Climate resilient and sustainable development of horticulture - Options and opportunities - A review
Bibliographic record
Abstract
Climatic changes and increasing climatic variability are likely to aggravate the problem of future food security by exerting pressure on agriculture. However, there are lot of uncertainties about the assessment of impact, adaptation and mitigation of climate change in agriculture. For the past some decades, the gaseous composition of earth's atmosphere is undergoing a significant change, largely through increased emissions from energy, industry and agriculture sectors; widespread deforestation as well as fast changes in land use and land management practices. These anthropogenic activities are resulting in an increased emission of radiatively active gases, viz. carbon dioxide ((CO2)), methane (CH4) and nitrous oxide (N2O), popularly known as the ‘greenhouse gases’ (GHGs). The global mean annual temperature at the end of the 20th century, as a result of GHG accumulation in the atmosphere, has increased by 0.4–0.7ºC above that recorded at the end of the 19th century. The Inter-Governmental Panel on Climate Change has projected the temperature increase to be between 1.1°C and 6.4°C by the end of the 21st Century (IPCC, 2007). The global warming is expected to lead to other regional and global changes in the climate-related parameters such as rainfall, soil moisture, and sea level. India responded to the challenge by reorganizing research and undertaking agricultural activities, creation of institutions, price support mechanism to the produce. The concept of Rainbow revolution (Green revolution, White revolution, Yellow revolution and Golden revolution together the Rainbow revolution) is an integrated development of crop cultivation, horticulture, forestry, fishery, poultry, animal husbandry and food processing industry.) have been possible due to technical interventions, new cultivars and production technology. Significant Technological changes in horticulture have been developed for coping with the climate changes. Climate change impacts have to be addressed in concerted and systematic manner in order to prepare the horticulture sector to face the imminent challenges of climate change. Mathematical models have been developed using available basic data on the crop response to different climatic factors, which have the potential to predict likely impact as well as suggest ways to overcome the problems to some extent, suggesting that impact will differ from region to region, depending upon current ecological and climatic conditions. With available knowledge and experience, it is possible to make agriculture a sustainable livelihood means - but this will require intensive efforts at ground level - local level where agriculture exists and it has to be made climate smart. Climate smart agriculture uses agriculture as a major tool for mitigation of GHG - CO2 by laying emphasis on its unique capacity to absorb CO2 and release Oxygen through photosynthesis process. It envisages to achieve this through increased cropping, by reducing rain fed areas through integrated water and river basin management and expansion of agriculture on wasteland, wetland, degraded fallow areas and introducing urban agriculture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".