Adoption and Use of Precision Agriculture in Brazil: Perception of Growers and Service Dealership
Bibliographic record
Abstract
Precision agriculture (PA) is growing considerably in Brazil. However, there is a lack of information regarding to PA adoption and use in the country. This study sought to: (i) investigate the perception of growers and service dealership about PA technologies; (ii) identify constraints to PA adoption; (iii) obtain information that might be useful to motivate producers and agronomists to use PA technologies in the crop production systems. A web-based survey approach method was used to collect data from farmers and services dealership involved with PA in several crop production regions of Brazil. We found that the growth of PA was linked to the agronomic and economic gains observed in the field; however, in some situations, the producers still can not measure the real PA impact in producer system. Economic aspects coupled with the difficulty to use of software and equipment proportioned by the lack of technical training of field teams, may be the main factors limiting the PA expansion in many producing regions of Brazil. Precision agriculture work carried out by dealership in Brazil is quite recent. The most services offered is gridding soil sampling, field mapping for lime and fertilizer application at variable rate. Many producers already have PA equipment loaded on their machines, but little explored, also restricting to fertilizers and lime application. Looking at the currently existing technologies and services offered by dealership, the PA use in Brazil could be better exploited, and therefore, a more rational use of non-renewable resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".