Modeling The Effects Of Post-flowering Heat Stress On Rice Growth And Yield Formation

Heat stress events are more frequent accompanied by the increasing global warming.Rice production will likely face enormous challenges against the short-term extreme heat stress,because of the warming temperature in its growth season and planting area.Heat waves that occur at critical stages of the reproductive phase have detrimental impacts on the grain yield of rice,threatening the food security.Crop growth models are effective tools to assess the rice growth and yield formation under future climate change,but have large uncertainties particularly under extreme environment.Therefore,accurate estimates of high temperature impacts are essential to evaluate the effects of climate change on rice productivity.Therefore,we conducted high temperature treatemtns of different temperature intensities and durations at different stages,to test the source of prediction uncertainties through multi-model comparison,and then improve the prediction performance of photosynthesis,dry matter accumulation and distribution,nitrogen accumulation and distribution,and yield formation under heat stress.In this study,we evaluated temperature functions for the grain setting used in 14 rice growth models against multi-year phytotron experiments with four heat treatments during flowering,with differences in the timing,intensity,and duration.We found that all models underestimated the negative effects of heat on grain yield.The variations among the models were much smaller than those between the means of the simulations and the observations,so crop model ensembles do not improve accuracy of yield prediction under heat stress.We used the observed seed setting rate at maturity instead of the simulated spikelets sterility which modified the grain number,partitioning index,and harvest index in different models,and the performance of predicted grain yield at 0 DAF and 6 DAF treatments improved greatly.The temperature response of grain-setting rate is the main cause of yield prediction uncertainty.We tested the model performance against different effective periods of temperature response functions for grain-setting rate(TRFGS).InfoCrop which used the daily mean temperature as the temperature variable for TRFGS,performed best within all models,and the suggested DVSe,a stage at which the effective period ends,ranged from 1.325 to 1.400.For models using average daily maximum temperature as the temperature variable for TRFGS(Horie-TRFGS model),the suggested DVSe ranged from 1.225 to 1.275.Even if DVSe was optimized using the suggested range,the Horie-TRFGS models still underestimated the negative impacts of high temperature.Occasional cool temperatures that occur during the sensitive period do not necessarily alleviate the negative impacts of heat events.Therefore,temperatures below a certain threshold were used for the calculation of the input temperature of the function for Horie-TRFGS models.Adjusting the sensitive periods of TRFGS and the threshold of input temperatures can improve the irreversible effects of short-term heat impacts on grain-setting rate,and meanwhile the performance of current rice models.Seed-setting rate of rice significantly decreases under high temperatures during the reproductive period largely due to heat-induced spikelet sterility.The effects depend largely on the timing and severity of heat stress,but the variation in the sensitivity of seed-setting rate in response to heat stress at different growth stages is not yet well simulated by most of the present crop models.The duration and intensity of heat stress were quantified by the heat degree-days(HDD),defined as the temperature sum above a critical temperature value,which varied from 35 to 36?,depending on the cultivars.The observed seed-setting rates were well expressed as a logistic function of HDD,but the temperature sensitivity parameter varied with the timing of heat stress and the spikelet positions on the panicle.The variation in timing of flowering was apparent among upper,middle and lower parts of the panicles:1 and 3-day delay of flowering for the spikelets on the middle and lower parts of the panicles,respectively,compared with upper ones.We therefore developed a simulation model that reflects the changes in the sensitivity of seed-setting rate to HDD to estimate the effects of heat stress with different intensities or durations at any time from flowering onward.The sensitivity to heat stress was greatest at around anthesis and decreased from then onward.The model with the stage-dependent parameters improved substantially the root mean square error(RMSE)and mean bias error(MBE)of seed-setting%from 20.1 and 6.0 to 7.6 and 0.2,respectively,compared to that with the stage-independent parameters.The proposed model needs to be tested under field conditions,but will be an important basis for accurate prediction of seed-setting rates in rice,which is critical for reliable estimates of crop production under climate change.The post-anthesis heat stress led to the early termination of grain filling,and then a large amount of carbohydrate and nitrogen accumulated in the stems,which closed to that in panicles under ambient.Therefore,the unusual stem is an alternative harvest organ under future climate change for feed and bio-fuel,whereas needs accurate assessments for strategy adaptation.We assumed that rice stem is re-activated as sink organ when grain filling ends,and possesses the storage capacity for a large amount of carbon and nitrogen.In this study,multi-year experiment datasets involving different temperature intensities,durations and rice varieties were used to modify simulation of biomass and nitrogen in crop growth dynamic model RiceGrow.The sink-source relationship under heat stress was re-evaluated,thus the process of linear filling of nitrogen in stems after the termination of grain filling is simulated,which improved the nitrogen distribution within rice organs.The normalized root mean squared error(NRMSE)of leaf nitrogen accumulation decreased from 6.5%to 3.2%while that of stem decreased from 11.5%to 3.8%.The normalized mean bias(NMBE)of leaf nitrogen accumulation changed from 3.2%to-2.8%,while that of stem decreased from-11.8%to-2%.Then the routines of leaf senescence driven by nitrogen distribution were integrated into the RiceGrow,which decreased the NRMSE of leaf biomass greatly from 31.8%to 6.9%,and that of stem from 7.8%to 3.8%,but still overestimated the leaf biomass slightly with the NMBE of 4.6%.The improved RiceGrow can well reproduce the biomass and nitrogen dynamics of different rice organs and shoot under the short-term extreme heat stress after flowering,which is critical for reliable estimates of rice yield formation under future climate change.Photosynthesis sub-routines in seven crop growth models were employed to test the maximum photosynthetic rate(Pmax)during and after short-term extreme heat stress in multi-year experiments.The biochemical models for photosynthetic rates(FvCB-type models)performed better than photosynthetic models based on light response curves(LRC-type models)with observed leaf nitrogen(area based or dry matter based),photosynthetically active radiation(PAR),leaf temperatures(Tleaf)or air temperature(Tair),and intercellular CO2 concentration(Ci).The prediction uncertainty during heat treatment was largely caused by the effects of temperatures and Ci on photosynthesis.Even if the Pmax-T response functions was calibrated using observed data,the LRC-type model which lacked the simulated effect of Ci on photosynthesis performed worse during the treatment.GECROS performed best among all models,which reproduced the dynamic photosynthesis during treatment well.In the coupled stomatal conductance(gs)model in GECROS,the FVPD which accounts for the effects of vapour pressure deficit(VPD)on Ci/Ca was improved as temperature-dependent instead of the constant in the original model,which greatly increased the prediction accuracy of Ci under heat stress.The leaf nitrogen response functions resulted in the large model variation after heat stress,which was reduced by calibrated nitrogen response functions based on active photosynthetic nitrogen.However,the decrease of carbon demand in sink organs under heat stress had a feedback effect on photosynthesis,which led to a decrease of Ci,and partially offset the positive effect of delayed leaf senescence on photosynthesis under extreme heat stress.We developed a correction for the sink feedback effects on photosynthesis[f(sink)]depending on the difference of linear accumulation rate of non-structural carbohydrates in stem and grain,which allow us to accurately simulate the Ci in FvCB-type photosynthetic models under different high temperature treatments.Moreover,thef(sink)also modify the overestimation in LRC-type models.The results provide an effective tool for the simulation of rice growth and yield formation under extreme heat stress,which are crucial for the accurate assessment of crop productivity under future climate change.