Coffee Production Systems: Evaluation of Intercropping System in Coffee Plantations in Rwanda
Bibliographic record
Abstract
Intercropping is an agricultural practice consisting in planting two or several crops in the same field simultaneously. This production system appeared to offer an excellent several advantages. While intercropping has been widely practiced since ancestral times, there was a lack of data in Rwanda on the kind of intercrops mostly used and on farmers’ perception of their utility and constraints. The main objectives of this work were (1) to assess the different food crops associated with coffee trees in Rwanda and (2) to determine the perception of farmers on the role of intercropping system. That was why a field survey was carried out between August 25th, 2014 and February 28th, 2015 in Kamonyi District of Rwanda. Seventy-five coffee producers were randomly selected and contacted to fill a questionnaire on their practices. The correlation between yield and pesticide application were performed using R version. The significance level P was set at 0.05. Results revealed that common beans (Pheseolus vulgaris L.) and soybeans (Glycine max L.) were the most coffee intercropped plants. It appeared that intercropping was practiced to ensure the production of staple crops beside coffee. Insecticide remained the main way to control coffee pests and there was thus an important work to find alternative solutions that are often ecologically non-disruptive. Plant breeders and extension agents should investigate plants that are suitable to intercrop with coffee trees in order to enhance the conservation agriculture.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".