Author Correction: Increasing CO2 threatens human nutrition.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

MORE PANICLES 3, a natural allele of OsTB1/FC1, impacts rice yield in paddy fields at elevated CO2 levels.

Improving crop yield potential through an enhanced response to rising atmospheric CO2 levels is an effective strategy for sustainable crop production in the face of climate change. Large-sized panicles (containing many spikelets per panicle) have been a recent ideal plant architecture (IPA) for high-yield rice breeding. However, few breeding programs have proposed an IPA under the projected climate change. Here, we demonstrate through the cloning of the rice (Oryza sativa) quantitative trait locus for MORE PANICLES 3 (MP3) that the improvement in panicle number increases grain yield at elevated atmospheric CO2 levels. MP3 is a natural allele of OsTB1/FC1, previously reported as a negative regulator of tiller bud outgrowth. The temperate japonica allele advanced the developmental process in axillary buds, moderately promoted tillering, and increased the panicle number without negative effects on the panicle size or culm thickness in a high-yielding indica cultivar with large-sized panicles. The MP3 allele, containing three exonic polymorphisms, was observed in most accessions in the temperate japonica subgroups but was rarely observed in the indica subgroup. No selective sweep at MP3 in either the temperate japonica or indica subgroups suggested that MP3 has not been involved and utilized in artificial selection during domestication or breeding. A free-air CO2 enrichment experiment revealed a clear increase of grain yield associated with the temperate japonica allele at elevated atmospheric CO2 levels. Our findings show that the moderately increased panicle number combined with large-sized panicles using MP3 could be a novel IPA and contribute to an increase in rice production under climate change with rising atmospheric CO2 levels.

Enhanced decreases in rice evapotranspiration in response to elevated atmospheric carbon dioxide under warmer environments.

A short period of exposure to elevated CO2 is known to decrease evapotranspiration via stomatal closure. Based on theoretical evaluation of a canopy transpiration model, we hypothesized that this decrease in the evapotranspiration of rice under elevated CO2 was greater under higher temperature conditions due to an increased sensitivity of transpiration to changes in CO2 induced by the greater vapour pressure deficit. In a temperature gradient chamber-based experiment, a 200 ppm increase in CO2 concentration led to 0.4 mm (-7%) and 1.5 mm (-15%) decreases in 12 h evapotranspiration under ambient temperature and high temperature (+3.7°C) conditions, respectively. Model simulations revealed that the greater vapour pressure deficit under higher temperature conditions explained the variations in the reduction of evapotranspiration observed under elevated CO2 levels between the temperature treatments. Our study suggests the utility of a simple modelling framework for mechanistic understanding of evapotranspiration and crop energy balance system under changing environmental conditions.

A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Low N level increases the susceptibility of PSI to photoinhibition induced by short repetitive flashes in leaves of different rice varieties.

The recovery from photoinhibition is much slower in photosystem (PS) I than in PSII; therefore, the susceptibility of PSI to photoinhibition is important with respect to photosynthetic production under special physiological conditions. Previous studies have shown that repetitive short-pulse (rSP) illumination selectively induces PSI photoinhibition. Depending on the growth light intensity or the variety/species of the plant, PSI photoinhibition is different, but the underlying mechanisms remain unknown. Here, we aimed to clarify whether the differences in the susceptibility of PSI to photoinhibition depend on environmental factors or on rice varieties and which physiological properties of the plant are related to this susceptibility. We exposed mature leaves of rice plants to rSP illumination. We examined the effects of elevated CO2 concentration and low N during growth on the susceptibility of PSI to photoinhibition and compared it in 12 different varieties. We fitted the decrease in the quantum yield of PSI during rSP illumination and estimated a parameter indicating susceptibility. Low N level increased susceptibility, whereas elevated CO2 concentration did not. The susceptibility differed among different rice varieties, and many indica varieties showed higher susceptibility than the temperate japonica varieties. Susceptibility was negatively correlated with the total chlorophyll content and N content. However, the decrease in P m ' value, an indicator of damaged PSI, was positively correlated with chlorophyll content. This suggests that in leaves with a larger electron transport capacity, the overall PSI activity may be less susceptible to photoinhibition, but more damaged PSI may accumulate during rSP illumination.

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 (Tbase ). 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.

Increasing CO2 threatens human nutrition.

Dietary deficiencies of zinc and iron are a substantial global public health problem. An estimated two billion people suffer these deficiencies, causing a loss of 63 million life-years annually. Most of these people depend on C3 grains and legumes as their primary dietary source of zinc and iron. Here we report that C3 grains and legumes have lower concentrations of zinc and iron when grown under field conditions at the elevated atmospheric CO2 concentration predicted for the middle of this century. C3 crops other than legumes also have lower concentrations of protein, whereas C4 crops seem to be less affected. Differences between cultivars of a single crop suggest that breeding for decreased sensitivity to atmospheric CO2 concentration could partly address these new challenges to global health.

Changes in grain protein and amino acids composition of wheat and rice under short-term increased [CO2 ] and temperature of canopy air in a paddy from East China.

Projected global climate change is a potential threat for food security. Both rising atmospheric CO2 concentrations ([CO2 ]) and temperatures have significant impacts on crop productivity, but the combined effects on grain quality are not well understood. We conducted an open-air field experiment to determine the impacts of elevated [CO2 ] (E-[CO2 ], up to 500 µmol mol-1 ) and warming (+2°C) on grain yield, protein and amino acid (AAs, acid digests) in a rice-winter wheat rotation system for 2 yr. E-[CO2 ] increased grain yield by 11.3% for wheat and 5.9% for rice, but decreased grain protein concentration by 14.9% for wheat and by 7.0% for rice, although E-[CO2 ] slightly increased the ratio of essential to nonessential AAs. With a consistent decline in grain yield, warming decreased protein yield, notably in wheat, despite a smaller increase in protein concentration. These results indicate that warming could partially negate the negative impact by E-[CO2 ] on grain protein concentration at the expense of grain yield; this tradeoff could not fully offset the negative effects of climate change on crop production.

Effects of Elevated Atmospheric CO2 on Respiratory Rates in Mature Leaves of Two Rice Cultivars Grown at a Free-Air CO2 Enrichment Site and Analyses of the Underlying Mechanisms.

Respiratory CO2 efflux and O2 uptake rates in leaves change in response to the growth CO2 concentration ([CO2]). The degrees of change vary depending on the responses of cellular processes such as nitrogen (N) assimilation and accumulation of organic acids to growth [CO2]. However, the underlying mechanisms remain unclear. Here, we examined the respiratory characteristics of mature leaves of two rice varieties with different yield capacities at different growth stages under ambient and elevated [CO2] conditions at a free-air CO2 enrichment site. We also examined the effect of increased water temperature on leaf respiration. We measured the rates of CO2 efflux and O2 uptake, and determined N contents, primary metabolite contents and maximal activities of respiratory enzymes. The leaf CO2 efflux rates decreased in plants grown at elevated [CO2] in both varieties, and were higher in high-yielding Takanari than in Koshihikari. The leaf O2 uptake rates showed little change with respect to growth [CO2] and variety. The increased water temperature did not significantly affect the CO2 efflux and O2 uptake rates. The N and amino acid contents were significantly higher in Takanari than in Koshihikari. The enhanced N assimilation in Takanari may have consumed more respiratory NADH, leading to higher CO2 efflux rates. In Koshihikari, the ratio of tricarboxylic acid (TCA) cycle intermediates changed and maximal activities of enzymes in the TCA cycle decreased at elevated [CO2]. Therefore, the decreased rates of CO2 efflux in Koshihikari may be due to the decreased activities of TCA cycle enzymes at elevated [CO2].

Increasing canopy photosynthesis in rice can be achieved without a large increase in water use-A model based on free-air CO2 enrichment.

Achieving higher canopy photosynthesis rates is one of the keys to increasing future crop production; however, this typically requires additional water inputs because of increased water loss through the stomata. Lowland rice canopies presently consume a large amount of water, and any further increase in water usage may significantly impact local water resources. This situation is further complicated by changing the environmental conditions such as rising atmospheric CO2 concentration ([CO2 ]). Here, we modeled and compared evapotranspiration of fully developed rice canopies of a high-yielding rice cultivar (Oryza sativa L. cv. Takanari) with a common cultivar (cv. Koshihikari) under ambient and elevated [CO2 ] (A-CO2 and E-CO2 , respectively) via leaf ecophysiological parameters derived from a free-air CO2 enrichment (FACE) experiment. Takanari had 4%-5% higher evapotranspiration than Koshihikari under both A-CO2 and E-CO2 , and E-CO2 decreased evapotranspiration of both varieties by 4%-6%. Therefore, if Takanari was cultivated under future [CO2 ] conditions, the cost for water could be maintained at the same level as for cultivating Koshihikari at current [CO2 ] with an increase in canopy photosynthesis by 36%. Sensitivity analyses determined that stomatal conductance was a significant physiological factor responsible for the greater canopy photosynthesis in Takanari over Koshihikari. Takanari had 30%-40% higher stomatal conductance than Koshihikari; however, the presence of high aerodynamic resistance in the natural field and lower canopy temperature of Takanari than Koshihikari resulted in the small difference in evapotranspiration. Despite the small difference in evapotranspiration between varieties, the model simulations showed that Takanari clearly decreased canopy and air temperatures within the planetary boundary layer compared to Koshihikari. Our results indicate that lowland rice varieties characterized by high-stomatal conductance can play a key role in enhancing productivity and moderating heat-induced damage to grain quality in the coming decades, without significantly increasing crop water use.

Rice grain yield and quality responses to free-air CO2 enrichment combined with soil and water warming.

Rising air temperatures are projected to reduce rice yield and quality, whereas increasing atmospheric CO2 concentrations ([CO2 ]) can increase grain yield. For irrigated rice, ponded water is an important temperature environment, but few open-field evaluations are available on the combined effects of temperature and [CO2 ], which limits our ability to predict future rice production. We conducted free-air CO2 enrichment and soil and water warming experiments, for three growing seasons to determine the yield and quality response to elevated [CO2 ] (+200 µmol mol(-1) , E-[CO2 ]) and soil and water temperatures (+2 °C, E-T). E-[CO2 ] significantly increased biomass and grain yield by approximately 14% averaged over 3 years, mainly because of increased panicle and spikelet density. E-T significantly increased biomass but had no significant effect on the grain yield. E-T decreased days from transplanting to heading by approximately 1%, but days to the maximum tiller number (MTN) stage were reduced by approximately 8%, which limited the panicle density and therefore sink capacity. On the other hand, E-[CO2 ] increased days to the MTN stage by approximately 4%, leading to a greater number of tillers. Grain appearance quality was decreased by both treatments, but E-[CO2 ] showed a much larger effect than did E-T. The significant decrease in undamaged grains (UDG) by E-[CO2 ] was mainly the result of an increased percentage of white-base grains (WBSG), which were negatively correlated with grain protein content. A significant decrease in grain protein content by E-[CO2 ] accounted in part for the increased WBSG. The dependence of WBSG on grain protein content, however, was different among years; the slope and intercept of the relationship were positively correlated with a heat dose above 26 °C. Year-to-year variation in the response of grain appearance quality demonstrated that E-[CO2 ] and rising air temperatures synergistically reduce grain appearance quality of rice.

Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.

KEY MESSAGE: It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in the prediction of phenotypes of plants.

A meta-analysis of leaf nitrogen distribution within plant canopies.

BACKGROUND AND AIMS: Leaf nitrogen distribution in the plant canopy is an important determinant for canopy photosynthesis. Although the gradient of leaf nitrogen is formed along light gradients in the canopy, its quantitative variations among species and environmental responses remain unknown. Here, we conducted a global meta-analysis of leaf nitrogen distribution in plant canopies. METHODS: We collected data on the nitrogen distribution and environmental variables from 393 plant canopies (100, 241 and 52 canopies for wheat, other herbaceous and woody species, respectively). KEY RESULTS: The trends were clearly different between wheat and other species; the photosynthetic nitrogen distribution coefficient (Kb) was mainly determined by leaf area index (LAI) in wheat, whereas it was correlated with the light extinction coefficient (KL) and LAI in other species. Some other variables were also found to influence Kb We present the best equations for Kb as a function of environmental variables and canopy characteristics. As a more simple function, Kb = 0·5KL can be used for canopies of species other than wheat. Sensitivity analyses using a terrestrial carbon flux model showed that gross primary production tended to be more sensitive to the Kb value especially when nitrogen content of the uppermost leaf was fixed. CONCLUSION: Our results reveal that nitrogen distribution is mainly driven by the vertical light gradient but other factors such as LAI also have significant effects. Our equations contribute to an improvement in the projection of plant productivity and cycling of carbon and nitrogen in terrestrial ecosystems.

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.

Elevated CO2 decreases the Photorespiratory NH3 production but does not decrease the NH3 compensation point in rice leaves.

The exchange of gaseous NH3 between the atmosphere and plants plays a pivotal role in controlling the global NH3 cycle. Photorespiration generates NH3 through oxygenation instead of carboxylation by the CO2-fixing enzyme, ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). The future increase in the atmospheric CO2 concentration, [CO2], is expected to reduce plant NH3 production by suppressing RuBisCO oxygenation (Vo). We measured the net leaf NH3 uptake rate (FNH3) across NH3 concentrations in the air (na) ranging from 0.2 to 1.6 nmol mol(-1) at three [CO2] values (190, 360 and 750 µmol mol(-1)) using rice plants. We analyzed leaf NH3 gas exchange using a custom-made whole-leaf chamber system, and determined the NH3 compensation point (γ), a measure of potential NH3 emission, as the x-intercept of the linear relationship of FNH3 as a function of na. Our γ values were lower than those reported for other plant species. γ did not decrease under elevated [CO2], although leaf NH4 (+) content decreased with decreasing Vo at higher [CO2]. This was also the case for γ estimated from the pH and NH4 (+) concentration of the leaf apoplast solution (γ'). γ' of rice plants, grown at elevated [CO2] for months in a free-air CO2 enrichment facility, was also not decreased by elevated [CO2]. These results suggest that suppression of RuBisCO oxygenation by elevated [CO2] does not decrease potential leaf NH3 emission in rice plants.

Do the rich always become richer? Characterizing the leaf physiological response of the high-yielding rice cultivar Takanari to free-air CO2 enrichment.

The development of crops which are well suited to growth under future environmental conditions such as higher atmospheric CO2 concentrations ([CO2]) is essential to meeting the challenge of ensuring food security in the face of the growing human population and changing climate. A high-yielding indica rice variety (Oryza sativa L. cv. Takanari) has been recently identified as a potential candidate for such breeding, due to its high productivity in present [CO2]. To test if it could further increase its productivity under elevated [CO2] (eCO2), Takanari was grown in the paddy field under season-long free-air CO2 enrichment (FACE, approximately 200 µmol mol(-1) above ambient [CO2]) and its leaf physiology was compared with the representative japonica variety 'Koshihikari'. Takanari showed consistently higher midday photosynthesis and stomatal conductance than Koshihikari under both ambient and FACE growth conditions over 2 years. Maximum ribulose-1,5-bisphosphate carboxylation and electron transport rates were higher for Takanari at the mid-grain filling stage in both years. Mesophyll conductance was higher in Takanari than in Koshihikari at the late grain-filling stage. In contrast to Koshihikari, Takanari grown under FACE conditions showed no decrease in total leaf nitrogen on an area basis relative to ambient-grown plants. Chl content was higher in Takanari than in Koshihikari at the same leaf nitrogen level. These results indicate that Takanari maintains its superiority over Koshihikari in regards to its leaf-level productivity when grown in elevated [CO2] and it may be a valuable resource for rice breeding programs which seek to increase crop productivity under current and future [CO2].

Soil and water warming accelerates phenology and down-regulation of leaf photosynthesis of rice plants grown under free-air CO2 enrichment (FACE).

To enable prediction of future rice production in a changing climate, we need to understand the interactive effects of temperature and elevated [CO2] (E[CO2]). We therefore examined if the effect of E[CO2] on the light-saturated leaf photosynthetic rate (Asat) was affected by soil and water temperature (NT, normal; ET, elevated) under open-field conditions at the rice free-air CO2 enrichment (FACE) facility in Shizukuishi, Japan, in 2007 and 2008. Season-long E[CO2] (+200 µmol mol(-1)) increased Asat by 26%, when averaged over two years, temperature regimes and growth stages. The effect of ET (+2°C) on Asat was not significant at active tillering and heading, but became negative and significant at mid-grain filling; Asat in E[CO2]-ET was higher than in ambient [CO2] (A[CO2])-NT by only 4%. Photosynthetic down-regulation at E[CO2] also became apparent at mid-grain filling; Asat compared at the same [CO2] in the leaf cuvette was significantly lower in plants grown in E[CO2] than in those grown in A[CO2]. The additive effects of E[CO2] and ET decreased Asat by 23% compared with that of A[CO2]-NT plants. Although total crop nitrogen (N) uptake was increased by ET, N allocation to the leaves and to Rubisco was reduced under ET and E[CO2] at mid-grain filling, which resulted in a significant decrease (32%) in the maximum rate of ribulose-1,5-bisphosphate carboxylation on a leaf area basis. Because the change in N allocation was associated with the accelerated phenology in E[CO2]-ET plants, we conclude that soil and water warming accelerates photosynthetic down-regulation at E[CO2].

Fully automated, high-throughput instrumentation for measuring the δ13C value of methane and application of the instrumentation to rice paddy samples.

RATIONALE: The stable carbon isotope ratio ((13)C/(12)C or δ(13)C value) of methane (CH4) produced in methanogenic environments contains information about primary source material, CH4 production pathways, degree of oxidation, and transport. However, the availability of δ(13)C-CH4 data is severely limited because isotope analysis methods are low throughput, owing primarily to the need for manual processing steps. High-throughput, fully automated measurement is necessary to facilitate the use of the δ(13)C signature in understanding CH4 biogeochemistry. METHODS: We modified a conventional continuous-flow (CF) gas chromatography/combustion/isotope ratio mass spectrometry (IRMS) instrument system by incorporating (i) automated sample injection, (ii) a newly developed temperature-control unit for preconcentration and cryofocus traps, and (iii) an automatic system for liquid-nitrogen refilling. The system, which could run unattended for 1 day, was used to obtain δ(13)C-CH4 data for CH4 samples collected from an irrigated rice paddy with an automated closed-chamber system. RESULTS: Using the fully automated CF-IRMS system, we measured δ(13)C-CH4 data for 77 samples during a 21.5-h continuous run (17 min per sample) with high precision (1σ = 0.11‰, reproducibility) and moderate consumption of liquid nitrogen (11 L). Application of the system to CH4 samples obtained from the rice paddy revealed distinct seasonal and diurnal variations in δ(13)C values with the highest temporal resolution ever reported. CONCLUSIONS: A fully automated, high-throughput system for the measurement of δ(13)C-CH4 values was developed and used to analyze air samples obtained from a rice paddy. Our results demonstrate the high potential of this system for obtaining δ(13)C data useful for process-level understanding of CH4 biogeochemistry with respect to spatiotemporal variation of CH4 sources and how that variation is affected by environmental and management factors.

Planting geometry as a pre-screening technique for identifying CO2 responsive rice genotypes: a case study of panicle number.

Identifying CO(2) responsive genotypes is a major target for enhancing crop productivity under future global elevated atmospheric CO(2) concentration ([CO(2)]). However, [CO(2)]-fumigation facilities are extremely expensive and are not easily accessible, and are limited in space for large-scale screening. Hence, reliable donors for initiating [CO(2)]-responsive breeding programs are not in place for crops, including rice. We propose a simple and novel phenotyping method for identifying [CO(2)]-responsive genotypes, and quantify the responsiveness to low planting density over 4-year trials across both temperate and tropical conditions. Panicle number per plant is the key determinant of grain yield and hence was the focus trait across all our trials. In temperate climate, a 3-season field screening using 127 diverse rice genotypes and employing two planting densities (normal and low density) was conducted. Two japonica genotypes were selected based on their higher responsiveness to low planting density as candidates for validating the proposed phenotyping protocol as a pre-screen for [CO(2)]-responsiveness. The approach using the two selected candidates and three standard genotypes was confirmed using a free-air CO(2) enrichment facility and temperature gradient chambers under elevated [CO(2)]. In tropical climate, we grew three rice cultivars, previously identified for their [CO(2)]-responsiveness, at two planting densities. The experiments provided confirmation that responsiveness to low planting density was correlated with that of [CO(2)]-responsiveness across both the temperate and tropical conditions. The planting density would be useful pre-screening method for testing large panels of diverse germplasm at low cost complemented by available CO(2) -control facilities for final validation of candidates from the pre-screens.

A dataset for analyzing the climate change response of grain quality of 48 Japanese rice cultivars with contrasting levels of heat tolerance.

Climate change has a significant impact on rice grain appearance quality; in particular, high temperatures during the grain filling period increase the rate of chalky immature grains, reducing the marketability of rice. Heat-tolerant cultivars have been bred and released to reduce the rate of chalky grain and improve rice quality under high temperatures, but the ability of these cultivars to actually reduce chalky grain content has never been demonstrated due to the lack of integrated datasets. Here, we present a dataset collected through a systematic literature search from publicly available data sources, for the quantitative analysis of the impact of meteorological factors on grain appearance quality of various rice cultivars with contrasted heat tolerance levels. The dataset contains 1302 field observations of chalky grain rates (%) - a critical trait affecting grain appearance sensitive to temperature shocks - for 48 cultivars covering five different heat-tolerant ranks (HTRs) collected at 44 sites across Japan. The dataset also includes the values of key meteorological variables during the grain filling period, such as the cumulative mean air temperature above the threshold temperature (TaHD), mean solar radiation, and mean relative humidity over 20 days after heading, obtained from a gridded daily meteorological dataset with a 1-km resolution developed by the National Agriculture and Food Research Organization. The dataset covers major commercial rice cultivars cultivated in Japan in different environmental conditions. It is a useful resource for analyzing the climate change impact on crop quality and assess the effectiveness of genetic improvements in heat tolerance. Its value has been illustrated in the research article entitled "Effectiveness of heat tolerance rice cultivars in preserving grain appearance quality under high temperatures - A meta-analysis", where the dataset was used to develop a statistical model quantifying the effects of high temperature on grain quality as a function of cultivar heat tolerance.