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Soybean varieties portfolio optimisation based on yield prediction

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Abstract
One of the biggest problems in agriculture is concerned with seed selection. Wrong choice of seed variety cannot be compensated with fertilisation, spraying or the use of mechanisation later in the season. The purpose of this work was to design the strategy for selecting soybean varieties that should be planted on the test farm in order to maximise yield in the following season, based on the knowledge acquired from heterogeneous historical data. We propose weighted histograms regression to predict the yield of different varieties and compare our method to conventional regression algorithms. Based on the predicted yield, we perform portfolio optimisation to come up with the optimal selection of seed varieties that is to be planted. Presented algorithms and results were produced within the Syngenta Crop Challenge. (C) 2016 Elsevier B.V. All rights reserved.
Keywords
ARTIFICIAL NEURAL-NETWORKS, WHEAT YIELD, CLASSIFICATION, SELECTION, MACHINE, MODEL, CORN, RISK, Yield prediction, Seed selection, Weighted histograms, Portfolio, optimization, Convex optimization

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Please use this url to cite or link to this publication:

MLA
Marko, Oskar, et al. “Soybean Varieties Portfolio Optimisation Based on Yield Prediction.” COMPUTERS AND ELECTRONICS IN AGRICULTURE, vol. 127, Elsevier, 2016, pp. 467–74.
APA
Marko, O., Brdar, S., Panic, M., Lugonja, P., & Crnojevic, V. (2016). Soybean varieties portfolio optimisation based on yield prediction. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 127, 467–474.
Chicago author-date
Marko, Oskar, Sanja Brdar, Marko Panic, Predrag Lugonja, and Vladimir Crnojevic. 2016. “Soybean Varieties Portfolio Optimisation Based on Yield Prediction.” COMPUTERS AND ELECTRONICS IN AGRICULTURE 127: 467–74.
Chicago author-date (all authors)
Marko, Oskar, Sanja Brdar, Marko Panic, Predrag Lugonja, and Vladimir Crnojevic. 2016. “Soybean Varieties Portfolio Optimisation Based on Yield Prediction.” COMPUTERS AND ELECTRONICS IN AGRICULTURE 127: 467–474.
Vancouver
1.
Marko O, Brdar S, Panic M, Lugonja P, Crnojevic V. Soybean varieties portfolio optimisation based on yield prediction. COMPUTERS AND ELECTRONICS IN AGRICULTURE. 2016;127:467–74.
IEEE
[1]
O. Marko, S. Brdar, M. Panic, P. Lugonja, and V. Crnojevic, “Soybean varieties portfolio optimisation based on yield prediction,” COMPUTERS AND ELECTRONICS IN AGRICULTURE, vol. 127, pp. 467–474, 2016.
@article{8587001,
  abstract     = {One of the biggest problems in agriculture is concerned with seed selection. Wrong choice of seed variety cannot be compensated with fertilisation, spraying or the use of mechanisation later in the season. The purpose of this work was to design the strategy for selecting soybean varieties that should be planted on the test farm in order to maximise yield in the following season, based on the knowledge acquired from heterogeneous historical data. We propose weighted histograms regression to predict the yield of different varieties and compare our method to conventional regression algorithms. Based on the predicted yield, we perform portfolio optimisation to come up with the optimal selection of seed varieties that is to be planted. Presented algorithms and results were produced within the Syngenta Crop Challenge. (C) 2016 Elsevier B.V. All rights reserved.},
  author       = {Marko, Oskar and Brdar, Sanja and Panic, Marko and Lugonja, Predrag and Crnojevic, Vladimir},
  issn         = {0168-1699},
  journal      = {COMPUTERS AND ELECTRONICS IN AGRICULTURE},
  keywords     = {ARTIFICIAL NEURAL-NETWORKS,WHEAT YIELD,CLASSIFICATION,SELECTION,MACHINE,MODEL,CORN,RISK,Yield prediction,Seed selection,Weighted histograms,Portfolio,optimization,Convex optimization},
  language     = {eng},
  pages        = {467--474},
  publisher    = {Elsevier},
  title        = {Soybean varieties portfolio optimisation based on yield prediction},
  url          = {http://dx.doi.org/10.1016/j.compag.2016.07.009},
  volume       = {127},
  year         = {2016},
}

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