benefits from heterosis in-yield formation and photosynthetic efficiency through optimizing canopy structure and improving leaf photosynthesis. However, the role of canopy structure and photosynthetic capacity in determining heterosis in biomass production and radiation use efficiency has not been separately clarified. We developed a quantitative framework based on a phytomer-based three-dimensional canopy photosynthesis model and simulated light capture and canopy photosynthetic production in scenarios with and without heterosis in either canopy structure or leaf photosynthetic capacity. The accumulated above-ground biomass of Jingnongke728 was 39% and 31% higher than its male parent, Jing2416, and female parent, JingMC01, while accumulated photosynthetically active radiation was 23% and 14% higher, correspondingly, leading to an increase of 13% and 17% in radiation use efficiency. The increasing post-silking radiation use efficiency was mainly attributed to leaf photosynthetic improvement, while the dominant contributing factor differs for male and female parents for heterosis in post-silking yield formation. This quantitative framework illustrates the potential to identify the key traits related to yield and radiation use efficiency and helps breeders to make selections for higher yield and photosynthetic efficiency.

Identification of photosynthetic parameters for superior yield of two super hybrid rice varieties: A cross-scale study from leaf to canopy.

Enhancing photosynthetic capacity is widely accepted as critical to advancing crop yield. Therefore, identifying photosynthetic parameters positively related to biomass accumulation in elite cultivars is the major focus of current rice research. In this work, we assessed leaf photosynthetic performance, canopy photosynthesis, and yield attributes of super hybrid rice cultivars Y-liangyou 3218 (YLY3218) and Y-liangyou 5867 (YLY5867) at tillering stage and flowering stage, using inbred super rice cultivars Zhendao11(ZD11) and Nanjing 9108 (NJ9108) as control. A diurnal canopy photosynthesis model was applied to estimate the influence of key environmental factors, canopy attributes, and canopy nitrogen status on daily aboveground biomass increment (AMDAY). Results showed that primarily the light-saturated photosynthetic rate at tillering stage contributed to the advancing yield and biomass of super hybrid rice in comparison to inbred super rice, and the light-saturated photosynthetic rate between them was similar at flowering stage. At tillering stage, the higher CO2 diffusion capacity, together with higher biochemical capacity (i.e., maximum carboxylation rate of Rubisco, maximum electron transport rate (J max), and triose phosphate utilization rate) favored leaf photosynthesis of super hybrid rice. Similarly, AMDAY in super hybrid rice was higher than inbred super rice at tillering stage, and comparable at flowering stage partially due to increased canopy nitrogen concentration (SLNave) of inbred super rice. At tillering stage, model simulation revealed that replacement of J max and g m in inbred super rice by super hybrid rice always had a positive effect on AMDAY, and the averaged AMDAY increment was 5.7% and 3.4%, respectively. Simultaneously, the 20% enhancement of total canopy nitrogen concentration through the improvement of SLNave (TNC-SLNave) resulted in the highest AMDAY across cultivars, with an average increase of 11.2%. In conclusion, the advancing yield performance of YLY3218 and YLY5867 was due to the higher J max and g m at tillering stage, and TCN-SLNave is a promising target for future super rice breeding programs.

Hybrid AI model for estimating the canopy photosynthesis of eggplants.

Modern models for estimating canopy photosynthetic rates (Ac) can be broadly classified into two categories, namely, process-based mechanistic models and artificial intelligence (AI) models, each category having unique strengths (i.e., process-based models have generalizability to a wide range of situations, and AI models can reproduce a complex process using data without prior knowledge about the underlying mechanism). To exploit the strengths of both categories of models, a novel "hybrid" canopy photosynthesis model that combines process-based models with an AI model was proposed. In the proposed hybrid model, process-based models for single-leaf photosynthesis and image analysis first transform raw inputs (environmental data and canopy images) into the single-leaf photosynthetic rate (AL) and effective leaf area index (Lc)), after which AL and Lc are fed into an artificial neural network (ANN) model to predict Ac. The hybrid model successfully predicted the diurnal cycles of Ac of an eggplant canopy even with a small training dataset and successfully reproduced a typical Ac response to changes in the CO2 concentration outside the range of the training data. The proposed hybrid AI model can provide an effective means to estimate Ac in actual crop fields, where obtaining a large amount of training data is difficult.

Use of 3D modeling to refine predictions of canopy light utilization: A comparative study on canopy photosynthesis models with different dimensions.

Canopy photosynthesis integrates leaf functional and structural traits in space and time and correlates positively with yield formation. Many models with different levels of architectural details ranging from zero-dimensional (0D) to three-dimensional (3D) have been developed to simulate canopy light interception and photosynthesis. Based on these models, a crop growth model can be used to assess crop yield in response to genetic improvement, optimized practices, and environmental change. However, to what extent do architectural details influence light interception, photosynthetic production, and grain yield remains unknown. Here, we show that a crop growth model with high-resolution upscaling approach in space reduces the departure of predicted yield from actual yield and refines the simulation of canopy photosynthetic production. We found crop yield predictions decreased by 12.0-48.5% with increasing the resolution of light simulation, suggesting that a crop growth model without architectural details may result in a considerable departure from the actual photosynthetic production. A dramatic difference in light interception and photosynthetic production of canopy between cultivars was captured by the proposed 3D model rather than the 0D, 1D, and 2D models. Furthermore, we found that the overestimation of crop yield by the 0D model is caused by the overestimation of canopy photosynthetically active radiation (PAR) interception and the RUE and that by the 1D and 2D model is caused by the overestimated canopy photosynthesis rate that is possibly related to higher predicted PAR and fraction of sunlit leaves. Overall, this study confirms the necessity of taking detailed architecture traits into consideration when evaluating the strategies of genetic improvement and canopy configuration in improving crop yield by crop modeling.

A canopy photosynthesis model based on a highly generalizable artificial neural network incorporated with a mechanistic understanding of single-leaf photosynthesis

• A canopy photosynthesis model was built based on an artificial neural network (ANN) • The model was combined with a process-based single-leaf photosynthesis model • The model could predict the canopy photosynthetic rate under various environments • The model was highly generalizable even when training data were scarce Crop productivity is largely dependent on canopy photosynthesis, which is difficult to measure at farming sites. Therefore, real-time estimation of the canopy photosynthetic rate ( A c ) is expected to facilitate effective farm management. For the estimation of A c , two types of mathematical models ( i.e ., process-based models and empirical models) have been used, although both types have their own weaknesses. Process-based models inevitably require many model parameters that are difficult to identify, while empirical models, including artificial neural network (ANN) models, have a low predictive ability outside of the range of training datasets. To overcome these weaknesses, we developed a hybrid canopy photosynthesis model that included components of both process-based models and ANN models. In this hybrid model, the single-leaf photosynthetic rate ( A L ) and leaf area index (LAI) were first estimated from information easily obtainable at farming sites: A L was estimated by the process-based model of A L ( i.e ., the biochemical photosynthesis model of Farquhar et al . (1980)) from environmental data (photosynthetic photon flux density (PPFD), air temperature ( T a ), humidity, and atmospheric CO 2 concentration ( C a )), and the LAI was estimated by an analysis of crop canopy imagery. As highly explainable information for A c , the estimated A L and LAI were input into the ANN model to estimate A c . As such, the ANN model learned the logical relationships between the inputs ( A L and LAI) and the output ( A c ). Detailed validation analysis using nine spinach A c datasets revealed that the hybrid ANN model can estimate A c accurately throughout the whole growth period, even when training and test datasets were obtained in different seasons under different CO 2 concentrations and based on training datasets of only three days. This study highlights the high generalizability of the hybrid ANN model, which is a prerequisite for practical application in environmentally controlled crop production.

Supra-optimal leaf area index of a temperate liana Pueraria lobata for competition with Solidago altissima at the expense of canopy photosynthesis.

Leaf area index (LAI) measured for the actual plant canopy is higher than the LAI that maximizes canopy photosynthesis (referred to as optimal LAI) because each individual can increase its light interception by unilaterally producing more leaf area. The LAI of an invasive woody vine Pueraria lobata (kudzu) is one of the highest among plant species, sometimes attaining nearly 10m2m-2. The high LAI casts heavy shade over neighboring plants, making their survival difficult. Interesting to note is that the high LAI also increases self-shading, thereby decreasing its own photosynthesis processes. In the present study, the influences of the high LAI on light interception and canopy photosynthesis, as well as on the inter-specific competition was investigated on a roadside P. lobata vegetation in Japan. With the aid of a canopy photosynthesis model and a sensitivity analysis, it was revealed that the actual LAI was 2.2-3.0 times higher than the optimal LAI for maximizing canopy photosynthesis. In the following year, a field experiment was conducted where a nearly optimal LAI was maintained throughout the growth period by regularly clipping the leaves of P. lobata. Ultimately, the field results revealed that even with a nearly optimal LAI, P. lobata was outcompeted by a competing alien weed, Solidago altissima (tall goldenrod). These results indicate that the supra-optimal leaf area, rather than maximum canopy carbon gain, makes P. lobata the dominating species in light-competing environments.

Scaling the leaf length-times-width equation to predict total leaf area of shoots.

Background and AimsAn individual plant consists of different-sized shoots, each of which consists of different-sized leaves. To predict plant-level physiological responses from the responses of individual leaves, modelling this within-shoot leaf size variation is necessary. Within-plant leaf trait variation has been well investigated in canopy photosynthesis models but less so in plant allometry. Therefore, integration of these two different approaches is needed.MethodsWe focused on an established leaf-level relationship that the area of an individual leaf lamina is proportional to the product of its length and width. The geometric interpretation of this equation is that different-sized leaf laminas from a single species share the same basic form. Based on this shared basic form, we synthesized a new length-times-width equation predicting total shoot leaf area from the collective dimensions of leaves that comprise a shoot. Furthermore, we showed that several previously established empirical relationships, including the allometric relationships between total shoot leaf area, maximum individual leaf length within the shoot and total leaf number of the shoot, can be unified under the same geometric argument. We tested the model predictions using five species, all of which have simple leaves, selected from diverse taxa (Magnoliids, monocots and eudicots) and from different growth forms (trees, erect herbs and rosette herbs).Key ResultsFor all five species, the length-times-width equation explained within-species variation of total leaf area of a shoot with high accuracy (R2 > 0.994). These strong relationships existed despite leaf dimensions scaling very differently between species. We also found good support for all derived predictions from the model (R2 > 0.85).ConclusionsOur model can be incorporated to improve previous models of allometry that do not consider within-shoot size variation of individual leaves, providing a cross-scale linkage between individual leaf-size variation and shoot-size variation.

Faster than expected Rubisco deactivation in shade reduces cowpea photosynthetic potential in variable light conditions

Cowpea is the major source of vegetable protein for rural populations in sub-Saharan Africa and average yields are not keeping pace with population growth. Each day, crop leaves experience many shade events and the speed of photosynthetic adjustment to this dynamic environment strongly affects daily carbon gain. Rubisco activity is particularly important because it depends on the speed and extent of deactivation in shade and recovers slowly on return to sun. Here, direct biochemical measurements showed a much faster rate of Rubisco deactivation in cowpea than prior estimates inferred from dynamics of leaf gas exchange in other species1–3. Shade-induced deactivation was driven by decarbamylation, and half-times for both deactivation in shade and activation in saturating light were shorter than estimates from gas exchange (≤53% and 79%, respectively). Incorporating these half-times into a model of diurnal canopy photosynthesis predicted a 21% diurnal loss of productivity and suggests slowing Rubisco deactivation during shade is an unexploited opportunity for improving crop productivity.

Coupling Photosynthetic Measurements with Biometric Data to Estimate Gross Primary Productivity (GPP) in Mediterranean Pine Forests of Different Post-Fire Age

Quantification of forest Gross Primary Productivity (GPP) is important for understanding ecosystem function and designing appropriate carbon mitigation strategies. Coupling forest biometric data with canopy photosynthesis models can provide a means to simulate GPP across different stand ages. In this study we developed a simple framework to integrate biometric and leaf gas-exchange measurements, and to estimate GPP across four Mediterranean pine forests of different post-fire age. We used three different methods to estimate the Leaf Area Index (LAI) of the stands, and monthly gas exchange data to calibrate the photosynthetic light response of the leaves. Upscaling of carbon sequestration at the canopy level was made by implementing a Big Leaf and a Sun/Shade model, using both average and variant (monthly) photosynthetic capacity values. The Big Leaf model simulations systematically underestimated GPP compared to the Sun/Shade model simulations. Our simulations suggest an increasing GPP with age up to a stand maturity stage. The shape of the GPP trend with stand age was not affected by the method used to parameterise the model. At the scale of our study, variability in stand and canopy structure among the study sites seems to be the key determinant of GPP.

Long-term estimation of the canopy photosynthesis of a leafy vegetable based on greenhouse climate conditions and nadir photographs

In horticultural crop production, accurately estimating the canopy photosynthetic rate (Ac) based on greenhouse climate conditions is of practical value for predicting and controlling crop growth and yield. We developed a method for continuously estimating Ac in a greenhouse from readily obtainable information (i.e., climatic data and canopy photographs) by combining a canopy photosynthesis model with image analysis. The canopy photosynthesis model was based on a sun/shade representation of the crop canopy combined with models of single-leaf photosynthesis, stomatal conductance, mass transfer and leaf energy balance. This combination allowed 1) the incorporation of all major climatic variables (i.e., radiation, CO2 concentration, air temperature, humidity, and wind velocity) into the Ac estimation and 2) the simultaneous estimation of Ac, canopy transpiration rate (Ec), and leaf temperature (TL). The leaf area index (Lc), which changes considerably throughout the growth period of a crop canopy, was evaluated nondestructively from the gap fractions of nadir digital photographs (i.e., the fractions of nonleaf area). The canopy photosynthesis model and image analysis results were combined and used to estimate the Ac, Ec, and TL of spinach canopies grown at ambient and elevated CO2 concentrations (c. 400 and 800 µmol mol−1, respectively). Regardless of the CO2 concentration, the estimates of Ac, Ec, and TL were in good agreement with measurements obtained with the open chamber method throughout the growth period. Analyzing the sensitivity of Ac to input variables revealed that the effects of climatic variables on Ac can vary considerably depending on the Lc and incoming radiation. The proposed method enables not only the estimation of Ac from readily obtainable information but also the quantitative evaluation of the effect of climate control on Ac in horticultural greenhouses.

Rubisco substitutions predicted to enhance crop performance through carbon uptake modelling.

Improving the performance of the CO2-fixing enzyme Rubisco is among the targets for increasing crop yields. Here, Earth system model (ESM) representations of canopy C3 and C4 photosynthesis were combined with species-specific Rubisco parameters to quantify the consequences of bioengineering foreign Rubiscos into C3 and C4 crops under field conditions. The 'two big leaf' (sunlit/shaded) model for canopy photosynthesis was used together with species-specific Rubisco kinetic parameters including maximum rate (Kcat), Michaelis-Menten constant for CO2 at ambient atmospheric O2 (Kc21%O2), specificity for CO2 to O2 (Sc/o), and associated heat activation (Ha) values. Canopy-scale consequences of replacing native Rubiscos in wheat, maize, and sugar beet with foreign enzymes from 27 species were modelled using data from Ameriflux and Fluxnet databases. Variation among the included Rubisco kinetics differentially affected modelled carbon uptake rates, and Rubiscos from several species of C4 grasses showed the greatest potential of >50% carbon uptake improvement in wheat, and >25% improvement in sugar beet and maize. This study also reaffirms the need for data on fully characterized Rubiscos from more species, and for better parameterization of 'Vcmax' and temperature response of 'Jmax' in ESMs.

Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum (STEMMUS–SCOPE v1.0.0)

Abstract. Root water uptake by plants is a vital process that influences terrestrial energy, water, and carbon exchanges. At the soil, vegetation, and atmosphere interfaces, root water uptake and solar radiation predominantly regulate the dynamics and health of vegetation growth, which can be remotely monitored by satellites, using the soil–plant relationship proxy – solar-induced chlorophyll fluorescence. However, most current canopy photosynthesis and fluorescence models do not account for root water uptake, which compromises their applications under water-stressed conditions. To address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere. The coupled model was evaluated with field measurements of maize and grass canopies. The results indicated that the simulation of land surface fluxes was significantly improved by the coupled model, especially when the canopy experienced moderate water stress. This finding highlights the importance of enhanced soil heat and moisture transfer, as well as dynamic root growth, on simulating ecosystem functioning.

Canopy position affects photosynthesis and anatomy in mature Eucalyptus trees in elevated CO2.

Leaves are exposed to different light conditions according to their canopy position, resulting in structural and anatomical differences with consequences for carbon uptake. While these structure-function relationships have been thoroughly explored in dense forest canopies, such gradients may be diminished in open canopies, and they are often ignored in ecosystem models. We tested within-canopy differences in photosynthetic properties and structural traits in leaves in a mature Eucalyptus tereticornis canopy exposed to long-term elevated CO2 for up to three years. We explored these traits in relation to anatomical variation and diffusive processes for CO2 (i.e., stomatal conductance, gs and mesophyll conductance, gm) in both upper and lower portions of the canopy receiving ambient and elevated CO2. While shade resulted in 13% lower leaf mass per area ratio (MA) in lower versus upper canopy leaves, there was no relationship between leaf Nmass and canopy gap fraction. Both maximum carboxylation capacity (Vcmax) and maximum electron transport (Jmax) were~18% lower in shaded leaves and were also reduced by ~ 22% with leaf aging. In mature leaves, we found no canopy differences for gm or gs, despite anatomical differences in MA, leaf thickness and mean mesophyll thickness between canopy positions. There was a positive relationship between net photosynthesis and gm or gs in mature leaves. Mesophyll conductance was negatively correlated with mean parenchyma length, suggesting that long palisade cells may contribute to a longer CO2 diffusional pathway and more resistance to CO2 transfer to chloroplasts. Few other relationships between gm and anatomical variables were found in mature leaves, which may be due to the open crown of Eucalyptus. Consideration of shade effects and leaf-age dependent responses to photosynthetic capacity and mesophyll conductance are critical to improve canopy photosynthesis models and will improve understanding of long-term responses to elevated CO2 in tree canopies.

Inferring CO2 fertilization effect based on global monitoring land-atmosphere exchange with a theoretical model

Rising atmospheric CO2 concentration ([CO2]) enhances photosynthesis and reduces transpiration at the leaf, ecosystem, and global scale via the CO2 fertilization effect. The CO2 fertilization effect is among the most important processes for predicting the terrestrial carbon budget and future climate, yet it has been elusive to quantify. For evaluating the CO2 fertilization effect on land photosynthesis and transpiration, we developed a technique that isolated this effect from other confounding effects, such as changes in climate, using a noisy time series of observed land-atmosphere CO2 and water vapor exchange. Here, we evaluate the magnitude of this effect from 2000 to 2014 globally based on constraint optimization of gross primary productivity (GPP) and evapotranspiration in a canopy photosynthesis model over 104 global eddy-covariance stations. We found a consistent increase of GPP (0.138 ± 0.007% ppm−1; percentile per rising ppm of [CO2]) and a concomitant decrease in transpiration (−0.073% ± 0.006% ppm−1) due to rising [CO2]. Enhanced GPP from CO2 fertilization after the baseline year 2000 is, on average, 1.2% of global GPP, 12.4 g C m−2 yr−1 or 1.8 Pg C yr−1 at the years from 2001 to 2014. Our result demonstrates that the current increase in [CO2] could potentially explain the recent land CO2 sink at the global scale.

생육 챔버를 이용하여 광도 및 이산화탄소 농도 변수를 갖는 상추(Lactuca sativa L.)의 군락 광합성 곡선의 효율적 도출 방법

For developing a canopy photosynthesis model, an efficient method to measure the photosynthetic rate in a growth chamber is required. The objective of this study was to develop a method for establishing canopy photosynthetic rate curves of romaine lettuce (Lactuca sativa L.) with light intensity and CO2 concentration variables using controlled growth chamber. The plants were grown in plant factory modules, and the canopy photosynthesis rates were measured in sealed growth chambers made of acrylic (1.0 x 0.8 x 0.5 m). First, the canopy photosynthetic rates of the plants were measured, and then the time constants were compared between two application methods: 1) changing light intensity (340, 270, 200, and 130 μmol·m-2·s-1) at a fixed CO2 concentration (1,000 μmol·mol-1) and 2) changing CO2 concentration (600, 1,000, 1,400, and 1,800 μmol·mol-1) at a fixed light intensity (200 μmol·m-2·s-1). Second, the canopy photosynthetic rates were measured by changing the light intensity at a CO2 concentration of 1,000 μmol·mol-1 and compared with those measured by changing the CO2 concentration at a light intensity of 200 μmol·m-2·s-1. The time constant when changing the CO2 concentration at the fixed light intensity was 3.2 times longer, and the deviation in photosynthetic rate was larger than when changing the light intensity. The canopy photosynthetic rate was obtained stably with a time lag of one min when changing the light intensity, while a time lag of six min or longer was required when changing the CO2 concentration. Therefore, changing the light intensity at a fixed CO2 concentration is more appropriate for short-term measurement of canopy photosynthesis using a growth chamber.

Alternate Trait‐Based Leaf Respiration Schemes Evaluated at Ecosystem‐Scale Through Carbon Optimization Modeling and Canopy Property Data

AbstractLeaf maintenance respiration (Rleaf,m) is a major but poorly understood component of the terrestrial carbon cycle (C). Earth systems models (ESMs) use simple sub‐models relating Rleaf,m to leaf traits, applied at canopy scale. Rleaf,m models vary depending on which leaf N traits they incorporate (e.g., mass or area based) and the form of relationship (linear or nonlinear). To simulate vegetation responses to global change, some ESMs include ecological optimization to identify canopy structures that maximize net C accumulation. However, the implications for optimization of using alternate leaf‐scale empirical Rleaf,m models are undetermined. Here we combine alternate well‐known empirical models of Rleaf,m with a process model of canopy photosynthesis. We quantify how net canopy exports of C vary with leaf area index (LAI) and total canopy N (TCN). Using data from tropical and arctic canopies, we show that estimates of canopy Rleaf,m vary widely among the three models. Using an optimization framework, we show that the LAI and TCN values maximizing C export depends strongly on the Rleaf,m model used. No single model could match observed arctic and tropical LAI‐TCN patterns with predictions of optimal LAI‐TCN. We recommend caution in using leaf‐scale empirical models for components of ESMs at canopy‐scale. Rleaf,m models may produce reasonable results for a specified LAI, but, due to their varied representations of Rleaf,mfoliar N sensitivity, are associated with different and potentially unrealistic optimization dynamics at canopy scale. We recommend ESMs to be evaluated using response surfaces of canopy C export in LAI‐TCN space to understand and mitigate these risks.

Nitrogen supply and other controls of carbon uptake of understory vegetation in a boreal Picea abies forest

In boreal forests, carbon (C) uptake by understory may be too large to be ignored and too variable in space to be assumed a constant fraction of the ecosystem gross primary production. To improve estimates of understory production in these ecosystems, we need to better account for its main controls. In this study, we estimated C uptake of field-layer vegetation, dominated by Vaccinium myrtillus, V. vitis-idaea, and Deschampsia flexuosa, in a boreal Picea abies stand in northern Sweden. Nitrogen (N) availability in the stand has been manipulated through annual N additions since 1996 at the rates of 0, 12.5, and 50 kg N ha−1 yr−1. To assess the relative importance of N supply, and interannual fluctuations in leaf biomass and weather, in controlling field-layer photosynthetic production, we calculated C uptake over eight growing seasons using a canopy photosynthesis model. Without N additions, tree leaf area index (L) was already high (8.5) and field-layer C uptake was small, 27 g C m−2 (or ∼3% of stand C uptake). An increase in tree L with N additions further reduced light availability for the understory, yet the concurrent increase in the relative abundance of the more physiologically active D. flexuosa sustained the contribution of the field-layer to stand photosynthetic production. Based on a literature survey, in which site quality or stand age generated a wide range in L, understory contribution to ecosystem C uptake increases linearly with the fraction of available light reaching the forest floor across high latitude forests. Understory contributes only ∼5% to ecosystem C uptake where trees intercept ∼80% of incoming light, increasing to 100% after clearcut tree harvest. While the availability of solar energy, both spatially and temporally, is the primary driver of understory production, our analyses suggest that the predicted increases in drought severity and frequency at high latitudes may affect understory communities more than trees. Future empirical and modeling studies should focus on functional and ecological responses to drought of not only trees but also understory species, which contribute to biodiversity and convert their photosynthates to important non-timber products.

A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties.

In current rice breeding programs, morphological parameters such as plant height, leaf length and width, leaf angle, panicle architecture, and tiller number during the grain filling stage are used as major selection targets. However, so far, there is no robust approach to quantitatively define the optimal combinations of parameters that can lead to increased canopy radiation use efficiency (RUE). Here we report the development of a three-dimensional canopy photosynthesis model (3dCAP), which effectively combines three-dimensional canopy architecture, canopy vertical nitrogen distribution, a ray-tracing algorithm, and a leaf photosynthesis model. Concurrently, we developed an efficient workflow for the parameterization of 3dCAP. 3dCAP predicted daily canopy RUE for different nitrogen treatments of a given rice cultivar under different weather conditions. Using 3dCAP, we explored the influence of three canopy architectural parameters-tiller number, tiller angle and leaf angle-on canopy RUE. Under different weather conditions and different nitrogen treatments, canopy architecture optimized by manipulating these parameters can increase daily net canopy photosynthetic CO2 uptake by 10-52%. Generally, a smaller tiller angle was predicted for most elite rice canopy architectures, especially under scattered light conditions. Results further show that similar canopy RUE can be obtained by multiple different parameter combinations; these combinations share two common features of high light absorption by leaves in the canopy and a high level of coordination between the nitrogen concentration and the light absorbed by each leaf within the canopy. Overall, this new model has potential to be used in rice ideotype design for improved canopy RUE.