Assessing the Effectiveness of Climate-Resilient Rice Varieties in Building Adaptive Capacity for Small-Scale Farming Communities in Assam
Bibliographic record
Abstract
Rice crop in Assam constitutes a significant portion of the cultivated area, covering around sixty percent of the total area. The state, like many others, confronts the repercussions of climate change, notably evident in recurrent floods that impact agricultural lands. The shifting climate, marked by rising temperatures and increased rainy days, poses threats to crop production. Despite witnessing overall productivity growth, the state grapples with persistent challenges related to flood-induced losses. In response to this, climate-resilient rice varieties were developed to withstand submergence. This study delves into the assessment of the impact of these climate-resilient rice varieties on yield, income, and adoption among farmers. Concentrating on Golaghat and Sivasagar districts, where 106 farmers were interviewed, the research addresses the prevalent challenges in rice cultivation due to changing rainfall patterns. The introduced varieties underwent demonstration in plots, and their effects on yield, income, and adoption were comprehensively evaluated. The study additionally scrutinized the technology and extension gaps in the area, utilizing various indices such as the technology gap, extension gap, technology index, and benefit-cost ratio to measure the efficacy of the introduced varieties. The findings of the study highlight disparities between recommended agricultural practices and the actual methods employed by farmers. Despite these challenges, the introduction of climate-resilient varieties resulted in a noteworthy increase in yield. Economic analysis revealed enhanced profitability from B:C ratio of 0.43 to1.06 and positive changes in economic indicators. The adoption and horizontal spread of these varieties were substantial, with a significant rise from 106 to 378 in the number of adopters and expanded cultivation areas. Overall, the study emphasizes the success of climate-resilient rice varieties in augmenting yield, income, and adoption among farmers. The positive economic changes, coupled with heightened awareness, underscore the importance of promoting such varieties. The study advocates for sustained efforts in disseminating climate-resilient varieties, emphasizing their pivotal role in enhancing farmers' climate resilience. Addressing the identified discrepancies in agricultural practices emerges as a crucial step toward fostering sustainability and optimizing crop yield in the region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".