A review of life cycle impacts and costs of precision agriculture for cultivation of field crops
Bibliographic record
Abstract
Assessing precision agriculture in crop production based on life cycle thinking and assessments allows for the consideration of multiple environmental as well as economic aspects at a systems level. Research at this intersection is, however, notably lacking. This review paper seeks to understand the current state of both environmental and economics research with respect to different agricultural crop production methods (orchard, vegetable, open field crop, etc.), regions, and the types of precision agriculture technologies applied in each context. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis method was used to answer three review questions to address a targeted subset of precision agriculture technologies relevant to field crop production, from both environmental and economic perspectives and at the global level. Fertilizer production/use and associated field-level emissions are the leading cause of environmental impacts in many life cycle impact categories, and energy and pesticide use also contribute significantly. For most environmental impact categories, the utilization of precision agriculture practices reduced these impacts as compared to conventional practices. Many precision agriculture technologies focus on nitrogen management, namely variable rate application of nutrients, but disproportionately in the context of high value crops. There is evidence that supports the notion that variable rate fertilization management leads to reduction in many but not necessarily all environmental impacts. Some studies reported no, or limited economic benefits associated with precision agriculture technologies, however overall results suggest that precision agriculture utilization delivers economic benefits either via cost savings, input savings, and/or increases to yield, margin, or profits. Variable rate technology is highlighted as a promising subset of precision agriculture technologies in terms of environmental impact reductions and economic benefits.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".