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Agronōmics: transforming crop science through digital technologies

  • R. Sylvester-Bradley (a1), D. R. Kindred (a1), B. Marchant (a2), S. Rudolph (a2), S. Roques (a1), A. Calatayud (a3), S. Clarke (a4) and V. Gillingham (a5)...
Abstract

Good progress in crop husbandry and science requires that impacts of field-scale interventions can be measured, analysed and interpreted easily and with confidence. The term ‘agronōmics’ describes the arena for research created by field-scale digital technologies where these technologies can enable effective commercially relevant experimentation. Ongoing trials with ‘precision-farm research networks’, along with new statistical methods (and associated software), show that robust conclusions can be drawn from digital field-scale comparisons, but they also show significant scope for improvement in the validity, accuracy and precision of digital measurements, especially those determining crop yields.

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Copyright
Corresponding author
E-mail: roger.sylvester-bradley@adas.co.uk
References
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AgGateway 2017. ADAPT. www.adaptframework.org (Retrieved 4/1/17).
Bloom, TM 1985. Bias in the measurement of crop performance. Aspects of applied Biology 10, Field trials methods and data handling 241258.
Carlson, TN and Ripley, DA 1997. On the relation between NDVI, fractional vegetation cover, and Leaf Area Index. Remote Sensing and Environment 62, 241252.
Deere, John 2016. Telematics. https://www.deere.co.uk/en_US/products/equipment/telematics/telematics.page (Retrieved 4/1/17).
Fisher, RA and Wishart, J 1930. The arrangement of field experiments and the statistical reduction of the results. Imperial Bureau of Soil Science, Tech. Comm 10, 123.
Griffin, TW, Dobbins, CL, Vyn, TJ, Florax, RJGM and Lowenberg-Deboer, JM 2008. Spatial analysis of yield monitor data: Case studies of on-farm trials and farm management decision making. Precision Agriculture 9 (5), 269.
Hicks, D, Vanden Heuvel, R and Fore, Z 1997. Analysis and Practical Use of Information from On-Farm Strip Trials. Better Crops 81, 1821.
Hoyles, D and Lamyman, T 2015. Yield Enhancement Network Videos. http://www.hlhltd.co.uk/yieldenhancementnetworkvideos.html (Retrieved 11/12/16).
Kindred, D and Sylvester-Bradley, R 2014. Using Precision Farming technologies to improve nitrogen management and empower on-farm learning. Aspects of Applied Biology 127, Precision Decisions for Profitable Cropping 173180.
Kindred, D, Sylvester-Bradley, R, Clarke, S, Roques, S, Smillie, I and Berry, P 2016a. Agronōmics – an arena for synergy between the science and practice of crop production. Paper presented at the 12th European IFSA Symposium at Harper Adams University. Pp. 12.
Kindred, DR, Hatley, D, Ginsburg, D, Catalayud, A, Storer, K, Wilson, L, Hockridge, B, Milne, A, Marchant, B, Miller, P and Sylvester-Bradley, R 2016b. Automating fertiliser N for cereals (Auto-N). AHDB Report 561. Pp. 196.
Lark, RM, Stafford, JV and Bolam, HC 1997. Limitations on the spatial resolution of yield mapping for combinable crops. Journal of Agricultural Engineering Research 66, 183193.
Lawes, RA and Bramley, RGV 2012. A simple method for the analysis of on-farm strip trials. Agronomy Journal 104 (2), 371.
Lewis, T 2014. How computer analysts took over at Britain’s top football clubs. The Observer, 9 March 2014.
Little, TM and Hills, FJ 1978. Agricultural Experimentation: Design and Analysis. John Wiley & Sons Ltd, West Sussex, England.
MacMillan, T and Benton, TG 2014. Engage farmers in research. Nature 509 (7498), 2527.
Qi, A, Ober, ES and Jaggard, KW 2012. Benchmarking sugar beet yields and the growers’ performance. British Sugar Beet Review 80, 36.
Ross, KW, Morris, DK and Johannsen, CJ 2008. A review of intra-field yield estimation from yield monitor data. Applied Engineering in Agriculture 24 (3), 309.
Rudolph, S, Marchant, PB, Gillingham, V, Kindred, D and Sylvester-Bradley, R 2016. Spatial Discontinuity Analysis’ a novel geostatistical algorithm for on-farm experimentation. In Proceedings of the 13th International Conference on Precision Agriculture Monticello, IL: USA International Society of Precision Agriculture.
Street, D 1990. Fisher’s contributions to agricultural statistics. Biometrics 46, 967–945.
Sylvester-Bradley, R 1991. Modelling and mechanisms for the development of agriculture. Aspects of Applied Biology 26, The Art and Craft of Modelling in Applied Biology 5567.
Sylvester-Bradley, R, Semenov, MA, Lawless, C, Jaggard, K and Qi, A 2005. Assessing predictive skill of models to optimise crop management and design. Final Report of Defra Project AR0909. Pp. 23.
Sylvester-Bradley, R and Kindred, D 2014. The Yield Enhancement Network: Philosophy and results from the first season. Aspects of Applied Biology 125, Agronomic Decision Making in an Uncertain Climate 125, 5362.
Sylvester-Bradley, R, Kindred, D, Smillie, I and Berry, PM 2016. Enhancement of European crop yields without agronomic ‘intensification’. Proceedings of the European Society of Agronomy 14th Congress, Edinburgh, 5–9 September 2016.
The University of Reading 2000. Concepts Underlying the Design of Experiments. Statistical Services Centre.
Whelan, B, Taylor, J and McBratney, A 2012. A ‘small strip’ approach to empirically determining management class yield response functions and calculating the potential financial ‘net wastage’ associated with whole-field uniform-rate fertiliser application. Field Crops Research 139, 4756.
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