Assessing the drivers of nitrogen fertilizer application in Panama to support sustainable nutrient management
Bibliographic record
Abstract
Nitrogen fertilizers are vital for global food production, but their overuse can degrade ecosystems and compromise agricultural productivity. Although fertilizer use is increasing in Latin America, research on the drivers of nitrogen application remains limited, particularly at subnational scales where management decisions are made. Using the Republic of Panama as a model system, this study integrates census data, government records, Landsat imagery, and interviews with farmers to identify the factors shaping nitrogen application across districts. Nitrogen inputs varied widely among crops, with bananas and pineapples exhibiting the highest mean application rates at 278 and 189 kg ha −1 yr −1 , while coffee, oranges, and pigeon pea received far lower amounts (<40 kg ha −1 yr −1 ). Agronomic factors were particularly influential, and both crop type and farm size emerged as significant predictors of nitrogen use. Notable, larger farms applied more nitrogen per hectare, as indicated by the positive elasticity of farm size where a 1 % increase in farm size corresponded to a 0.35 % increase in nitrogen application, likely reflecting greater resource availability and the use of more input-intensive production systems. In contrast, districts with greater adoption of conservation practices applied less nitrogen overall, underscoring the role of sustainable management in reducing fertilizer inputs. For example, model predictions indicate that increasing conservation adoption from 15 % to 25 % at the district level is associated with a 39 % reduction in nitrogen application. We also generated the first district-level nitrogen fertilizer application map for Panama, revealing substantial spatial variation ranging from 10.34 to 1032.60 kg ha −1 yr −1 , with central and western districts showing the highest values. These findings underscore the complexity of fertilizer use across Panama and emphasize the need to consider farm structure, crop composition, and conservation practices when designing strategies for sustainable nutrient management, providing an essential baseline for future policy and decision making. • District-level drivers of nitrogen fertilizer application assessed in Panama. • Crop type and farm size are key determinants of nitrogen application rates. • Widespread conservation practices reduce nitrogen application at district scale. • First national map shows high spatial variation (10.34–1032.60 kg N ha −1 yr −1 ). • Results support tailored policies for sustainable nutrient application 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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".