Simple equations for dynamic models of the effects of CO2 and O3 on light-use efficiency and growth of crops

Importance of the description of light interception in crop growth models

Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modeling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement Project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from three-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KellC). Results showed that the KellC model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.

Towards a multiscale crop modelling framework for climate change adaptation assessment

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Increased light-use efficiency sustains net primary productivity of shaded coffee plants in agroforestry system

In agroforestry systems, shade trees strongly affect the physiology of the undergrown crop. However, a major paradigm is that the reduction in absorbed photosynthetically active radiation is, to a certain extent, compensated by an increase in light-use efficiency, thereby reducing the difference in net primary productivity between shaded and non-shaded plants. Due to the large spatial heterogeneity in agroforestry systems and the lack of appropriate tools, the combined effects of such variables have seldom been analysed, even though they may help understand physiological processes underlying yield dynamics. In this study, we monitored net primary productivity, during two years, on scales ranging from individual coffee plants to the entire plot. Absorbed radiation was mapped with a 3D model (MAESPA). Light-use efficiency and net assimilation rate were derived for each coffee plant individually. We found that although irradiance was reduced by 60% below crowns of shade trees, coffee light-use efficiency increased by 50%, leaving net primary productivity fairly stable across all shade levels. Variability of aboveground net primary productivity of coffee plants was caused primarily by the age of the plants and by intraspecific competition among them (drivers usually overlooked in the agroforestry literature) rather than by the presence of shade trees.

Ozone damage, detoxification and the role of isoprenoids - new impetus for integrated models

High concentrations of ozone (O3) can have significant impacts on the health and productivity of agricultural and forest ecosystems, leading to significant economic losses. In order to estimate this impact under a wide range of environmental conditions, the mechanisms of O3 impacts on physiological and biochemical processes have been intensively investigated. This includes the impact on stomatal conductance, the formation of reactive oxygen species and their effects on enzymes and membranes, as well as several induced and constitutive defence responses. This review summarises these processes, discusses their importance for O3 damage scenarios and assesses to which degree this knowledge is currently used in ecosystem models which are applied for impact analyses. We found that even in highly sophisticated models, feedbacks affecting regulation, detoxification capacity and vulnerability are generally not considered. This implies that O3 inflicted alterations in carbon and water balances cannot be sufficiently well described to cover immediate plant responses under changing environmental conditions. Therefore, we suggest conceptual models that link the depicted feedbacks to available process-based descriptions of stomatal conductance, photosynthesis and isoprenoid formation, particularly the linkage to isoprenoid models opens up new options for describing biosphere-atmosphere interactions.

Accounting for the decrease of photosystem photochemical efficiency with increasing irradiance to estimate quantum yield of leaf photosynthesis

Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by non-photosynthetic pigments or to some data points being beyond the linear range of the irradiance response, both causing an underestimation of Φ CO2LL. We demonstrate here that a decrease in photosystem (PS) photochemical efficiency with increasing irradiance, even at very low levels, is another source of error that causes a systematic underestimation of Φ CO2LL. A model method accounting for this error was developed, and was used to estimate Φ CO2LL from simultaneous measurements of gas exchange and chlorophyll fluorescence on leaves using various combinations of species, CO2, O2, or leaf temperature levels. The conventional linear regression method under-estimated Φ CO2LL by ca. 10-15%. Differences in the estimated Φ CO2LL among measurement conditions were generally accounted for by different levels of photorespiration as described by the Farquhar-von Caemmerer-Berry model. However, our data revealed that the temperature dependence of PSII photochemical efficiency under low light was an additional factor that should be accounted for in the model.

Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO(2) concentration on crop productivity

BACKGROUND

Process-based ecophysiological crop models are pivotal in assessing responses of crop productivity and designing strategies of adaptation to climate change. Most existing crop models generally over-estimate the effect of elevated atmospheric [CO2], despite decades of experimental research on crop growth response to [CO2].

ANALYSIS

A review of the literature indicates that the quantitative relationships for a number of traits, once expressed as a function of internal plant nitrogen status, are altered little by the elevated [CO2]. A model incorporating these nitrogen-based functional relationships and mechanisms simulated photosynthetic acclimation to elevated [CO2], thereby reducing the chance of over-estimating crop response to [CO2]. Robust crop models to have small parameterization requirements and yet generate phenotypic plasticity under changing environmental conditions need to capture the carbon-nitrogen interactions during crop growth.

CONCLUSIONS

The performance of the improved models depends little on the type of the experimental facilities used to obtain data for parameterization, and allows accurate projections of the impact of elevated [CO2] and other climatic variables on crop productivity.

Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation

Assessments of the relationships between crop productivity and climate change rely upon a combination of modelling and measurement. As part of this review, this relationship is discussed in the context of crop and climate simulation. Methods for linking these two types of models are reviewed, with a primary focus on large-area crop modelling techniques. Recent progress in simulating the impacts of climate change on crops is presented, and the application of these methods to the exploration of adaptation options is discussed. Specific advances include ensemble simulations and improved understanding of biophysical processes. Finally, the challenges associated with impacts and adaptation research are discussed. It is argued that the generation of knowledge for policy and adaptation should be based not only on syntheses of published studies, but also on a more synergistic and holistic research framework that includes: (i) reliable quantification of uncertainty; (ii) techniques for combining diverse modelling approaches and observations that focus on fundamental processes; and (iii) judicious choice and calibration of models, including simulation at appropriate levels of complexity that accounts for the principal drivers of crop productivity, which may well include both biophysical and socio-economic factors. It is argued that such a framework will lead to reliable methods for linking simulation to real-world adaptation options, thus making practical use of the huge global effort to understand and predict climate change.