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

Optimized irrigation and fertilization can mitigate negative CO2 impacts on seed yield and vigor of hybrid maize

Seed yield and vigor of hybrid maize determine the planting, yield, and quality of maize, and consequently affect food, nutrition, and livelihood security; however, the response of seed yield and vigor to climate change is still unclear. We established an optimization-simulation framework consisting of a water‑nitrogen crop production function, a seed vigor and a gridded process-based model to optimize irrigation and nitrogen fertilization management, and used it to evaluate seed yield and vigor in major seed production locations of China, the USA, and Mexico. This framework could reflect the influence of water and nitrogen inputs at different stages on seed yield and vigor considering the spatio-temporal variability of climate and soil properties. Projected seed yield and vigor decreased by 5.8-9.0 % without adaptation by the 2050s, due to the 1.3-5.8 % decrease in seed number and seed protein concentration. Seed yield was positively correlated with CO2 and negatively correlated with temperature, while seed vigor depended on the response of components of seed vigor to climatic factors. Under optimized management, the direct positive effects of temperature on seed protein concentration and CO2 on seed number were strengthened, and the direct negative effects of temperature on seed number and CO2 on seed protein concentration were weakened, which mitigated the reductions in both seed yield and vigor. Elevated CO2 was projected to exacerbate the 2.6 % seed vigor reduction and mitigate the 2.9 % seed yield loss without adaptation, while optimized management could increase seed yield by 4.1 % and mitigate the 2.2 % seed vigor reduction in the Hexi Corridor of China, and decrease the seed yield and vigor reduction by 2.4-5.8 % in the USA and Mexico. Optimized management can strengthen the positive and mitigate the negative effects of climate change on irrigated hybrid maize and inform high-yield and high-quality seed production globally.

Rising atmospheric carbon dioxide concentrations increase gaps of rice yields between low- and middle-to-high-income countries

The rising carbon dioxide concentrations are expected to increase future rice yields. However, variations in the CO2 fertilization effect (CFE) between rice subspecies and the influence of concurrent global warming introduce uncertainty in future global rice yield projections. Here we conducted a meta-analysis of rising carbon dioxide field experiments and employed crop modelling to assess future global rice yields for the top 14 rice producing countries. We found a robust parabolic relationship between rice CFE and temperature, with significant variations between rice subspecies. Our projections indicate that global rice production in the 2050s is expected to increase by 50.32 million tonnes (7.6%) due to CFE compared with historical production. Because low-income countries will experience higher temperatures, the gaps (difference of Δyield) between middle-to-high-income and low-income countries are projected to widen from the 2030s to the 2090s under elevated carbon dioxide. These findings underscore the critical role of CFE and emphasize the necessity to increase investments in research and technology for rice producing systems in low-income countries.

Triose phosphate utilization in leaves is modulated by whole-plant sink-source ratios and nitrogen budgets in rice

Triose phosphate utilization (TPU) is a biochemical process indicating carbon sink-source (im)balance within leaves. When TPU limits leaf photosynthesis, photorespiration-associated amino acid exports probably provide an additional carbon outlet and increase leaf CO2 uptake. However, whether TPU is modulated by whole-plant sink-source relations and nitrogen (N) budgets remains unclear. We address this question by model analyses of gas-exchange data measured on leaves at three growth stages of rice plants grown at two N levels. Sink-source ratio was manipulated by panicle pruning, by using yellower-leaf variant genotypes, and by measuring photosynthesis on adaxial and abaxial leaf sides. Across all these treatments, higher leaf N content resulted in the occurrence of TPU limitation at lower intercellular CO2 concentrations. Photorespiration-associated amino acid export was greater in high-N leaves, but was smaller in yellower-leaf genotypes, panicle-pruned plants, and for abaxial measurement. The feedback inhibition of panicle pruning on rates of TPU was not always observed, presumably because panicle pruning blocked N remobilization from leaves to grains and the increased leaf N content masked feedback inhibition. The leaf-level TPU limitation was thus modulated by whole-plant sink-source relations and N budgets during rice grain filling, suggesting a close link between within-leaf and whole-plant sink limitations.

Crop/Plant Modeling Supports Plant Breeding: I. Optimization of Environmental Factors in Accelerating Crop Growth and Development for Speed Breeding

The environmental conditions in customered speed breeding practice are, to some extent, empirical and, thus, can be further optimized. Crop and plant models have been developed as powerful tools in predicting growth and development under various environments for extensive crop species. To improve speed breeding, crop models can be used to predict the phenotypes resulted from genotype by environment by management at the population level, while plant models can be used to examine 3-dimensional plant architectural development by microenvironments at the organ level. By justifying the simulations via numerous virtual trials using models in testing genotype × environment × management, an optimized combination of environmental factors in achieving desired plant phenotypes can be quickly determined. Artificial intelligence in assisting for optimization is also discussed. We admit that the appropriate modifications on modeling algorithms or adding new modules may be necessary in optimizing speed breeding for specific uses. Overall, this review demonstrates that crop and plant models are promising tools in providing the optimized combinations of environment factors in advancing crop growth and development for speed breeding.

Neglecting acclimation of photosynthesis under drought can cause significant errors in predicting leaf photosynthesis in wheat

Extreme climatic events, such as heat waves, cold snaps and drought spells, related to global climate change, have become more frequent and intense in recent years. Acclimation of plant physiological processes to changes in environmental conditions is a key component of plant adaptation to climate change. We assessed the temperature response of leaf photosynthetic parameters in wheat grown under contrasting water regimes and growth temperatures (T_growth ). Two independent experiments were conducted under controlled conditions. In Experiment 1, two wheat genotypes were subjected to well-watered or drought-stressed treatments; in Experiment 2, the two water regimes combined with high, medium and low T_growth were imposed on one genotype. Parameters of a biochemical C₃ -photosynthesis model were estimated at six leaf temperatures for each factor combination. Photosynthesis acclimated more to drought than to T_growth . Drought affected photosynthesis by lowering its optimum temperature (T_opt ) and the values at T_opt of light-saturated net photosynthesis, stomatal conductance, mesophyll conductance, the maximum rate of electron transport (J_max ) and the maximum rate of carboxylation by Rubisco (V_cmax ). T_opt for V_cmax was up to 40°C under well-watered conditions but 24-34°C under drought. The decrease in photosynthesis under drought varied among T_growth but was similar between genotypes. The temperature response of photosynthetic quantum yield under drought was partly attributed to photorespiration but more to alternative electron transport. All these changes in biochemical parameters could not be fully explained by the changed leaf nitrogen content. Further model analysis showed that both diffusional and biochemical parameters of photosynthesis and their thermal sensitivity acclimate little to T_growth , but acclimate considerably to drought and the combination of drought and T_growth . The commonly used modelling approaches, which typically consider the response of diffusional parameters, but ignore acclimation responses of biochemical parameters to drought and T_growth , strongly overestimate leaf photosynthesis under variable temperature and drought.

Three new methods indicate that CO2 concentration affects plant respiration in the range relevant to global change

Short-term responses of plant dark respiration to carbon dioxide concentration ([CO₂]) in the range anticipated in the atmosphere with global change remain controversial, primarily because it is difficult to convincingly eliminate the many possible sources of experimental error in measurements of carbon dioxide or oxygen exchange rates. Plant dark respiration is a major component of the carbon balance of many ecosystems. In seedlings without senescent tissue, the rate of loss of dry mass during darkness indicates the rate of respiration. This method of measuring respiration was used to test for [CO₂] effects on respiration in seedlings of three species with relatively large seeds. The time it took respiration to exhaust substrates and cause seedling death in darkness was used as an indicator of respiration rate in four other species with smaller seeds. The third method was measuring rates of CO₂ exchange in excised petioles sealed in a cuvette submerged in water to prevent leaks. Petioles were utilized as the plant tissue type with the most reliable rates of respiration, for excised tissue. The rate of loss of dry mass in the dark decreased with increasing [CO₂] in the range of 200-800 μmol mol^-1 in all three large-seeded species. The seedling survival time in the dark increased with [CO₂] in the same concentration range in all four of the smaller-seeded species. Respiration rates of excised petioles of several species also decreased over this [CO₂] range. The data provide new evidence that the rate of dark respiration in plant tissue often decreases with increasing [CO₂] in the 200-800 μmol mol^-1 range.

Inherent trait differences explain wheat cultivar responses to climate factor interactions: New insights for more robust crop modelling

Climate change predictions foresee a combination of rising CO₂ , temperature and altered precipitation. Effects of single climatic variables are well defined, but the importance of combined variables and genotypic effects is less known, although pivotal for assessing climate change impacts, for example, with crop growth models. This study provides developmental and physiological data from combined climatic factors for two distinct wheat cultivars (Paragon and Gladius), as a basis to improve predictions for climate change scenarios. The two cultivars were grown in controlled climate chambers in a fully factorial setup of atmospheric CO₂ concentration, growth temperature and watering regime. The cultivars differed considerably in their developmental rate, response pattern and the parameters responsible for most of their variation. The growth of Paragon was linked to climatic effects on photosynthesis and mainly affected by temperature. Paragon was overall more negatively affected by all treatment combinations compared to Gladius. Gladius was mostly affected by watering regime. The cultivars' acclimation strategies to climate factors varied significantly. Thus, considering a single factor is an oversimplification very likely impacting the accuracy of crop growth models. Intraspecific crop variation could help understanding genotype by environment variation. Cultivars with high phenotypic plasticity may have greater resilience against climatic variability.

The case for improving crop carbon sink strength or plasticity for a CO2-rich future

Atmospheric CO₂ concentration [CO₂] has increased from 260 to 280μmolmol^-1 (level during crop domestication up to the industrial revolution) to currently 400 and will reach 550μmolmol^-1 by 2050. C3 crops are expected to benefit from elevated [CO₂] (e-CO₂) thanks to photosynthesis responsiveness to [CO₂] but this may require greater sink capacity. We review recent literature on crop e-CO₂ responses, related source-sink interactions, how abiotic stresses potentially interact, and prospects to improve e-CO₂ response via breeding or genetic engineering. Several lines of evidence suggest that e-CO₂ responsiveness is related either to sink intrinsic capacity or adaptive plasticity, for example, involving enhanced branching. Wild relatives and old cultivars mostly showed lower photosynthetic rates, less downward acclimation of photosynthesis to e-CO₂ and responded strongly to e-CO₂ due to greater phenotypic plasticity. While reverting to such archaic traits would be an inappropriate strategy for breeding, we argue that substantial enhancement of vegetative sink vigor, inflorescence size and/or number and root sinks will be necessary to fully benefit from e-CO₂. Potential ideotype features based on enhanced sinks are discussed. The generic 'feast-famine' sugar signaling pathway may be suited to engineer sink strength tissue-specifically and stage-specifically and help validate ideotype concepts. Finally, we argue that models better accounting for acclimation to e-CO₂ are needed to predict which trait combinations should be targeted by breeders for a CO₂-rich world.

Photosynthesis in a Changing Global Climate: Scaling Up and Scaling Down in Crops

Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO2 in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO2 assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO2], salinity, and ozone. The "omics" revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production.

Substantial differences occur between canopy and ambient climate: Quantification of interactions in a greenhouse-canopy system

Organ temperature and variation therein plays a key role in plant functioning and its responses to e.g. climate change. There is a strong feedback between organ, especially leaf, temperature and the climate within the canopy (canopy climate), which in turn interacts with the climate outside the canopy (ambient climate). For greenhouses, the determinants of this interplay and how they drive differences between canopy and ambient climate are poorly understood. Yet, as many experiments on both regular greenhouse crops and field crops are done in greenhouses, this is crucial to know. Therefore, we designed an experiment to quantify the differences between ambient and canopy climate and leaf temperature. A path analysis was performed to quantify the interactions between components of the greenhouse canopy-climate system. We found that with high radiation the canopy climate can be up to 5°C cooler than the ambient climate, while for cloudy days this was only 2°C. Canopy relative humidity (RH) was up to 25% higher compared to ambient RH. We showed that radiation is very important for these climate differences, but that this effect could be partly counteracted by turning off supplementary light (i.e. due to its indirect effects e.g. changing light distribution). Leaf temperature was substantially different, both higher and lower, from the canopy air temperature. This difference was determined by leaf area index (LAI), temperature of the heating pipe and the use of supplementary light, which all strongly influence radiation, either shortwave or thermal radiation. The difference between leaf and ambient air temperature could be decreased by decreasing the LAI or increasing the temperature of the heating pipe.

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.

The acclimation of leaf photosynthesis of wheat and rice to seasonal temperature changes in T-FACE environments

Crops show considerable capacity to adjust their photosynthetic characteristics to seasonal changes in temperature. However, how photosynthesis acclimates to changes in seasonal temperature under future climate conditions has not been revealed. We measured leaf photosynthesis (A_n ) of wheat (Triticum aestivum L.) and rice (Oryza sativa L.) grown under four combinations of two levels of CO₂ (ambient and enriched up to 500 µmol/mol) and two levels of canopy temperature (ambient and increased by 1.5-2.0°C) in temperature by free-air CO₂ enrichment (T-FACE) systems. Parameters of a biochemical C₃ -photosynthesis model and of a stomatal conductance (g_s ) model were estimated for the four conditions and for several crop stages. Some biochemical parameters related to electron transport and most g_s parameters showed acclimation to seasonal growth temperature in both crops. The acclimation response did not differ much between wheat and rice, nor among the four treatments of the T-FACE systems, when the difference in the seasonal growth temperature was accounted for. The relationships between biochemical parameters and leaf nitrogen content were consistent across leaf ranks, developmental stages, and treatment conditions. The acclimation had a strong impact on g_s model parameters: when parameter values of a particular stage were used, the model failed to correctly estimate g_s values of other stages. Further analysis using the coupled g_s -biochemical photosynthesis model showed that ignoring the acclimation effect did not result in critical errors in estimating leaf photosynthesis under future climate, as long as parameter values were measured or derived from data obtained before flowering.

Assessing the impact of global climate changes on irrigated wheat yields and water requirements in a semi-arid environment of Morocco

The present work aims to quantify the impact of climate change (CC) on the grain yields of irrigated cereals and their water requirements in the Tensift region of Morocco. The Med-CORDEX (MEDiterranean COordinated Regional Climate Downscaling EXperiment) ensemble runs under scenarios RCP4.5 (Representative Concentration Pathway) and RCP8.5 are first evaluated and disaggregated using the quantile-quantile approach. The impact of CC on the duration of the main wheat phenological stages based on the degree-day approach is then analyzed. The results show that the rise in air temperature causes a shortening of the development cycle of up to 50 days. The impacts of rising temperature and changes in precipitation on wheat yields are next evaluated, based on the AquaCrop model, both with and without taking into account the fertilizing effect of CO₂. As expected, optimal wheat yields will decrease on the order of 7 to 30% if CO₂ concentration rise is not considered. The fertilizing effect of CO₂ can counterbalance yield losses, since optimal yields could increase by 7% and 13% respectively at mid-century for the RCP4.5 and RCP8.5 scenarios. Finally, water requirements are expected to decrease by 13 to 42%, mainly in response to the shortening of the cycle. This decrease is associated with a change in temporal patterns, with the requirement peak coming two months earlier than under current conditions.

Is triose phosphate utilization involved in the feedback inhibition of photosynthesis in rice under conditions of sink limitation?

This study aimed to understand the physiological basis of rice photosynthetic response to C source-sink imbalances, focusing on the dynamics of the photosynthetic parameter triose phosphate utilization (TPU). Here, rice (Oriza sativa L.) indica cultivar IR64 were grown in controlled environment chambers under current ambient CO2 concentration until heading, and thereafter two CO2 treatments (400 and 800 μmol mol-1) were compared in the presence and absence of a panicle-pruning treatment modifying the C sink. At 2 weeks after heading, photosynthetic parameters derived from CO2 response curves, and non-structural carbohydrate content of flag leaf and internodes were measured three to four times of day. Spikelet number per panicle and flag leaf area on the main culm were recorded. Net C assimilation and TPU decreased progressively after midday in panicle-pruned plants, especially under 800 μmol mol-1 CO2. This TPU reduction was explained by sucrose accumulation in the flag leaf resulting from the sink limitation. Taking together, our findings suggest that TPU is involved in the regulation of photosynthesis in rice under elevated CO2 conditions, and that sink limitation effects should be considered in crop models.

Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity

Elevated carbon-dioxide concentration [eCO2] is a key climate change factor affecting plant growth and yield. Conventionally, crop modeling work has evaluated the effect of climatic parameters on crop growth, without considering CO2. It is conjectured that a novel multimodal ensemble approach may improve the accuracy of modelled responses to eCO2. To demonstrate the applicability of a multimodel ensemble of crop models to simulation of eCO2, APSIM, CropSyst, DSSAT, EPIC and STICS were calibrated to observed data for crop phenology, biomass and yield. Significant variability in simulated biomass production was shown among the models particularly at dryland sites (44%) compared to the irrigated site (22%). Increased yield was observed for all models with the highest average yield at dryland site by EPIC (49%) and lowest under irrigated conditions (17%) by APSIM and CropSyst. For the ensemble, maximum yield was 45% for the dryland site and a minimum 22% at the irrigated site. We concluded from our study that process-based crop models have variability in the simulation of crop response to [eCO2] with greater difference under water-stressed conditions. We recommend the use of ensembles to improve accuracy in modeled responses to [eCO2].

Nitrogen application is required to realize wheat yield stimulation by elevated CO2 but will not remove the CO2 -induced reduction in grain protein concentration

Elevated CO₂ (eCO₂ ) generally promotes increased grain yield (GY) and decreased grain protein concentration (GPC), but the extent to which these effects depend on the magnitude of fertilization remains unclear. We collected data on the eCO₂ responses of GY, GPC and grain protein yield and their relationships with nitrogen (N) application rates across experimental data covering 11 field grown wheat (Triticum aestivum) cultivars studied in eight countries on four continents. The eCO₂ -induced stimulation of GY increased with N application rates up to ~200 kg/ha. At higher N application, stimulation of GY by eCO₂ stagnated or even declined. This was valid both when the yield stimulation was expressed as the total effect and using per ppm CO₂ scaling. GPC was decreased by on average 7% under eCO₂ and the magnitude of this effect did not depend on N application rate. The net effect of responses on GY and protein concentration was that eCO₂ typically increased and decreased grain protein yield at N application rates below and above ~100 kg/ha respectively. We conclude that a negative effect on wheat GPC seems inevitable under eCO₂ and that substantial N application rates may be required to sustain wheat protein yields in a world with rising CO₂ .

Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields

We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42-77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments.

Crop quality under rising atmospheric CO2

Crops grown under elevated CO₂ (eCO₂) typically exhibit enhanced yields but at the same time decreased nutritional quality. The latter effect has often been explained as a growth dilution phenomenon, but this cannot be the only process involved since crop nutrient concentrations are decreased also when production is unaffected by eCO₂. We review the current knowledge on eCO₂ effects on crop nutritional quality with focus on the current understanding of the possible mechanisms and processes causing these effects. Emphasis is on crop nitrogen (N) and protein concentrations but effects on other nutrients and how they compare with those on N are also covered.

An ecophysiologically based mapping model identifies a major pleiotropic QTL for leaf growth trajectories of Phaseolus vulgaris

Crop modeling, a widely used tool to predict plant growth and development in heterogeneous environments, has been increasingly integrated with genetic information to improve its predictability. This integration can also shed light on the mechanistic path that connects the genotype to a particular phenotype under specific environments. We implemented a bivariate statistical procedure to map and identify quantitative trait loci (QTLs) that can predict the form of plant growth by estimating cultivar-specific growth parameters and incorporating these parameters into a mapping framework. The procedure enables the characterization of how QTLs act differently in response to developmental and environmental cues. We used this procedure to map growth parameters of leaf area and mass in a mapping population of the common bean (Phaseolus vulgaris L.). Different sets of QTLs are responsible for various aspects of growth, including the initiation time of growth, growth rate, inflection point and asymptotic growth. A major QTL of a large effect was identified to pleiotropically affect trait expression in distinct environments and different traits expressed on the same organism. The integration of crop models and QTL mapping through our statistical procedure provides a powerful means of building a more precise predictive model of genotype-phenotype relationships for crops.

Do all leaf photosynthesis parameters of rice acclimate to elevated CO2 , elevated temperature, and their combination, in FACE environments?

Leaf photosynthesis of crops acclimates to elevated CO₂ and temperature, but studies quantifying responses of leaf photosynthetic parameters to combined CO₂ and temperature increases under field conditions are scarce. We measured leaf photosynthesis of rice cultivars Changyou 5 and Nanjing 9108 grown in two free-air CO₂ enrichment (FACE) systems, respectively, installed in paddy fields. Each FACE system had four combinations of two levels of CO₂ (ambient and enriched) and two levels of canopy temperature (no warming and warmed by 1.0-2.0°C). Parameters of the C₃ photosynthesis model of Farquhar, von Caemmerer and Berry (the FvCB model), and of a stomatal conductance (g_s ) model were estimated for the four conditions. Most photosynthetic parameters acclimated to elevated CO₂ , elevated temperature, and their combination. The combination of elevated CO₂ and temperature changed the functional relationships between biochemical parameters and leaf nitrogen content for Changyou 5. The g_s model significantly underestimated g_s under the combination of elevated CO₂ and temperature by 19% for Changyou 5 and by 10% for Nanjing 9108 if no acclimation was assumed. However, our further analysis applying the coupled g_s -FvCB model to an independent, previously published FACE experiment showed that including such an acclimation response of g_s hardly improved prediction of leaf photosynthesis under the four combinations of CO₂ and temperature. Therefore, the typical procedure that crop models using the FvCB and g_s models are parameterized from plants grown under current ambient conditions may not result in critical errors in projecting productivity of paddy rice under future global change.

Improving the use of crop models for risk assessment and climate change adaptation

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1.Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk?2.Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output.3.Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.

Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

The CO₂ fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO₂] (E-[CO₂]) by comparison to free-air CO₂ enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO₂] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO₂] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO₂] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO₂] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO₂] × N interactions is necessary to better evaluate management practices under climate change.

Elevated CO2 alters distribution of nodal leaf area and enhances nitrogen uptake contributing to yield increase of soybean cultivars grown in Mollisols

Understanding how elevated CO2 affects dynamics of nodal leaf growth and N assimilation is crucial for the construction of high-yielding canopy via breeding and N management to cope with the future climate change. Two soybean cultivars were grown in two Mollisols differing in soil organic carbon (SOC), and exposed to ambient CO2 (380 ppm) or elevated CO2 (580 ppm) throughout the growth stages. Elevated CO2 induced 4-5 more nodes, and nearly doubled the number of branches. Leaf area duration at the upper nodes from R5 to R6 was 4.3-fold greater and that on branches 2.4-fold higher under elevated CO2 than ambient CO2, irrespective of cultivar and soil type. As a result, elevated CO2 markedly increased the number of pods and seeds at these corresponding positions. The yield response to elevated CO2 varied between the cultivars but not soils. The cultivar-specific response was likely attributed to N content per unit leaf area, the capacity of C sink in seeds and N assimilation. Elevated CO2 did not change protein concentration in seeds of either cultivar. These results indicate that elevated CO2 increases leaf area towards the upper nodes and branches which in turn contributes yield increase.

Responses to atmospheric CO2 concentrations in crop simulation models: a review of current simple and semicomplex representations and options for model development

Elevated atmospheric CO₂ concentrations ([CO₂ ]) cause direct changes in crop physiological processes (e.g. photosynthesis and stomatal conductance). To represent these CO₂ responses, commonly used crop simulation models have been amended, using simple and semicomplex representations of the processes involved. Yet, there is no standard approach to and often poor documentation of these developments. This study used a bottom-up approach (starting with the APSIM framework as case study) to evaluate modelled responses in a consortium of commonly used crop models and illuminate whether variation in responses reflects true uncertainty in our understanding compared to arbitrary choices of model developers. Diversity in simulated CO₂ responses and limited validation were common among models, both within the APSIM framework and more generally. Whereas production responses show some consistency up to moderately high [CO₂ ] (around 700 ppm), transpiration and stomatal responses vary more widely in nature and magnitude (e.g. a decrease in stomatal conductance varying between 35% and 90% among models was found for [CO₂ ] doubling to 700 ppm). Most notably, nitrogen responses were found to be included in few crop models despite being commonly observed and critical for the simulation of photosynthetic acclimation, crop nutritional quality and carbon allocation. We suggest harmonization and consideration of more mechanistic concepts in particular subroutines, for example, for the simulation of N dynamics, as a way to improve our predictive understanding of CO₂ responses and capture secondary processes. Intercomparison studies could assist in this aim, provided that they go beyond simple output comparison and explicitly identify the representations and assumptions that are causal for intermodel differences. Additionally, validation and proper documentation of the representation of CO₂ responses within models should be prioritized.

Inorganic nitrogen form: a major player in wheat and Arabidopsis responses to elevated CO2

Critical for predicting the future of primary productivity is a better understanding of plant responses to rising atmospheric carbon dioxide (CO2) concentration. This review considers recent results on the role of the inorganic nitrogen (N) forms nitrate (NO3-) and ammonium (NH4+) in determining the responses of wheat and Arabidopsis to elevated atmospheric CO2 concentration. Here, we identify four key issues: (i) the possibility that different plant species respond similarly to elevated CO2 if one accounts for the N form that they are using; (ii) the major influence that plant-soil N interactions have on plant responses to elevated CO2; (iii) the observation that elevated CO2 may favor the uptake of one N form over others; and (iv) the finding that plants receiving NH4+ nutrition respond more positively to elevated CO2 than those receiving NO3- nutrition because elevated CO2 inhibits the assimilation of NO3- in shoots of C3 plants. We conclude that the form and amount of N available to plants from the rhizosphere and plant preferences for the different N forms are essential for predicting plant responses to elevated CO2.

Can the Responses of Photosynthesis and Stomatal Conductance to Water and Nitrogen Stress Combinations Be Modeled Using a Single Set of Parameters?

Accurately predicting photosynthesis in response to water and nitrogen stress is the first step toward predicting crop growth, yield and many quality traits under fluctuating environmental conditions. While mechanistic models are capable of predicting photosynthesis under fluctuating environmental conditions, simplifying the parameterization procedure is important toward a wide range of model applications. In this study, the biochemical photosynthesis model of Farquhar, von Caemmerer and Berry (the FvCB model) and the stomatal conductance model of Ball, Woodrow and Berry which was revised by Leuning and Yin (the BWB-Leuning-Yin model) were parameterized for Lilium (L. auratum × speciosum "Sorbonne") grown under different water and nitrogen conditions. Linear relationships were found between biochemical parameters of the FvCB model and leaf nitrogen content per unit leaf area (Na), and between mesophyll conductance and Na under different water and nitrogen conditions. By incorporating these Na-dependent linear relationships, the FvCB model was able to predict the net photosynthetic rate (An) in response to all water and nitrogen conditions. In contrast, stomatal conductance (gs) can be accurately predicted if parameters in the BWB-Leuning-Yin model were adjusted specifically to water conditions; otherwise gs was underestimated by 9% under well-watered conditions and was overestimated by 13% under water-deficit conditions. However, the 13% overestimation of gs under water-deficit conditions led to only 9% overestimation of An by the coupled FvCB and BWB-Leuning-Yin model whereas the 9% underestimation of gs under well-watered conditions affected little the prediction of An. Our results indicate that to accurately predict An and gs under different water and nitrogen conditions, only a few parameters in the BWB-Leuning-Yin model need to be adjusted according to water conditions whereas all other parameters are either conservative or can be adjusted according to their linear relationships with Na. Our study exemplifies a simplified procedure of parameterizing the coupled FvCB and gs model that is widely used for various modeling purposes.

Nutrient constraints on terrestrial carbon fixation: The role of nitrogen

Carbon dioxide (CO₂) concentrations in the earth's atmosphere are projected to rise from current levels near 400ppm to over 700ppm by the end of the 21st century. Projections over this time frame must take into account the increases in total net primary production (NPP) expected from terrestrial plants, which result from elevated CO₂ (eCO₂) and have the potential to mitigate the impact of anthropogenic CO₂ emissions. However, a growing body of evidence indicates that limitations in soil nutrients, particularly nitrogen (N), the soil nutrient most limiting to plant growth, may greatly constrain future carbon fixation. Here, we review recent studies about the relationships between soil N supply, plant N nutrition, and carbon fixation in higher plants under eCO₂, highlighting key discoveries made in the field, particularly from free-air CO₂ enrichment (FACE) technology, and relate these findings to physiological and ecological mechanisms.

Impact of climate change on crop yield and role of model for achieving food security

In recent times, several studies around the globe indicate that climatic changes are likely to impact the food production and poses serious challenge to food security. In the face of climate change, agricultural systems need to adapt measures for not only increasing food supply catering to the growing population worldwide with changing dietary patterns but also to negate the negative environmental impacts on the earth. Crop simulation models are the primary tools available to assess the potential consequences of climate change on crop production and informative adaptive strategies in agriculture risk management. In consideration with the important issue, this is an attempt to provide a review on the relationship between climate change impacts and crop production. It also emphasizes the role of crop simulation models in achieving food security. Significant progress has been made in understanding the potential consequences of environment-related temperature and precipitation effect on agricultural production during the last half century. Increased CO2 fertilization has enhanced the potential impacts of climate change, but its feasibility is still in doubt and debates among researchers. To assess the potential consequences of climate change on agriculture, different crop simulation models have been developed, to provide informative strategies to avoid risks and understand the physical and biological processes. Furthermore, they can help in crop improvement programmes by identifying appropriate future crop management practises and recognizing the traits having the greatest impact on yield. Nonetheless, climate change assessment through model is subjected to a range of uncertainties. The prediction uncertainty can be reduced by using multimodel, incorporating crop modelling with plant physiology, biochemistry and gene-based modelling. For devloping new model, there is a need to generate and compile high-quality field data for model testing. Therefore, assessment of agricultural productivity to sustain food security for generations is essential to maintain a collective knowledge and resources for preventing negative impact as well as managing crop practises.

Responses of wheat and rice to factorial combinations of ambient and elevated CO2 and temperature in FACE experiments

Elevated CO2 and temperature strongly affect crop production, but understanding of the crop response to combined CO2 and temperature increases under field conditions is still limited while data are scarce. We grew wheat (Triticum aestivum L.) and rice (Oryza sativa L.) under two levels of CO2 (ambient and enriched up to 500 μmol mol(-1) ) and two levels of canopy temperature (ambient and increased by 1.5-2.0 °C) in free-air CO2 enrichment (FACE) systems and carried out a detailed growth and yield component analysis during two growing seasons for both crops. An increase in CO2 resulted in higher grain yield, whereas an increase in temperature reduced grain yield, in both crops. An increase in CO2 was unable to compensate for the negative impact of an increase in temperature on biomass and yield of wheat and rice. Yields of wheat and rice were decreased by 10-12% and 17-35%, respectively, under the combination of elevated CO2 and temperature. The number of filled grains per unit area was the most important yield component accounting for the effects of elevated CO2 and temperature in wheat and rice. Our data showed complex treatment effects on the interplay between preheading duration, nitrogen uptake, tillering, leaf area index, and radiation-use efficiency, and thus on yield components and yield. Nitrogen uptake before heading was crucial in minimizing yield loss due to climate change in both crops. For rice, however, a breeding strategy to increase grain number per m(2) and % filled grains (or to reduce spikelet sterility) at high temperature is also required to prevent yield reduction under conditions of global change.

An overview of available crop growth and yield models for studies and assessments in agriculture

The scientific community offers numerous crop models with different levels of sophistication. In such a wide range of crop models, users should have the possibility to choose the most suitable, in terms of detail, scale and representativeness, to their objectives. However, even when an appropriate choice is made, model limitations should be clarified such that modelling studies are put in the proper perspective and robust applications are achieved. This work is an overview of available models to simulate crop growth and yield. A summary matrix with more than 70 crop models is provided, storing the main model characteristics that can help users to choose the proper tool according to their purposes. Overall, we found that two main aspects of models, despite their importance, are not always clear from the published references, i.e. the versatility of the models, in terms of reliable transferability to different conditions, and the degree of complexity. Hence, the developers of models should be encouraged to pay more attention to clarifying the model limitations and limits of applicability, and users should make an effort in proper model selection, to save time often devoted to iteration of tuning steps to force an inappropriate model to be adapted to their own purpose.

The optimal atmospheric CO2 concentration for the growth of winter wheat (Triticum aestivum)

This study examined the optimal atmospheric CO2 concentration of the CO2 fertilization effect on the growth of winter wheat with growth chambers where the CO2 concentration was controlled at 400, 600, 800, 1000, and 1200 ppm respectively. I found that initial increase in atmospheric CO2 concentration dramatically enhanced winter wheat growth through the CO2 fertilization effect. However, this CO2 fertilization effect was substantially compromised with further increase in CO2 concentration, demonstrating an optimal CO2 concentration of 889.6, 909.4, and 894.2 ppm for aboveground, belowground, and total biomass, respectively, and 967.8 ppm for leaf photosynthesis. Also, high CO2 concentrations exceeding the optima not only reduced leaf stomatal density, length and conductance, but also changed the spatial distribution pattern of stomata on leaves. In addition, high CO2 concentration also decreased the maximum carboxylation rate (Vc(max)) and the maximum electron transport rate (J(max)) of leaf photosynthesis. However, the high CO2 concentration had little effect on leaf length and plant height. The optimal CO2 fertilization effect found in this study can be used as an indicator in selecting and breeding new wheat strains in adapting to future high atmospheric CO2 concentrations and climate change.

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

CO2 dose-response functions for wheat grain, protein and mineral yield based on FACE and open-top chamber experiments

Data from three Swedish open-top chamber and four German FACE experiments were combined to derive response functions for elevated CO2 (eCO2) effects on Cd, Zn, Mn, protein, grain yield, grain mass and grain number of wheat. Grain yield and grain number were increased by ∼6% and ∼7%, respectively, per 100 ppm CO2; the former effect was linked to plant nitrogen status. Grain mass was not influenced by eCO2, whereas Cd concentration was reduced. Unlike Zn, Mn and protein, effects on Cd yield were not related to effects on grain yield. Yields of Mn, Zn and (weakly) protein were positively affected by eCO2. For protein, grain yield, grain mass and grain number, the results were consistent among the FACE and OTC experiments. A key conclusion was that yields of essential nutrients were enhanced (Mn > Zn > protein), although less than grain yield, which would not be expected from a simple dilution model.

Constructing a framework for risk analyses of climate change effects on the water budget of differently sloped vineyards with a numeric simulation using the Monte Carlo method coupled to a water balance model

Grapes for wine production are a highly climate sensitive crop and vineyard water budget is a decisive factor in quality formation. In order to conduct risk assessments for climate change effects in viticulture models are needed which can be applied to complete growing regions. We first modified an existing simplified geometric vineyard model of radiation interception and resulting water use to incorporate numerical Monte Carlo simulations and the physical aspects of radiation interactions between canopy and vineyard slope and azimuth. We then used four regional climate models to assess for possible effects on the water budget of selected vineyard sites up 2100. The model was developed to describe the partitioning of short-wave radiation between grapevine canopy and soil surface, respectively, green cover, necessary to calculate vineyard evapotranspiration. Soil water storage was allocated to two sub reservoirs. The model was adopted for steep slope vineyards based on coordinate transformation and validated against measurements of grapevine sap flow and soil water content determined down to 1.6 m depth at three different sites over 2 years. The results showed good agreement of modeled and observed soil water dynamics of vineyards with large variations in site specific soil water holding capacity (SWC) and viticultural management. Simulated sap flow was in overall good agreement with measured sap flow but site-specific responses of sap flow to potential evapotranspiration were observed. The analyses of climate change impacts on vineyard water budget demonstrated the importance of site-specific assessment due to natural variations in SWC. The improved model was capable of describing seasonal and site-specific dynamics in soil water content and could be used in an amended version to estimate changes in the water budget of entire grape growing areas due to evolving climatic changes.

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

BACKGROUND AND AIMS

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress.

METHODS

Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions.

KEY RESULTS

To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait 'total crop nitrogen uptake' (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10-36 % more yield than those based on markers for yield per se.

CONCLUSIONS

This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions.

Comment on 'improving ecophysiological simulation models to predict the impact of elevated CO(2) concentration on crop productivity' by X. Yin

SCOPE

The recent publication by Yin (2013; Annals of Botany 112: 465-475) referred to in the title above provides an excellent review of modelling approaches to predict the impact of elevated CO2 on crop productivity, as well as on the controversy regarding whether yield responses observed in free-air CO2 enrichment (FACE) experiments are indeed lower than those from chamber-based experiments. However, the wheat experiments in the example of fig. 1 in Yin's paper had a flaw as the control plots lacked blowers that were in the FACE plots, which warmed the FACE plots at night and hastened plant development. This Viewpoint seeks to highlight this fact, and to comment on the relative merits of FACE and enclosure experiments.