Preserving soil and crop resources by increasing cropping intensity and decreasing tillage
Bibliographic record
Abstract
Soils perform many essential functions for plant growth, water storage, and water and air quality. Future generations depend on our crop and soil management practices to maintain or improve the soil’s ability to perform these essential functions while providing the food and fiber needed by society. Unfortunately, many soils have been degraded1 because we either did not understand the negative effects of certain management practices or we lacked better alternatives. In recent years, scientific research, farmer innovation, and new commercial technologies have allowed improved soil management in many regions. In spite of this progress, additional improvements in soil management will be required to restore and then maintain the functional capacity of the soil resource. The Great Plains of Canada and the US is a major agricultural region producing sorghum, corn, wheat, and other small grains. This productive region is also highly susceptible to the vagaries of climate and destructive natural forces, such as those that gave rise to the Dust Bowl in the 1930s. The continental climate of the Great Plains and western Corn Belt is characterized by highly variable precipitation and temperature, both within and among years. Farmers who settled in this region, utilizing practices developed in more humid regions, experienced a high incidence of crop failure. In order to survive in the harsh, erratic environment of the Great Plains, different management practices were needed. Crop–fallow was developed as a practice to reduce the risk of crop failure due to water stress by allowing soil water to accumulate during the fallow period for use by the subsequent crop. Crop–fallow, using intensive tillage for seedbed preparation and weed control, soon became the dominant practice throughout the region. While the incidence of crop failure declined, the loss of organic matter, susceptibility of soil to wind and water erosion during the fallow period, development of extensive saline seeps, and the need for two growing seasons to produce a crop led many to conclude that crop– fallow was not a sustainable system. As herbicides and minimum tillage implements became available, research was initiated throughout the region to develop more intensive cropping systems that kept the land under crops every year. Initial results from these studies demonstrated that annual yields and economic returns were greater with more intensive cropping2. In addition, wind erosion was reduced and precipitation use efficiency increased3. The impact of more intensive cropping on the soil resource was difficult to assess as changes in many soil properties occur slowly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".