Did someone say “farmer-centric”? Digital tools for spatially distributed on-farm experimentation

Author:

Bramley Robert G. V.ORCID,Song Xinxin,Colaço André F.,Evans Katherine J.,Cook Simon E.

Publisher

Springer Science and Business Media LLC

Subject

Agronomy and Crop Science,Environmental Engineering

Reference85 articles.

1. Adams ML, Cook SE (2000) On-farm experimentation: application of different analytical techniques for interpretation. In: Robert PC, Rust RH, Larson WE (eds) Proceedings of the 5th International Conference on Precision Agriculture. Agronomy Society of America, Madison, pp 1–17

2. Adams ML, Cook SE, Caccetta PA, Pringle MJ (1999) Machine learning methods in site-specific management research: an Australian case study. In: Robert PC, Rust RH, Larson WE (eds) Proceedings of the 4th International Conference on Precision Agriculture. American Society of Agronomy, Crop Science Society of America and Soil Science Society of America, Madison, pp 1321–1333

3. Alesso CA, Cipriotti PA, Bollero GA, Martin NF (2019) Experimental designs and estimation methods for on-farm research: a simulation study of corn yields at field scale. Agron J 111:1–12. https://doi.org/10.2134/agronj2019.03.0142

4. Amrhein V, Greenland S, McShane B (2019) Retire statistical significance. Nature 567:305–307. https://doi.org/10.1038/d41586-019-00857-9

5. Bakar KS, Jin H, Henderson B, Bramley RGV, Gobbett DL, Ratcliff CEM (2021) An algorithm to identify the appropriate size of a neighbourhood when analysing spatially distributed field experiments by local cokriging. Comput Electron Agr 188:106328. https://doi.org/10.1016/j.compag.2021.106328

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