IDEAS home Printed from https://ideas.repec.org/a/eee/agisys/v169y2019icp58-70.html
   My bibliography  Save this article

Modeling growth, development and yield of Sugarbeet using DSSAT

Author

Listed:
  • Anar, Mohammad J.
  • Lin, Zhulu
  • Hoogenboom, Gerrit
  • Shelia, Vakhtang
  • Batchelor, William D.
  • Teboh, Jasper M.
  • Ostlie, Michael
  • Schatz, Blaine G.
  • Khan, Mohamed

Abstract

Sugarbeet (Beta vulgaris) is considered as one of the most viable feedstock alternatives to maize for biofuel production since herbicide resistant sugarbeet was deregulated by the United States Department of Agriculture in 2012. So far, only a few sugarbeet simulation models have been developed and these models are limited to application for local regions. The Decision Support System for Agrotechnology Transfer (DSSAT) provides a common framework for a cropping system study and currently has crop modules for >40 crops. However, DSSAT currently does not include a model for sugarbeet. In this study, the Crop and Environment REsource Synthesis (CERES) Beet model was modified and incorporated into the current version of the Cropping System Model (CSM) to simulate growth, development, and yield of sugarbeet. The PEST optimizer was used for parameter estimation, transferability evaluation, and predictive uncertainty analysis. The sugarbeet model was evaluated with two sets of experimental data collected in two different regions and under different environmental conditions; one in Romania (Southeastern Europe) during 1997–1998 and the other in North Dakota, US (North America) during 2014–2016. After model calibration for specific cultivars, the CSM-CERES-Beet model performed well for the simulation of leaf area index, leaf number, leaf or top weight, and root weight for both datasets (NSE = 0.144–0.976, rRMSE = 0.127–1.014). Uncertainty analysis revealed that the calibrated CSM-CERES-Beet consistently over-predicted leaf number with false confidence, although measured leaf number also showed a significant variability. The model was successfully applied for predicting yield for six different sugarbeet cultivars grown in North Dakota during the 2014 to 2016 growing seasons. CSM-CERES-Beet could be applied for predicting sugarbeet yield for different soil and climatic conditions and various management scenarios for the Red River Valley in the US and other regions with environmental conditions favorable for sugarbeet production.

Suggested Citation

  • Anar, Mohammad J. & Lin, Zhulu & Hoogenboom, Gerrit & Shelia, Vakhtang & Batchelor, William D. & Teboh, Jasper M. & Ostlie, Michael & Schatz, Blaine G. & Khan, Mohamed, 2019. "Modeling growth, development and yield of Sugarbeet using DSSAT," Agricultural Systems, Elsevier, vol. 169(C), pages 58-70.
    • Handle: RePEc:eee:agisys:v:169:y:2019:i:c:p:58-70
    DOI: 10.1016/j.agsy.2018.11.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308521X18303597
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agsy.2018.11.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hunt, L. A. & White, J. W. & Hoogenboom, G., 2001. "Agronomic data: advances in documentation and protocols for exchange and use," Agricultural Systems, Elsevier, vol. 70(2-3), pages 477-492.
    2. Vandendriessche, H. J., 2000. "A model of growth and sugar accumulation of sugar beet for potential production conditions: SUBEMOpoII. Model performance," Agricultural Systems, Elsevier, vol. 64(1), pages 21-35, April.
    3. Yang, J.M. & Yang, J.Y. & Liu, S. & Hoogenboom, G., 2014. "An evaluation of the statistical methods for testing the performance of crop models with observed data," Agricultural Systems, Elsevier, vol. 127(C), pages 81-89.
    4. Baey, Charlotte & Didier, Anne & Lemaire, Sébastien & Maupas, Fabienne & Cournède, Paul-Henry, 2014. "Parametrization of five classical plant growth models applied to sugar beet and comparison of their predictive capacity on root yield and total biomass," Ecological Modelling, Elsevier, vol. 290(C), pages 11-20.
    5. Timsina, J. & Humphreys, E., 2006. "Performance of CERES-Rice and CERES-Wheat models in rice-wheat systems: A review," Agricultural Systems, Elsevier, vol. 90(1-3), pages 5-31, October.
    6. Ma, L. & Hoogenboom, G. & Ahuja, L.R. & Ascough II, J.C. & Saseendran, S.A., 2006. "Evaluation of the RZWQM-CERES-Maize hybrid model for maize production," Agricultural Systems, Elsevier, vol. 87(3), pages 274-295, March.
    7. Li, Zhuo Ting & Yang, J.Y. & Drury, C.F. & Hoogenboom, G., 2015. "Evaluation of the DSSAT-CSM for simulating yield and soil organic C and N of a long-term maize and wheat rotation experiment in the Loess Plateau of Northwestern China," Agricultural Systems, Elsevier, vol. 135(C), pages 90-104.
    8. Hettinga, W.G. & Junginger, H.M. & Dekker, S.C. & Hoogwijk, M. & McAloon, A.J. & Hicks, K.B., 2009. "Understanding the reductions in US corn ethanol production costs: An experience curve approach," Energy Policy, Elsevier, vol. 37(1), pages 190-203, January.
    9. Miyake, Saori & Smith, Carl & Peterson, Ann & McAlpine, Clive & Renouf, Marguerite & Waters, David, 2015. "Environmental implications of using ‘underutilised agricultural land’ for future bioenergy crop production," Agricultural Systems, Elsevier, vol. 139(C), pages 180-195.
    10. Khaembah, E.N. & Brown, H.E. & Zyskowski, R. & Chakwizira, E. & de Ruiter, J.M. & Teixeira, E.I., 2017. "Development of a fodder beet potential yield model in the next generation APSIM," Agricultural Systems, Elsevier, vol. 158(C), pages 23-38.
    11. Vandendriessche, H. J., 2000. "A model of growth and sugar accumulation of sugar beet for potential production conditions: SUBEMOpo I. Theory and model structure," Agricultural Systems, Elsevier, vol. 64(1), pages 1-19, April.
    12. Rinaldi, Michele & Ventrella, Domenico & Gagliano, Caterina, 2007. "Comparison of nitrogen and irrigation strategies in tomato using CROPGRO model. A case study from Southern Italy," Agricultural Water Management, Elsevier, vol. 87(1), pages 91-105, January.
    13. Foteinis, Spyros & Kouloumpis, Victor & Tsoutsos, Theocharis, 2011. "Life cycle analysis for bioethanol production from sugar beet crops in Greece," Energy Policy, Elsevier, vol. 39(9), pages 4834-4841, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mompremier, R. & Her, Y. & Hoogenboom, G. & Migliaccio, K. & Muñoz-Carpena, R. & Brym, Z. & Colbert, R.W. & Jeune, W., 2021. "Modeling the response of dry bean yield to irrigation water availability controlled by watershed hydrology," Agricultural Water Management, Elsevier, vol. 243(C).
    2. Prabakaran, G. & Vaithiyanathan, D. & Ganesan, Madhavi, 2021. "FPGA based effective agriculture productivity prediction system using fuzzy support vector machine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 1-16.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khaembah, E.N. & Brown, H.E. & Zyskowski, R. & Chakwizira, E. & de Ruiter, J.M. & Teixeira, E.I., 2017. "Development of a fodder beet potential yield model in the next generation APSIM," Agricultural Systems, Elsevier, vol. 158(C), pages 23-38.
    2. Hamidreza Kamali & Shahrokh Zand-Parsa, 2017. "Estimation of Sugar Beet Yield and its Dry Matter Partitioning Under Different Irrigation and Nitrogen Levels," Modern Applied Science, Canadian Center of Science and Education, vol. 11(1), pages 143-143, September.
    3. Liang, Hao & Lv, Haofeng & Batchelor, William D. & Lian, Xiaojuan & Wang, Zhengxiang & Lin, Shan & Hu, Kelin, 2020. "Simulating nitrate and DON leaching to optimize water and N management practices for greenhouse vegetable production systems," Agricultural Water Management, Elsevier, vol. 241(C).
    4. Anshuman Gunawat & Devesh Sharma & Aditya Sharma & Swatantra Kumar Dubey, 2022. "Assessment of climate change impact and potential adaptation measures on wheat yield using the DSSAT model in the semi-arid environment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 2077-2096, March.
    5. Kadiyala, M.D.M. & Jones, J.W. & Mylavarapu, R.S. & Li, Y.C. & Reddy, M.D., 2015. "Identifying irrigation and nitrogen best management practices for aerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models," Agricultural Water Management, Elsevier, vol. 149(C), pages 23-32.
    6. Malik, Wafa & Dechmi, Farida, 2019. "DSSAT modelling for best irrigation management practices assessment under Mediterranean conditions," Agricultural Water Management, Elsevier, vol. 216(C), pages 27-43.
    7. Liang, Hao & Qi, Zhiming & Hu, Kelin & Li, Baoguo & Prasher, Shiv O., 2018. "Modelling subsurface drainage and nitrogen losses from artificially drained cropland using coupled DRAINMOD and WHCNS models," Agricultural Water Management, Elsevier, vol. 195(C), pages 201-210.
    8. Li, Zhuoting & Yang, J.Y. & Drury, C.F. & Yang, X.M. & Reynolds, W.D. & Li, Xiaogang & Hu, Chunsheng, 2017. "Evaluation of the DNDC model for simulating soil temperature, moisture and respiration from monoculture and rotational corn, soybean and winter wheat in Canada," Ecological Modelling, Elsevier, vol. 360(C), pages 230-243.
    9. Devkota, Mina & Devkota, Krishna Prasad & Kumar, Shiv, 2022. "Conservation agriculture improves agronomic, economic, and soil fertility indicators for a clay soil in a rainfed Mediterranean climate in Morocco," Agricultural Systems, Elsevier, vol. 201(C).
    10. De Laporte, Aaron V. & Ripplinger, David G., 2019. "The effects of site selection, opportunity costs and transportation costs on bioethanol production," Renewable Energy, Elsevier, vol. 131(C), pages 73-82.
    11. Singh, Anil Kumar & Tripathy, Rojalin & Chopra, Usha Kiran, 2008. "Evaluation of CERES-Wheat and CropSyst models for water-nitrogen interactions in wheat crop," Agricultural Water Management, Elsevier, vol. 95(7), pages 776-786, July.
    12. Liang, Hao & Chen, Qing & Liang, Bin & Hu, Kelin, 2020. "Modeling the effects of long-term reduced N application on soil N losses and yield in a greenhouse tomato production system," Agricultural Systems, Elsevier, vol. 185(C).
    13. Bao, Yawen & Hoogenboom, Gerrit & McClendon, Ron & Vellidis, George, 2017. "A comparison of the performance of the CSM-CERES-Maize and EPIC models using maize variety trial data," Agricultural Systems, Elsevier, vol. 150(C), pages 109-119.
    14. Li, Zhuo Ting & Yang, J.Y. & Drury, C.F. & Hoogenboom, G., 2015. "Evaluation of the DSSAT-CSM for simulating yield and soil organic C and N of a long-term maize and wheat rotation experiment in the Loess Plateau of Northwestern China," Agricultural Systems, Elsevier, vol. 135(C), pages 90-104.
    15. Devkota, Mina & Devkota, Krishna Prasad & Paudel, Gokul Prasad & Krupnik, Timothy J. & McDonald, Andrew James, 2024. "Opportunities to close wheat yield gaps in Nepal's Terai: Insights from field surveys, on-farm experiments, and simulation modeling," Agricultural Systems, Elsevier, vol. 213(C).
    16. Gupta, Rishabh & Mishra, Ashok, 2019. "Climate change induced impact and uncertainty of rice yield of agro-ecological zones of India," Agricultural Systems, Elsevier, vol. 173(C), pages 1-11.
    17. Wang, Haidong & Cheng, Minghui & Liao, Zhenqi & Guo, Jinjin & Zhang, Fucang & Fan, Junliang & Feng, Hao & Yang, Qiliang & Wu, Lifeng & Wang, Xiukang, 2023. "Performance evaluation of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under various drip fertigation regimes," Agricultural Water Management, Elsevier, vol. 276(C).
    18. Saseendran, S.A. & Ahuja, Lajpat R. & Ma, Liwang & Trout, Thomas J. & McMaster, Gregory S. & Nielsen, David C. & Ham, Jay M. & Andales, Allan A. & Halvorson, Ardel D. & Chávez, José L. & Fang, Quanxia, 2015. "Developing and normalizing average corn crop water production functions across years and locations using a system model," Agricultural Water Management, Elsevier, vol. 157(C), pages 65-77.
    19. Mompremier, R. & Her, Y. & Hoogenboom, G. & Migliaccio, K. & Muñoz-Carpena, R. & Brym, Z. & Colbert, R.W. & Jeune, W., 2021. "Modeling the response of dry bean yield to irrigation water availability controlled by watershed hydrology," Agricultural Water Management, Elsevier, vol. 243(C).
    20. Nasca, J.A. & Feldkamp, C.R. & Arroquy, J.I. & Colombatto, D., 2015. "Efficiency and stability in subtropical beef cattle grazing systems in the northwest of Argentina," Agricultural Systems, Elsevier, vol. 133(C), pages 85-96.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agisys:v:169:y:2019:i:c:p:58-70. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agsy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.