Changing Trends in Cultivation Practices and Adoption of Climate Adaptive Farming in Eastern Nepal
Bibliographic record
Abstract
Climate change impacts are likely to affect the agricultural production leading to further food insecurity. In this context, the trend of cereal production with climate variables was studied in order to understand the linkages between climate change and crop productivity. The study was conducted in three districts of Sagarmatha zone, namely Solukhumbu (mountain region), Okhaldhunga (hill region) and Saptari (Terai region) representing three ecological zones in Nepal. A household survey (295 households), focus group discussions and key informant interviews were used to collect data on the history of the cultivation systems, varieties of crop grown, trends on crop yield, and adaptation to climate change. Results showed farmers’ introduction of high yielding varieties of crops and vegetables due to economic benefit, while traditional varieties are no longer cultivated. The infestation of pest attack is increasingly seen since two decades, while few pests were reported to be disappeared. Although majority of farmers in Saptari and Okhaldhunga districts used pesticides as per the prescribed doses, pesticide use is still random in Solukhumbu district. The multiple comparisons of means showed that there is a significant difference in the average production of rice and maize since 30 years until recently (p<0.05) in these three districts. The average production of rice, maize and wheat increased with decreasing average annual temperature and rainfall in Saptari district since 30 years. In contrast, in Okhaldhunga and Solukhumbu districts, the average production of three cereal crops increased with increasing average annual temperature and rainfall. With the late arrival of the monsoon, farmers have adopted coping strategies particularly for rice cultivation through occasional shift in crop planting dates and selection of shorter duration crop varieties that can be harvested early.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| 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".