Land Cover and Land Use Change in the US Prairie Pothole Region Using the USDA Cropland Data Layer
Bibliographic record
Abstract
The Prairie Pothole Region (PPR) is a biotically important region of grassland, wetland, and cropland that traverses the Canada-US border. Significant amounts of grasslands and wetlands within the PPR have been converted to croplands in recent years due to increasing demand for biofuels. We characterized land dynamics across the US portion of the PPR (US–PPR) using the USDA Crop Data Layer (CDL) for 2006–2018. We also conducted a comparative analysis between two epochs (1998–2007 & 2008–2017) of the CDL data time series in the North Dakotan portion of the US–PPR. The CDL revealed the western parts of the US–PPR have been dominated by grass/pasture, to the north it was spring wheat, to the east and southern half, soybeans dominated, and to the south it was corn (maize). Nonparametric trend analysis on the major crop and land cover types revealed statistically significant net decreases in the grass/pasture class between 2006 and 2018, which accounts for more than a quarter of grass/pasture area within the US–PPR. Other crops experiencing significant decreases included sunflower (-5%), winter wheat (-3%), spring wheat (-2%), and durum wheat (-1%). The combined coverage of corn and soybeans exhibited significant net increases in 23.5% of its cover; whereas, the individual significant net increases were 5% for corn and 11% for soybeans. Hotspots of increase in corn and soybeans were distributed across North and South Dakota. Other crop/land covers with huge significant increases include other hay/non-alfalfa (15%), and alfalfa (11%), which appear to be associated with the sharp increase in larger dairy operations, mostly in Minnesota. Wetland area increased 5% in the US–PPR, due to increased precipitation as well as inundation associated with Devils Lake in North Dakota. Hotspots of decreasing grass/pasture area were evident across the study area. Comparative trend analysis of two epochs (1998–2007 vs. 2008–2017) in North Dakota revealed that grass/pasture cover showed a negligible net trend (-0.3 %) between 1998 and 2007; whereas, there was a statistically significant decrease of more than 30% between 2008 and 2017. Combined coverage of corn and soybeans experienced statistically significant net increases in both epochs: 11% greater during 1998–2007 and 17% greater during 2008–2017. Recent sharp losses of grasslands and smaller wetlands combined the expansion of corn, soybeans, and alfalfa bode ill for wildlife habitat and require a re-examination of agricultural and energy policies that have encouraged these land transitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".