Spatial Drivers of Crop Diversification in Andhra Pradesh, India
Bibliographic record
Abstract
The adoption of crop diversification stands as a crucial strategy to counter the challenges posed by climate variability and market fluctuations, playing a pivotal role in enhancing food security, elevating agrarian income, and promoting sustainable farming practices. A detailed examination of crop diversification patterns in Andhra Pradesh unveils a nuanced agricultural landscape, with Prakasam district emerging as a leader in diversification, in contrast to more modest levels in Nellore and East Godavari. Srikakulam displays a moderate level, while Vizianagaram and Visakhapatnam showcase commendable crop diversity. The application of spatial regression model in understanding determinants of Simpson Diversity Index (SDI) in Andhra Pradesh enriches comprehension of the intricate interplay between ecological and spatial factors. In the spatial lag model, significant determinants influencing SDI include temperature, revealing that higher maximum temperatures correlate with reduced ecological diversity. Fertilizer use, particularly phosphorous and potassium, exhibits a positive association with increased SDI. The Gross Area Irrigated (GAI) emerges as a significant contributor, indicating that higher irrigation levels foster greater ecological diversity. Access to long-term loans positively influences crop diversity, empowering farmers to engage in experimental and diversified agricultural practices. Conversely, higher wage rates for both genders negatively impact ecological diversity, potentially due to intensified and specialized farming practices. The negative correlation between the Public Distribution System (PDS) and SDI suggests that areas with PDS procurement exhibit lower crop diversity. Comparing the spatial lag and error models, the former exhibited significant Likelihood Ratio for spatial lag dependence indicating meaningful influence from neighbouring districts, suggesting spatial interdependence—a contrast to the spatial error model. The robust spatial interdependence underscores need for nuanced and targeted policy initiatives. These initiatives should champion and promote diversified agricultural practices, considering the interdependence among neighbouring districts. Such policies would contribute to both resilience and sustainability of agricultural landscape in Andhra Pradesh, reflecting an intricate balance between regional dynamics and broader ecological considerations.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".