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

Pathways to identify and reduce uncertainties in agricultural climate impact assessments

Both climate and impact models are essential for understanding and quantifying the impact of climate change on agricultural productivity. Multi-model ensembles have highlighted considerable uncertainties in these assessments, yet a systematic approach to quantify these uncertainties is lacking. We propose a standardized approach to attribute uncertainties in multi-model ensemble studies, based on insights from the Agricultural Model Intercomparison and Improvement Project. We find that crop model processes are the primary source of uncertainty in agricultural projections (over 50%), excluding unquantified hidden uncertainty that is not explicitly measured within the analyses. We propose multidimensional pathways to reduce uncertainty in climate change impact assessments.

Impacts of Climate Change on Rice Grain: A Literature Review on What Is Happening, and How Should We Proceed?

More than half of the people on Earth get their calories, proteins, and minerals from rice grains. Staple increases in the quantity and quality of rice grains are key to ending hunger and malnutrition. Rice production, however, is vulnerable to climate change, and the climate on Earth is becoming more fluctuating with the atmospheric change induced by human activities. As a result, the impacts of climate change on rice grain (ICCRG) have sparked widespread concern. In order to reveal the development and the trend in the study on the ICCRG, a bibliometric analysis was conducted. The results showed that both the model simulations and the field experiment-based observations, as reflected by APSIM (the Agricultural Production Systems sIMulator) and free-air carbon dioxide (CO₂) enrichment, are of concern to researchers worldwide, especially in China, India, the United States, and Japan. Different types of warming include short-term, nighttime, soil and water, and canopy, and their interactions with other climate factors, such as CO₂, or agronomic factors, such as nitrogen level, are also of concern to researchers. Spatiotemporal variations in changing weather and regional adaptations from developed and developing countries are challenging the evaluation of ICCRG from an economic perspective. In order to improve the efficacy of breeding adaptable cultivars and developing agronomic management, interdisciplinary studies integrating molecular biology, plant physiology, agronomy, food chemistry, ecology, and socioeconomics are needed.

Genome-Wide Identification of Kiwifruit SGR Family Members and Functional Characterization of SGR2 Protein for Chlorophyll Degradation

The STAY-GREEN (SGR) proteins play an important role in chlorophyll (Chl) degradation and are closely related to plant photosynthesis. However, the availability of inadequate studies on SGR motivated us to conduct a comprehensive study on the identification and functional dissection of SGR superfamily members in kiwifruit. Here, we identified five SGR genes for each of the kiwifruit species [Actinidia chinensis (Ac) and Actinidia eriantha (Ae)]. The phylogenetic analysis showed that the kiwifruit SGR superfamily members were divided into two subfamilies the SGR subfamily and the SGRL subfamily. The results of transcriptome data and RT-qPCR showed that the expression of the kiwifruit SGRs was closely related to light and plant developmental stages (regulated by plant growth regulators), which were further supported by the presence of light and the plant hormone-responsive cis-regulatory element in the promoter region. The subcellular localization analysis of the AcSGR2 protein confirmed its localization in the chloroplast. The Fv/Fm, SPAD value, and Chl contents were decreased in overexpressed AcSGR2, but varied in different cultivars of A. chinensis. The sequence analysis showed significant differences within AcSGR2 proteins. Our findings provide valuable insights into the characteristics and evolutionary patterns of SGR genes in kiwifruit, and shall assist kiwifruit breeders to enhance cultivar development.

Protecting plant nutrition from the effects of climate change

Climate change adversely affects plant nutrition, which serves as a major hurdle in the production of enough nutritious food to meet the needs of the growing global population. Here, we discuss how various climatic stressors impact nutrient homeostasis and how natural variation studies can yield resilient crop production systems to ensure future food security.

Response of rice growth and leaf physiology to elevated CO2 concentrations: A meta-analysis of 20-year FACE studies

The Free Air CO₂ Enrichment (FACE) facility enables the study of plant responses to climate change under open field conditions. This meta-analysis was conducted to quantitatively assess the effects of elevated CO₂ concentration ([CO₂]) on 47 variables describing rice growth physiology and whether CO₂ effects were influenced by cultivar, plant growth stage, nitrogen application rate or temperature. On average, elevated [CO₂] increased root and shoot biomass by 28% and 19%, respectively. Among shoot organs, the [CO₂]-induced increase in leaf biomass was only 9%, significantly smaller than a 24% increase in stems or a 25% increase in panicles. The higher biomass for FACE rice was consistent with the stimulation in plant height (4%), maximum tiller number (11%), leaf area index (9%) and light-saturated photosynthetic rate (Asat, 22%). When compared within rice groups, hybrid rice showed the greatest CO₂ response in growth and leaf physiological variables. Elevated [CO₂] increased plant biomass and Asat at each rice growth stage, but the increment tended to decline with the advancement of rice growth and development. The increase in aboveground biomass at elevated [CO₂] was enhanced by a higher nitrogen supply but reduced with a temperature elevation of 1-2 °C. Rice growth benefited more from elevated [CO₂] in Chinese FACE studies than in Japanese FACE studies, which may result from the different cultivars and nitrogen application rates used in the two countries. Combined with a previous meta-analysis of the rice yield response to FACE, the [CO₂] level predicted in the middle of this century will improve rice productivity by stimulating leaf photosynthesis. However, the effects of CO₂ on the photosynthetic rate and rice growth tend to shrink over the plant life cycle. Selecting heat-resistant, high-yield hybrid rice cultivars with large sink capacity, supplemented with appropriate nitrogen input, will maximize the CO₂ fertilizer effect in the future.

Is Current Research on How Climate Change Impacts Global Food Security Really Objective?

Global food insecurity is becoming more severe under the threat of rising global carbon dioxide concentrations, increasing population, and shrinking farmlands and their degeneration. We acquired the ISI Web of Science platform for over 31 years (1988-2018) to review the research on how climate change impacts global food security, and then performed cluster analysis and research hotspot analysis with VosViewer software. We found there were two drawbacks that exist in the current research. Firstly, current field research data were defective because they were collected from various facilities and were hard to integrate. The other drawback is the representativeness of field research site selection as most studies were carried out in developed countries and very few in developing countries. Therefore, more attention should be paid to developing countries, especially some African and Asian countries. At the same time, new modified mathematical models should be utilized to process and integrate the data from various facilities and regions. Finally, we suggested that governments and organizations across the world should be united to wrestle with the impact of climate change on food security.

Elevated atmospheric CO2 concentration triggers redistribution of nitrogen to promote tillering in rice

Elevated atmospheric CO2 concentration (eCO2) often reduces nitrogen (N) content in rice plants and stimulates tillering. However, there is a general consensus that reduced N would constrain rice tillering. To resolve this contradiction, we investigated N distribution and transcriptomic changes in different rice plant organs after subjecting them to eCO2 and different N application rates. Our results showed that eCO2 significantly promoted rice tillers (by 0.6, 1.1, 1.7, and 2.1 tillers/plant at 0, 75, 150, and 225 kg N ha-1 N application rates, respectively) and more tillers were produced under higher N application rates, confirming that N availability constrained tillering in the early stages of growth. Although N content declined in the leaves (-11.0 to -20.7 mg g-1) and sheaths (-9.8 to -28.8 mg g-1) of rice plants exposed to eCO2, the N content of newly emerged tillers on plants exposed to eCO2 equaled or exceeded the N content of tillers produced under ambient CO2 conditions. Apparently, the redistribution of N within the plant per se was a critical adaptation strategy to the eCO2 condition. Transcriptomic analysis revealed that eCO2 induced less extensive alteration of gene expression than did N application. Most importantly, the expression levels of multiple N-related transporters and receptors such as nitrate transporter NRT2.3a/b and NRT1.1a/b were differentially regulated in leaf and shoot apical meristem, suggesting that multiple genes were involved in sensing the N signal and transporting N metabolites to adapt to eCO2. The redistribution of N in different organs could be a universal adaptation strategy of terrestrial plants to eCO2.

Field experiments and model simulation based evaluation of rice yield response to projected climate change in Southeastern China

Evaluating the impact of climate change factors, especially temperature and carbon dioxide (CO₂), on rice yield is essential to ensure future food security. Because of the wide biogeographical distribution of rice, such evaluations are conducted exclusively through modeling efforts. However, geographical forecasts could, potentially, be improved by the inclusion of field-based data on projected increases in temperature and CO₂ concentration from a given rice-growing region. In this study, the latest version of the ORYZA (v3) crop model was evaluated with additional yield data obtained from a temperature-controlled free-air CO₂ enrichment system (T-FACE) in Southeastern China. ORYZA (v3) results were then evaluated in the context of phase five of the Coupled Model Intercomparison Project (CMIP5) for representative concentration pathways (RCP) 4.5 and RCP 8.5 using five global change models (GCMs). Our findings indicate that climate change, i.e., inclusion of CO₂ and temperature effects, decreased mean rice yield by 3.5%, and 9.4% for RCP 4.5; and by 10.5 and 47.9% for RCP 8.5 for the scenarios in the 2050s and 2080s, respectively. The CO₂ fertilizer effect partially compensated but did not offset the negative impacts of rising temperature on rice yields. Warmer temperatures were the primary factor that influenced yield by adversely affecting the spikelet fertility factor and spikelet number. Overall, climate change would have positive effects on rice yields until the middle-century in Southeastern China but negative effects were noted by the end of the century. These results may be of interest for informing policy makers and developing appropriate strategies to improve future rice productivity for this region of China.

Current rice models underestimate yield losses from short-term heat stresses

Crop production will likely face enormous challenges against the occurrences of extreme climatic events projected under future climate change. Heat waves that occur at critical stages of the reproductive phase have detrimental impacts on the grain yield formation of rice (Oryza sativa). Accurate estimates of these impacts are essential to evaluate the effects of climate change on rice. However, the accuracy of these predictions by crop models has not been extensively tested. In this study, we evaluated 14 rice growth models against four year phytotron experiments with four levels of heat treatments imposed at different times after flowering. We found that all models greatly underestimated the negative effects of heat on grain yield, suggesting that yield projections with these models do not reflect food shocks that may occur under short-term extreme heat stress (SEHS). As a result, crop model ensembles do not help to provide accurate estimates of grain yield under heat stress. We examined the functions of grain-setting rate response to temperature (TRF_GS) used in eight models and showed that adjusting the effective periods of TRF_GS improved the model performance, especially for models simulating accumulative daily temperature effects. For TRF_GS which uses daily maximum temperature averaged for the effective period, the models provided better grain yield estimates by using maximum temperatures averaged only when daily maximum temperatures exceeded the base temperature (T_base ). An alternative method based on heating-degree days and stage-dependent heat sensitivity parameters further decreased the prediction uncertainty of grain yield under heat stress, where stage-dependent heat sensitivity was more important than heat dose for model improvement under SEHS. These results suggest the limitation of the applicability of existing rice models to variable climatic conditions and the urgent need for an alternative grain-setting function accounting for the stage-dependent heat sensitivity.

Narrowing uncertainties in the effects of elevated CO2 on crops

Plant responses to rising atmospheric carbon dioxide (CO₂) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO₂ concentrations. Recent concerted efforts have narrowed the uncertainties in CO₂-induced crop responses so that climate change impact simulations omitting CO₂ can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO₂ and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.

A general non-rectangular hyperbola equation for photosynthetic light response curve of rice at various leaf ages

Photosynthetic light response (PLR) curves of leaves are usually fitted by non-rectangular hyperbola (NRH) equation, and those fitted NRH parameters may change with leaf aging. The objectives of this study were 1) to reveal the response of NRH parameters of rice leaves, light-saturated net photosynthetic rate (P_nmax), quantum yield of assimilation (φ), dark respiration rate (R_d) and convexity of the curve (k), to leaf age; and 2) to improve the performance of NRH equation in simulating the PLR curves for leaves at various ages. The PLR for rice leaves at ages of 3-53 days were measured, and the general NRH equation was developed by incorporating the relationship between NRH parameters and leaf age into the NRH equation. The results showed that the NRH parameters of P_nmax, φ and R_d increased rapidly to maximum at approximately 10 days and then declined linearly toward the age of 53 days. However, the value of k was not sensitive to leaf age. The general NRH equation can be used to simulate leaf PLR continuously along with leaf aging.

A High-Yielding Rice Cultivar "Takanari" Shows No N Constraints on CO2 Fertilization

Enhancing crop yield response to elevated CO2 concentrations (E-[CO2]) is an important adaptation measure to climate change. A high-yielding indica rice cultivar "Takanari" has recently been identified as a potential candidate for high productivity in E-[CO2] resulting from its large sink and source capacities. To fully utilize these traits, nitrogen should play a major role, but it is unknown how N levels influence the yield response of Takanari to E-[CO2]. We therefore compared grain yield and quality of Takanari with those of Koshihikari, a standard japonica cultivar, in response to Free-Air CO2 enrichment (FACE, +200 μmol mol-1) under three N levels (0, 8, and 12 g m-2) over three seasons. The biomass of both cultivars increased under E-[CO2] at all N levels; however, the harvest index decreased under E-[CO2] in the N-limited treatment for Koshihikari but not for Takanari. The decreased harvest index of Koshihikari resulted from limited enhancement of spikelet number under N-limitation. In contrast, spikelet number increased in E-[CO2] in Takanari even without N application, resulting in significant yield enhancement, averaging 18% over 3 years, whereas Koshihikari exhibited virtually no increase in yield in E-[CO2] under the N-limited condition. Grain appearance quality of Koshihikari was severely reduced by E-[CO2], most notably in N-limited and hot conditions, by a substantial increase in chalky grain, but chalky grain % did not increase in E-[CO2] even without N fertilizer. These results indicated that Takanari could retain its high yield advantage over Koshihikari with limited increase in chalkiness even under limited N conditions and that it could be a useful genetic resource for improving N use efficiency under E-[CO2].

Atmospheric CO₂ Concentration and Other Limiting Factors in the Growth of C₃ and C₄ Plants

It has been widely observed that recent increases in atmospheric CO₂ concentrations have had, so far, a positive effect on the growth of plants. This is not surprising since CO₂ is an important nutrient for plant matter, being directly involved in photosynthesis. However, it is also known that the conditions which have accompanied this increase in atmospheric CO₂ concentration have also had significant effects on other environmental factors. It is possible that these other effects may emerge as limiting factors which could act to prevent plant growth. This may involve complex interactions between prevailing sunlight and water conditions, variable temperatures, the availability of essential nutrients and the type of synthetic pathway for the plant species. The issue of concern to this investigation is if we should be worried about a possible shift in the C₃-C₄ paradigm driven by changes in the atmospheric CO₂ concentration, or if some other factor, such as water scarcity, is much more relevant within a 30-year time frame. If an opinion is needed on what will have the worst effect on the survival of the planet between the scarcity of water or the reduced efficiency of C₃ plants to sequester CO₂, the issue of water is the more incisive.

Global warming and rice production in Asia: Modeling, impact prediction and adaptation

Since the projection of global warming emerged in 1980s with the potential of laying enormous impacts on agriculture and food security of the world, we have conducted experimental and modeling studies for clarifying its effects on rice production in Asia and for developing adaptive rice production technologies. On the basis of measurement of rice responses to climate and carbon dioxide concentration ([CO2]), the dynamic process model named SIMRIW was developed to predict global warming effects on irrigated rice. The model predicted differential regional effects of the projected global warming by doubling [CO2] on the rice yield over Asia, and indicated that high tolerance to heat-induced spikelet sterility and high yield potential under elevated [CO2] are the two important characteristics required for rice genotypes adaptive to global warming environment. Further, genetic traits associated with these characteristics and their genetic resources for breeding adaptive genotypes were identified from diverse rice germplasms. This article reviews our initiative studies in the light of the recent studies, and points out further research that is needed for better understanding and overcoming of this unprecedentedly large problem.

Multimodel ensembles improve predictions of crop-environment-management interactions

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.