Evaluación ingenieril, agronómica y económica de la labranza cero en Venezuela
Bibliographic record
Abstract
Venezuela counts with around 300.000 ha under no-tillage farming. It is practically impossible to carry out conventional tillage in Venezuelan agriculture due to the problem of friability of the farm soils, together with timeliness factor, the high involved cost, physical, chemical, biological and thermal damages carried out. The specific objectives are the evaluation of the no-tillage with the purpose of appreciating some parameters: (a) engineering, (b) agronomic and (c) economic that governs their process. The engineering factors valued were: weigh, efficiency, field capacity, width, power required and operation speed. The agronomic: fertilization, pH, organic matter and soil structures. The economic: analysis including the timeliness factor (0,0002 day-1), the optimum width by maxim and minimum, energy consumption, comparisons with the lease, costs of acquisition, fertilization and conventional mechanization with relationship to the cultivation of corn. It was applied the PERT, the point of same opportunity and the regression analysis to interpret the variance among cost parameters. Among the economic results the PERT diagram was established for the processes of zero tillage in Venezuela, fixed costs of 18652,33 USD/year, total cost for the unit tractor-seeder of 27261,93 USD/year (34,42 USD/ha), equilibrium lease for 742,25 ha/year, optimum width between 4 and 10 m for of 5000 ha maximum. Among the engineering results, the field capacity was of 4,95 ha/h, speed of 11 km/h, the price of the direct seeder between 5.648,91-10.119,85 USD/m and an efficiency of 75%. Agronomics: the evaluated parameters produced advantageous results. It is concluded: (a) the zero-tillage reduced the costs, (b) the problems of the opportune time of operation are highly reduced, (c) the structure of the soil is favored, (d) it is cheaper to leasing and (e) the operational process of the productive process is lessened.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".