Utilizing Science and Technology in Agriculture to Ensure the Enhancement of Quality of Life Through Food Security, Improved Nutrition and Sustainable Livelihoods.
Bibliographic record
Abstract
Ensuring food, nutrition, and income security is of paramount importance for India, a country where over 50 percent of the workforce is employed in agriculture, contributing approximately 17 percent to the GDP.Food security encompasses more than just production availability; it encompasses ensuring nutritional well-being for the populace and financial stability for farmers.Throughout history, agricultural advancements driven by science and technology have significantly influenced India's agricultural land scape, spanning various revolutions such as the green, white, blue, rainbow, and golden revolutions.India has achieved notable progress in terms of agricultural production, productivity, and availability of essential commodities like food grains, horticul tural produce, milk, meat, and fish, largely owing to technology-driven development and governmental initiatives.The Ministry of Agriculture and Farmers Welfare has spearheaded flagship programs and production -oriented schemes like the National Food Security Mission and the National Mission on Oilseeds, aimed at promoting technology adoption and bridging yield gaps.However, amidst a scenario of increasing population and diminishing land and water resources due to climate change, the challenges are growing.Climate change is expected to exacerbate issues such as high temperatures, unpredictable weather patterns, the emergence of new pests and diseases, and threats such as rising sea levels and glacier melt.Addressing these challenges requires robust suppor t for research and development to deliver science-based solutions that enhance the quality of life for all, including farmers who not only produce food but also rely on it for their livelihoods.Present Scenario food production : Out of total geographical area of 328.7 million hectares (as per the land use statistics 2015-16) of which about 140 million hectares is reported as net sown area and about 195 million hectares is the gross cropped area with a cropping intensity of 139%.The net irrigated area is 68 million hectares.The total food grains production increased from 218.11 million tonne in 2009-10 to 284.8 million tonne during 2017-18 and touched all times high food grain production.This accomplishment was a result of the determined efforts of all stakeholders in making latest crop production & protection technologies available to farmers and providing postharvest marketing support.The total area coverage of food grain crops during kharif 2018 (as on 12.10.2018) is 107.2millionha, which is 105% of normal area sown.However, the total production is expected to be higher because of better science based production technologies and spread of high yielding varieties.Although area continue to be the same to 140 ± 2 million hectare for the last 40 years, but production has increased apparently.It gives a lot of satisfaction that production of food crops has increased 5.5 times, horticulture 11.5 times since 1950-51.Many of the crops which were not known before have emerged as important crop and India has become a leader.But the challenges ahead are much greater than before.Shortage of oilseeds and rising price of food is cause of concern.The impact of climate change is likely to increase in terms of high temperature, uncertainty of weather, emergence of new pests and diseases.How we can address the increasing food needs of the increasing population and reducing highly unequal social satisfaction.But there is a need to address new challenges that transcend the traditional decision making horizons of producers, consumers and policy-makers. Quality of life is linked with food, nutritional and income security :The quality of life and health of any nation is directly linked to food and nutritional security, which is the back bone of national prosperity and well-being of the people.There is direct relationship between food consumption levels and poverty.In rural context, agriculture development for small and marginal farmer is the most important dimension of livelihood.The diversification of agriculture for food e.g., cereals, pulses, edible oil yielding, fruits, vegetables, medicinal and fodder crops are necessary to meet the food& nutritional requirements and augment income to farmers to meet the income security.According to some projections, the demand for fruits and vegetables would increase from 265 million tonnes to 300 million tonnes.Given the shifts in consumption patterns, towards non-cereal food, and even to non-food, it is felt that the demand projections of the Ministry of Agriculture on food grains of around 350 million tonne for 2030 are Journal of Survey in Fisheries Sciences 08 (3) 446-450 2022
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".