Agricultural biodiversity as a livelihood strategy? The case of wastewater-irrigated vegetable cultivation along the Musi River in periurban Hyderabad, India
Bibliographic record
Abstract
Abstract Agricultural biodiversity ensures the nutritional basis upon which humankind depends and therefore plays an important role in ecological and socioeconomic contexts. The rates of loss however are alarming. For this case study, diversity in vegetable varieties in wastewater and groundwater-irrigated gardens along the Musi River was mapped and compared. Interviews with local farmers were conducted to better understand the decisions behind their crop choices. Most farmers interviewed used a highly intensive, short-term cropping system. Their work exposed them to pollutants like pesticides and industrial effluents. Their land tenure situation was insecure and they were faced with fluctuating prices of inputs such as seeds, pesticides and fertilizers. The perception of agricultural biodiversity among these farmers was positive, mostly for economic reasons, but also because it was seen as strengthening resilience against negative ecological impacts. Agricultural biodiversity was thus part of the livelihood strategy as it helped to mitigate vulnerability. However, it should be assured that industrial effluences are separated from the domestic effluent which can be profitable for urban and periurban farming. Cultivating a high diversity of crops in a sustainable way requires specialised knowledge. Therefore, meaningful ways of assisting the periurban farmers would be field schools and support through agricultural extension services.
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 | 0.000 | 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 | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".