Crop Estimation Through Resource And Environment Synthesis Research Articles

Assessment of Future Climate and Kharif Paddy Yields using Ceres-Rice in the State of Andhra Pradesh

Climate change considerably impacts water needs for agricultural production, particularly in paddy crops (Oryza Sativa). The paddy crop response is uncertain and also heterogeneous due to climate change. Climate variables rainfall and temperature directly impact crop productivity. A precise understanding of crop yields is required for agricultural production management to plan sustainable food demand in the future at the state level. The climate change scenario's impact on rice yield at 0.25° × 0.25° spatial resolution was assessed in Andhra Pradesh and presented in this paper. The use of representative concentration Pathway 8.5 scenario in projections made by the Global Climate Model (GCM) were downscaled for mid-century (2048-2078) using statistical tools. In this study, the Cropping System Model (CSM) and Crop Estimation through Resource and Environment Synthesis (CERES) modules for rice, as part of the Decision Support System for Agro-technology Transfer (DSSAT) package, were utilized. The use of the Regional Crop Yield Estimation System (RCYES) for the Cropping System Model (CSM) within the Decision Support System for Agro-technology Transfer (DSSAT) was facilitated through Python in this study. It is observed that rainfall will decrease during winter and pre-monsoon seasons related to the baseline period (1988–2018) for RCP 8.5. From July to October, there was a significant increase in rainfall. The most considerable change in the rain was 50.7 mm in September. A notable variation between the maximum and minimum temperatures of 2.3 and 2.5 degrees Celsius in June and April respectively. Rainfall is expected to increase in Anantapur, Kurnool and Nellore districts during the mid-century 2040's. The correlation between the baseline and DES mean yield was 0.87, with a maximum yield of 0.86 and a minimum yield of 0.82. Decrease paddy yields by up to 10.7% in West Godavari, Krishna, Guntur, Nellore and Prakasam districts. At the same time, an increase in paddy yields up to 9.8% is anticipated in Srikakulam, Visakhapatnam, Vizianagaram, East Godavari, Anantapur, Chittoor, Kadapa, and Kurnool. In contrast, a maximum decrease of 189.9 mm of rainfall is expected in the Vizianagaram district. These results could assist in devising adaptation measures to reduce the negative effect of climate change on rice crops in Andhra Pradesh.

Future changes in rice yield over Kerala using climate change scenario from high resolution global climate model projection

The impact of climate change on agricultural yield is one amongst the major concerns the world is witnessing. Our study focusses on rice yield prediction for an agricultural research station in Kerala with the help of climate change scenario input from the Meteorological Research Institute (MRI) Global Climate Model (GCM) projection under Representative Concentration Pathway 8.5 (RCP8.5). We have used Cropping System Model (CSM) Crop Estimation through Resource and Environment Synthesis (CERES) Rice within Decision Support System for Agrotechnology Transfer (DSSAT) package for predicting the yield. Our study has the novelty of using very high-resolution climate data from a model which is highly skilful in capturing the present-day climate features and climatic trends over India (in particular, over the Western Ghats), as input for simulating the future crop yield. From this study, we find that the rice yield decreases due to rise in temperature and reduction in rainfall, thereby reducing the crops maturity time in the future. Based on our results, the adaptation measures suggested to achieve better yield under future warming conditions are: (i) to opt for alternative rice varieties which have tolerance to high temperatures and consume less water, and (ii) shifting of planting date to the most appropriate window.

Agronomic management and climate change scenario simulations on productivity of rice, maize and wheat in central Nepal using DSSAT ver 4.5 crop model

Average productivity of 3.50 t/ha of rice, 2.50 t/ha of maize and 2.45 t/ha of wheat in Nepal have been very less than their potential productivity for which précised agronomic management and changing climatic scenarios have been reported the most challenging factors at present. Cropping system Model (CSM)-Crop Estimation through Resource and Environment Synthesis (CERES)- Rice, Maize and Wheat, embedded under Decision Support System for Agro-technology Transfer (DSSAT) ver. 4.5 was evaluated from a datasets of farmers’ field experimentations of the central Nepal (Terai-Nawalpur and mid-hill-Kaski districts), and showed high sensitivity of model over change in different agronomic management and climate change scenarios. Model calibration was done by using maximum attainable yield treatments for all tested cultivars while validation was accomplished by using the remaining treatments for predicting growth, phenology and yield of all crop cultivars and results were found perfectly matched with the observed results. Further, the different agronomic management options and climate change scenarios as advocated by IPCC for 2020, 2050 and 2080 from base line of 1995 was studied to simulate the growth and yield performance of diverse crop cultivars. The hybrids and short duration cultivars of all three cereals were found more affected due to climate change than the local and long duration crop cultivars. The model simulation results obtained on rice, maize and wheat using DSSAT ver 4.5 model highlighted that there is utmost importance to develop new climate ready crop cultivars to feed the future generation over different climate change scenarios as suggested by IPCC, 2007 and the simulation results should be extrapolated to the major domains of similar agro-ecozones in Nepal. It is suggested that CSM- CERES- model would be reliable and valid approach for getting strategic decision support system especially with regards to the climate change adaptation measures in Nepal.

Coupling of a Regional Climate Model with a Crop Development Model and Evaluation of the Coupled Model across China

In this study, the CERES (Crop Estimation through Resource and Environment Synthesis) crop model was coupled with CLM3.5, the land module of the regional climate model RegCM4. The new coupled model was named RegCM4 CERES; and in this model, crop type was further divided into winter wheat, spring wheat, spring maize, summer maize, early rice, late rice, single rice, and other crop types based on each distribution fraction. The development of each crop sub-type was simulated by the corresponding crop model separately, with each planting and harvesting date. A simulation test using RegCM4 CERES was conducted across China from 1999 to 2008; a control test was also performed using the original RegCM4. Data on crop LAI (leaf area index), soil moisture at 10 cm depth, precipitation, and 2 m air temperature were collected to evaluate the performance of RegCM4 CERES. The evaluation provided comparison of single-station time series, regional distributions, seasonal variations, and statistical indices for RegCM4 CERES. The results revealed that the coupled model had an excellent ability to simulate the phonological changes and spatial variations in crops. The consideration of dynamic crop development in RegCM4 CERES corrected the wet bias of the original RegCM4 over North China and the cold bias over South China. However, the degree of improvement was minimal and the statistical indices for RegCM4 CERES were roughly the same as the original RegCM4.

Optimizing the Sowing Date and Irrigation Strategy to Improve Maize Yield by Using CERES (Crop Estimation through Resource and Environment Synthesis)-Maize Model

Summer maize (Zea mays L.) is a widely cultivated crop in the arid and semi-arid Guanzhong region of China. However, due to the spatial and temporal variation in rainfall, the seasonal maize yield varies substantially and occasionally is not economical for poor farmers to produce. Recent water-saving agricultural practices were developed by the government to make it possible to apply supplementary irrigation at optimum sowing dates to maximize maize production under limited rainfall in the region. CERES (Crop Estimation through Resource and Environment Synthesis)-maize model was used to identify the appropriate irrigation strategies, crop growth stages and sowing dates for sustainable maize production. Model calibration process were carried out for full irrigation treatments of four growing seasons, (2012–2015). The data used for calibration included: Crop phenology, grain yield, aboveground biomass and leaf area index. The calibration phase model showed good agreement between simulated and observed values, with normalized root mean square error (nRMSE) ranging from 4.51% to 14.5%. The performance of the calibrated model was evaluated using the field data of grain yield, aboveground biomass, leaf area index and water use efficiency. The performance of the model during evaluation was satisfactory with acceptable nRMSE error ranging from 7% to 10%. Soil moisture content was evaluated for full irrigation treatments for both 2012 and 2013 seasons. With results showing that soil moisture content below 35 cm layer was well simulated with nRMSE, 0.57 to 0.86 respectively. Appropriate simulated sowing dates for higher production and water productivity were from 14 to 24 June. The proper amount and timing of irrigation water application was 100 mm at the flowering stage, and 100 mm at the grain filling stage respectively. Summer maize yield can be improved by adjusting the sowing date and applying supplementary irrigation when precipitation cannot meet the crop water demand in the Guanzhong Plain.

Application of csm- ceres-maize model for seasonal and multi-decadal predictions of maize yield in under subtropical condition of Chitwan, Nepal

The average maize yield of 2.5 t/ha in sub-tropical terai and inner terai of Nepal has been very less than its potential yield of about 5.0 t/ha, for which changing climatic scenarios have been reported the critical factors. Cropping system Model (CSM)-Crop Estimation through Resource and Environment Synthesis (CERES)-Maize, embedded under Decision Support System for Agro-technology Transfer (DSSAT) ver. 4.2 was evaluated from a datasets of field experimentation by growing four diverse maize genotypes viz. full season OPV (Rampur Composite), Quality Protein Maize (Posilo Makai-1), Hybrid (Gaurav) and Pop corn (Pool-12) under three different planting dates (September 1, October 1 and November 1) in 2009-10 at Rampur Campus, Chitwan. The experiment was laid out in two factor factorial randomized complete block design (RCBD) with three replications in slightly acidic (pH 6.7) sandy loam soil having low soil available N( 0.49%) and K (148 kg/ha) and medium P (16.3 kg/ha) status. The ancillary and yield data obtaining from field experiment was analyzed from the M-Stat C software and recorded that Gaurav hybrid produced significantly higher yield under September 1 planting (5.86 t/ha) followed by Posilo Makai 1 (5.55 t/ha), Rampur Composite (5.1t/ha) and the least with Pool-12 (3.45 t/ha). Further, the heat use efficiency of diverse maize genotypes were also calculated by using the mean temperature based accumulative heat unit system and found the stable yields only with Rampur Composite for all planting dates and the rest genotypes were suitable only to the early winter plantings. Model calibration was done by using September 1 planting date for all 4 maize genotypes while validation was accomplished by using the remaining treatments for predicting growth and yield of different maize genotypes. The year 2006- 07 was found 13, 18, 23 and 7% higher in producing the maize yield than the standard year 2009-10 for Rampur Composite, Posilo Makai-1, Gaurav and Pool-12, respectively. Further, the different climate change scenarios as advocated by IPCC (2007) for 2020, 2050 and 2080 from base line of 2009-10 was studied to simulate the growth and yield performance of diverse maize genotypes with September 1 planting date and found that there would be increment in winter maize yield up to 2020 scenario of climate change and the drastic yield loss would be on 2050 to 2080 scenarios under the present levels of agronomic management options and urged for the new climate change adaptation and mitigation production technologies.Journal of Maize Research and Development (2015) 1(1):86-97DOI: http://dx.doi.org/10.5281/zenodo.34283

Simulating climate change impacts and potential adaptations on rice yields in the Sichuan Basin, China

Rice (Oryza) is a staple food in China, and rice yield is inherently sensitive to climate change. It is of great regional and global importance to understand how and to what degree climate change will impact rice yields and to determine the adaptation options effectiveness for mitigating possible adverse impacts or for taking advantage of beneficial changes. The objectives of this study are to assess the climate change impact, the carbon dioxide (CO2) fertilization effect, and the adaptation strategy effectiveness on rice yields during future periods (2011–2099) under the newly released Representative Concentration Pathway (RCP) 4.5 scenario in the Sichuan Basin, one of the most important rice production areas of China. For this purpose, the Crop Estimation through Resource and Environment Synthesis (CERES)-Rice model was applied to conduct simulation, based on high-quality meteorological, soil and agricultural experimental data. The modeling results indicated a continuing rice reduction in the future periods. Compared to that without incorporating of increased CO2 concentration, a CO2 fertilization effect could mitigate but still not totally offset the negative climate change impacts on rice yields. Three adaptive measures, including advancing planting dates, switching to current high temperature tolerant varieties, and breeding new varieties, could effectively offset the negative climate change impacts with various degrees. Our results will not only contribute to inform regional future agricultural adaptation decisions in the Sichuan Basin but also gain insight into the mechanism of regional rice yield response to global climate change and the effectiveness of widely practiced global thereby assisting with appropriate adaptive strategies.

Development of a rice yield prediction system over Bhubaneswar, India: combination of extended range forecast and CERES-rice model

Use of seasonal and sub-seasonal forecast products of experimental extended range forecast system (ERFS) in crop models is investigated for improving the rice grain yield prediction skill for the ensuing monsoon season in the experimental station at Bhubaneswar, India. A stochastic disaggregation is used to downscale seasonal and monthly forecast products in daily weather sequences. These weather series are taken as input in Crop Estimation through Resource and Environment Synthesis (CERES)-rice crop simulation model for the crop yield prediction at different stages of forecast: June–September (4 month forecast), July–September (3 month forecast), August–September (2 month forecast) and monthly forecast for September (1 month forecast). To avoid a technological trend in historical yield data, yields simulated with observed weather data have been used as the benchmark (observed rice yield) to evaluate the yields simulated using experimental ERFS forecasts. The findings recommend the efficiency of forecast products to capture year-to-year variability in observed rice yield at experimental stations. A significant enhancement in the prediction skill is noticed as the season advances due to incorporation of observed weather data, reducing uncertainty of yield prediction. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.

Performance evaluation of CERES-Wheat model in Guanzhong Plain of Northwest China

Winter wheat (Triticum durum Desf.) is one of the most important food crops in Guanzhong Plain of Northwest China. Irrigation and fertilizer are essential for wheat growth in the region because of inadequate precipitation and unfertile soil in the region. However, in most cases optimal yields are not gained due to unreasonable irrigation and fertilizer applications. A decision-supporting tool is required to facilitate local farmers to evaluate irrigation and fertilizer uses and their impact on yield. In this study, CERES (Crop Estimation through Resource and Environment Synthesis)-Wheat model, a process-based model, was adapted for predicting yield of winter wheat calibrated using plot experiments in the region. Water and nitrogen (N) limitation conditions were included in the experiments conducted in Yangling on small test plots during the growing years of 2009–2010, 2010–2011 and 2011–2012. Results showed that the simulated aboveground biomass, grain yield, leaf area index (LAI), canopy N, cumulative evapotranspiration (ET), water use efficiency (WUE) and nitrogen partial fertilizer productivity (NPFP) deviations from the measured were acceptable with normalized root mean square error (NRMSE) below 21%. LAI rose as N application increased and the synergistic impact of irrigation and N was exerted on LAI. ET and canopy N at maturity increased with irrigation although N application was different for 2010–2011 and 2011–2012. The response of both ET and canopy N to fertilizer N showed the similar trend. Besides, the impact of irrigation on ET was affected by different N applications under low irrigation conditions. WUE and NPFP were exerted a great influence on by N fertilizer and the impact of irrigation on them was not significant. Model sensitivity analysis indicated that the grain yield was very sensitive to field capacity among the factors except for the date-related factors and planting date had a significant influence on grain yield by comparison with the factors related to the date. Therefore, the model can be used for the region to assess and optimize winter wheat yield with respect to irrigation and N applications as well as other influential factors.

Modeling Nitrogen and Water Management Effects in a Wheat‐Maize Double‐Cropping System

Excessive N and water use in agriculture causes environmental degradation and can potentially jeopardize the sustainability of the system. A field study was conducted from 2000 to 2002 to study the effects of four N treatments (0, 100, 200, and 300 kg N ha(-1) per crop) on a wheat (Triticum aestivum L.) and maize (Zea mays L.) double cropping system under 70 +/- 15% field capacity in the North China Plain (NCP). The root zone water quality model (RZWQM), with the crop estimation through resource and environment synthesis (CERES) plant growth modules incorporated, was evaluated for its simulation of crop production, soil water, and N leaching in the double cropping system. Soil water content, biomass, and grain yield were better simulated with normalized root mean square errors (NRMSE, RMSE divided by mean observed value) from 0.11 to 0.15 than soil NO(3)-N and plant N uptake that had NRMSE from 0.19 to 0.43 across these treatments. The long-term simulation with historical weather data showed that, at 200 kg N ha(-1) per crop application rate, auto-irrigation triggered at 50% of the field capacity and recharged to 60% field capacity in the 0- to 50-cm soil profile were adequate for obtaining acceptable yield levels in this intensified double cropping system. Results also showed potential savings of more than 30% of the current N application rates per crop from 300 to 200 kg N ha(-1), which could reduce about 60% of the N leaching without compromising crop yields.

Downscaling of Seasonal Precipitation for Crop Simulation

Abstract A nonhomogeneous hidden Markov model (NHMM) is used to make stochastic simulations of March–August daily rainfall at 10 stations over the southeastern United States, 1923–98. Station-averaged observed daily rainfall amount is prescribed as an input to the NHMM, which is then used to disaggregate the rainfall in space. These rainfall simulations are then used as inputs to a Crop Estimation through Resource and Environment Synthesis (CERES) crop model for maize. Regionally averaged yields derived from the NHMM rainfall simulations are found to correlate very highly (r = 0.93) with those generated by the crop model using observed rainfall; stationwise correlations range between 0.44 and 0.74. Rainfall and crop simulations are then constructed under increasing degrees of temporal smoothing applied to the regional rainfall input to the NHMM, designed to exclude the submonthly weather details that would be unpredictable in seasonal climate forecasts. Regional yields are found to be remarkably insensitive to this temporal smoothing; even with 90-day low-pass-filtered inputs to the NHMM, resulting yields are still correlated at 0.85 with the baseline simulation, whereas stationwise correlations range between 0.18 and 0.68. From these findings, it is expected that regional maize yields over the southeastern United States will be largely insensitive to year-to-year details of subseasonal rainfall variability; they should be downscalable, in principle, using an NHMM from climate forecasts archived at daily resolution, with the important caveat that the latter need to be skillful enough at the 90-day time scale. As a by-product of the analysis, subseasonal-to-interdecadal summer rainfall variability over the southeastern United States is interpretable in terms of six discrete weather states indicative of a monsoonlike climate regime. Low-simulated-yield years are found to be associated with delayed summer rainfall onset.

Assessing the impacts of climate change on rice yields in the main rice areas of China

This paper assesses the impact of climate change on irrigated rice yield using B2 climate change scenario from the Regional Climate Model (RCM) and CERES-rice model during 2071--2090. Eight typical rice stations ranging in latitude, longitude, and elevation that are located in the main rice ecological zones of China are selected for impact assessment. First, Crop Estimation through Resource and Environment Synthesis (CERES)-rice model is validated using farm experiment data in selected stations. The simulated results represent satisfactorily the trend of flowering duration and yields. The deviation of simulation within ± 10% of observed flowering duration and ± 15% of observed yield. Second, the errors of the outputs of RCM due to the difference of topography between station point and grid point is corrected. The corrected output of the RCM used for simulating rice flowering duration and yield is more reliable than the not corrected. Without CO2 direct effect on crop, the results from the assessment explore that B2 climate change scenario would have a negative impact on rice yield at most rice stations and have little impacts at Fuzhou and Kunming. To find the change of inter-annual rice yield, a preliminary assessment is made based on comparative cumulative probability at low and high yield and the coefficient variable of yield between the B2 scenario and baseline. Without the CO2 direct effect on rice yield, the result indicates that frequency for low yield would increase and it reverses for high yield, and the variance for rice yield would increase. It is concluded that high frequency at low yield and high variances of rice yield could pose a threat to rice yield at most selected stations in the main rice areas of China. With the CO2 direct effect on rice yield, rice yield increase in all selected stations.

Simulation of Leaching Losses in the Nitrogen Cycle

The CERES (Crop Estimation through Resource and Environment Synthesis) family of crop models predicts cereal growth, development, and yield. CERES simulates nitrogen (N) as a yield‐limiting macronutrient. Because N leaching is an economic and environmental concern, this study evaluated if CERES can be used to predict N leaching under different N management scenarios: background leaching in unfertilized corn (Zea mays L.), alfalfa (Medicago sativa L.) residue mineralization, and till versus no‐till management. Data were collected during a 7‐yr field experiment on tillage practices in a maize–alfalfa–maize succession. Sensitivity analyses were performed for decomposition rates of the different residue pools and the relative proportions of carbohydrate, cellulose, and lignin in the residues. During the last 5 yr, under corn, CERES accurately simulated nitrate leaching from the no‐till lysimeters. Nitrate leaching was underestimated in the tillage treatments, possibly because CERES does not simulate tillage. The model is not very sensitive to the decomposition rates and to the composition of the residues.

Testing effectiveness of four simulation models for estimating nitrates and water in two soils

Information is required on the usefulness and limitations of simulation models for estimating nitrate-nitrogen (NO3-N) and water status of soil in different agroecological zones of western Canada. Therefore, four simulation models (CERES, EPIC, NLEAP and NTRM) were used to predict distribution of NO3-N and water in the soil profile from long-term spring wheat (Triticum aestivum L.) rotations for the 1990 growing season at Melfort and Scott, Saskatchewan. The models estimated NO3-N and water levels in the crop rooting zone (0–1.5 m) of the soil profiles generally well. However, EPIC underestimated NO3-N levels in the top 0.3 m for fallow (F) rotations at Melfort, whereas NLEAP overestimated amounts in the same depth increment of Melfort soil for the majority of rotations. In addition, CERES and NTRM markedly overestimated NO3-N levels in the Melfort soil solum (0–0.6 m) for the unfertilized continuous wheat (W) rotation. Soil water content was overestimated in the top 0.3-m layer of Melfort soil for the two W rotations by CERES and EPIC and for all rotations by NTRM. As well, EPIC markedly overestimated water content in the crop rooting zone for both rotations at Scott. There were differences among models in accurately simulating NO3-N and water status of the soil profile, but these differences were not great. Overall, these models should be effective in estimating soil NO3-N and water content for wheat cropping systems, thereby aiding agronomic decisions and environmental impact assessments. Key words: Crop Estimation through Resource and Environment Synthesis (CERES), Erosion/Productivity Impact Calculator (EPIC), nitrogen, Nitrogen Leaching and Economic Analysis Package (NLEAP), Nitrogen, Tillage, and Residue Management (NTRM), Triticum aestivum L.