CERES-Rice Model Research Articles

Parametrization of Models and Use of Estimated Global Solar Radiation Data in the Irrigated Rice Yield Simulation

Abstract The objective of this study was to evaluate the use of estimated global solar radiation data in the simulations of potential yield of irrigated rice. Global solar radiation was estimated by four empirical models, based on air temperature, and a meteorological satellite derivated. The empirical models were calibrated and validated for 10 sites, representative of the six rice regions of the State of Rio Grande do Sul - Brazil. To evaluate the impact of the radiation estimates on irrigated rice yield simulations, the CERES-Rice model, calibrated for four cultivars, was used. The estimates of global solar radiation of the empirical models based on the air temperature showed deviations, from the observed values, of 20 to 30% and the estimated by satellite deviations of more than 30%. The global solar radiation data estimated by the Hargreaves and Samani, Donatelli and Campbell and derived satellite (PowerNasa) type air temperature-based empirical models can be used as input data in simulation models of crop growth, development and productivity of irrigated rice.

Validação do modelo Ceres-Rice para a cultivar IRGA 424 no Rio Grande do Sul

The objective of this paper was to calibrate and validate the CERES-Rice model in relation to the productivity estimate for the cultivar IRGA 424 in Rio Grande do Sul, using only information found in the literature. CERES-Rice is a model that simulates the development and productivity of a cultivar. To achieve this goal, it is necessary to obtain the genetic coefficients of the cultivar through the calibration process. The genetic coefficients describe the behavior of the plant in the soil-plant-atmosphere system. The GENCALC module present in the DSSAT was used for the calibration. After the calibration process with 30% of the assembled experiments, the validation was performed in the others. The results for productivity were considered acceptable, since most of the values were below 15%, only one of the results showed an error of 16,88%. Note that the model did not consider neither phenological stages nor fertilization or water issues in the calibration and validation process.

Assessing the climate change and its impact on rice yields of Haridwar district using PRECIS RCM data

In the past few decades, continuous intervention with the environmental landscape in the form of land use practices (water diversions, deforestation, local agriculture practices, industrialization etc.) in the Haridwar district of Uttarakhand, India, has impacted the region on various accounts. This is likely to be further aggravated in view of the increasing variability in the weather condition. Thus, there is urgent need felt to quantitatively asses the future climatic scenario for initiating and effectively undertaking the adaptation strategies for safe and sustained agricultural growth. For undertaking this study, PRECIS RCM data was downloaded for Roorkee grid station for the period (1961–2090). This PRECIS RCM data was biased corrected by merging two different bias correction methods which involves correcting mean and standard deviation simultaneously. Observed and bias corrected daily data was tested for their significance at 95% probability of occurrence employing various statistical methods such as correlation coefficient, mean bias error, normalized mean squared error, and Z and F statistical tests. These tests revealed that the difference between the observed and bias corrected PRECIS RCM data was insignificant. Trend analysis was done using Mann-Kendall’s test and Theil Sen’s Slope. Analysis of the bias corrected data revealed that the Roorkee station will, in general, record increasing trends in rainfall (at 5.83 mm/year), maximum temperature (at 0.05 °C/year), and minimum temperature (at 0.04 °C/year). This indicates that by 2090 AD present normal rainfall of 1006 mm/year will rise to 1447 mm/year; maximum temperature of 30.1 °C will rise to 33.9 °C and minimum temperature of 17.5 °C will rise to 20.5 °C by 2090. This weather scenario cautions at the future agronomic practices for sustained productivity. Grain yield of rice cv. Sharbati for the period 2014–2090 was simulated by running DSSAT CERES rice model with PRECIS RCM weather data of Haridwar district and RCP CO2 emission scenarios. The simulation result showed that if the crop is grown with the existing soil and crop management conditions, rice productivity of Haridwar in general will decrease 31.7 kgs/ha/year. This could be attributed to the climate change and decreased rice yields from increased maximum temperature. In the case of RCP 2.6 and RCP 4.5, the effect of CO2 on grain yields is more pronounced compared to RCP 6.0 and RCP 8.5. With the higher CO2 concentrations (above 450 ppm), the rice crop cv. Sharbati did not show much CO2 fertilization effect. DSSAT CERES rice simulations under climate change and higher CO2 concentrations showed that the yields will decline with the advancing climate change but CO2 intervention will compensate the loss in yield.

Climate change impact assessment on three major crops in the north-central region of Bangladesh using DSSAT

The potential consequences of climate change emanated from global warming are very alarming; the greatest concern is the potentially disastrous consequences on crop agriculture and food security in many parts of the world. Bangladesh is a country highly susceptible to climate change, but information in this regard is still inadequate. This study investigated the effects of climate change on three major crops – wheat, potato and rice – in the north-central region of Bangladesh. Two climate change scenarios, A2 and B2, of the Intergovernmental Panel on Climate Change (IPCC) were generated by employing MAGICC/SCENGEN model together with the observed climate data of the region. The growth and yield of the crops were simulated using DSSAT CERES-Wheat, SUBSTOR-Potato and CERES-Rice models under the present and projected future changing climatic conditions. For a predicted 5.32ºC increase in temperature in the year 2100, the yield of wheat, rice and potato would decrease by 47.6%, 67.8% and 38.6%, respectively. The increased temperature would accelerate physiological maturity of the crops as reflected by their reduced length of growing season (LGS) by 1.20% to 18.5%. The reduced LGS would reduce seasonal evapotranspiration (ET) of the crops by shortening time-span for ET generation. Due to dominant yield reduction over ET reduction, the water use efficiency (WUE) for grain/tuber and biomass yields would decrease with the changing climate. The reduced crop yields are an indicative of a potential future risk of food security in Bangladesh. The results of this study can therefore guide to adopt coping mechanisms in the light of climate change to ensure future food security of the country. Keywords: climate change, DSSAT, MAGICC/SCENGEN model, CERES model, SUBSTOR model, growing season length, crop-water use DOI: 10.25165/j.ijabe.20181104.3331 Citation: Rahman A, Mojid M A, Banu S. Climate change impact assessment on three major crops in the north-central region of Bangladesh using DSSAT. Int J Agric & Biol Eng, 2018; 11(4): 135-143.

Effect of Elevated Air Temperature and Carbon Dioxide Levels on Dry Season Irrigated Rice Productivity in Bangladesh

Agricultural productivity is affected by air temperature and CO2 concentration. The relationships among grain yields of dry season irrigated rice (Boro) varieties (BRRI dhan28, BRRI dhan29 and BRRI dhan58) with increased temperatures and CO2 concentrations were investigated for futuristic crop management in six regions of Bangladesh using CERES-Rice model (DSSATv4.6). Maximum and minimum temperature increase rates considered were 0°C, +1°C, +2°C, +3°C and +4°C and CO2 concentrations were ambient (380), 421, 538, 670 and 936 ppm. At ambient temperature and CO2 concentration, attainable grain yields varied from 6506 to 8076 kg·ha-1 depending on rice varieties. In general, grain yield reduction would be the highest (13% - 23%) if temperature rises by 4°C and growth duration reduction would be 23 - 33 days. Grain yield reductions with 1°C, 2°C and 3°C rise in temperature are likely to be compensated by increased CO2 levels of 421, 538 and 670 ppm, respectively. In future, the highest reduction in grain yield and growth duration would be in cooler region and the least in warmer saline region of the country. Appropriate adaptive techniques like shifting in planting dates, water and nitrogen fertilizer management would be needed to overcome climate change impacts on rice production.

Impact of climatic parameters on simulated yield of rice at Faizabad of U.P.

Impact of climatic parameters on grain yield of rice cultivars under three dates of transplanting (30th June, 15th July and 30th July) was assessed at Faizabad Uttar Pradesh using calibrated and validated CERES-Rice model by incremental change in climatic parameters. The increase in sunshine duration and solar radiation caused increase in the yield and vice versa, while increase in maximum and minimum temperatures caused decrease in the yield. Among the sowing dates, the impact of sunshine duration under early transplanted rice (30th June) was higher than late transplanted rice (30th July). While the reverse trend was observed in case of solar radiation. The effect of maximum and minimum temperatures under early transplanted rice was also higher than that of late transplanted rice.

Shifting Planting Dates and Fertilizer Application Rates as Climate Change Adaptation Strategies for Two Rice Cultivars in Cambodia

We attempted to assess the impact of climate change on rice yields in Cambodia and to investigate adaptation strategies to climate change including more drastically shifting the planting dates and considering more fertilizer application levels. The potential yields of two wet season rice cultivars (Sen Pidao and Phka Rumduol) under two climate change scenarios in Cambodia were simulated using the CERES-Rice model. Field experiments conducted at the Cambodian Agricultural Research and Development Institute (CARDI), in 2010, 2011, and 2013 and climate variables from the HadGEM3-RA model were collected for this study. Compared with the baseline (1991-2000), yields of Sen Pidao rice will decrease under climate change and yields of Phka Rumduol rice could increase or decrease depending on fertilizer rates and the periods (2040s, 2050s, and 2080s). In general, the variations in the simulated effects of climate change on yields were more sensitive at fertilizer N100-N200 and less sensitive at fertilizer N0-N50. It is likely that forward shifts of planting date from the baseline plating date for the two cultivars in the future can be more benefitted than backward shifts. It is concluded that the CERES-Rice model can be useful to provide efficacious adaptation strategies in Cambodia.

Evaluation of CERES-Rice model for Upper Brahmaputra Valley Zone of Assam

Evaluation of CERES-Rice model for Upper Brahmaputra Valley Zone of Assam

Quantifying the impact of climate change on crop yield and water footprint of rice in the Nam Oon Irrigation Project, Thailand

Northeast Thailand makes a significant contribution to fragrant and high-quality rice consumed within Thailand and exported to other countries. The majority of rice is produced in rainfed conditions while irrigation water is supplied to rice growers in the dry season. This paper quantifies the potential impact of climate change on the water footprint of rice production using the DSSAT (CERES-Rice) crop growth model for the Nam Oon Irrigation Project located in Northeast Thailand. Crop phenology data was obtained from field experiments and used to set up and validate the CERES-Rice model. The present and future water footprint of rice, the amount of water evaporated during the growing period, was calculated under current and future climatic condition for the irrigation project area. The outputs of three regional climate models (ACCESS-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM) for scenarios RCP 4.5 and RCP 8.5 were downscaled using quantile mapping method. Simulation results show a considerably high increase in the water footprint of KDML-105 and RD-6 rice varieties ranging from 56.5 to 92.2% and 27.5 to 29.7%. respectively for the future period under RCP 4.5, and 71.4 to 76.5% and 27.9 to 37.6%, respectively under RCP 8.5 relative to the simulated baseline water footprint for the period 1976–2005. Conversely, the ChaiNat-1 variety shows a decrease in projected water footprint of 42.1 to 39.4% under RCP 4.5 and 38.5 to 31.7% under RCP 8.5. The results also indicate a huge increase in the future blue water footprint, which will consequently cause a high increment in the irrigation water requirement in order to meet the plant's evaporation demand. The research outcome highlights the importance of proper adaptation strategies to reduce or maintain acceptable water footprints under future climate conditions.

Response of rice production to climate change based on self-adaptation in Fujian Province

SUMMARYClimate change has greatly affected agricultural production, and will lead to further changes in cropping system, varietal type and cultivation techniques for each region. The potential effects of climate change on rice production in Fujian Province, China, were explored in the current study with CERES-Rice model and climate-change scenarios, based on the self-adaptation of rice production. The results indicated that simulated yields of early rice in the double-rice region in south-eastern Fujian under scenarios A2, B2 and A1B increased by 15·9, 18·0 and 19·2%, respectively, and correspondingly those of late rice increased by 9·2, 7·4 and 7·4% when self-adaptation adjustment was considered, compared to scenarios without that consideration. In the double-rice region in north-western Fujian, simulated yields of early rice increased by 21·2, 20·5 and 18·9% and those of late rice by 14·7, 14·8 and 7·2% under scenarios A2, B2 and A1B, respectively, when self-adaptation was considered, compared to without consideration. Similar results were obtained for the single-rice region in the mountain areas of north-western Fujian, correspondingly increasing by 4·9, 5·0 and 2·9% when self-adaptation was considered compared to when it was not. In this single-rice region, double rice might be grown in the future at the Changting site under scenarios A1 and B2. When the self-adaptation adjustment was considered, the simulated overall output of rice crops in Fujian under scenarios A2, B2 and A1B increased by 5·9, 5·2 and 5·1%, respectively. Thus, more optimistic results were obtained when the self-adaptation ability of rice production was considered.

Evaluation of CERES-Rice Model in Simulation of Rice Growth under Constraint Irrigation and Nitrogen Fertilizer Conditions

Evaluation of CERES-Rice Model in Simulation of Rice Growth under Constraint Irrigation and Nitrogen Fertilizer Conditions

Evaluation of the CERES-Rice Model for Precision Nitrogen Management for Rice in Northeast China

Over-application of nitrogen (N) in rice (Oryza sativaL.) production in China is common, leading to low N use efficiency (NUE) and high environmental risks. The objective of this work was to evaluate the ability of the CERES-Rice crop growth model to simulate N response in the cool climate of Northeast China, with the long term goal of using the model to develop optimum N management recommendations. Nitrogen experiments were conducted from 2011–2015 in Jiansanjiang, Heilongjiang Province in Northeast China. The CERES-Rice model was calibrated for 2014 and 2015 and evaluated for 2011 and 2013 experiments. Overall, the model gave good estimations of yield across N rates for the calibration years (R2=0.89) and evaluation years (R2=0.73). The calibrated model was then run using weather data from 2001–2015 for 20 different N rates to determine the N rate that maximized the long term marginal net return (MNR) for different N prices. The model results indicated that the optimum mean N rate was 120–130 kg N ha–1, but that the simulated optimum N rate varied each year, ranging from 100 to 200 kg N ha–1. Results of this study indicated that the CERES-Rice model was able to simulate cool season rice growth and provide estimates of optimum regional N rates that were consistent with field observations for the area.

A robust parameter approach for estimating CERES-Rice model parameters for the Vietnam Mekong Delta

Rice crop models, such as CERES-Rice, are useful tools to predict rice growth and understand the effects of climatic and management changes on rice yield. As these models involve complex processes, non-linearity and interdependence of parameters may result in high uncertainty in model results. In this study, we tested the CERES-Rice model in the Vietnam Mekong Delta for two rice cultivars (Jasmine 85 and VD20) in four experimental sites. We applied a two-stage calibration procedure. In the first stage, initial sets of good performing parameters were identified. In the second stage, the good performing parameter sets and their minimum-maximum ranges were refined using the Robust Parameter Estimation (ROPE) approach. We found that in the first calibration stage, the majority of the parameter sets that performed well in the calibration sites performed poorly in the validation sites. For example, the simulated rice yields were within +/−5% of the observed yields in the calibration sites. But in the validation sites, the same parameter sets resulted in the differences of −77% to 19% for Jasmine 85 and −47% to 9.5% for VD20. This is because these parameter sets had a broad range of values, which compensated each other well in calibration but were less successful to do so in validation. When the ROPE was applied, the value ranges of the parameters narrowed down and the model performance in validation improved. In general, the parameter ranges identified by ROPE are considered more robust and have higher probability to perform better. Our positive results show a good potential of the ROPE approach for calibration of the CERES-Rice model, which can also be applied with similar other crop growth models. The robust parameter value ranges derived in this study may be used as reference parameter values for future applications of the model in the region.

Rice phenology and growth simulation using CERES-Rice model under the agro-climate of upper Brahmaputra valley of Assam

Crop growth simulation models, properly validated against experimental data have the potential for facilitating strategic decision making in agriculture. Such validated models can also make use of the information generated for site specific experiments and trials to other sites and for different time durations. For proper calibration and evaluation of crop simulation models, there is a need for collection of a comprehensive minimum set of data on soil, weather and crop management in all agronomic experiments. Keeping this in view, data from seven field experiments conducted at Jorhat (26° 47' N, 94°12' E; 87 m amsl) during 1998-2005 for long duration rice cultivar Ranjit grown under rainfed conditions were collected. Genetic coefficients required for running the CERES-Rice v4.5 model were derived and the performance of the model under the climate of upper Brahmaputra valley was evaluated. These results indicate that the CERES Rice v4.5 model is capable of estimating growth stages and grain yield of rice cultivar Ranjit in the climatic conditions of upper Brahmaputra valley with reasonable accuracy. Hence, the model have the potential for its use as a tool in making various strategic and tactical decisions related to agricultural planning in the state.

Performance of CERES-rice model for prediction of different rice cultivars at Navsari

Filed experiment was conducted during kharif season of 2012 at college farm of Navsari Agricultural University, (Gujarat) to investigate the CERES-Rice model validation for three rice cultivars, viz., V1 - Jaya, V2 - Gurjari and V3 - GNR-2 with three different dates of transplanting, viz., D1- 12 July, 2012, D2- 27 July, 2012 and D3-11 August, 2012 and two nitrogen levels, viz., N1-75 kg/ha and N2-100 kg/ha. The experiment was laid out in a split plot design. The model was subsequently validated against observed data from field experiment. From the response of CERES-Rice model it was found that among the genotypes prediction accuracy for cv. Gurjari at third date of transplanting at 100 kg N level was better in respect to panicle initiation with percent error PE (-4.25%), anthesis (-3.40%) and beginning of grain filling (1.05%). But for physiological maturity stage, cv. GNR-2 was found better at third date of transplanting at same N level with PE (-0.97%) as comparison to other treatments.

Evaluation of CERES-Rice model for the selected rice varieties of Kerala

A field experiment was conducted at Thrissur during kharif seasons of 2013 and 2014 using two rice varieties (Jyothi and Kanchana) and five dates of planting (June 5th, June 20th, July 5th, July 20th and August 5th). CERES-Rice model of DSSAT v 4.6 was used to simulate the phenology and grain yield of rice. Calibration of genetic coefficients was done with 6000 iterations and then the model was validated. The model could simulate the phenology of both the rice varieties with RMSE ranging between 0.89 to 1.92 for physiological maturity.The model overestimated the grain yield of rice cultivars which need further refinement.

Calibration and validation of CERES-rice model for different rice cultivars at Navsari

Calibration and validation of CERES-rice model for different rice cultivars at Navsari

Simulation of rice yield response to elevated carbon dioxide and temperature for different cultivars in the cold region of China.

This paper was aimed to assess the potential impacts of rising temperature and CO2 concentration on the production of different rice cultivars in the cold region of China, Heilongjiang Province. Total three representative rice varieties with different maturity types were selected to conduct the simulation experiments according to the required accumulated temperature. Daily weather data and open top chamber (OTC) test yield data for year 2013 were used to initialize CERES-Rice model parameters. CERES-Rice model was executed to simulate the influence of climate change on early-mature, mid-mature and late-mature rice production under fixed weather scenarios, which consisted of three CO2 concentrations (i.e. 390, 450 and 550 μmol·mol-1) and four temperature rise levels (i.e. 1, 2, 3 and 4 ℃). Results showed that with the increase in concentration of CO2, the rice yield would increase. With the rise in temperature, early-mature rice yield would decline significantly. However, mid-mature and late-mature rice yield would increase at first and then gra-dually decline. Without considering the effect of CO2 fertilization, except that the medium and late varieties under 1 ℃ warming would slightly increase the yield by 3.1% and 0.27% respectively, yield under the other treatments would reduce. The most serious reduction occurred to early-mature rice, which decreased up to 57.7% when the temperature increased by 4 ℃, while mid-mature rice and late-mature rice yields decreased about 10%. Considering the effect of CO2 fertilization, mid-mature and late-mature rice yields would even increase by 0.75% and 3.2% at 450 μmol·mol-1 CO2 under 2 ℃ warming, respectively. Mid-mature rice yield would still increase 4.5% under 3 ℃ warming and late-mature rice yield would also increase 0.39% under 4 ℃ warming at 550 μmol·mol-1 CO2. However, it was identified that early-mature rice yield would always sharply decrease with temperature increasing regardless of the effect of CO2 fertilization. Similarly, CO2 fertilization effects could improve rice yield to certain extent with temperature increasing. However, the diffe-rence among the varieties in response to CO2 fertilization effect was not significant. The contribution rate of CO2 fertilization effect on rice yield was less than 10%.

Application of CERES-Rice Model to Identify Optimum Sowing Window for Different Rice Varieties under Aerobic Culture

Planting window concept is a useful approach in identifying suitable planting time and appropriate varieties to ensure crop success under aerobic irrigated rice conditions. CERES-Rice model was validated with experimental data collected during kharif 2012 and 2013 to ensure its fitness in the region in order to simulate the growth and development of rice varieties under aerobic environment of Telangana State and to determine the impact of various sowing windows and different duration group rice varieties on grain yield. The simulation scenarios showed that under irrigated aerobic conditions, 17thJune sown crop predicted the highest average grain yield of 5689 kg ha-1 and it was on par up to 29thJune sown crop. The median yield decreased consistently with each delay in time of sowing. Among the varieties the extra short durationcv JGL 17004 and short duration cvMTU 1010 have wider sowing window with highest grain yield at8thJuly and 1st Julysowings respectively.Whereasthe medium duration cv JGL 11470 and long duration cvMTU 1061 have narrow sowing window from 10thJune to 1stJuly with highest grain yield when sown on 10thJune. Based on seasonal analysis, it was identified the optimum sowing window for extra short and short duration cultivars was wider from 10thJune to 26thAugust where as for medium and long duration cultivars was narrow from 10thJune to 1stJuly to obtain optimum grain yields under aerobic environment.

Assessing rice productivity and adaptation strategies for Southeast Asia under climate change through multi-scale crop modeling

Rice (Oryza sativa L.) is one of the most important staple food crops in Southeast Asia, a region that is also particularly vulnerable to climate change. We introduced a multi-scale crop modeling approach to assess the impacts of climate change on future rice yields in Southeast Asia. National- and farmer-level adaptation strategies may be developed by combining the advantages from regional- and field-scale crop models. Climate variables collected from the COordinated Regional climate Downscaling EXperiment (CORDEX)-East Asia were used as inputs to run the GLAM-Rice and CERES-Rice crop models. Simulations produced by the GLAM-Rice model identified Cambodia as the country in Southeast Asia where the reduction in rice yields under climate change will be the largest (a decrease of approximately 45% in the 2080s under RCP 8.5, relative to the baseline period 1991–2000) without adequate adaptation. The results of the model simulations considering the CO2 fertilization effect showed that improved irrigation will largely increase rice yields (up to 8.2–42.7%, with the greatest increases in yields in Cambodia and Thailand) in the 2080s under RCP 8.5 compared to a scenario without irrigation. In addition, the grid cell that will benefit the most (12.6 °N and 103.8 °E) was identified through further investigation of the spatial distribution of the effects of irrigation for Cambodia. For this grid cell, the CERES-Rice model was used to develop the best combination of adaptation measures. The results show that while a doubled application rate of nitrogen fertilizer (100kgNha−1) will increase rice yields by 3.9% in the 2080s under the RCP 4.5 scenario for the Sen Pidao cultivar, a decrease in rice yield was projected for the Phka Rumduol cultivar under RCP 4.5. For both cultivars, the results show that additional adaptation strategies besides the 100kgNha−1 fertilizer application rate and planting adjustment should be applied in order to offset all of the negative projected impacts of climate change on rice yields in the 2080s under RCP 8.5. It is concluded that this study can be useful to enhance food security in Southeast Asia by providing informed recommendations for efficacious adaptation strategies.