Predicting fodder crop yield in semi-arid Mongolia using Landsat data: a multivariate analysis approach
Bibliographic record
Abstract
Crop yield prediction enables appropriate crop management in response to climate change and is essential for assessing food security and economic efficiency at all levels, from individual farmers to governments. Mongolian cropland yields face several challenges that remain underexplored through spatial analysis, including insufficient data, planning difficulties, harsh climatic conditions, and limited access to advanced technologies. This study aimed to predict crop yields at the local level using Landsat time-series data (2014–2023), along with climate and soil data. Key variables (NDVI, soil parameters, and hydrothermal coefficient) were selected based on prior research on crop yield prediction. Soil parameters (humus, humus depth, soil reaction pH, nitrogen, phosphorus, and potassium) and climate factors (precipitation, temperature) were analyzed to predict crop yield using regression analysis. Two models were developed: a multiple linear regression (MLR) model incorporating the normalized difference vegetation index (NDVI) and another without NDVI. The results showed that the coefficient of determination (R2) from the multivariate regression analysis was 0.91 for the model including NDVI and 0.74 for the model excluding NDVI. These findings can enhance early yield prediction, optimize resource allocation, and support timely decision-making in agricultural management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".