Simulação do rendimento de grãos de arroz irrigado em cenários de mudanças climáticas

O objetivo deste trabalho foi simular o rendimento de grãos de arroz em cenários de mudanças climáticas com aumento da atual concentração de CO2 na atmosfera e da temperatura média do ar em Santa Maria, RS, e verificar as implicações nas recomendações de época de semeadura. Foram utilizados cenários de mudanças climáticas, para os próximos 100 anos, com o dobro da quantidade de CO2 e com aumentos de temperatura do ar de 1, 2, 3, 4 e 5°C. O rendimento de grãos da cultura do arroz foi simulado com o modelo InfoCrop. As simulações foram realizadas para três cultivares de arroz (IRGA 421, IRGA 417 e EPAGRI 109) em sete datas de semeadura, com intervalos mensais, de 20 de julho até 20 de janeiro. Um aumento no rendimento de grãos de arroz irrigado foi observado nos cenários de mudanças climáticas simulado para as três cultivares, com maior incremento na muito precoce (IRGA 421) e menor na de ciclo longo (EPAGRI 109). Se as mudanças climáticas se confirmarem, o período de semeadura recomendado atualmente para cultivares de arroz irrigado deverá ser ampliado

Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice

We consider predictions of the impact of climate warming on rice development times in Sri Lanka. Themajor emphasis is on the uncertainty of the predictions, and in particular on the estimation of meansquared error of prediction. Three contributions to mean squared error are considered. The first is param-eter uncertainty that results from model calibration. To take proper account of the complex data structure,generalized least squares is used to estimate the parameters and the variance-covariance matrix of theparameter estimators. The second contribution is model structure uncertainty, which we estimate usingtwo different models. An ANOVA analysis is used to separate the contributions of parameter and modeluncertainty to mean squared error. The third contribution is model error, which is estimated usinghindcasts. Mean squared error of prediction of time from emergence to maturity, for baseline +2◦C,is estimated as 108 days2, with model error contributing 86 days2, followed by model structure uncer-tainty which contributes 15 days2and parameter uncertainty which contributes 7 days2. We also showhow prediction uncertainty is reduced if prediction concerns development time averaged over years, orthe difference in development time between baseline and warmer temperatures

Erratum to ‘Monitoring canopy micrometeorology in diverse climates to improve the prediction of heat-induced spikelet sterility in rice under climate change’

The publisher regrets displaying an erroneous Figure 10A. The original and correct Figure 10A is as follows. The publisher would like to apologise for any inconvenience caused

Monitoring canopy micrometeorology in diverse climates to improve the prediction of heat-induced spikelet sterility in rice under climate change

Rice is well adapted to a wide range of climates, but is highly susceptible to heat during flowering. However, there are uncertainties in assessing the occurrence of heat-induced spikelet sterility (HISS) and the impact of climate change. One reason is the gap between the ambient air temperature and the panicle temperature, which determines the magnitude of HISS in field studies. To improve our understanding of this gap, we established a multi-site monitoring network (MINCERnet) to measure canopy micrometeorology and heat stress in the major rice growing regions (Sub-Saharan Africa; South, Southeast, and East Asia; and the USA). MINCERnet assessed the processes that determine panicle temperature and the resulting HISS in open fields using the same cultivars (‘IR64’, ‘N22’, and ‘IR52’) and a standard system (MINCER) for micrometeorological monitoring under diverse climates. By using the MINCERnet data in the canopy heat-balance model (IM2PACT), we confirmed that the canopy and panicle transpiration and the resulting evaporative cooling strongly affected the gap between the ambient air temperature and the panicle temperature, and that the HISS rate in open fields could be predicted accurately in diverse climates by using the mean panicle temperature during the flowering hours. The “oasis effect” in the broad sense, that is, evaporative cooling and the increase of relative humidity, which is nested at the various levels along the continuum from the landscape to the panicle, formed temperature and relative humidity gradients along the continuum in response to different climatic conditions. The heat-balance characteristics (i.e., a stronger evaporative cooling under drier climate conditions) suggested that the risk of HISS caused by global warming will increase more in wetter climates, where panicle temperatures tended to increase. Thus, accurate relative humidity data as well as air temperature will be required, along with spatial downscaling, to permit accurate prediction of rice heat stress and yield. HISS prediction using an approach based on the panicle temperature as input for models and monitoring of canopy micrometeorology will reduce uncertainties in rice yield prediction and the response of yield to various climate change adaptation measures

CO2-response function of radiation use efficiency in rice for climate change scenarios

The objective of this work was to evaluate a generalized response function to the atmospheric CO2 concentration [f(CO2)] by the radiation use efficiency (RUE) in rice. Experimental data on RUE at different CO2 concentrations were collected from rice trials performed in several locations around the world. RUE data were then normalized, so that all RUE at current CO2 concentration were equal to 1. The response function was obtained by fitting normalized RUE versus CO2 concentration to a Morgan-Mercer-Flodin (MMF) function, and by using Marquardt's method to estimate the model coefficients. Goodness of fit was measured by the standard deviation of the estimated coefficients, the coefficient of determination (R²), and the root mean square error (RMSE). The f(CO2) describes a nonlinear sigmoidal response of RUE in rice, in function of the atmospheric CO2 concentration, which has an ecophysiological background, and, therefore, renders a robust function that can be easily coupled to rice simulation models, besides covering the range of CO2 emissions for the next generation of climate scenarios for the 21st century