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Environment Systems and Decisions

, Volume 38, Issue 4, pp 458–470 | Cite as

Hierarchical modeling of seed variety yields and decision making for future planting plans

  • Huaiyang Zhong
  • Xiaocheng Li
  • David Lobell
  • Stefano Ermon
  • Margaret L. Brandeau
Article
  • 57 Downloads

Abstract

Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced. This prediction mechanism is then integrated with a weather forecasting model and three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. The model was applied to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. The prediction model achieved a median absolute error of 235 kg/ha and thus provides good estimates for input into the decision models. The decision models identified the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer’s risk aversion level. More generally, the models can support farmers in decision making about which seed varieties to plant.

Keywords

Crop selection Yield prediction Hierarchical modeling Machine learning Random forest Stochastic decision model 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Australian Center for Precision Agriculture (2010) A general introduction to precision agriculture. http://www.agriprecisione.it/wp-content/uploads/2010/11/general_introduction_to_precision_agriculture.pdf
  2. Bunge J (2014) Big data comes to the farm, sowing mistrust. Wall Str J. https://www.wsj.com/articles/no-headline-available-1393372266?tesla=y
  3. Food and Agriculture Organization of the United Nations (2015) Soybean worldwide production. http://www.fao.org/faostat/en/#data/QC/visualize
  4. Gandhi N, Petkar O, Armstrong LJ (2016) Rice crop yield prediction using artificial neural networks. IEEE Technol Innov ICT Agric Rural Dev 2016:105–110.  https://doi.org/10.1109/TIAR.2016.7801222 CrossRefGoogle Scholar
  5. Institute for Operations Research and the Management Sciences (2016) Syngenta crop challenge in analytics. https://www.ideaconnection.com/syngenta-crop-challenge/challenge.php
  6. International Food Policy Research Institute (2017) Food security. http://www.ifpri.org/topic/food-security
  7. Knox SW (2018) Machine learning: a concise introduction. Wiley series in probability and statistics. Wiley, HobokenCrossRefGoogle Scholar
  8. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira E, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25. Curran Associates, Inc., pp 1097–1105. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
  9. Kumar R, Singh MP, Kumar P, Singh JP (2015) Crop selection method to maximize crop yield rate using machine learning technique. In: 2015 international conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM), pp 138–145Google Scholar
  10. Rajak RK, Pawar A, Pendke M, Shinde P, Rathod S, Devare A (2017) Crop recommendation system to maximize crop yield using machine learning technique. Int Res J Eng Technol 4(12):950–953Google Scholar
  11. Sujjaviriyasup T, Pitiruek K (2013) Agricultural product forecasting using machine learning approach. Int J Math Anal 7(38):1869–1875CrossRefGoogle Scholar
  12. Syngenta (2016) Crop challenge winners announced. http://www.syngenta-us.com/thrive/news/crop-challenge-winners.html
  13. United Nations (2015) Sustainable development goals. 17 goals to transform our world. http://www.un.org/sustainabledevelopment/sustainable-development-goals/
  14. World Food Programme (2017) Zero hunger. http://www1.wfp.org/zero-hunger

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Huaiyang Zhong
    • 1
  • Xiaocheng Li
    • 1
  • David Lobell
    • 2
  • Stefano Ermon
    • 3
  • Margaret L. Brandeau
    • 1
  1. 1.Department of Management Science and EngineeringStanford UniversityStanfordUSA
  2. 2.Department of Earth System ScienceStanford UniversityStanfordUSA
  3. 3.Computer Science DepartmentStanford UniversityStanfordUSA

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