Machine learning and optimization based decision-support tool for seed variety selection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Link
https://link.springer.com/content/pdf/10.1007/s10479-022-04995-8.pdf
Reference58 articles.
1. Awoye, O. A. (2016). Markowitz minimum variance portfolio optimization using new machine learning methods. PHD Thesis.
2. Bansal, S., Gutierrez, G. J., & Keiser, J. R. (2017). Using experts’ noisy quantile judgments to quantify risks: Theory and application to agri-business. Operations Research, 65(5), 1115–1130.
3. Bansal, S., & Nagarajan, M. (2017). Product portfolio management with production flexibility in agribusiness. Operations Research, 65(4), 914–930.
4. Barkley, A., Peterson, H. H., & Shroyer, J. (2010). Wheat variety selection to maximize returns and minimize risk: An application of portfolio theory. Journal of Agricultural and Applied Economics, 42(1), 39–55.
5. Barkley, A., Tack, J., Nalley, L. L., Bergtold, J., Bowden, R., & Fritz, A. (2014). Weather, disease, and wheat breeding effects on Kansas wheat varietal yields, 1985 to 2011. Agronomy Journal, 106(1), 227–235.
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