CERES-Rice Model Research Articles

Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

The accurate estimation of grain yield in rice breeding is crucial for breeders to screen and select qualified cultivars. In this study, a low-cost unmanned aerial vehicle (UAV) platform mounted with an RGB camera was carried out to capture high-spatial resolution images of rice canopy in rice breeding. The random forest (RF) regression techniques were used to establish yield models by using (1) only color vegetation indices (VIs), (2) only phenological data, and (3) fusion of VIs and phenological data as inputs, respectively. Then, the performances of RF models were compared with the manual observation and CERES-Rice model. The results indicated that the RF model using VIs only performed poorly for estimating yield; the optimized RF model that combined the use of phenological data and color VIs performed much better, which demonstrated that the phenological data significantly improved the model performance. Furthermore, the yield estimation accuracy of 21 rice cultivars that were continuously planted over three years in the optimal RF model had no significant difference (p > 0.05) with that of the CERES-Rice model. These findings demonstrate that the RF model, by combining phenological data and color Vis, is a potential and cost-effective way to estimate yield in rice breeding.

Heat stress may cause a significant reduction of rice yield in China under future climate scenarios

Extreme heat events have become more frequent and severe under climate change and seriously threaten rice growth. Most existing crop models tend to underestimate the impacts of heat stress on rice yields. Heat stress modules in crop models have not been extensively explored, particularly on a large scale. This study modeled rice growth under heat stress at the flowering and filling stages through two heat stress models which coupled into the CERES-Rice model. We evaluated the advanced model with provincial statistics and Gridded Observed Rice Yield. Our improved CERES-Rice model produced more accurate estimates on rice yield than the original model evidenced by an increased correlation coefficient (R) of 12.72% and d-index of 0.02%. The RMSE and MAE decreased by 5.94% and 6.01%, respectively. Most pseudo positive correlations between rice yield and the number of heat days were corrected to the negative ones by the improved model. The future projections from the improved model signifies multi-model ensemble yield projection without CO2 effect (MME-I-NOCO2) has an apparent fall from 2020 to 2099 under RCP4.5, RCP6.0 and RCP8.5 with the decreasing percentages of 6%, 14%, and 37%, respectively, whereas the decreasing trend (12%) only occurs under RCP8.5 with CO2 effect (MME-I-CO2). The apparently decreasing trends of yield projection from MME-I-NOCO2 will occur in most rice-planted regions of China with the decreasing rate < 50 kg/ha/a especially in the central-south and southern cropping regions, and this decreasing trend will be slowed down for MME-I-CO2. Relative to rice yield of historical period, rice yield variations of MME-I-NOCO2 for different growing seasons show a downward trend with the decrease of approximately 54%, 60%, and 43%, respectively. Our study highlights the importance of modeling crop yields under heat stress to food security, agricultural adaptation and mitigation to climate change.

Evaluating and Adapting Climate Change Impacts on Rice Production in Indonesia: A Case Study of the Keduang Subwatershed, Central Java

Predicting the effect of climate change on rice yield is crucial as global food demand rapidly increases with the human population. This study combined simulated daily weather data (MarkSim) and the CERES-Rice crop model from the Decision Support System for Agrotechnology Transfer (DSSAT) software to predict rice production for three planting seasons under four climate change scenarios (RCPs 2.6, 4.5, 6.0, and 8.5) for the years 2021 to 2050 in the Keduang subwatershed, Wonogiri Regency, Central Java, Indonesia. The CERES-Rice model was calibrated and validated for the local rice cultivar (Ciherang) with historical data using GenCalc software. The model evaluation indicated good performance with both calibration (coefficient of determination (R2) = 0.89, Nash–Sutcliffe efficiency (NSE) = 0.88) and validation (R2 = 0.87, NSE = 0.76). Our results suggest that the predicted changing rainfall patterns, rising temperature, and intensifying solar radiation under climate change can reduce the rice yield in all three growing seasons. Under RCP 8.5, the impact on rice yield in the second dry season may decrease by up to 11.77% in the 2050s. Relevant strategies associated with policies based on the results were provided for decision makers. Furthermore, to adapt the impact of climate change on rice production, a dynamic cropping calendar, modernization of irrigation systems, and integrated plant nutrient management should be developed for farming practices based on our results in the study area. Our study is not only the first assessment of the impact of climate change on the study site but also provides solutions under projected rice shortages that threaten regional food security.

Response of Crop Water Requirement and Yield of Irrigated Rice to Elevated Temperature in Bangladesh

In the changing climatic condition, temperature is the most vulnerable parameter and is projecting a trend of increase in the future. Crop growth and development process depend largely on air temperature. This study aims to determine the role of increasing air temperature in yield, crop water requirement (CWR), and other agronomic parameters of irrigated rice. Ceres-rice model associated in the Decision Support System for Agrotechnology Transfer (DSSAT) was used in 15 different locations of Bangladesh. Grain yield, growth duration, and crop water requirement of widely cultivated irrigated rice (Boro rice) variety BRRI dhan28 were analysed in normal temperature and elevated air temperature by 1°C, 2°C, 3°C, and 4°C. The result revealed detrimental effect of elevated temperature on growth duration and grain yield. The estimated highest growth duration reduction of 30 days was found in Moulvibazar for 4°C temperature rise. The grain yield reduction was projected by 0–17%, 16–35%, 31–49%, and 39–61% from the normal condition if the seasonal mean temperature increased by 1°C, 2°C, 3°C, and 4°C, respectively. The country average crop water requirement was found to be 405 mm of which the highest 445 mm and the lowest 358 mm were recorded in Moulvibazar and Chandpur, respectively. The study revealed that the country average rice CWR reduced by 5%, 8%, 12%, and 17% over the normal condition for 1°C, 2°C, 3°C, and 4°C rising temperature, respectively. For 1°C temperature rise, BRRI dhan28 life span shortened by 6.4 days, grain yield reduced by 695 kg, and estimated CWR decreased by 14 mm. The projected declining CWR indicated that irrigated rice will require less irrigation water, but it will cause considerable yield loss under elevated temperature. Though elevated temperature will save huge irrigation water used in country-wide Boro rice cultivation, the crop developers need to introduce new heat-tolerant cultivar to minimize yield loss.

Modelling rice yield from biochar-inorganic fertilizer amended fields

Rice yield is vulnerable to climatic changes. High concentrations of atmospheric CO 2 is noted to increase yield by improving upon nutrient uptake and water use efficiency of plants. Temperature variations also affect plant growth and eventually crop yield as it has an effect on respiration and photosynthesis. Soil fertility is an important parameter affecting the production of most crops and rice is not an exception. The cost as well as the environmental consequences of using inorganic fertilizers makes it necessary to use other soil amendments such as biochar. Field experiments are often time consuming and involves a lot of drudgery. This research was therefore conducted to model the quantities of biochar-inorganic fertilizer combinations that give optimum yields of rice and assess the effect of changes in climatic parameters on the yield of rice. CERES-Rice model was used to simulate the yield of rice using different combinations of biochar-inorganic fertilizer as soil amendments. Effects of changes in climatic parameters on the yield of rice were also simulated. The yield of rice from the simulations were sensitive to the soil amendments especially the biochar. Treatments with no biochar had relatively low rice yields compared with those with biochar. The average simulated yields were 1.5 t/ha, 5.6 t/ha and 5.5 t/ha for treatments with 0 t/ha, 20 t/ha and 30 t/ha of biochar respectively. Increasing or decreasing rainfall resulted in reduction in the yield of rice. Increasing carbon dioxide concentrations generally led to an increase in the simulated yields of rice. On the other hand, decreasing the carbon dioxide concentrations resulted in decreased simulated rice yields. It was observed that increasing temperature resulted in a reduction in rice yield for the various treatments. The reduction in the yield of rice ranged from 7.64% to 34 .67%. • Biochar-inorganic fertilizer improve upon simulated rice yield. • Increase in temperature results in a decrease in simulated rice yield. • Increase in carbon dioxide results in an increase in simulated rice yield. • An increase in solar radiation results in an increase in rice yield.

Simulating upland rice yield at diverse temperatures using DSSAT4.7-CERES-Rice crop model under changing climatic conditions in southern Thailand

Declaration of climate emergency reminds that the main source of variations in worldwide food production sourced from developing countries and is of utmost concern. Process-based models use simplified functions to express the interactions between upland rice growth and the major environmental factors (i.e., climate, soils, and management) that affect crops yield. This research was conducted to investigate the DSSAT-CERES-Rice model for simulating the impact of different temperature (28˚C, 30˚C and 32˚C) on upland grain yield (Dawk Pa-yawm, Mai Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm). The results showed that the temperature significantly affected the grain yield, flowering, and maturity date. The highest grain yield bearing genotype was Bow leb Nahng (6235.80 kgha -1 ) with the highest variation between the genotypes. At maximum temperature 32˚C simulated grain yield varied from (3194-6669) kgha -1 , at high temperature 30˚C it varied from 3252-6667 kgha -1 while at moderate temperature 28˚C 3189-6711 kgha -1 was the observed range. However, results indicated that moderate temperature i.e., 28˚C gave the highest simulated grain yield. Thus, it was demonstrated that the CERES-Rice model was more useful as a tool for simulating grain yield under the changing different temperature conditions.

Predicting the Water Requirement for Rice Production as Affected by Projected Climate Change in Bihar, India

Climate change is a well-known phenomenon all over the globe. The influence of projected climate change on agricultural production, either positive or negative, can be assessed for various locations. The present study was conducted to investigate the impact of projected climate change on rice’s production, water demand and phenology for the state of Bihar, India. Furthermore, this study assessed the irrigation water requirement to increase the rice production by 60%, for the existing current climate scenario and all the four IPCC climate change scenarios (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) by the 2050s (2050–2059). Various management practices were used as adaptation methods to analyze the requirement of irrigation water for a 60% increase in rice production. The climate data obtained from the four General Circulation Models (GCMs) (bcc_csm1.1, csiro_mk3_6_0, ipsl_cm5a_mr and miroc_miroc5) were used in the crop growth model, with the Decision Support System for Agrotechnology Transfer (DSSAT) used to simulate the rice yield, phenological days and water demand under all four climate change scenarios. The results obtained from the CERES-Rice model in the DSSAT, corresponding to all four GCMs, were ensembled together to obtain the overall change in yield, phenology and water demand for 10 years of interval from 2020 to 2059. We investigated several strategies: increasing the rice’s yield by 60% with current agronomic practice; increasing the yield by 60% with conservation agricultural practice; and increasing the rice yield by 30% with current agronomic practice as well as with conservation agricultural practices (assuming that the other 30% increase in yield would be achieved by reducing post-harvest losses by 30%). The average increase in precipitation between 2020 and 2059 was observed to be 5.23%, 13.96%, 9.30% and 9.29%, respectively, for RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5. The decrease in yield during the 2050s, from the baseline period (1980–2004), was observed to be 2.94%, 3.87%, 4.02% and 5.84% for RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5, respectively. The irrigation requirement was predicted to increase by a range of 39% to 45% for a 60% increase in yield using the current agronomic practice in current climate scenario and by 2050s with all the four climate change scenarios from the baseline period (1980–2004). We found that if we combine both conservation agriculture and removal of 30% of the post-harvest losses, the irrigation requirement would be reduced by 26% (45 to 19%), 20% (44 to 24%), 21% (43 to 22%), 22% (39 to 17%) and 20% (41 to 21%) with current climate scenario, RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 conditions, respectively. This combination of conservation practices suggests that the irrigation water requirement can be reduced by a large percentage, even if we produce 60% more food under the projected climate change conditions.

Evaluation of DSSAT model (CERES rice) on rice production: A review

Rice is the staple food for over half of the world population. Crop models potentially offer a means to readily explore management options to increase yield, and to determine trade-off between yield, resource-use efficiency and environmental outcomes. This paper reviews the performance of CERES-Rice model in different regions of the world in relation to their potential application towards increasing resource use efficiency and yield of rice. In this article, the CERES-Rice model evaluation by using the simulated and observed values on crop phenology (anthesis, physiological maturity) and final grain yield mainly over Asian countries by different authors has been compiled and described. Mainly the model was evaluated based on different statistical measures such as RMSE and D-index. Several datasets for the prediction of grain yield and phenological period across different parts of Asia were examined. This particular model predicted those with high-accuracy (nRMSE1-5% for anthesis and 1-4% for physiological maturity days). For various data sets for grain yield, the nRMSE varied between 0.05–5.00 percent with error percentage of 2-5%. The model sometimes over-estimated or under-estimated the values of grain yield, especially under water stress conditions.

Improvement of the CERES-Rice model using controlled experiments and a Meta-analysis

Extreme heat has occurred more frequently in recent years and will intensify in the future, and this change has serious impacts on rice (Oryza sativa L.) yields. Thus, it was crucial to evaluate its influence on rice yield reductions. Recent papers have shown that a lack of experimental data makes it difficult for most crop models to capture the impacts of heat stress. Therefore, this paper explored how to improve the performance of crop models under extreme heat stress based on the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Rice model. This study primarily focused on (i) quantifying spikelet fertility based on daily temperature and durations derived from controlled experiments, (ii) improving the performance of the CERES-Rice model under extreme heat stress, and (iii) simulating historical and future rice yields using the improved model. Specifically, a meta-analysis method was utilized to build a new heat stress function between spikelet fertility and temperature and heat day duration with high realization. Subsequently, independent artificial controlled experiments at two sites were proposed to calibrate and validate the CERES-Rice model. The results showed a higher R2 (> 0.739) and a lower RMSE that was reduced by 38~68% after incorporating the new heat stress function in the CERES-Rice model compared with that of the original model. Furthermore, a historical simulation (1980–2010) demonstrated that an improved CERES-Rice model could better capture rice yield in response to extreme heat. Using an ensemble of five climate model datasets and four Representative Concentration Pathways (RCPs), the analysis of the projected future (2020–2099) rice yield showed that the rice yield reduction caused by high temperature was considerable; however, the rice yields were overestimated by 34% and 18%, respectively, at the two sites. Some regions rarely affected by heat are likely to experience yield reductions in the future due to climate change.

Simulating the impact of climate change on rice yield using DSSAT model

Rice (Oryza sativa L.) is the second most important food grain after wheat in World. A decline in productivity of rice in recent years has been ascribed to decrease in soil organic carbon and reserve of nutrients, non-uniform distribution of rainfall, and increase in temperature and carbon dioxide because of climate change. To assess the impact of climate change on rice yield, crop simulation DSSAT model CERES – rice was calibrated and well evaluated for short and medium duration varieties through field experimental data at Prayagraj, India. Using past 8 years (2012 – 2019) weather data, the CERES-Rice model predicted higher yield variability of the medium duration variety (130 days) “NDR – 359” as compared to the short duration (90 days) variety “NDR – 97” under irrigated condition of Prayagraj, India. However NDR – 359 simulated the higher mean yield. With increase in atmospheric CO2 level by 150 ppm, the grain yield of NDR – 359 and NDR – 97 was increased by 7.16% and 5.86% respectively under irrigated condition. Increase in average air temperature by 3ºC resulted a decline in yield of short duration variety but an increase in yield of the medium duration variety. The medium duration variety showed better adaptability to climate change than the short duration varieties under optimum input management condition.

Development of production management strategies for a long-duration rice variety: Rajendra Mahsuri—using crop growth model, DSSAT, for the state of Bihar, India

Increasing crop production is an inevitable demand of a current growing population all over the world. Implementation of best field crop practices potentially enables farmers to achieve that desired increase in crop production. The CERES-Rice model in Decision Support System for Agrotechnology Transfer was used in this study with 10 years (2006–2015) of field experimental data collected from the CIMMYT (International Maize and Wheat Improvement Center)–BISA (Borlaug Institute for South Asia), Pusa, Bihar, research farm to provide the water stress impact on crop production, and best management strategies to improve the rice yield, followed by the calibration (2006–2010) and validation (2011–2015) from the collected field experimental data. The genetic coefficients were developed for the rice variety, Rajendra Mahsuri. The normalized root-mean-square error (RMSEn) and d-index values were obtained to be 2.73% and 0.62, respectively, for prediction of yield with the model performance efficiency (ME) of 75% in the range of 5 years of validation studies. The model indicated that water stress during vegetative and maturity phase decreased rice yield by 24% and 33%, respectively. However, the water stress during reproductive stage showed the largest reduction in the yield by 43%, and hence, must be avoided. Several management strategies were investigated to determine their impact on rice yield. Optimum transplanting date was found to be during the month of June for the highest yield of Rajendra Mahsuri rice. Application of crop residue up to 2500 kg/ha would increase the yield by 21.94%, compared to the management practices where no residue is applied in the field. Crop row spacing of 15–25 cm increased rice yield by 16.47–18.49%, and for maximum yield, optimum planting depth was found to be 2–4 cm. Additionally, keeping a ponding depth of 4–6 cm before 10 days of harvesting would aid in maximizing the rice yield by 10.70–16.10%, compared to no ponding or more than 6 cm of ponding conditions in the field.

Assessment of future climate variability and potential adaptation strategies on yield of peanut and Kharif rice in eastern India

Temperature and CO2 are the two most important climate parameters that affect crop yield directly. In this study, the impact of these two parameters on the productivity of peanut and rice under the sub-humid and sub-tropical condition of Eastern India were assessed using experimental data and the DSSAT v4.5 CROPGRO-Peanut and CERES-Rice model. Experimental results of peanut and rice with four sowing dates of each were used as the baseline scenarios. The future weather data from the CSIRO-Mk3–6.0 model for RCP 4.5 and 8.5 was used to the peanut and rice crop model to simulate the yield using future weather data for the periods 2020, 2050, and 2080, and change in yield was compared with the baseline (1980–2013) at Kharagpur, West Bengal, India. The results reveal that rising temperature has negative effect on the growth and developmental phases of crop, and it brings early flowering. Concurrently, increase in temperature causes reduction of crop yield due to pollen sterility and poor pollen growth during reproductive growth stage. The CO2 concentrations used in the model, as projected by IPCC, were 390, 420, 530, and 650 ppm for the years 1980–2013 (baseline), 2020, 2050, and 2080, respectively. However, simulation was made using the model for percent change in mean temperature of 3.36 °C for 2020, 7.59 °C for 2050, and 10.4 °C for 2080. The model simulation reveals that elevated CO2 concentration of 420, 530 and 650 ppm showed gradual increase in the grain yield and biomass yield with shifting in sowing dates for peanut crop. On the other hand, for rice crop, though the crop yield increases gradually, the total biomass yield would reduce in future climate change scenario.

Climate change impacts and adaptations for fine, coarse, and hybrid rice using CERES-Rice

Climate change has become a threatening issue for major field crops of Pakistan, especially rice. A 2 years' (2014 and 2015) field trial was conducted on fine, coarse, and hybrid rice at Research Area, Department of Agronomy, University of Agriculture, Faisalabad following the split-plot design. Data of growth, yield, and yield components were collected to calibrate and evaluate the CERES-Rice model under Decision Support System for Agro-technology Transfer (DSSAT). Two cultivars of each type of fine, coarse, and hybrid rice were transplanted with interval of fortnight from May to September during 2014 and 2015. The model was calibrated with non-stressed sowing data during the year 2014 and evaluated with the data of 2015. Climate change scenarios were generated for mid-century (2040-2069) under representative concentration pathway (RCP8.5) using different general circulation models (GCMs) (baseline, cool dry, hot dry, cool wet, hot wet, and middle) were using different General Circulation Models (GCMs). CERES-Rice calibration and evaluation results were quite good to simulate impacts of climate change and to formulate adaptations during 2040-2069 (mid-century). Simulations of all GCMs showed an average increase of 3 °C in average temperature as compared to baseline (1980-2010). Likewise, there would be an average increase of 107.6 mm in rainfall than baseline. The future rise in temperature will reduced the paddy yield by 10.33% in fine, 18-54% in coarse and 24-64% in hybrid rice for mid-century under RCP8.5. To nullified deleterious effects of climate change, some agronomic and genetics adaptation strategies were evaluated with CERES-rice during mid-century. Paddy yield of fine rice was increased by 15% in cool dry and 5% in hot dry GCM. Paddy yield of coarse rice was improved by 15% and 9% under cool dry and hot dry climatic conditions, respectively, with adaptations. For hybrid rice, paddy yield was enhanced by 15% and 0.3% with cool wet and hot dry climatic conditions, respectively. Hot dry climatic conditions were the most threatening for rice crop in rice producing areas of Punjab, Pakistan.

Modeling Climate Change Impacts on Rice Growth and Yield under Global Warming of 1.5 and 2.0 °C in the Pearl River Delta, China

In this study, the potential climate change impacts on rice growth and rice yield under 1.5 and 2.0 °C warming scenarios, respectively, are simulated using the Ceres-Rice Model based on high-quality, agricultural, experimental, meteorological and soil data, and the incorporation of future climate data generated by four Global Climate Models (GCMs) in the Pearl River Delta, China. The climatic data is extracted from four Global Climate Models (GCMs) namely: The Community Atmosphere Model 4 (CAM4), The European Centre for Medium-Range Weather Forecasts-Hamburg 6 (ECHAM6), Model for Interdisciplinary Research On Climate 5 (MIROC5) and the Norwegian Earth System Model 1 (NorESM1). The modeling results show that climate change has major negative impacts on both rice growth and rice yields at all study sites. More specifically, the average of flowering durations decreases by 2.8 days (3.9 days), and the maturity date decreases by 11.0 days (14.7 days) under the 1.5 °C and (2.0 °C) warming scenarios, respectively. The yield for early mature rice and late mature rice are reduced by 292.5 kg/ha (558.9 kg/ha) and 151.8 kg/ha (380.0 kg/ha) under the 1.5 °C (2.0 °C) warming scenarios, respectively. Adjusting the planting dates of eight days later and 15 days earlier for early mature rice and late mature rice are simulated to be adaptively effective, respectively. The simulated optimum fertilizer amount is about 240 kg/ha, with different industrial fertilizer and organic matter being applied.

Quantifying Spatio-Temporal Patterns of Rice Yield Gaps in Double-Cropping Systems: A Case Study in Pearl River Delta, China

Quantifying the contributing and limiting factors of yield potential is of vital importance, and the closure of existing yield gaps on currently available agricultural land is regarded as the most effective measure to meet future food demands. In this study, the CERES-Rice model and long-term rice yield records of 12 sites from 1981 to 2010 were combined together to investigate the spatial and temporal distributions of yield potential, yield attainable, yield actual, and yield gaps for double cropping rice in the Pearl River Delta (PRD), China. The evaluated yield potential of all the sites ranged from 7500 to 14,900 kg/ha, while yield attainable was from 6400 to 12,665 kg/ha, and yield actual was from 4000 to 7000 kg/ha. The yield gaps between yield potential and yield actual, yield potential and yield attainable, and yield attainable and yield actual were projected to be 3500 kg/ha, 1400 kg/ha, and 2100 kg/ha, respectively. The decrease of yield potential was due to the increasing temperature for early mature rice and the prolonged sunshine hours for the yield potential of late mature rice, respectively. The social–economic impacts of yield actual were also assessed, and adaptive measures were simulated so that the yield would certainly increase.

基于CERES-Rice模型的湖南省一季稻极端高温损失评估及适应性措施

基于CERES-Rice模型的湖南省一季稻极端高温损失评估及适应性措施

Climate change impacts on rice productivity in the Mekong River Delta

Rice is consumed by more people than any other grain. Globally, Vietnam is one of the largest exporters of rice, with the majority of production occurring in the tropical, low-lying Mekong River Delta. Agriculture in the Mekong River Delta is susceptible to yield losses from rising temperatures, sea level rise, and land use change as urban expansion replaces productive farmland. Most studies that assess climate change impacts to rice paddy yields are conducted at global- or continental-scales, and use general information on management practices to simulate production. Here, we use management information from farmers and published information on soils collected in Can Tho, a centrally-located province in the Mekong Delta. These data, along with projected mid-century (2040–2069) climate data for the RCP4.5 and RCP8.5 greenhouse gas emissions scenarios, are used to drive the Decision Support System for Agrotechnology Transfer (DSSAT) platform to project future rice paddy yields using the CERES-Rice model. The results indicate that yields decline for all three rice-growing seasons in Can Tho city for both emissions scenarios when CO2 fertilization is not considered (5.5–8.5% annually on average depending on the emissions scenario). Increasing irrigation and fertilizer did not offset these losses, but simulated CO2 fertilization did compensate for yield declines caused by increasing temperatures (yields were modeled to be up 23% higher when CO2 fertilization is considered). However, we caution that estimated yield gains from CO2 fertilization are optimistic, and these modeled values do not consider rises in ozone, which can diminish yields. Continued and future dam construction could negatively affect agriculture in the region, and current government policies prohibit rice paddy farmers from diversifying their livelihoods to adapt to these changes. Monitoring rice agroecosystems at a fine-scale, as this study does, is necessary to best capture the impact that varying management practices can have on local yields. When these differences are captured, future impacts of climate change can be modeled more effectively so that local policymakers can make informed decisions about how to offset yield losses and use farmland more efficiently.

Improving High-Latitude Rice Nitrogen Management with the CERES-Rice Crop Model

Efficient use of nitrogen (N) fertilizer is critically important for China’s food security and sustainable development. Crop models have been widely used to analyze yield variability, assist in N prescriptions, and determine optimum N rates. The objectives of this study were to use the CERES-Rice model to simulate the N response of different high-latitude, adapted flooded rice varieties to different types of weather seasons, and to explore different optimum rice N management strategies with the combinations of rice varieties and types of weather seasons. Field experiments conducted for five N rates and three varieties in Northeast China during 2011–2016 were used to calibrate and evaluate the CERES-Rice model. Historical weather data (1960–2014) were classified into three weather types (cool/normal/warm) based on cumulative growing degree days during the normal growing season for rice. After calibrating the CERES-Rice model for three varieties and five N rates, the model gave good simulations for evaluation seasons for top weight (R2 ≥ 0.96), leaf area index (R2 ≥ 0.64), yield (R2 ≥ 0.71), and plant N uptake (R2 ≥ 0.83). The simulated optimum N rates for the combinations of varieties and weather types ranged from 91 to 119 kg N ha−1 over 55 seasons of weather data and were in agreement with the reported values of the region. Five different N management strategies were evaluated based on farmer practice, regional optimum N rates, and optimum N rates simulated for different combinations of varieties and weather season types over 20 seasons of weather data. The simulated optimum N rate, marginal net return, and N partial factor productivity were sensitive to both variety and type of weather year. Based on the simulations, climate warming would favor the selection of the 12-leaf variety, Longjing 21, which would produce higher yield and marginal returns than the 11-leaf varieties under all the management strategies evaluated. The 12-leaf variety with a longer growing season and higher yield potential would require higher N rates than the 11-leaf varieties. In summary, under warm weather conditions, all the rice varieties would produce higher yield, and thus require higher rates of N fertilizers. Based on simulation results using the past 20 years of weather data, variety-specific N management was a practical strategy to improve N management and N partial factor productivity compared with farmer practice and regional optimum N management in the study region. The CERES-Rice crop growth model can be a useful tool to help farmers select suitable precision N management strategies to improve N-use efficiency and economic returns.

Effect of Irrigation Method on Adaptation Capacity of Rice to Climate Change in Subtropical India

Water management technologies under projected climate change will play key role in sustainable rice production. Modeling approach was used to assess the impact of climate change on rice production under drip irrigation (DIR) and conventional puddle transplanted (PTR) in subtropical India. The genotype coefficients of CERES-Rice model (cv. Naveen) were determined and tested using experimental data for the years 2012–2014. Close match between the observed and simulated values was recorded during both the years which led to higher d-index (> 0.95) and lower normalized RMSE (RMSEn) values. Under the projected climate change scenarios (RCP 4.5 and RCP 8.5), grain yield reduced over the period 2020–2080, with higher decline in RCP 8.5. Over the period, higher nitrogen (N) use efficiency in DIR led to lower yield reduction over PTR. Among the different adaptation measures, higher fertilizer N dose was able to mitigate negative impact of temperature rise up to 3.3 °C over base period, beyond which grain yield was significantly reduced. Results of the simulations for the different sowing dates stated higher reduction in grain yield with delayed sowing in DIR as well as in PTR for both (RCP 4.5 and 8.5) climate change scenarios. However, early sowing resulted in better crop establishment in DIR leading to better yield compared to PTR in both the climate change scenarios.

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.