Accessibility navigation

Predicting the probability that higher profits could be achieved by adopting PA

Murdoch, A. J., Todman, L. ORCID:, Mahmood, S. A., Karampoiki, M., Paraforos, D. S., Antognelli, S., Guidotti, D., Ranieri, E., Ahrholz, T., Petri, J. and Engel, T. (2021) Predicting the probability that higher profits could be achieved by adopting PA. In: Precision agriculture '21, July 2021, Budapest, pp. 941-947, (9789086863631)

Text - Accepted Version
· Please see our End User Agreement before downloading.


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.3920/978-90-8686-916-9_113


Algorithms were developed to predict spatial variation of yield and quality within winter wheat crops intended for bread-making. Bayesian networks were used to predict spatial probability maps of yield and quality based on data sources including yield maps, fertiliser applications, soil variables and Sentinel 2 satellite data. Results presented here for five UK fields show that there was a 65% likelihood of achieving a grain protein premium with variable rate nitrogen application compared to 50% with uniform N. Achieving this premium would increase revenues by £150/ha. A similar comparison for five German fields did not demonstrate a higher probability of profit.

Item Type:Conference or Workshop Item (Paper)
Divisions:Life Sciences > School of Agriculture, Policy and Development > Department of Crop Science
ID Code:103132
Uncontrolled Keywords:wheat, maps, grain yield, grain protein, probability, Bayesian Network


Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation