Evaluation of CERES-Wheat and CropSyst models for water–nitrogen interactions in wheat crop

Singh, Anil Kumar; Tripathy, Rojalin; Chopra, Usha Kiran

doi:10.1016/j.agwat.2008.02.006

Cited by 114 publications

(71 citation statements)

References 26 publications

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“…For all crop growing seasons, prediction of dates were good. Similar results were reported by Singh et al (2008) for the New Delhi area. The calibrated crop model parameters are shown in Table 3.…”

Section: Model Calibrationsupporting

confidence: 80%

“…However, reports on the validation of the CropSyst model under different water and N availability conditions in arid and semi-arid regions are very few (Pathak et al, 2006;Singh et al, 2008). Evaluations under different N management conditions, and water-nitrogen interactions in wheat crop have been done by Roberto et al (2006) and Singh et al (2008), respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the fact that wheat crop simulation models are now widely applied in monitoring and planning agricultural resources, CropSyst parameters for wheat are limited in the sense that they refer to older model versions (Giardini et al, 1998), to specific pedoclimatic environments (Pannkuk et al, 1998), and sometimes lack the complete list of crop parameters. The model has been widely applied to cereals and other cropping systems (Stöckle et al, 1994;Pala et al, 1996;Donatelli et al, 1997;Giardini et al, 1998;Pannkuk et al, 1998;Confalonieri and Bechini 2004;Wang et al, 2006;Benli et al, 2007;Singh et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Evaluations under different N management conditions, and water-nitrogen interactions in wheat crop have been done by Roberto et al (2006) and Singh et al (2008), respectively. Reports on the validation of CropSyst for crop growth and yield under Iranian conditions are scanty.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimizing Wheat Water Productivity as Affected by Irrigation and Fertilizer-nitrogen Regimes in an Arid Environment

Montazar

Mohseni²

2011

JAS

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The present study evaluated the applicability of the CropSyst model under variable climatic, irrigation, and fertilizer-nitrogen regimes. The objective was to analyze wheat productivity responses to water and N-application for optimizing water productivity in an arid irrigated environment. Evaluation analysis showed that the model provided very satisfactory estimates for the emergence, flowering and physiological maturity dates. The performance of the model was reasonable as demonstrated by the close correspondence between simulated grain yield, biomass accumulation, seasonal ET, and irrigation water productivity (WP I ) with measured data. The normalized root mean square error ranged between 5 and 10% for most of the parameters. Overall, the Willmott index of agreement between simulated and observed values of grain yield, biomass and seasonal ET were 0.99, 0.98 and 0.97, respectively. The validated model was employed to assess interactive effects of irrigation and fertilizer N on grain yield and water productivity indices. Scenario analyses indicated that WP I and WP ET (ET water productivity) ranged from 0.16 to 2.07 kg m -3 , and from 0.07 to 1.49 kg m -3 , respectively. For predicting the best N and water application practices for maximization of water productivity, the best option found by the model was application of water and nitrogenous fertilizer in 70% and 90% of the required values, respectively, for WP I , and equal to the required values (100%) for WP ET . The simulations demonstrated that the current wheat productivity of 5.0 Mg ha -1 obtained by the local farmers can be achieved at 140 kg ha -1 fertilizer N and 30% deficit irrigation regime with a WP I of 1.73 kg m -3 . The CropSyst model can be applied to derive best management options in terms of N and irrigation application of wheat under arid conditions.

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Section: Model Calibrationsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Wheat Water Productivity as Affected by Irrigation and Fertilizer-nitrogen Regimes in an Arid Environment

Montazar

Mohseni²

2011

JAS

View full text Add to dashboard Cite

show abstract

“…The water and nitrogen sub-models have feedback effects on plant growth and development. This model has been widely applied to field sites to assess crop potential productivity [51,52] and the influence of climate change on grain yields [40,41], farmland water, and fertilizer management [53].…”

Section: Crop Growth Modelmentioning

confidence: 99%

The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation

Jiang

Chen

et al. 2014

Remote Sensing

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To improve crop model performance for regional crop yield estimates, a new four-dimensional variational algorithm (POD4DVar) merging the Monte Carlo and proper orthogonal decomposition techniques was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)-Wheat model. Two winter wheat yield estimation procedures were conducted on a field plot and regional scale to test the feasibility and potential of the POD4DVar-based strategy. Winter wheat yield forecasts for the field plots showed a coefficient of determination (R 2 ) of 0.73, a root mean square error (RMSE) of 319 kg/ha, and a relative error (RE) of 3.49%. An acceptable yield at the regional scale was estimated with an R 2 of 0.997, RMSE of 7346 tons, and RE of 3.81%. The POD4DVar-based strategy was more accurate and efficient than the EnKF-based strategy. In addition to crop yield, other critical crop variables such as the biomass, harvest index, evapotranspiration, and soil organic carbon may also be estimated. The present study thus introduces a promising approach for operationally monitoring regional crop growth and predicting yield. Successful application of this assimilation model at regional scales must focus on uncertainties derived from the crop model, model inputs, data assimilation algorithm, and assimilated observations.

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Implementation of an automatic time‐series calibration method for the DSSAT wheat models to enhance multi‐model approaches

2020

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Multi-modeling (MM) approaches allow increasing modeling accuracy through a combination of different modeling structures for the simulation of plant growth and yield. The Decision Support System for Agrotechnology Transfer (DSSAT) 4.7 modeling platform currently includes three different wheat (Triticum aestivum L.) models (CERES, N-Wheat, and Cropsim). However, the main obstacle for using an MM approach is the calibration procedure. Calibration is time consuming and complex, especially if the user is not familiar with all three models. It results in a subjective calibration optimum and might discriminate models if the user is less trained. To avoid these conflicts, an automated calibration program which optimizes cultivar coefficients based on the root means square error (RMSE) of time-series data was developed to ensure objective calibration results across three different wheat models and to highlight the potential of MM approaches for decision support in the future. Model calibration was performed on a 4-yr nitrogen wheat fertilizer trial (0-240 kg ha −1 N) in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-index = .93) and evaluation dataset (d-index = .81). By comparing different years, the MM approach improved modeling accuracy in most cases. Especially in the drought season of 2018, the MM approach revealed higher modeling accuracy for yield (d-index = .61) in contrast to a single simulation of CERES (d-index = .34) and Cropsim (d-index = .39). This demonstrated the advantage of an MM approach as different modeling structures could compensate for errors that occur in single modeling approaches.

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Evaluation of CERES-Wheat and CropSyst models for water–nitrogen interactions in wheat crop

Cited by 114 publications

References 26 publications

Optimizing Wheat Water Productivity as Affected by Irrigation and Fertilizer-nitrogen Regimes in an Arid Environment

Optimizing Wheat Water Productivity as Affected by Irrigation and Fertilizer-nitrogen Regimes in an Arid Environment

The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation

Implementation of an automatic time‐series calibration method for the DSSAT wheat models to enhance multi‐model approaches

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