Economic Assessment of Technology Adoption in Oil Palm Plantations from Colombia
Bibliographic record
Abstract
Adopting technology regarding agricultural crops has traditionally been associated with high costs. Producers have thus often abstained from adopting better agronomical practices and have consequently lost the benefits they could otherwise have obtained by implementing better criteria for managing their crops.This research builds on results by Ruiz et al., (2017) who found three typologies of oil palm lots, regarding adoption of technology and yields on oil palm crops from Colombia. This work was aimed at evaluating the typologies found by Ruiz et al. (2017) from an economic standpoint by using different economic assessment methods, in order to determine the benefits of technology adoption at the Colombian oil palm agroindustry. The methods used were aimed at estimating: unit cost, net present value (NPV), net income, land use efficiency, generation of income and competitiveness.Results indicate that the cost of producing a ton of fresh fruit bunches from oil palms (FFB) on lots having high adoption of technology was 2.5% to 8% lower when compared to lots having lower adoption of technology (Typologies 2 and 3. respectively). Technology adoption enables greater yearly net income to be obtained in mature oil palm crops in typology 1, than the one obtained at typology 2 and typology 3. The adoption of technology allows the grower to obtain net income equivalent to a legally-established yearly minimum wage (LEYMW), using less land. Finally, it was concluded that at average CPO prices for the period 2005-2015, the Colombian growers that participated in this study, may be competitive at the European market, which is the main destination of Colombian exports of crude palm oil (CPO).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".