IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p7768-d847946.html
   My bibliography  Save this article

Impacts of Harvesting Age and Pricing Schemes on Economic Sustainability of Cassava Farmers in Thailand under Market Uncertainty

Author

Listed:
  • Warut Pannakkong

    (School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand)

  • Parthana Parthanadee

    (Department of Agro-Industrial Technology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand)

  • Jirachai Buddhakulsomsiri

    (School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand)

Abstract

This paper involves an analysis to determine appropriate cassava harvest practices and choose a pricing scheme between farmers and factories, cassava yards, and collectors in Thailand. Harvest practices represent all activities from land preparation to harvest. A key decision that governs the amount of resources required during cassava life cycle is the cassava’s harvesting age. The harvesting age can be from eight to 18 months in two patterns: fixed age, e.g., harvest every 12 months, and variable age, e.g., harvest at an age between 10 and 14 months. After harvesting, there are two common pricing schemes to consider, which are weight-based and starch-content-based. Factors that affect the two decisions made by Thai farmers at a given time are the market price, which highly varies within a season and between seasons, and yields in terms of weight and starch content, both of which change with cassava’s age and/or harvest month. Economic sustainability measure for Thai farmers is the average monthly profit that the farmers gain over cassava harvest cycle under uncertain market price. To handle uncertainties, a simulation model is constructed to imitate cassava planting activities from cultivation to harvest. The purpose is to evaluate various harvesting ages and two pricing schemes under uncertain cassava market prices. Market prices in 15 seasons (2006–2021) are grouped using the k -mean clustering into four price scenarios. As cassava grows in the simulation, the required resources are consumed until the decisions on harvesting time and pricing scheme are made with estimated selling probability under different price scenarios and uncertainty in cassava yield. Through simulation, harvesting age and pricing scheme that are most profitable and robust-to-system-variation are determined. Finally, a guideline for Thai farmers to choose a pricing scheme is developed based on the sensitivity analysis of the simulation model.

Suggested Citation

  • Warut Pannakkong & Parthana Parthanadee & Jirachai Buddhakulsomsiri, 2022. "Impacts of Harvesting Age and Pricing Schemes on Economic Sustainability of Cassava Farmers in Thailand under Market Uncertainty," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7768-:d:847946
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/7768/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/13/7768/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Simon Lidberg & Tehseen Aslam & Leif Pehrsson & Amos H. C. Ng, 2020. "Optimizing real-world factory flows using aggregated discrete event simulation modelling," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 888-912, December.
    2. Stehfest, Elke & Heistermann, Maik & Priess, Joerg A. & Ojima, Dennis S. & Alcamo, Joseph, 2007. "Simulation of global crop production with the ecosystem model DayCent," Ecological Modelling, Elsevier, vol. 209(2), pages 203-219.
    3. Yu, Suiran & Tao, Jing, 2009. "Energy efficiency assessment by life cycle simulation of cassava-based fuel ethanol for automotive use in Chinese Guangxi context," Energy, Elsevier, vol. 34(1), pages 22-31.
    4. Mobini, Mahdi & Sowlati, Taraneh & Sokhansanj, Shahab, 2011. "Forest biomass supply logistics for a power plant using the discrete-event simulation approach," Applied Energy, Elsevier, vol. 88(4), pages 1241-1250, April.
    5. Ibrahim L. Kadigi & Khamaldin D. Mutabazi & Damas Philip & James W. Richardson & Jean-Claude Bizimana & Winfred Mbungu & Henry F. Mahoo & Stefan Sieber, 2020. "An Economic Comparison between Alternative Rice Farming Systems in Tanzania Using a Monte Carlo Simulation Approach," Sustainability, MDPI, vol. 12(16), pages 1-22, August.
    6. Abdulmalek, Fawaz A. & Rajgopal, Jayant, 2007. "Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study," International Journal of Production Economics, Elsevier, vol. 107(1), pages 223-236, May.
    7. Linker, Raphael & Ioslovich, Ilya & Sylaios, Georgios & Plauborg, Finn & Battilani, Adriano, 2016. "Optimal model-based deficit irrigation scheduling using AquaCrop: A simulation study with cotton, potato and tomato," Agricultural Water Management, Elsevier, vol. 163(C), pages 236-243.
    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. Apichaya Lilavanichakul & Rangrong Yoksan, 2023. "Development of Bioplastics from Cassava toward the Sustainability of Cassava Value Chain in Thailand," Sustainability, MDPI, vol. 15(20), pages 1-21, October.

    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. 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).
    2. Kelly, T.D. & Foster, T. & Schultz, David M., 2023. "Assessing the value of adapting irrigation strategies within the season," Agricultural Water Management, Elsevier, vol. 275(C).
    3. Ran, Hui & Kang, Shaozhong & Li, Fusheng & Du, Taisheng & Tong, Ling & Li, Sien & Ding, Risheng & Zhang, Xiaotao, 2018. "Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 438-450.
    4. Sacchelli, Sandro & De Meo, Isabella & Paletto, Alessandro, 2013. "Bioenergy production and forest multifunctionality: A trade-off analysis using multiscale GIS model in a case study in Italy," Applied Energy, Elsevier, vol. 104(C), pages 10-20.
    5. Jayaram, Jayanth & Das, Ajay & Nicolae, Mariana, 2010. "Looking beyond the obvious: Unraveling the Toyota production system," International Journal of Production Economics, Elsevier, vol. 128(1), pages 280-291, November.
    6. Ifeoluwa Elemure & Hom Nath Dhakal & Michel Leseure & Jovana Radulovic, 2023. "Integration of Lean Green and Sustainability in Manufacturing: A Review on Current State and Future Perspectives," Sustainability, MDPI, vol. 15(13), pages 1-25, June.
    7. Phemelo Tamasiga & Helen Onyeaka & Adenike Akinsemolu & Malebogo Bakwena, 2023. "The Inter-Relationship between Climate Change, Inequality, Poverty and Food Security in Africa: A Bibliometric Review and Content Analysis Approach," Sustainability, MDPI, vol. 15(7), pages 1-35, March.
    8. Jiang, Dong & Wang, Qian & Ding, Fangyu & Fu, Jingying & Hao, Mengmeng, 2019. "Potential marginal land resources of cassava worldwide: A data-driven analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 167-173.
    9. Abir Fatallah & Julie Stal-Le Cardinal & Jean-Louis Ermine & Jean-Claude Bocquet, 2010. "Using product design methods in designing and validating entreprise models," Post-Print hal-02448273, HAL.
    10. Zhang, Qin & Zhou, Dequn & Zhou, Peng & Ding, Hao, 2013. "Cost Analysis of straw-based power generation in Jiangsu Province, China," Applied Energy, Elsevier, vol. 102(C), pages 785-793.
    11. Branislav Micieta & Honorata Howaniec & Vladimíra Binasova & Marta Kasajova & Miroslav Fusko, 2021. "Increasing Work Efficiency in a Manufacturing Setting Using Gemba Walk," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 4), pages 601-620.
    12. Eriksson, Anders & Eliasson, Lars & Sikanen, Lauri & Hansson, Per-Anders & Jirjis, Raida, 2017. "Evaluation of delivery strategies for forest fuels applying a model for Weather-driven Analysis of Forest Fuel Systems (WAFFS)," Applied Energy, Elsevier, vol. 188(C), pages 420-430.
    13. Palander, Teijo & Haavikko, Hanna & Kärhä, Kalle, 2018. "Towards sustainable wood procurement in forest industry – The energy efficiency of larger and heavier vehicles in Finland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 100-118.
    14. Yang, Jia & Ren, Wei & Ouyang, Ying & Feng, Gary & Tao, Bo & Granger, Joshua J. & Poudel, Krishna P., 2019. "Projection of 21st century irrigation water requirement across the Lower Mississippi Alluvial Valley," Agricultural Water Management, Elsevier, vol. 217(C), pages 60-72.
    15. Fritz, Steffen & See, Linda & Bayas, Juan Carlos Laso & Waldner, François & Jacques, Damien & Becker-Reshef, Inbal & Whitcraft, Alyssa & Baruth, Bettina & Bonifacio, Rogerio & Crutchfield, Jim & Rembo, 2019. "A comparison of global agricultural monitoring systems and current gaps," Agricultural Systems, Elsevier, vol. 168(C), pages 258-272.
    16. Luoman Pu & Shuwen Zhang & Jiuchun Yang & Liping Chang & Shuting Bai, 2019. "Spatio-Temporal Dynamics of Maize Potential Yield and Yield Gaps in Northeast China from 1990 to 2015," IJERPH, MDPI, vol. 16(7), pages 1-18, April.
    17. Sultana, Arifa & Kumar, Amit, 2012. "Optimal siting and size of bioenergy facilities using geographic information system," Applied Energy, Elsevier, vol. 94(C), pages 192-201.
    18. Mosayeb Dashtpeyma & Reza Ghodsi, 2021. "Forest Biomass and Bioenergy Supply Chain Resilience: A Systematic Literature Review on the Barriers and Enablers," Sustainability, MDPI, vol. 13(12), pages 1-21, June.
    19. Cheng, Kun & Ogle, Stephen M. & Parton, William J. & Pan, Genxing, 2013. "Predicting methanogenesis from rice paddies using the DAYCENT ecosystem model," Ecological Modelling, Elsevier, vol. 261, pages 19-31.
    20. Chen, Mengting & Linker, Raphael & Wu, Conglin & Xie, Hua & Cui, Yuanlai & Luo, Yufeng & Lv, Xinwei & Zheng, Shizong, 2022. "Multi-objective optimization of rice irrigation modes using ACOP-Rice model and historical meteorological data," Agricultural Water Management, Elsevier, vol. 272(C).

    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:gam:jsusta:v:14:y:2022:i:13:p:7768-:d:847946. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.