Soil Fertility on an Agricultural Frontier: The US Great Plains, 1880–2000
Bibliographic record
Abstract
Abstract In contrast to most long-settled agricultural landscapes, the US Great Plains presents a rare example of well-documented agricultural colonization of new land. The Census of Agriculture provides detailed information about evolving grassland farm systems from the beginning of agricultural expansion and then at some two dozen time points between 1880 and the present. From early sod-busting, through drought and depression, and into late-twentieth-century modernization, it is possible to track how farmers used their land in any county. Treating farmland as an agroecosystem, a hybrid human-natural landscape, this article asks how farmers captured, altered, and replenished soil fertility. Did they extract more soil nitrogen than they returned, or did they maintain a balance? The article assesses land use from a soil nutrients perspective in several plains environments to capture variation in climate (especially rainfall), native soil quality, and availability of irrigation water. It traces farm management strategies through time to understand agricultural crises, growth periods, and technological transitions in the context of soil fertility. Soil management on an agricultural frontier was markedly different from that in places that had been farmed for centuries. A shortage of people and livestock and an abundance of deep, rich soils in the plains informed farmers’ calculations as they juggled labor, capital, and market forces against family and financial strategies. Uniform methods of estimating and representing soil nutrient processes make possible a direct comparison of the relative sustainability of historical agroecosystems.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".