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Experiences from using big datasets in the
American Midwest to optimise variety selection
and soybean yield prediction
Oskar Marko
ABOUT BIOSENSE
•R&D institute for IT in biosystems
•Serbia – leader in production of non-GMO soybean
•70 permanent staff
•Diversified income
•24 ongoing H2020 projects
•No 1 in Eastern Europe
ABOUT BIOSENSE
•R&D institute for IT in biosystems
•Serbia – leader in production of non-GMO soybean
•70 permanent staff
•Diversified income
•24 ongoing H2020 projects
•No 1 in Eastern Europe
ABOUT BIOSENSE
•Antares: €28M, 7 years
•European CoE for Advanced Technologies in Sustainable Agriculture
•People, equipment, accelerator, Sentinel data hub…
ABOUT BIOSENSE
•Antares: €28M, 7 years
•European CoE for Advanced Technologies in Sustainable Agriculture
•People, equipment, accelerator, Sentinel data hub…
2017 WINNERS
FAO: +70% by 2050
Increasing the yield not a problem, but comes at a price
Sustainability
Seed selection is the most efficient way of increasing the yield
AGRICULTURAL CHALLENGES
PROBLEM STATEMENT
•>100,000 samples
•5/180 seed varieties
•Maximise yield
•Minimise risk
DATA
WEIGHTED HISTOGRAMS REGRESSION (WHR)
•System determinative
•Training Dataset: F1-F5
•Evaluation Farm: FE
•Voting
•Votes weighted by similarity
WEIGHTED HISTOGRAMS REGRESSION (WHR)
•System determinative
•Training Dataset: F1-F5
•Evaluation Farm: FE
•Voting
•Votes weighted by similarity
WEIGHTED HISTOGRAMS
PDF of yield at Evaluation Farm for each feature
DEFINING SIMILARITY
Similarity of individual features
COMPARISON OF REGRESSION ALGORITHMS
[bu/ac]
COMPARISON OF REGRESSION ALGORITHMS
[bu/ac]
PORTFOLIO OPTIMISATION
•Objective 1: high yield
•Objective 2: stable yield
•Trade-off
•Tools from economics
•Portfolio optimisation
SEED SELECTION
PORTFOLIO OPTIMISATION IN FOLKLORE
•GER: Man kann nicht auf zwei Hochzeiten tanzen One can’t dance at two weddings
•YU: Ne možeš imati i jare i pare You can't have both the goatling and the money
•ITA: Volere la botte piena e la moglie ubriaca To want the barrel full and the wife drunk
•FRA: Vouloir le beurre et l'argent du beurre (et le sourire de la crémière) To want the butter and the money from the butter (and a smile from the shopkeeper)
•ENG: Don’t put all your eggs in one basket
PORTFOLIO OPTIMISATION
•Harry Markowitz (1952) – Nobel prize (1990)
•Diversification of investments
•Investments = soybean varieties
•Return = yield
•Source of risk?
PORTFOLIO OPTIMISATION
PORTFOLIO OPTIMISATION
•Predicted yields for the next year
•Covariance matrix
PORTFOLIO OPTIMISATION
PORTFOLIO OPTIMISATION
Efficient frontier
PORTFOLIO OPTIMISATION
RESULTS
•Is 5% good enough?
•Average person consumes 1.46 bu/yr
•The US could feed a country the size of Mexico
SYNGENTA SERBIA
“Wrong choice of seeds cannot be compensated later in season“
Additional investment ≈ 0
CROP CHALLENGE 2017
•Farmer’s point of view -> retailers’ point of view
•Right amount of supplies
•Too much – dead stocks
•Too little – missed opportunity
•Minimise transport costs
FEATURE IMPORTANCE
Midwest climate analysis
Weather in the top 5
WEATHER SCENARIOS
•Climate
•15 historical weather instances from Region dataset
•1 WS = solar radiance, temperature, precipitation in 1 year
•Assumptions:
•1. No other weather scenarios
•2. All weather scenarios equally probable
PORTFOLIO OPTIMISATION
Pareto optimal portfolios (blue)
Farmer’s risk profile defined by PI
High PI = ready to take risks
Low PI = not ready to take risks
RESULTS
Strategy Risk Improvement
Choose the best variety for all subregions (benchmark)
7.52 6.72%
Portfolio optimisation based on local PI
7.60 7.91%
Portfolio optimisation based on globally optimal PI (18.25)
7.96 9.53%
Assumptions that farmers will actually buy the optimal seeds
SPATIAL DISTRIBUTION
FUTURE WORK
Thank you for your attention!
Oskar Marko