No‐Tillage Crop Production: A Revolution in Agriculture!
Bibliographic record
Abstract
For thousands of years, agriculture and tillage were considered synonymous. It was simply not thought possible to grow crops without first tilling the soil before planting and for weed control. The advent of modern herbicides permitted no‐tillage (NT) to be developed and practiced on actual working family farms. No‐tillage is generally defined as planting crops in unprepared soil with at least 30% mulch cover. Adoption of NT after its successful demonstration in the 1950s was slow. However, with better planters, herbicides, and accumulated experience, NT began to be widely adopted in the 1980s in the United States and then in Australia, South America, and Canada. Today, approximately 23% of the total cropland in the United States is planted using NT. No‐tillage has revolutionized agricultural systems because it allows individual producers to manage greater amounts of land with reduced energy, labor, and machinery inputs. At the same time, NT is a very effective erosion control measure and improves water and fertilizer use efficiency so that many crops yield better under NT than under tilled systems. Tillage, like crops, can be rotated but the benefits of NT are most likely to be realized with continuous application. We review some of the early work that led to the development of NT and how NT impacts the crop, soil, hydrology, and farm economics. While highly sustainable, there are still many challenges that remain for researchers to solve so the benefits of NT can be realized on expanded land area and for more crops, worldwide.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".