A three-stage approach for co-designing diversified cropping systems with farmers: the case study of lentil-wheat intercropping
Bibliographic record
Abstract
There is an increasing need for more sustainable and diversified cropping systems while guaranteeing adequate crop yields and economic viability for European farms. The intercropping of lentil with wheat can be a valuable agroecological practice for stabilizing crop yields and improving weed control; however, this requires better knowledge about the technical viability, suitable varieties, sowing density, management practices for different conditions, and the feasibility of these solutions for farmers. In this paper, we present a three-stage participatory approach aimed at involving farmers in the evaluation and design of knowledge-intensive agroecological cropping systems and applied it to the case of wheat-lentil intercropping. The proposed approach is articulated into three connected stages involving experiments at different scales (plot, field and farm) and with different grades of interactions among farmers and researchers regarding the design of experiments and the evaluation of the results. In the first stage, we set up controlled plot experiments at an experimental station allowing all interested farmers to observe and comment on the various treatments that were investigated during dedicated events. This stage tested the potential of intercropping to improve the sustainability of the local farming system and provide a solid scientific background to the ecosystem services provided by wheat-lentil intercropping, such as crop production, yield stability, and weed control. While being agronomically beneficial, the technical feasibility and economic benefits of wheat-lentil intercropping have still to be proven. Therefore, based on the results obtained from the first stage and the feedback of local farmers on the opportunities and weaknesses of the on-station application of wheat-lentil intercropping, a second experiment was carried out using commercial agriculture machines with the objective to test the technical viability of intercropping at a larger scale. In the final third stage, we set up a co-designed on-farm experiment aimed at supporting a farmer in establishing lentil-wheat intercropping adapted to the farm conditions. This approach demonstrated that gradually involving farmers in the experimental process, starting from evaluating the most promising agroecological solutions on station to implementing them on farms, supports a successful agroecological transition of farms towards more diversified cropping systems.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".