Small, But Significant Declines In Crop Productivity On Low Fertility Soils
Bibliographic record
Abstract
Agricultural yields are susceptible to losses during extreme weather events related to climate change, jeopardizing food security. Yield losses may be mediated by underlying quality or variation of agricultural land in soil fertility, topography, drainage, and growing degree days. For instance, crops grown on poor quality land may be more susceptible to the negative consequences of climate variation as compared to crops grown on high quality land. This study investigated yield response for corn, soybeans, and pasture to different land qualities across Ontario from 2011 to 2017. Yield is approximated using the Normalized Difference Vegetation Index (NDVI), which is a satellite-derived measure of biomass production. For three focal crops (soybean, corn, and pasture), the average maximum NDVI and the coefficient of variance (CV) of maximum NDVI were aggregated at a provincial and county scale for each land quality classification. Relatively stable CV values were evident across all land qualities, yet certain counties showed greater variation in productivity on poor quality land suggesting greater susceptibility to extreme weather. Over 7 years, there were small but significant declines in NDVI in response to poor quality land for all three crop types. This suggests that agricultural producers cannot overcome the biophysical limitations of poor quality land on crop yield. Understanding differential crop productivity responses to land quality can help producers mitigate crop losses to climatic variation, thus equally stabilizing food availability.
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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 | 0.001 | 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".