CERES-Rice Model Research Articles

Evaluating the effects of the CERES-Rice model to simulate upland rice (Oryza sativa L.) yield under different plant density and nitrogen management strategies in Fogera Plain, Northwest Ethiopia

This study assessed the optimal nitrogen (N) fertilizer rate and planting density for the well-adapted upland rice cultivar NERICA_4 on the Fogera Plain. The primary objective was to evaluate the effects of varied planting densities and N-fertilizer rates on upland rice yield and other agronomic parameters. A two-year field study (2020 and 2021) was conducted at the Fogera Rice Research Field Station, testing nine plant densities (75, 87, and 98; 72, 82, and 91; 70, 79, and 89 plants per m2 and two N rates (115 and 138 kg N ha−1). The Crop Simulation Model Crop Environment Resource Synthesis (CSM-CERES-Rice) within the Decision Support System for Agrotechnology Transfer (DSSAT) framework was calibrated and validated using site-specific weather, soil, crop, and agronomic management data from the experiment. Results on the subsequent RMSE, RMSEn, and d index values during the calibration phase were 0.074 t ha−1, 1.82 %, and 0.86 of grain yield; 0.307 t ha−1, 3.36 %, and 0.87 of by-product yield; 0.489 t ha−1, 3.74 %, and 0.79 of top dry biomass yield; and 0.28, 8.24 %, and 0.63 of leaf area index values, respectively. Whereas results on the corresponding RMSE, RMSEn, and d index values during the evaluation phase were: 0.58 t ha−1, 1.33 %, and 0.90 of grain yield; 0.69 t ha−1, 0.58 %, and 0.99 of by-product yield; 0.678 t ha−1, 4.36 %, and 0.67 of top dry biomass yield; and 0.75, 13.92 %, and 0.74 of leaf area index, respectively. The findings of the long-term simulation showed that a 23 % increase in grain yield was achieved with 138 kg N ha−1 and 87 plants per m2 of planting density, as compared to 115 kg N ha−1 and 75 plants per m2 of plant density. The recommended optimum plant density and N fertilizer rate were 138 kg N ha−1 with PD2 of plant density for upland rice production in the Fogera Plain.

Evaluation of CERES –Rice Model for Simulating Rice Yield and Phenophases

In this study, the performance of the CERES-Rice model in simulating the growth and yield of the Rajeshwari variety in the Raipur district of Chhattisgarh, India, was evaluated. Utilizing observed data from 2021 and 2022, the model was calibrated and validated using key parameters such as days to anthesis, physiological maturity, and yield. Calibration involved adjusting genetic coefficients to improve simulation accuracy, with validation ensuring the model's reliability beyond the calibration period. The comparison between observed and simulated data for crop performance parameters showed that the model performed reasonably well. For days to Anthesis, the RMSE was 4.32 with a d-stat of 0.59, and an error of 5.4%. For Days to Panicle initiation, the RMSE was 1.83, the d-stat was 0.82, and the error was -4.7%. For days to PM, the RMSE was 6.7, the d-stat was 0.65, and the error was 3.0%. Yield showed an RMSE of 472.4, a d-stat of 0.81, and an error of 7.7%. F The mean simulated values closely matched the observed means, indicating overall good model accuracy. In this study, fine tuning the genetic coefficients of CERES rice model for the variety Rjeshwari was done and can be used for further studies and applications.

Yield gap analysis and the relative importance of factors explaining yield variability in paddy fields

Closing the yield gap of strategic crops can facilitate achieving sustainable solutions to alleviate hunger, ensure food security, and enhance nutrition. This study aimed to quantify factors affecting rice yield gap in northern Iran using experimental and survey data on yields and management practices. The CERES-Rice model, calibrated with data (2011, 2016, 2019) from two regions, was used to estimate potential yield (Yp) of different rice cultivars. Yield gaps were calculated as the difference between economic yield potential and the average yield attained by farmers. The calibration process showed that cultivar coefficients varied across rice varieties. The simulated yields (2012–2021) showed that the mean potential yields were 13362 kg ha−1 and 8818 kg ha−1 for the late and early-maturing cultivars, respectively. High temperature was the most important yield defining factor across the rice growing areas. The yield gap was 3751 kg ha−1 and 2830 kg ha−1 in the late- and early-maturing cultivars, respectively. Nitrogen fertilizer rate, number of fertilizer applications, phosphorus fertilizer rate, transplanting date, potassium fertilizer rate, and seedling age were the top factors influencing the yield gap. Among the management factors, rice fertilizer management explained about 65% of the yield variability. Together, transplanting date and seedling age explained 15.3% of the variability in rice production, while pest, disease, and weed damage accounted for 9.6%. Breaking down the yield gap into affecting factors allows growers to improve the production system by providing the best crop management according to their facilities and capabilities. This approach is essential for optimizing agricultural productivity and achieving sustainable food production goals.

Optimizing transplanting window for higher productivity of short and medium duration rice cultivars in Punjab, India using CERES-Rice model

Optimizing transplanting window for higher productivity of short and medium duration rice cultivars in Punjab, India using CERES-Rice model

Assessment of CSM–CERES–Rice as a Decision Support Tool in the Identification of High-Yielding Drought-Tolerant Upland Rice Genotypes

Drought is considered as one of the critical abiotic stresses affecting the growth and productivity of upland rice. Advanced and rapid identification of drought-tolerant high-yielding genotypes in comparison to conventional rice breeding trials and assessments can play a decisive role in tackling climate-change-associated drought events. This study has endeavored to explore the potential of the CERES–Rice model as a decision support tool (DST) in the identification of drought-tolerant high-yielding upland rice genotypes. Two experiments mentioned as potential experiment (1) for model calibration under optimum conditions and an experiment for yield assessment (2) with three irrigation treatments, (i) a control (100% field capacity [FC]), (ii) moderate stress (70% FC), and (iii) severe stress (50 % FC), were conducted. The results from the yield assessment experiment indicated that the grain yield of the studied genotypes decreased by 24–62% under moderate stress and by 43–78% under severe stress as compared to the control. The values for the drought susceptibility index (DSI) ranged 0.54–1.38 for moderate stress and 0.68–1.23 for severe stress treatment. Based on the DSI and relative yield, genotypes Khao/Sai, Dawk Kham, Dawk Pa–yawm, Goo Meuang Luang, and Mai Tahk under moderate stress and Dawk Kha, Khao/Sai, Nual Hawm, Dawk Pa–yawm, and Bow Leb Nahag under severe stress were among the top five drought-tolerant genotypes as well as high-yielding genotypes. The model accurately simulated grain yield under different irrigation treatments with normalized root mean square error < 10%. An inverse relationship between simulated drought stress indices and grain yield was observed in the regression analysis. Simulated stress indices and water use efficiency (WUE) under different irrigation treatments revealed that the identified drought-tolerant high-yielding genotypes had lower values for stress indices and an increasing trend in their WUE indicating that the model was able to aid in decision support for identifying drought-tolerant genotypes. Simulating the drought stress indices could assist in predicting the response of a genotype under drought stress and the final yield at harvest. The results support the idea that the model could be used as a DST in the identification of drought-tolerant high-yielding genotypes in stressed as well as non-stressed conditions, thus assisting in the genotypic selection process in rice crop breeding programs.

Grain Yield and Water Productivity of Irrigated Rice Affected by Transplanting Dates in Bangladesh

Selection of suitable transplanting window is essential for getting desired crop yield and optimizing irrigation water. This study was conducted in four different locations of Bangladesh (Gazipur, Mymensingh, Cumilla and Bogura districts) to investigate the effect of transplanting period on irrigation water productivity during irrigated rice (Boro) cultivation. Ceres-rice model incorporated in DSSAT was used to estimate rice grain yield and agronomic parameters for Boro 2016-17. Daily weather data and soil data were collected from Bangladesh Meteorological Department (BMD) and Soil Resource Development Institute (SRDI). The estimated irrigation scheduling using CROPWAT- 8.0 model was used as input to the DSSAT model. Rainfall distribution showed only about 22% (2% in winter and 20% in pre-monsoon) of annual rainfall occurred in irrigated rice growing period. Delay transplanting after 15 December, the cultivar BRRI dhan28 faced higher mean daily temperature resulted shorter life span. The increased seasonal mean temperature by 2.8ºC in Gazipur and Bogura and 2.6ºC in Mymensingh and Cumilla from 15 December to 01 March reduced growth duration by 24 days in Gazipur, Mymensingh and Bogura and by 26 days in Cumilla district. Cumilla received the maximum rainfall, however Gazipur experienced the lowest among the four study locations. The received rainfall amount increased with the advancement of transplanting date from 15 December. The increased rainfall reduced the irrigation demand of the cultivar. On the contrary, reduced growth duration due to delay transplanting decreased the grain yield. Transplanting up to 1 February produced almost similar grain yield, while irrigation demand decreased from 15 December transplanting. Water productivity showed increasing trend for late transplanting. Considering grain yield and irrigation water productivity, 15 January to 1 February transplanting were found suitable transplanting period for the study locations. Rice crop establishment within the recommended period could be optimized the grain yield and irrigation water productivity in the selected study locations. Thus, maximum coverage of rainfall can be reduced the irrigation demand. Consequently, it may help to optimize groundwater use and to arrest the groundwater mining. Bangladesh Rice J. 25 (2) : 21-30, 2021

Optimizing water-saving irrigation schemes for rice (Oryza sativa L.) using DSSAT-CERES-Rice model

Rice is one of the major crops in China, and enhancing the rice yield and water use efficiency is critical to ensuring food security in China. Determining how to optimize a scientific and efficient irrigation and drainage scheme by combining existing technology is currently a hot topic. Crop growth models can be used to assess actual or proposed water management regimes intended to increase water use efficiency and mitigate water shortages. In this study, a CERES-Rice model was calibrated and validated using a two-year field experiment. Four irrigation and drainage treatments were designed for the experiment: alternate wetting and drying (AWD), controlled drainage (CD), controlled irrigation and drainage for a low water level (CID1), and controlled irrigation and drainage for a high water level (CID2). According to the indicators normalized root mean square error (NRMSE) and index of agreement (d), the calibrated CERES-Rice model accurately predicted grain yield (NRMSE=6.67%, d=0.77), , shoot biomass (NRMSE = 3.37%, d = 0.77), actual evapotranspiration (ETa) (NRMSE = 3.83%, d = 0.74), irrigation volume (NRMSE = 15.56%, d = 0.94), and leaf area index (NRMSE = 9.69%, d = 0.98) over 2 a. The calibrated model was subsequently used to evaluate rice production in response to the four treatments (AWD, CD, CID1, and CID2) under 60 meteorological scenarios which were divided into wet years (22 a), normal years (16 a), and dry years (22 a). Results showed that the yield of AWD was the largest among four treatments in different hydrological years. Relative to that of AWD, the yield of CD, CID1, and CID2 were respectively reduced by 5.7%, 2.6%, 8.7% in wet years, 9.2%, 2.3%, 8.6% in normal years, and 9.2%, 3.8%, 3.9% in dry years. However, rainwater use efficiency and irrigation water use efficiency were the greatest for CID2 in different hydrological years. The entropy-weighting TOPSIS model was used to optimize the four water-saving irrigation schemes in terms of water-saving, labor-saving and high-yield, based on the simulation results of the CERES-Rice model in the past 60 a. These results showed that CID1 and AWD were optimal in the wet years, CID1 and CID2 were optimal in the normal and dry years. These results may provide a strong scientific basis for the optimization of water-saving irrigation technology for rice. Key words: CERES-Rice, controlled irrigation and drainage, water-saving, long-term weather data, water use efficiency DOI: 10.25165/j.ijabe.20231602.7361 Citation: Gao S K, Gu Q Q, Gong X W, Li Y B, Yan S F, Li Y Y. Optimizing water-saving irrigation schemes for rice (Oryza sativa L.) using DSSAT-CERES-Rice model. Int J Agric & Biol Eng, 2023; 16(2): 142-151.

Analysis of Potential Yield and Yield gap of Kharif Rice in Prayagraj district of Uttar Pradesh using CERES-Rice Model

Analysis of Potential Yield and Yield gap of Kharif Rice in Prayagraj district of Uttar Pradesh using CERES-Rice Model

Determination of Nitrogen Application Ratio and Sowing Time for Improving the Future Yield of Double-Harvest Rice in Nanchang Based on the DSSAT-CERES-Rice Model

Climate change is a very serious threat to the agricultural sector and potentially brings new problems to the sustainability of agricultural production systems. This paper aims to know how to improve crop yield by changing the nitrogen application ratio and sowing time under future climate change conditions based on the CERES-Rice model. The CERES-Rice model was calibrated and validated with a three-year field experiment (2018–2020), which was coupled with four N rates (50, 100, 150, and 200 kg/ha) and three different N ratios (B:T:S = 3:1:0; B:T:S = 5:3:2; B:T:S = 6:3:1). The results showed that the CERES-Rice model had better simulation effect on the phenophase (n-RMSE < 15%, d > 0.9 and R2 = 0.978) and yield (n-RMSE < 10%, d > 0.9 and R2 = 0.910) of double-harvest rice. The calibrated model was used to evaluate the growth period and yield of double-harvest rice under the RCP4.5 climate scenario and the results revealed that future yields of double-harvest rice in Nanchang are lower than those in experimental years, especially for early rice. Adjusting the nitrogen application ratio and sowing time can improve the yield of double-harvest rice to a certain extent, and the nitrogen application ratio of 5:3:2 has the best effect. In 2021–2035, the best yield of double-harvest rice can be obtained when the sowing date of early rice is about 15 days earlier and the sowing date of late rice is about 10 days earlier than the experiment year. From 2035 to 2050, the sowing date of early rice and late rice will be advanced by about 10 days, and the total yield of double-harvest rice will be higher. In 2050–2070, the total yield of double-harvest rice may reach the best when the sowing date is delayed by 10–15 days. Therefore, reasonably changing the sowing date of double-harvest rice and the nitrogen application regime of early rice can be used as a possible adaptive strategy to cope with the yield reduction in double-harvest rice in future climate scenarios.

Application of the CERES-Rice Model for Rice Yield Gap Analysis

Aim: This study aimed to quantify and identify the trends in the yield gaps over the 15 years (2006-07 to 2020-21) in the Karimnagar district of Telangana State in India. The DSSAT v4.7.5 CERES-Rice model was used to calculate the potential yields and then yield gaps were calculated. For the yield gaps, linear and compound growth rates were determined.
 Results: (Crop Environment Resource Synthesis) CERES-Rice model has simulated the potential yields with the given weather and soil data of Karimnagar district in India. Yield gap I has recorded positive trend, whereas the yield gap II and total yield gaps have recorded negative trend. Linear and Compound growth rates for yield gap I, yield gap II and total yield gaps were calculated in which only yield gap I showed positive growth rates whereas yield gap II and total yield gaps showed negative growth rates. However, at 5% level, the growth rates in yield gap-II are significant.
 Conclusion: It has been observed that, in Karimnagar district of Telangana, the production of rice has increased during the period under study, however there are yield gaps in rice. Using the DSSAT (Decision-support system for Agro-technology Transfer) model, potential rice yields in the Karimnagar district were predicted. There was no much variation in the yield gap I and it was noticed negative trends in yield gap II and total yield gaps. These gaps are to be filled to boost the productivity.

Estimating rice yield by assimilating UAV-derived plant nitrogen concentration into the DSSAT model: Evaluation at different assimilation time windows

Accurately simulating crop growth and estimating yield play a paramount role in policy decision making and agriculture management. To improve the accuracy of rice yield estimation at the field scale, plant nitrogen concentration (PNC), a highly nitrogen-related variable, was derived from the UAV-based imagery through a two-year rice experiment by using the random forest (RF) algorithm; then the PNC data was assimilated into the CERES-Rice model with a particle swarm optimization (PSO) method. In this study, the sensitivity of assimilation process to the acquisition time of UAV remotely sensed PNC data, including the vegetative stage, heading stage, ripening stage and the whole growth stage, was investigated. The results showed that the relationship between the estimated and measured PNC after data assimilation at the vegetative stage was stronger than that at other growth stages. Moreover, assimilating PNC at the early growth stage could better simulate the dynamics of PNC with no nitrogen (N) stress. Due to the assumption of soil homogeneity under various N fertilization treatments, all data assimilation strategies had the tendency to overestimate PNC in the N stress condition. In addition, the accuracy of yield estimation obtained by assimilating PNC at the vegetative stage was the highest. Accordingly, the assimilation of PNC data at the early rice growth stage could provide a great potential for improving yield estimation.

Simulation of rice yield with resource conserving technologies for early, mid and end centuries under changing climatic conditions using DSSAT model

Rice (Oryza sativa L.) is one of the major crop of India and the lifeline of the Eastern states. The challenges however are to produce more food for the increasing population by utilizing limited resources and maintain it under climate change conditions. This can be achieved by devising appropriate agro-techniques. We calibrated and evaluated CERES-Rice model of Decision Support System for Agro-Technology Transfer (DSSAT) for different resource conserving technologies (RCTs) to assess their effects on yield of transplanted rice under projected climate change scenarios. Yields were projected for 2030, 2050 and 2070 using data downloaded from MarkSim® DSSAT weather file generator for RCP 4.5 climate change scenario. The simulated result indicated that the productivity of transplanted rice under resource conserving technologies is likely to increase by 11.6-14.9% in 2030, 18.6-22.3% in 2050 and 17.6-20.2% in 2070 compared to the present yield levels (2015) with current management practices. Yield can further be enhanced through adoption of appropriate resource conservation technologies (RCTs). The DSSAT model predicted the effects of RCTs in transplanted rice very well and can be a useful tool for evaluating the effects of climate change on rice under these managements.

Application of the WRF-DSSAT Modeling System for Assessment of the Nitrogen Fertilizer Used for Improving Rice Production in Northern Thailand

The cultivation of rice under irrigation provides fundamental sustenance for nearly half of the world’s population. Rice yields need to increase in order to maintain the rapidly growing population and meet growing food requirements. In this research, we applied the coupled atmospheric–crop model, which is based on the WRF and CERES-Rice models, to find the appropriate nitrogen fertilizer level for improving rice yield in northern Thailand. The model was conducted from June to December in 2011 and 2015. To evaluate the model’s capability, the output from the model, including meteorological data (i.e., precipitation and temperature) and rice production, was compared to actual observation data. The modeling system showed an acceptable level of output for statistical examination; for example, the R2 values were 0.93, 0.76, and 0.97 for precipitation, temperature, and rice production, respectively. To assess the optimization of the nitrogen fertilizer level, we designed nine experiments: control cases and other cases that were multiplied by a factor of 2–10 times the nitrogen fertilizer levels. The model suggested that we can produce substantial rice yields by increasing nitrogen fertilizer levels by 12 kg/ha.

GLUEOS: A high performance computing system based on the orchestration of containers for the GLUE parameter calibration of a crop growth model

The cultivar parameters of crop growth models are typically estimated through calibration procedures based on a global optimization method such as Generalized Likelihood Uncertainty Estimation (GLUE). A system that supports parallel processing would accelerate the calibration of cultivar parameters using GLUE, which requires a large amount of computation. In the present study, a framework for a distributed computing system was proposed to aid calibration of model parameters, which was referred to as Generalized Likelihood Uncertainty Estimation Orchestration System (GLUEOS). GLUEOS was designed to manage multiple containers in a distributed computing system and to provide a simple web-based interface. In a case study, the cultivar parameters of the CERES-Rice model were calibrated for a rice cultivar Shindongjin using GLUEOS. The calibration procedures were repeated 100 times to take into account the uncertainties of parameter estimates. The elapsed wall time for the parameter calibration process was compared between two computing systems including low end single board (LESB) and high end desktop (HEDT) computers. It was found that GLUEOS completed with less wall time for HEDT than LESB. However, the absolute time difference between these systems was considerably small on average, e.g., 22 s, when 32 containers were used for a single round of calibration procedure. The computing efficiency of GLUEOS was affected by the configurations for the containers, e.g., the number of containers per node. The mean value of calibrated parameter sets resulted in reliable estimate of heading dates for both calibration and validation datasets. These results suggested that GLUEOS would allow for calibration of cultivar parameters with less wall time for computation and minimum expertise required for distributed computing. Utilizing GLUEOS with other crop models would facilitate a wider range of studies for any research that requires parameter calibration.

Evaluating the impact of intra-seasonal change in temperature and solar radiation on phenology and yield of rice using CERES-rice model in Punjab

Rice is a staple food of global as well as Indian population and its productivity is vulnerable to climate change. Keeping in view the observed trends in climate variability, phenology and yield of rice were simulated using CERES-Rice model under climatic scenarios of changes in temperature (+0.5, +1.0, +1.5, +2.0, +2.5, +3.0 °C from normal) and solar radiation (+2.5, +5.0, +10.0, +12.5, +15.0, +20.0% from normal) and their combined interactive effects during differential growth periods, i.e., whole season, vegetative phase, grain filling phase, 0-30 days after transplanting (DAT), 30-60 DAT, 60-90 DAT. In general, with a rise in temperature from normal, the CERES-Rice model predicted advancement in phenological development in rice and vice versa. In general, the increase in yield of rice cultivars for different transplanting dates was observed with decrease in temperature and increase in radiation and vice-versa. With rise in temperature from normal, CERES-Rice model showed maximum decrease in grain yield of rice during whole season change followed by grain filling phase change and vegetative phase change in decreasing order. Among the growth periods, maximum decrease in yield was observed in 0-30 DAT (early vegetative) followed by 30-60 DAT (late vegetative) and 60-90 DAT (grain filling) in decreasing order. Under the conditions of heat and radiative stress, grain filling phase and early vegetative phase was more vulnerable. The CERES-Rice model predictions showed that rice cv. PR-118 (long duration cultivar) was more tolerant to heat and radiative stress than cv. PR-115 (short duration cultivar) and hence may be recommended for cultivation due to its tolerant traits towards maintaining its good grain yield and harvest index.

Climate change impact assessment and developing adaptation strategies for rice crop in western zone of Tamil Nadu

The CERES-Rice model in the Decision Support System for Agro-technology Transfer (DSSAT) was used for assessing the impact of climate change on rice and developing adaptation strategies to sustain rice production in western zone of Tamil Nadu. Model results showed that the rice yield reduction ranged from 4 -56 % with increase in temperature from 1 to 5°C respectively from the current climate under different dates of planting from 1st June to 15th July. The different sowing windows tested as adaptation strategy to climate change indicated that the change in yield from current condition under early, normal, late planting during kharif season was -21.2, -15.0 and -16.3 % respectively for 3°C increase in temperature and 650 ppm CO2 enrichment, during rabi season, it was -9.2, +10.2 and +11.0 % respectively. To manage the water crisis under changing climatic conditions, different methods of cultivation viz., transplanted rice (TRC- conventional), direct sown rice (DSR), alternate wetting and drying method (AWD), system of rice intensification (SRI) and aerobic rice cultivation (ARC) was simulated. Higher grain yield was registered under SRI with water saving of 16 and 7.8% respectively during kharif and rabi seasons.

Review on Plant Physiology and Crop Modeling for the Response of Rice Crop to Climate Change

Climate change impacts the sustainability of agriculture. Physiological processes are very essential for improving crop modeling. The objective of this paper was to assess the integrated effects of crop modeling and crop physiology on the response of rice crop to climate change. Some models have weakness and this weakness rectified by better understanding of physiological features which is correlated to the growth and development of the plant. Process-based dynamic crop models are able to estimate a range of crop response to the environment and to assess the biophysical effects of future climate scenarios on growth and yield. Crop modelling has the potential to enable society to assess the efficacy of G × E technologies to mitigate and adapt crop production systems to climate change. An advanced integration of crop modeling and crop physiology will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales. High night temperature increases carbon loss from night respiration and reduces the activity of source–sink enzymes resulting in lower biomass, poor grain filling, and reduced grain weight of rice. ORYZA and CERES-Rice models are the most familiar physiological based rice models currently used for rice crop modeling studies. Interaction between plant physiology and modeling is essential to improving the existing models, for creating new models, and for improving predictions on crop responses to climate changes and variability. Keywords: Crop modeling, Physiology, climate change, crop response, Rice DOI: 10.7176/JNSR/13-4-01 Publication date: February 28 th 2022

Simulation of yield of rice cultivars under variable agronomic management options using ceres-rice and infocrop-rice models in irrigated plains of Punjab

Dynamic simulation models viz. CERES-Rice and Info Crop-Rice were used to simulate phenology and yield of rice crop. The models were calibrated and validated for rice cultivars (PR115 and PR118) transplanted with three dates, i.e., 15 June, 22 June and 29 June. The statistical evaluation of simulated results by the models revealed good agreement between observed and simulated values. The d-stat values for the days taken to anthesis and maturity and grain yield in CERES-Rice model were 0.92, 0.83 and 0.88%, respectively whereas in InfoCrop-Rice model were 0.94, 0.82 and 0.84%, respectively. The Root Mean Square Error (RMSE) for grain yield was 277.10 kg/ha in CERES-Rice and 328.31 kg/ha in InfoCrop-Rice. The CERES-Rice and InfoCrop-Rice model predicted a rice yield of 6770 kg/ha and 6070kg/ha, respectively for a 15 June transplanted crop which was applied urea fertilizer @ 225.5 kg/ha and cultivated with a plant population stand of 33.3 plants m−2.

Modeling the effects of elevation and precipitation on Rice (Oryza sativa L.) production considering multiple planting methods and cultivars in Central China

In this study, we investigated the effects of elevation and precipitation on rice (Oryza sativa L.) production using the Crop Environment Resource Synthesis (CERES)-Rice model in Hubei province, China. We divided our study area into four zones based on elevation and precipitation. For each zone, our simulations were conducted using three planting methods: dry direct-seeded rice (DDSR), wet direct-seeded rice (WDSR), and transplanted-flooded rice (TFR), with three rice cultivars of different growth duration: Yangliangyou6 (long-duration), Huanghuazhan (mid-duration), and Lvhan1 (short-duration). Additionally, the optimal irrigation strategy for WDSR was determined with the CERES-Rice model. Our results indicated that the yields of WDSR with the optimal irrigation strategy were comparable with those of TFR in low-elevation regions but were less than the TFR yields in high-elevation areas. Furthermore, the rice yields increased at first and then decreased with increasing elevation, which was affected by growing period length and photosynthesis rate. Compared with the other two cultivars, the short-duration cultivar may be more suitable for growing in high-elevation regions. In addition, high precipitation could facilitate the cultivation of the long-duration cultivar in low-elevation regions, as it gives DDSR a yield potential comparable to that of WDSR for the short-duration cultivar in high-elevation regions. This study could help farmers choose optimal field management practices based on elevation and precipitation, ensuring sustainable and improved rice production.

Global Sensitivity Analysis for CERES-Rice Model under Different Cultivars and Specific-Stage Variations of Climate Parameters

Global sensitivity analysis (SA) has become an efficient way to identify the most influential parameters on model results. However, the effects of cultivar variation and specific-stage variations of climate conditions on model outputs still remain unclear. In this study, 30 indica hybrid rice cultivars were simulated in the CERES-Rice model; then the Sobol’ method was used to perform a global SA on 16 investigated parameters for three model outputs (anthesis day, maturity day, and yield). In addition, we also compared the differences in the sensitivity results under four specific-stage variations (vegetative phase, panicle-formation phase, ripening phase, and the whole growth season) of climate conditions. The results indicated that (1) parameter Tavg, G4, and P2O are the most influential parameters for all model outputs across cultivars during the whole growth season; (2) under the vegetative-phase variation of climate parameters; the variability of model outputs is mainly controlled by parameter P2O and Tavg; (3) under the panicle-formation-phase or ripening-phase variation of climate parameters, parameter P2O was the dominant variable for all model outputs; (4) parameter PORM had a considerable effect (the total sensitivity index, STi; STi>0.05) on yield regardless of the various specific-stage variations of the climate parameters. Findings obtained from this study will contribute to understanding the comprehensive effects of crop parameters on model outputs under different cultivars and specific-stage variations of climate conditions.