Research Progress on Heat Stress of Rice at Flowering Stage

Chen

et al. 2019

Agronomy Journal

Self Cite

The photoperiod sensitivity of rice (Oryza sativa L.) cultivars plays an important role in rice growth and yield formation. To ascertain the different effects of sowing dates on spikelet formation for different photoperiod‐sensitive genotypes, 10 sowing dates were selected to assess differences in spikelet differentiation and degeneration for photoperiod‐sensitive cultivars yongyou 538 (YY538) and xiushui134 (XS134) and photoperiod‐insensitive cultivar chujing27 (CJ27). The yield of the cultivars differed significantly among different sowing dates and was primarily influenced by the spikelet number per panicle. With different sowing dates, shorter daylengths, and increasing temperatures significantly shortened the duration from sowing to heading for YY538 and XS134, while that of CJ27 was less changed and influenced by the average temperature. Rice growth duration was the primary factor limiting dry matter accumulation (DMA), which was correlated with the number of differentiated spikelets (NDS) and the proportion of degenerated spikelets (PGS) in photoperiod‐sensitive varieties. High temperature weather with a relatively large temperature gap and strong solar radiation promoted spikelet differentiation in all three cultivars. Extremely high and low temperatures aggravated spikelet degeneration, but YY538 and XS134 were not exposed to low temperatures because their duration of growth was shortened to a greater extent than that of CJ27 with late sowing dates. Under different sowing dates, the NDS and PGS were evaluated by partial least squares regression (PLSR) with DMA, rice growth duration, temperature, and solar radiation. These results provide a reference for sowing date adjustment with climate changes in different regions. Core Ideas Rice yield with different sowing dates was determined by spikelet number. Rice growth duration was the primary factor affecting dry matter accumulation. High temperature and more dry weight increased spikelet differentiation. Sowing photoperiod‐sensitive cultivars at a late date avoided chilling injury. Spikelet formation can be predicted by partial least squares regression with sowing dates.

Section: Discussion Effects Of Rice Sowing Date On Growth Duration Ancontrasting

confidence: 99%

Sowing Dates have Different Effects on Spikelet Formation among Different Photoperiod‐Sensitive Rice Genotypes

Chen

et al. 2019

Agronomy Journal

Self Cite

“…Global warming has led to an increase in extreme weather events in recent decades (Wang et al, 2019). Extreme weather conditions, such as high temperature and drought, have greatly affected rice production and food security worldwide.…”

Section: Introductionmentioning

confidence: 99%

“…indica accounts for more than 70% of rice production globally (Zhang et al, 2016b). It is, however, very sensitive to high-temperature stress during almost all growth stages, particularly the flowering period (Wang et al, 2019;Zafar et al, 2018). Rice grain yields decline by 10% for each 1 C increase in the minimum night time temperature during the growing period (Peng et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic and proteomic profiles of II YOU 838 (Oryza sativa) provide insights into heat stress tolerance in hybrid rice

Huang

et al. 2020

PeerJ

Heat stress is an increasing threat to rice production worldwide. To investigate the mechanisms of heat tolerance in hybrid rice and their contributions to rice heterosis, we compared the transcriptome of the hybrid rice II YOU 838 (II8) with the transcriptomes of its parents Fu Hui 838 (F8) and II-32A (II3) after heat stress at 42 °C for 0 h, 24 h, 72 h and 120 h. We also performed a proteomic analysis in II8 after heat stress at 42 °C for 24 h. The transcriptome data revealed time-dependent gene expression patterns under the heat stress conditions, and the heat stress response of II8 was greatly different from those of its parents. Gene ontology analysis of the differentially expressed genes that were clustered using k-means clustering showed that most of the up-regulated genes were involved in responses to stimuli, cell communication, and metabolic and transcription factor activities, whereas the down-regulated genes were enriched in photosynthesis and signal transduction. Moreover, 35 unique differentially abundant proteins, including a basic helix-loop-helix transcription factor (bHLH96), calmodulin-binding transcription activator, heat shock protein (Hsp70), and chaperonin 60 (CPN60), were detected in the proteomic analysis of II8 under heat stress. The co-regulatory analysis revealed novel genes and pathways involved in heat tolerance, namely, ferredoxin-NADP reductase, peroxidases, mitogen-activated protein kinase kinase kinase, and heat shock factor (HSF)–Hsp network. Members of the Hsp and HSF families had over-dominant expression patterns in the hybrid compared with its parents, to help maintain the higher photosynthesis and antioxidant defense systems in the hybrid. Our study suggests that the complex HSF–Hsp regulatory network contribute to the heat tolerance of the hybrid rice.

“…With increasing concerns about global warming, the impacts of higher temperature on rice production have become a major focus in many rice-producing countries in tropical, subtropical, and temperate regions [1][2][3][4][5][6][7][8][9]. In China, extreme high temperature in 2003 caused about 5 million tonnes of rice yield loss [10], while unusual temperatures (>40°C) in many areas of Kanto and Tokai regions of Japan resulted in 25% spikelet sterility in 2007 [11].…”

Section: Introductionmentioning

confidence: 99%

Estimating Rice Panicle Temperature with Three-Layer Model

Advances in Meteorology

Oue

Luo

et al. 2020

Rice panicle temperature (Tp) is a key factor for studying high temperature impacts on spikelet sterility. Comparing with measuring Tp by hand, a Tp simulation model could obtain Tp data readily. The two-layer energy budget model which divides the soil layer and canopy layer was widely used to predict rice canopy temperature (Tc), but panicle existed mostly in the upper layer canopy, and we have proved that Tc was different from the upper layer canopy temperature (Tc1), and the upper layer must be separated from the whole canopy for the purpose of estimating Tp. Thus, we developed the three-layer model, contained upper canopy layer with panicle (50–100 cm), lower rice canopy layer (10–40 cm), and water surface layer (≤10 cm) to estimate Tp with general meteorological and vegetation growth data. There were two steps to estimate Tp. The first step was calculating Tc1 and lower layer canopy temperature (Tc2) by solving heat balance equations with canopy resistances. And the second step was estimating Tp with following parameters: (a) the inclination factors of leaves and panicles (F1, F2, and Fp) which were decided by fitting the calculated transmissivity of downward solar radiation (TDSR) to the measured TDSR, (b) the aerodynamic resistance between the panicle and atmosphere (rap) denoted by wind speed, (c) the panicle resistance for transpiration (rp) denoted by days after heading, and (d) air temperature and humidity at the panicle’s height (Tac1 and eac1) calculated from the resistances of the pathways of sensible and latent heat fluxes in accordance with Ohm’s law. The model simulated fairly well the Tc1, Tc2, and Tp with root mean square errors (RMSEs) of 0.76°C, 0.75°C, and 0.81°C, respectively, where RMSE of measured Tp and predicted Tp by integrated micrometeorology model for panicle and canopy temperature (IM2PACT) including two-layer model was 1.27°C. This model was validated well by two other rice cultivars, and thus, it demonstrated the three-layer model was a new feasible way to estimate Tp.