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

Röll, Georg; Memic, Emir; Graeff‐Hönninger, Simone

doi:10.1002/agj2.20328

Cited by 10 publications

(1 citation statement)

References 70 publications

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“…The GLUE (Generalized Likelihood Uncertainty Estimation) program is used to estimate genotype-specific coefficients for the DSSAT crop models. It is a Bayesian estimation method that uses Monte Carlo sampling from prior distributions of the coefficients and a Gaussian likelihood function to determine the best coefficients based on the simulated and observed yield values [27]. The uncertainties in predictions of the impact of future climate change on the yield of spring maize in the main production areas of China were evaluated using the existing field corn genotype coefficient and soil parameter database contained within the DSSAT and field data collected from the agrometeorological stations near the study sites [28].…”

Section: Methods To Manage the Uncertainty Of Simulationsmentioning

confidence: 99%

Yield Response of Spring Maize under Future Climate and the Effects of Adaptation Measures in Northeast China

Koimbori

Wang

Pan

et al. 2022

Plants

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Agriculture production has been found to be the most sensitive sector to climate change. Northeast China (NEC) is one of the world’s major regions for spring maize production and it has been affected by climate change due to increases in temperature and decreases in sunshine hours and precipitation levels over the past few decades. In this study, the CERES-Maize model-v4.7 was adopted to assess the impact of future climatic change on the yield of spring maize in NEC and the effect of adaptation measures in two future periods, the 2030s (2021 to 2040) and the 2050s (2041 to 2060) relative to the baseline (1986 to 2005) under RCP4.5 and RCP8.5 scenarios. The results showed that increased temperatures and the decreases in both the precipitation level and sunshine hours in the NEC at six representative sites in the 2030s and 2050s periods based on RCP4.5 and RCP8.5 climate scenarios would shorten the maize growth durations by (1–38 days) and this would result in a reduction in maize yield by (2.5–26.4%). Adaptation measures, including altered planting date, supplemental irrigation and use of cultivars with longer growth periods could offset some negative impacts of yield decrease in maize. For high-temperature-sensitive cultivars, the adoption of early planting, cultivar change and adding irrigation practices could lead to an increase in maize yield by 23.7–43.6% and these measures were shown to be effective adaptation options towards reducing yield loss from climate change. The simulation results exhibited the effective contribution of appropriate adaptation measures in eliminating the negative impact of future climate change on maize yield.

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Section: Methods To Manage the Uncertainty Of Simulationsmentioning

confidence: 99%

Yield Response of Spring Maize under Future Climate and the Effects of Adaptation Measures in Northeast China

Koimbori

Wang

Pan

et al. 2022

Plants

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Comprehensive evaluation of the DSSAT‐CSM‐CERES‐Wheat for simulating winter rye against multi‐environment data in Germany

Shawon,

Memic,

Kottmann

et al. 2024

Agronomy Journal

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Crop models are valuable tools for simulating and assessing genotype‐by‐environment interactions. In most studies, these models are parameterized based on crop data from a few sites and years, which often limits their applicability to a broader geographic context. Therefore, we utilize countrywide multi‐environment variety trial data in this study to implement a genotype‐specific model parameterization for winter rye (Secale cereale L.) in Germany. We use the Crop and Environment REsource Synthesis (CERES) model originally used for wheat available in the decision support system for agrotechnology transfer (DSSAT) framework and adapt and evaluate it for rye. Calibration and evaluation involved a comprehensive agronomic trial datasets for the rye cultivar Palazzo, encompassing 194 site‐years of experiments covering various cereal production regions in Germany. The parameterization followed a structured approach, encompassing phenology, growth, and yield‐specific coefficients. The parameterized CSM‐CERES‐Rye (where CSM is cropping system model) demonstrated reasonable accuracy in simulating critical crop parameters, including aboveground biomass, leaf area index, tiller, grain number, unit seed weight, and grain yield. The model is available for diverse model‐based assessments of rye cultivation, including evaluating crop management, analyzing crop rotations, and assessing rye's suitability across varied environments, making it valuable for sustainable agriculture and decision‐making.

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Evaluation of different crop model-based approaches for variable rate nitrogen fertilization in winter wheat

et al. 2022

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Several remote sensing-based methods have been developed to apply site-specific nitrogen (N) fertilization in crops. They consider spatial and temporal variability in the soil-plant-atmosphere continuum to modulate N applications to the actual crop nutrient status and requirements. However, deriving fertilizer N recommendations exclusively from remote proximal and remote sensing data can lead to substantial inaccuracies and new, more complex approaches are needed.Therefore, this study presents an improved approach that integrates crop modelling, proximal sensing and forecasts weather data to manage site-specific N fertilization in winter wheat. This improved approach is based on four successive steps: (1) optimal N supply is estimated through the DSSAT crop model informed with a combination of observed and forecast weather data; (2) actual crop N uptake is estimated using proximal sensing; (3) N prescription maps are created merging crop model and proximal sensing information, considering also the contribution of the soil N mineralisation; (4) N-Variable Rate Application (N-VRA) is implemented in the field. A VRA method based on DSSAT fed with historical weather data and a business-as- usual uniform fertilization were also compared.The methods were implemented in a 23.4 ha field in Northern Italy, cropped to wheat and characterized by large soil variability in texture and organic matter content. Results indicated that the model-based approaches consistently led to higher yields, agronomic efficiencies and gross margins than the uniform N application rate. Furthermore, the proximal sensing-based approach allowed capturing of the spatial variability in crop N uptake and led to a substantial reduction of the spatial variability in yield and protein content. This study grounds the development of web-based software as a friendly tool to optimize the N variable rate application in winter cereals.

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

Cited by 10 publications

References 70 publications

Yield Response of Spring Maize under Future Climate and the Effects of Adaptation Measures in Northeast China

Yield Response of Spring Maize under Future Climate and the Effects of Adaptation Measures in Northeast China

Comprehensive evaluation of the DSSAT‐CSM‐CERES‐Wheat for simulating winter rye against multi‐environment data in Germany

Evaluation of different crop model-based approaches for variable rate nitrogen fertilization in winter wheat

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