Future Farm: Improving farmer confidence in targeted N management through automated sensing and decision support
Technical report
Bramley, Rob, Whelan, Brett, Colaco, André, McCarthy, Alison, Richetti, Jonathan, Lawes, Roger, Fajardo, Mario, Bender, Asher, Pothula, Anand, Baillie, Craig, Perry, Eileen, Fitzgerald, Glenn, Clancy, Alex, Leo, Stephen and Grace, Peter. 2022. Future Farm: Improving farmer confidence in targeted N management through automated sensing and decision support. Australia. Grains Research and Development Corporation.
| Title | Future Farm: Improving farmer confidence in targeted N management through automated sensing and decision support |
|---|---|
| Report Type | Technical report |
| Research Report Category | Industry |
| Authors | Bramley, Rob, Whelan, Brett, Colaco, André, McCarthy, Alison, Richetti, Jonathan, Lawes, Roger, Fajardo, Mario, Bender, Asher, Pothula, Anand, Baillie, Craig, Perry, Eileen, Fitzgerald, Glenn, Clancy, Alex, Leo, Stephen and Grace, Peter |
| Number of Pages | 62 |
| Year | 2022 |
| Publisher | Grains Research and Development Corporation |
| Place of Publication | Australia |
| Abstract | The Future Farm Project was established to re-examine and improve the way in which soil and crop sensors, supplemented by other sources of useful/available data, are used to inform decisions about input management and to provide a way of automating the process from data acquisition, through analysis, to the formulation and implementation of decision options. In particular, using nitrogen (N) fertilizer management as a ‘use-case’, the project sought to enable enhanced grower confidence in N decision making through the adaptive generation of site-specific management models. A key element of these is their increased and improved use of in-season field monitored data (soil, crop, climatic), historic on-farm data, external public and private data and automation of decision rules in software that may potentially be linked to real-time application equipment. This was considered important given the pre-project perception that a lack of farmer confidence in precision agriculture-based decision making was constraining adoption of precision agriculture (PA) approaches to management of grains-based farming systems. This lack of adoption was in spite of the potential of PA approaches as a counter to farm labour shortages, the need to optimise resource use efficiency as a means of maintaining or enhancing farm profitability and the finding through an exhaustive modelling exercise, that the error associated with prediction of N fertilizer requirement based on expected yield was of the order of 50 kg N/ha. The project developed and/or evaluated several frameworks through which N decisions might be made using remote and proximal sensing technologies and other data sources (e.g. seasonal weather, historical yield, etc). Some frameworks followed mechanistic agronomic thinking whilst others were underpinned by new data-driven methods, ‘artificial intelligence’ (AI) and ‘machine learning’ (ML). A critical element of each was the use of an on-farm experiment comprising ‘N-rich’ and ‘N-zero’ strips which, along with the standard rate used by a farmer at any given site, enabling prediction of the spatially variable crop response and estimation of the economically optimum N rate (EONR). |
| ANZSRC Field of Research 2020 | 300206. Agricultural spatial analysis and modelling |
| 4611. Machine learning | |
| 460304. Computer vision | |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia |
| University of Sydney | |
| University of Southern Queensland | |
| Agriculture Victoria | |
| Queensland University of Technology |
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