IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i7p2917-d342053.html
   My bibliography  Save this article

Recovering Forecast Distributions of Crop Composition: Method and Application to Kentucky Agriculture

Author

Listed:
  • GwanSeon Kim

    (College of Agriculture, Arkansas State University, Jonesboro, AR 72467, USA)

  • Mehdi Nemati

    (School of Public Policy, University of California, Riverside, CA 92521, USA)

  • Steven Buck

    (Department of Agricultural Economics, University of Kentucky, Lexington, KY 40546, USA)

  • Nicholas Pates

    (Department of Agricultural Economics, University of Kentucky, Lexington, KY 40546, USA)

  • Tyler Mark

    (Department of Agricultural Economics, University of Kentucky, Lexington, KY 40546, USA)

Abstract

This paper proposes a novel application of the multinomial logit (MNL) model using Cropland Data Layer and field-level boundaries to estimate crop transition probabilities, which are used to generate forecast distributions of total acreage for five major crops produced in the state of Kentucky. These forecasts distributions have a wide range of applications that, besides providing interim acreage estimates ahead of the June Acreage Survey, can inform the ability of producers to incorporate new crops in the land-use rotation, investments in location-specific capital and input distribution as well informing the likelihood of adverse water quality events from nutrient run-off.

Suggested Citation

  • GwanSeon Kim & Mehdi Nemati & Steven Buck & Nicholas Pates & Tyler Mark, 2020. "Recovering Forecast Distributions of Crop Composition: Method and Application to Kentucky Agriculture," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2917-:d:342053
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/7/2917/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/7/2917/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. JunJie Wu & Richard M. Adams & Catherine L. Kling & Katsuya Tanaka, 2004. "From Microlevel Decisions to Landscape Changes: An Assessment of Agricultural Conservation Policies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 26-41.
    2. Vogel, Fred A. & Bange, Gerald A., 1999. "Understanding USDA Crop Forecasts," USDA Miscellaneous 320799, United States Department of Agriculture.
    3. Good, Darrel L. & Irwin, Scott H., 2011. "USDA Corn and Soybean Acreage Estimates and Yield Forecasts: Dispelling Myths and Misunderstandings," Marketing and Outlook Briefs 183528, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    4. Hendricks, Nathan P. & Sinnathamby, Sumathy & Douglas-Mankin, Kyle & Smith, Aaron & Sumner, Daniel A. & Earnhart, Dietrich H., 2014. "The environmental effects of crop price increases: Nitrogen losses in the U.S. Corn Belt," Journal of Environmental Economics and Management, Elsevier, vol. 68(3), pages 507-526.
    5. Douglas J. Miller, 1999. "An Econometric Analysis of the Costs of Sequestering Carbon in Forests," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 812-824.
    6. Good , Darrel & Irwin, Scott, 2011. "USDA Corn and Soybean Acreage Estimates and Yield Forecasts: Dispelling Myths and Misunderstandings," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 1, March.
    7. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, November.
    8. Aurbacher, Joachim & Dabbert, Stephan, 2011. "Generating crop sequences in land-use models using maximum entropy and Markov chains," Agricultural Systems, Elsevier, vol. 104(6), pages 470-479, July.
    9. Jayson L. Lusk & Jesse Tack & Nathan P. Hendricks, 2018. "Heterogeneous Yield Impacts from Adoption of Genetically Engineered Corn and the Importance of Controlling for Weather," NBER Chapters, in: Agricultural Productivity and Producer Behavior, pages 11-39, National Bureau of Economic Research, Inc.
    10. Castellazzi, M.S. & Wood, G.A. & Burgess, P.J. & Morris, J. & Conrad, K.F. & Perry, J.N., 2008. "A systematic representation of crop rotations," Agricultural Systems, Elsevier, vol. 97(1-2), pages 26-33, April.
    11. Thornton, P. K. & Jones, P. G., 1998. "A conceptual approach to dynamic agricultural land-use modelling," Agricultural Systems, Elsevier, vol. 57(4), pages 505-521, August.
    12. Michael K. Adjemian & Aaron Smith, 2012. "Using USDA Forecasts to Estimate the Price Flexibility of Demand for Agricultural Commodities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(4), pages 978-995.
    13. Johansson, Robert & Effland, Anne & Coble, Keith, 2017. "Falling Response Rates to USDA Crop Surveys: Why It Matters," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 7, January.
    14. Good, Darrel & Irwin, Scott, 2015. "Progression of USDA Corn and Soybean Acreage Estimates and Prospects for Final Estimates for 2015," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 5, October.
    15. Ian W. Hardie & Peter J. Parks, 1997. "Land Use with Heterogeneous Land Quality: An Application of an Area Base Model," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 299-310.
    16. Egelkraut, Thorsten M. & Garcia, Philip & Irwin, Scott H. & Good, Darrel L., 2003. "An Evaluation of Crop Forecast Accuracy for Corn and Soybeans: USDA and Private Information Agencies," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 35(1), pages 1-17, April.
    17. Nathan P. Hendricks & Aaron Smith & Daniel A. Sumner, 2014. "Crop Supply Dynamics and the Illusion of Partial Adjustment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(5), pages 1469-1491.
    18. Karlen, D. L. & Hurley, E. & Andrews, S & Cambardella, C. & Meek, M. & Duffy, Michael & Mallarenio, A., 2006. "Crop Rotation Effects on Soil Quality at Three Northern Corn/Soybean Locations," Staff General Research Papers Archive 12580, Iowa State University, Department of Economics.
    19. Matis, J. H. & Saito, T. & Grant, W. E. & Iwig, W. C. & Ritchie, J. T., 1985. "A Markov chain approach to crop yield forecasting," Agricultural Systems, Elsevier, vol. 18(3), pages 171-187.
    Full references (including those not matched with items on IDEAS)

    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. Berna Karali & Scott H. Irwin & Olga Isengildina‐Massa, 2020. "Supply Fundamentals and Grain Futures Price Movements," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 548-568, March.
    2. Catherine L. Kling & Raymond W. Arritt & Gray Calhoun & David A. Keiser, 2016. "Research Needs and Challenges in the FEW System: Coupling Economic Models with Agronomic, Hydrologic, and Bioenergy Models for Sustainable Food, Energy, and Water Systems," Center for Agricultural and Rural Development (CARD) Publications 16-wp563, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    3. Isengildina, Olga & Irwin, Scott H. & Good, Darrel L., 2013. "Do Big Crops Get Bigger and Small Crops Get Smaller? Further Evidence on Smoothing in U.S. Department of Agriculture Forecasts," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 45(1), pages 1-13, February.
    4. Langpap, Christian & Wu, JunJie, 2014. "Impacts of Changes in Federal Crop Insurance Programs on Land Use and Environmental Quality," 2014 AAEA: Crop Insurance and the 2014 Farm Bill Symposium: Implementing Change in U.S. Agricultural Policy, October 8-9, 2014, Louisville, KY 186643, Agricultural and Applied Economics Association.
    5. Karali, Berna & Isengildina-Massa, Olga & Irwin, Scott H. & Adjemian, Michael K. & Johansson, Robert, 2019. "Are USDA reports still news to changing crop markets?," Food Policy, Elsevier, vol. 84(C), pages 66-76.
    6. Sandler, Austin M. & Rashford, Benjamin S., 2018. "Misclassification error in satellite imagery data: Implications for empirical land-use models," Land Use Policy, Elsevier, vol. 75(C), pages 530-537.
    7. Nicholas J. Pates & Nathan P. Hendricks, 2021. "Fields from Afar: Evidence of Heterogeneity in United States Corn Rotational Response from Remote Sensing Data," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1759-1782, October.
    8. Roger Claassen & Christian Langpap & JunJie Wu, 2017. "Impacts of Federal Crop Insurance on Land Use and Environmental Quality," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(3), pages 592-613.
    9. Hendricks, Nathan P. & Er, Emrah, 2018. "Changes in cropland area in the United States and the role of CRP," Food Policy, Elsevier, vol. 75(C), pages 15-23.
    10. Hendricks, Nathan P. & Janzen, Joseph P. & Smith, Aaron, 2013. "Futures Prices in Supply Analysis Reconsidered," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150696, Agricultural and Applied Economics Association.
    11. Gaurav Arora & Hongli Feng & Christopher J. Anderson & David A. Hennessy, 2020. "Evidence of climate change impacts on crop comparative advantage and land use," Agricultural Economics, International Association of Agricultural Economists, vol. 51(2), pages 221-236, March.
    12. Isengildina-Massa, Olga & Cao, Xiang & Karali, Berna & Irwin, Scott H. & Adjemian, Michael & Johansson, Robert C., 2021. "When does USDA information have the most impact on crop and livestock markets?," Journal of Commodity Markets, Elsevier, vol. 22(C).
    13. CARPENTIER, Alain & GOHIN, Alexandre & SCKOKAI, Paolo & THOMAS, Alban, 2015. "Economic modelling of agricultural production: past advances and new challenges," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement (RAEStud), Institut National de la Recherche Agronomique (INRA), vol. 96(1), March.
    14. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    15. An N. Q. Cao & Michel A. Robe, 2022. "Market uncertainty and sentiment around USDA announcements," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(2), pages 250-275, February.
    16. Ji, Yongjie & Rabotyagov, Sergey & Kling, Catherine L., 2014. "Crop Choice and Rotational Effects: A Dynamic Model of Land Use in Iowa in Recent Years," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170366, Agricultural and Applied Economics Association.
    17. Ji, Yongjie & Rabotyagov, sergey & Valcu-Lisman, Adriana, 2015. "Estimating Adoption of Cover Crops Using Preferences Revealed by a Dynamic Crop Choice Model," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205799, Agricultural and Applied Economics Association.
    18. MacEwan, Duncan & Howitt, Richard E., 2012. "Behavioral Salinity Response: Estimating Salinity Policies from Remote Sensed Micro-Data," 2012 Conference (56th), February 7-10, 2012, Fremantle, Australia 124331, Australian Agricultural and Resource Economics Society.
    19. Fishe, Raymond P.H. & Smith, Aaron, 2019. "Do speculators drive commodity prices away from supply and demand fundamentals?," Journal of Commodity Markets, Elsevier, vol. 15(C), pages 1-1.
    20. Konstantinos Metaxoglou & Aaron Smith, 2020. "Productivity Spillovers From Pollution Reduction: Reducing Coal Use Increases Crop Yields," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 259-280, January.

    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:12:y:2020:i:7:p:2917-:d:342053. 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.