TECHNOLOGY AND POLICY IMPACTS ON ECONOMIC PERFORMANCE, NUTRIENT FLOWS AND SOIL EROSION AT WATERSHED LEVEL:THE CASE OF GINCHI IN ETHIOPIA
Bibliographic record
Abstract
A dynamic bio-economic model is used to examine natural resource use, the resulting nutrient balances and economic outcomes in a poor country under a range of technological and policy intervention scenarios. With limited technological intervention over a twelve year planning period, incomes rise by 50% from a very low base and average per ha nutrient balances stand at –58kgs for nitrogen, -32kgs for phosphorous and –114kgs for potassium. Associated soil losses are 31 tons per ha. With a set of new technologies involving use of new high yielding crop varieties, agro-forestry, animal manure and inorganic fertilizers, construction of a communal drain to reduce water logging and some limited land user rights, results show a tenfold increase in incomes, 20% decline in aggregate erosion levels and an increase in the dependence on livestock for dung manure, oxen draft, milk and ready cash over time. Moreover, a minimum daily calorie intake of 2000 per adult equivalent is met from on-farm outputs and per ha nutrient balances after intervention are as low as –25kgsN, -14kgsP and –68kgsK on the average. There is hence an obvious reduction in nutrient losses despite the higher reliance on the watershed for subsistence food requirements. The bias towards replenishment of nitrogen and phosphorous nutrients at the expense of potassium may, however, not be resolved. Emissions (leaching, gaseous losses, and erosion) could be higher than immissions (atmospheric deposition, nitrogen fixation) in both situations. From a policy perspective, these results imply an increasing need for a more secure land tenure policy than currently prevailing and provision of credit to ensure uptake of the above land management technology packages. They also imply a shift from a general approach to land management to a relatively more site specific approach that emphasizes spatial and inter-temporal variability in input use based on land quality. Such variable rate technology may be an efficient nutrient management strategy as it enables farmers to apply optimal rates of fertilizer for each field and in each period. Moreover, residual nutrient loading is simultaneously reduced. Implementation of such a strategy may be difficult in a developing country situation but an attempt to do so may yield results that are significantly better than at present.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".