The profitability of site-specific fertilisation based on Sure Grow Solutions – A Canadian case study
Bibliographic record
Abstract
This paper presents the outcome from a case study analysis for a Canadian farm that does site-specific fertilisation (SSF), a precision farming approach which takes into consideration the spatial variability of soils. The economic results for three years of wheat and canola production are compared to a neighbouring farm, which is practicing conventional broadcast application of fertilisers. Since no additional investments in machinery are needed, the annual variable cost is 6 CAD/acre. In the standard case, the average profit is 30 CAD/acre. The rather pronounced difference in the effects from SSF application in wheat vs. canola leads one to question whether this is a crop-related systematic outcome or instead represents something more random. Sensitivity analyses generated two main insights. First, the economics of SSF are sensitive to a modification in commodity prices – a 50 % cut would reduce the average profit to about 9 CAD/acre. Second, another scenario calculation in which no-till is assumed to generate a 5% increase in yields suggests that the net profit would be just 7 CAD/acre. Given the existence of so many uncertainties, this paper calls for more farm-based economic analysis of SSF, one which should also include a comparison of different service providers for application maps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".