Modeling intensification decisions in the Kilombero Valley floodplain: A Bayesian belief network approach.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to or identifying determinants of intensification and their interrelationships. The Bayesian network is learned as in the referenced paper. The vertices are:
(No, Yes);
(25-35, 35-45, 45-55, >55);
(ApplyFertilizer, ApplyImprovedSeed, CropMultipleTimes, None, UseIrrigation, UseIrrigationAndFertilizerApplication);
(<30%, 30-60%, >60%);
(Maize, Rice, RiceAndMaize, RiceMaizeAndVegit, Vegitables, VegitAndMaize, VegitAndRice);
(<15km, 15-30km, >30km);
(0, 0-800, 800-861.111, 861.111-1111.11);
(0 to 1000, 1000 to 1200, 1200 to 1500, 1500 to 1900);
(AgroPastoralist, Diversifier, Subsistence);
(0-160, 160-280, 280-600, 600-15800);
(<120, 120-220, 220-400, >400);
(None, <30%, >30%);
(<10%, 10-60%, >60%);
(<3Ha, 3-6Ha, 6-9Ha, >9Ha);
(<4, 4-7, >7);
(14-18, 18-23, 23-32);
Gebrekidan, B. H., Heckelei, T., & Rasch, S. (2023). Modeling intensification decisions in the Kilombero Valley floodplain: A Bayesian belief network approach. Agricultural Economics, 54(1), 23-43.