Rice yields decline with higher night temperature from global warming.
Ontology highlight
ABSTRACT:
SUBMITTER: Peng S
PROVIDER: S-EPMC454199 | biostudies-other | 2004 Jul
REPOSITORIES: biostudies-other
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Project description:Increasing night-time temperatures are a major threat to sustaining global rice (Oryza sativa L.) production. A simultaneous increase in [CO2] will lead to an inevitable interaction between elevated [CO2] (e[CO2]) and high night temperature (HNT) under current and future climates. Here, we conducted field experiments to identify [CO2] responsiveness from a diverse indica panel comprising 194 genotypes under different planting geometries in 2016. Twenty-three different genotypes were tested under different planting geometries and e[CO2] using a free-air [CO2] enrichment facility in 2017. The most promising genotypes and positive and negative controls were tested under HNT and e[CO2] + HNT in 2018. [CO2] responsiveness, measured as a composite response index on different yield components, grain yield, and photosynthesis, revealed a strong relationship (R2 = 0.71) between low planting density and e[CO2]. The most promising genotypes revealed significantly lower (P < 0.001) impact of HNT in high [CO2] responsive (HCR) genotypes compared to the least [CO2] responsive genotype. [CO2] responsiveness was the major driver determining grain yield and related components in HCR genotypes with a negligible yield loss under HNT. A systematic investigation highlighted that active selection and breeding for [CO2] responsiveness can lead to maintained carbon balance and compensate for HNT-induced yield losses in rice and potentially other C3 crops under current and future warmer climates.
| S-EPMC8774858 | biostudies-literature
Project description:High temperature stress during rice reproductive development results in yield losses. Reduced grain yield and grain quality has been associated with high temperature stress, and specifically with high night-time temperatures (HNT). Characterizing the impact of HNT on the phenotypic and metabolic status of developing rice seeds can provide insights into the mechanisms involved in yield and quality decline. Here, we examined the impact of warmer nights on the morphology and metabolome during early seed development in six diverse rice accessions. Seed size was sensitive to HNT in four of the six genotypes, while seed fertility and seed weight were unaffected. We observed genotypic differences for negative impact of HNT on grain quality. This was evident from the chalky grain appearance due to impaired packaging of starch granules. Metabolite profiles during early seed development (3 and 4 days after fertilization; DAF) were distinct from the early grain filling stages (7 and 10 DAF) under optimal conditions. We observed that accumulation of sugars (sucrose, fructose, and glucose) peaked at 7 DAF suggesting a major flux of carbon into glycolysis, tricarboxylic acid cycle, and starch biosynthesis during grain filling. Next, we determined hyper (HNT > control) and hypo (HNT < control) abundant metabolites and found 19 of the 57 metabolites to differ significantly between HNT and control treatments. The most prominent changes were exhibited by differential abundance of sugar and sugar alcohols under HNT, which could be linked to a protective mechanism against the HNT damage. Overall, our results indicate that combining metabolic profiles of developing grains with yield and quality parameters under high night temperature stress could provide insight for exploration of natural variation for HNT tolerance in the rice germplasm.
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Project description:Animal physiology, ecology and evolution are affected by temperature and it is expected that community structure will be strongly influenced by global warming. This is particularly relevant in the tropics, where organisms are already living close to their upper temperature limits and hence are highly vulnerable to rising temperature. Here we present data on upper temperature limits of 34 tropical marine ectotherm species from seven phyla living in intertidal and subtidal habitats. Short term thermal tolerances and vertical distributions were correlated, i.e., upper shore animals have higher thermal tolerance than lower shore and subtidal animals; however, animals, despite their respective tidal height, were susceptible to the same temperature in the long term. When temperatures were raised by 1°C hour(-1), the upper lethal temperature range of intertidal ectotherms was 41-52°C, but this range was narrower and reduced to 37-41°C in subtidal animals. The rate of temperature change, however, affected intertidal and subtidal animals differently. In chronic heating experiments when temperature was raised weekly or monthly instead of every hour, upper temperature limits of subtidal species decreased from 40°C to 35.4°C, while the decrease was more than 10°C in high shore organisms. Hence in the long term, activity and survival of tropical marine organisms could be compromised just 2-3°C above present seawater temperatures. Differences between animals from environments that experience different levels of temperature variability suggest that the physiological mechanisms underlying thermal sensitivity may vary at different rates of warming.
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Project description:Under the dual impacts of climate change and COVID-19, there are great risks to the world's food security. Rice is one of the three major food crops of the world. Assessing the impact of climate change on future rice production is very important for ensuring global food security. This article divides the world's main rice-producing regions into four regions and uses a multivariate nonlinear model based on historical economic and climatic data to explore the impacts of historical extreme climatic events and economic factors on rice yield. Based on these historical models, future climatic data, and economic data under different shared socioeconomic pathways (SSPs), the yields of four major rice-producing regions of the world under different climate change scenarios (SSP126, SSP245, and SSP585) are predicted. The research results reveal that under different climate change scenarios, extreme high-temperature events (Tx90p) and extreme precipitation events (Rx5day, R99pTOT) in the four major rice-producing regions have an upward trend in the future. Extreme low-temperature events (Tn10p) have a downward trend. In the rice-producing regions of Southeast Asia and South America, extreme precipitation events will increase significantly in the future. The prediction results of this model indicate that the rice output of these four major rice-producing regions will show an upward trend in the future. Although extreme precipitation events will have a negative impact on rice production, future increases in rice planting areas, economic development, and population growth will all contribute to an increase in rice production. The increase in food demand caused by population growth also brings uncertainty to global food security. This research is helpful for further understanding climate change trends and risks to global rice-production areas in the future and provides an important reference for global rice-production planning and risk management.
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Project description:Background:To study the impact of climate warming on crops, it is crucial to have a warming equipment suitable for their field environment. A facility is needed that can provide suitable combinations of different temperatures at reasonable cost for large plots. Results:Here, an additional field warming facility option named the hot-blast warming facility (HBWF), which comprised heaters, blowers, wind breaks, and a control board was developed. An application case based on HBWF was carried out to assess elevated temperature effects on rice in Central China during 2015 and 2016. We tested four elevated temperature treatments on four rice cultivars under paddy field conditions and measured yield and its components. Heating convection air directly, the facility could increase the temperature of the rice canopy up to 1-2 °C, which could properly simulate global warming. Considering the costs, the HBWF reduced the operating costs because of its relatively lower power consumption (0.164 kW/m2), which was 80% lower than that of Free Air Temperature Increase. Our results demonstrate that the HBWF could build a 25 m2 homogeneous heating area and had little effect on the relative humidity under a paddy field environment. Warming treatments significantly reduced the grain yield by 4.4-22.7% in 2015, and 30.8-61.9% in 2016, compared to the control. The main contribution to the significant decrease of the grain yields was the decrease in seed setting rate. Moreover, a reduction of 1000-grain weight led to the decline in grain yield. The increasing ranges of the temperature simulated by HBWF were stable in different years, however, whether the elevated treatments demonstrated significant difference on rice growth mainly decided by the basic atmospheric temperature (as the control) during the growth period. Conclusions:The new warming facility is suitable for field trials to assess elevated temperature combinations and provides an extra equipment option for use in elevated temperature research in the future.
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Project description:Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.
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Project description:Global warming is posing a threat to animals. As a large group of widely distributed poikilothermal animals, insects are liable to heat stress. How insects deal with heat stress is worth highlighting. Acclimation may improve the heat tolerance of insects, but the underlying mechanism remains vague. In this study, the high temperature of 39 °C was used to select the third instar larvae of the rice leaf folder Cnaphalocrocis medinalis, an important insect pest of rice, for successive generations to establish the heat-acclimated strain (HA39). The molecular mechanism of heat acclimation was explored using this strain. The HA39 larvae showed stronger tolerance to 43 °C than the unacclimated strain (HA27) persistently reared at 27 °C. The HA39 larvae upregulated a glucose dehydrogenase gene, CmGMC10, to decrease the reactive oxygen species (ROS) level and increase the survival rate under heat stress. The HA39 larvae maintained a higher activity of antioxidases than the HA27 when confronted with an exogenous oxidant. Heat acclimation decreased the H2O2 level in larvae under heat stress which was associated with the upregulation of CmGMC10. The rice leaf folder larvae may acclimate to global warming via upregulating CmGMC10 to increase the activity of antioxidases and alleviate the oxidative damage of heat stress.
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Project description:Rice yield and quality are adversely affected by increasing global surface temperature, and are strongly attributed to high night temperature (HNT) than high daytime temperature. However, the molecular mechanism underlying the heat-tolerant characteristics of rice remains unclear. In the present study, we compared the proteomes of heat-tolerant and -sensitive lines of rice at early milky stage using an iTRAQ method. We have identified 38 differentially expressed proteins between the two lines, of which 32 proteins have been functionally annotated in NCBI and/or the UniProt database. These proteins were then classified into seven functional subgroups, which include signal transduction, transcript regulation, oxidation, defense response, transport, energy metabolism, and biosynthesis. Further analysis indicated that HNT stress could disrupt the redox equilibrium of plant cells, which in turn triggers the calcium-dependent protein kinase and COP9 signalosome, thereby regulating downstream genes/proteins that are involved in the HNT response. The candidate proteins may provide genetic resources for the improvement of heat-tolerant characteristics in rice, and the proposed model for signal transduction and transcriptional regulation may facilitate in the elucidation of the molecular mechanism underlying the response to HNT stress in rice.
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Project description:Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future.
| S-EPMC5788319 | biostudies-literature
Project description:BackgroundRice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed.ResultsWe use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area.ConclusionsWe have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.
| S-EPMC8783510 | biostudies-literature