The DSSAT crop modeling ecosystem

Hoogenboom, Gerrit; Porter, Cheryl; Boote, Kenneth J.; Shelia, Vakhtang; Wilkens, Paul W.; Singh, Upendra; White, Jeffrey W.; Asseng, Senthold; Lizaso, Jon; Moreno, Lamilla; Pavan, Willingthon; Ogoshi, Richard; Hunt, L. A.; Tsuji, Gordon Y.; Jones, James W.; Usda-Ars, Usa

doi:10.19103/as.2019.0061.10

Cited by 335 publications

(359 citation statements)

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“…The cropping system model (CSM) CERES-Maize [48], one of 42 crop simulation models within the Decision Support System for Agrotechnology Transfer (DSSAT) v. 4.7.5 [25,49], was used for this study. The DSSAT incorporates the dynamics of soil-plant-atmosphere interactions and simulates maize growth, development, and yield as functions of genotype, weather, soil, and crop management information [25].…”

Section: Ceres-maize Modelmentioning

confidence: 99%

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

et al. 2019

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Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach.

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Section: Ceres-maize Modelmentioning

confidence: 99%

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

et al. 2019

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“…Depending upon the aim of the model following are the different classes of crop simulation models,  Decision Support System for Agrotechnology Transfer (DSSAT) Models [23,24]: It is a software application which has crop simulation model for 42 different crops (Version 4.7). The input data to this software is daily weather data, soil surface and profile information, and detailed crop management with output as experimental data so that farmers can compare the predicted and actual output.…”

Section: Types Of Crop Simulation Modelsmentioning

confidence: 99%

“…The input data to this software is daily weather data, soil surface and profile information, and detailed crop management with output as experimental data so that farmers can compare the predicted and actual output. With the help of DSSAT farmers can avail the information on crops and new farm practices to achieve the high crop yield [23,24].…”

Section: Types Of Crop Simulation Modelsmentioning

confidence: 99%

Agriculture Crop Simulation Models Using Computational Intelligence

Kosamkar¹,

Kulkarni²

2019

IJCET

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Variation in the climatic conditions is the major hurdle in the Agriculture sector to attain high crop yield. The Crop simulation models portray the stage-wise growth of crop with the respective environment condition. The crop simulation models help the farmer to make better decisions for improving the crop yield. Artificial Intelligence, Data mining and Computational Intelligent are becoming more prominent in the agriculture field for decision making because of emerging technology such as GIS, Satellite data and remote sensing data in agriculture. This paper reviews information on crop simulation models using computational intelligence and their application. It also reviews the different types of crop simulation models and their limitation in Agriculture. It also discusses the different crop simulation models in details. Considering the emerging technology in the agriculture field we discussed the future trends of crop simulation models.

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“…This equation is primarily based on temperature and includes extraterrestrial radiation. Yet, many crop models do not use any of the FAO‐recommended equations but rather use the Priestley‐Taylor approach: LPJ‐GUESS (Smith et al, ), LPJmL (Bondeau et al, ), APSIM (Keating et al, ), GLAM (Challinor et al, ), PEGASUS (Deryng et al, ), STICS (Brisson et al, ) and DSSAT (Jones et al, ; Hoogenboom et al, ). Similar to the PM equation, the Priestley‐Taylor (PT) equation is based on net radiation, but it does not include wind speed or relative humidity.…”

Section: Introductionmentioning

confidence: 99%

Impact of ET₀ method on the simulation of historical and future crop yields: a case study of millet growth in Senegal

Ramarohetra¹,

Sultan

2017

Intl Journal of Climatology

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The reference evapotranspiration (ET 0 ) is an integrated climatic variable from which many crop models derive simulated crop yields. In most of these models, different equations are parameterized leaving the choice of the equation to the user. However, the impact of the choice of the ET 0 equations on crop yield prediction has been little studied.The present study proposes a sensitivity analysis of the impact of the choice of the ET 0 equation on simulated millet yields using SARRA-H crop model over 12 Senegalese stations representative of the Sudano-Sahelian climate conditions of West Africa.Priestley-Taylor, a modified Priestley-Taylor and Hargreaves equations lead to simulated yields up to 19% than those calculated using the Penman-Monteith equation. Despite high biases in wind speed, among the tested methods, the Penman-Monteith method remains the most robust to derive ET 0 and yield over the major part of Senegal, Hargreaves equation being more appropriated under dry climates. The choice of ET 0 formulation introduces uncertainties representing 8% of baseline yield regardless of precipitation changes; for wet conditions these uncertainties approach 30% of the overall climate change impact. The choice of ET 0 equation is increasingly important, with local temperature changes out to 4 ∘ C, while extreme changes above 6 ∘ C depend less on the ET 0 equation.

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The DSSAT crop modeling ecosystem

Cited by 335 publications

References 0 publications

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

Agriculture Crop Simulation Models Using Computational Intelligence

Impact of ET₀ method on the simulation of historical and future crop yields: a case study of millet growth in Senegal

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The DSSAT crop modeling ecosystem

Cited by 335 publications

References 0 publications

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

Agriculture Crop Simulation Models Using Computational Intelligence

Impact of ET0 method on the simulation of historical and future crop yields: a case study of millet growth in Senegal

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Product

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Impact of ET₀ method on the simulation of historical and future crop yields: a case study of millet growth in Senegal