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- P. Venkatesh
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Jha, Girish Kumar
- Who cultivates traditional paddy varieties and why? Findings from Kerala, India
Abstract Views :286 |
PDF Views:123
Authors
Shenaz Rasheed
1,
P. Venkatesh
1,
Dharam Raj Singh
1,
V. R. Renjini
1,
Girish Kumar Jha
1,
Dinesh Kumar Sharma
2
Affiliations
1 Division of Agricultural Economics, ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, IN
2 Centre for Environment Science and Climate Resilient Agriculture, ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, IN
1 Division of Agricultural Economics, ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, IN
2 Centre for Environment Science and Climate Resilient Agriculture, ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, IN
Source
Current Science, Vol 121, No 9 (2021), Pagination: 1188-1193Abstract
Traditional paddy varieties are climate resilient, local stress-tolerant, low-input intensive and valuable sources of genetic diversity that have been under the threat of extinction from rising preferences for high yielding varieties. However, farmers in few pockets of the globe continue to cultivate traditional paddy varieties. This study therefore is an attempt at investigating who cultivates them and why they do so, through the survey of 225 paddy farmers in Wayanad district of Kerala. Results revealed that traditional paddy varieties were grown mainly by marginal and tribal farmers for chief purposes of self-consumption, and for associated traditional values and conservation. Farmers’ varietal selection decisions were found to be influenced by varietal traits related to consumption aspects, consumer demand, pest and disease resistance. Therefore, by cultivating traditional paddy varieties, farmers have been conserving these valuable genetic resources on-farm. However, stronger concerted institutional interventions are required for full-fledged, systematic and sustained in situ conservation of agricultural biodiversityKeywords
Agrobiodiversity, in-situ conservation, traditional paddy varieties, varietal traits.References
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- Rasheed, S., Venkatesh, P., Singh, D. R., Renjini, V. R., Jha, G. K. and Sharma, D. K., Ecosystem valuation and eco-compensation for conservation of traditional paddy ecosystems and varieties in Kerala, India. Ecosyst. Serv., 2021, 49, 101272.
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- Nutrient intake disparities among public distribution system beneficiaries in the Bundelkhand region
Abstract Views :75 |
PDF Views:25
Authors
Surjya Kanta Roy
1,
Satyapriya
1,
Venu Lenin
1,
Sitaram Bishnoi
1,
Girish Kumar Jha
1,
Pramod Kumar
1,
Sujay B. Kademani
2,
P. N. Fatheen Abrar
1,
Amandeep Ranjan
1
Affiliations
1 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Institute of Agricultural Biotechnology, Ranchi 834 010, IN
1 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Institute of Agricultural Biotechnology, Ranchi 834 010, IN
Source
Current Science, Vol 127, No 2 (2024), Pagination: 214-221Abstract
There is a consistent association between dietary diversity, both in terms of quantity and pattern of food consumption, and inadequate growth, development and long-term health outcomes. A total of 16 villages in the Bundelkhand region were chosen to evaluate calorie and nutrition intake using a 24-hour recall time. Caloric and nutrient intake in Chitrakoot district deviated from the recommended dietary allowance, with insufficient dietary diversity. This deficiency, excluding protein and phosphorus consumption, adversely affected the under-nutrition status among beneficiaries of the public distribution system.Keywords
Calorie intake, household dietary diversity, nutrient gap, public distribution system, under-nutrition.- Artificial intelligence for crop yield prediction: a bibliometric analysis
Abstract Views :131 |
Authors
Affiliations
1 The Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India; ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 Division of Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
3 Division of Agricultural Economics, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
4 Division of Computer Application, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
1 The Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India; ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 Division of Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
3 Division of Agricultural Economics, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
4 Division of Computer Application, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 126, No 10 (2024), Pagination: 1245-1253Abstract
The synergy between artificial intelligence (AI) and agricultural sciences has garnered substantial attention, especially in the realm of crop yield prediction. The present bibliometric analysis examines the worldwide research trends about the application of AI in predicting crop yields. The global literature on crop yield prediction using AI published between 1997 and 2022 is searched in the Scopus database. Five hundred and forty research articles were used to compile the analysis; they were located in the Scopus database and processed through the VOSviewer. Our research reveals a significant surge in scholarly publications, particularly focusing on countries including China, the United States, India and Canada. These research endeavours aim to apply AI methodologies for forecasting agricultural produce yields in tandem with developments in remote sensing technologies that facilitate more accurate yield predictions. These insights offer a valuable reference for researchers and illuminate potential directions for future investigations in the domain of AI-based crop yield predictionKeywords
: Artificial intelligence, bibliometric analysis, crop yield prediction, deep learning, machine learning, remote sensing, VOSviewer.Full Text
- Economic Incentives for Sustainable Legume Production in India: A Valuation Approach Internalizing Risk Sharing and Environmental Benefits
Abstract Views :310 |
PDF Views:137
Authors
Affiliations
1 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Institute of Soil and Water Conservation, Research Centre, Bellary 583 104, IN
1 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Institute of Soil and Water Conservation, Research Centre, Bellary 583 104, IN
Source
Current Science, Vol 119, No 7 (2020), Pagination: 1184-1189Abstract
The present study estimates the social cost of growing paddy, wheat and legumes as Rs 9484, 8804 and 1281 per ha respectively. Monetized value of overall risk in paddy, wheat and legumes is Rs 716, 650 and 1738 per ha respectively. An economic incentive consisting of risk and social benefits, to the tune of Rs 8611 and 9225 per ha over wheat and paddy respectively, should be provided for the production of legumes. The study highlights the need to internalize environmental benefits of legumes vis-à-vis competing crops, and accordingly cultivation of legumes needs to be encouraged through a proper mechanism of incentivization.Keywords
Economic Incentive, Environmental Benefits, Legumes, Risk Sharing, Rice, Wheat.References
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- A Hybrid Approach for Forecasting Mustard Price having Long-Memory Property
Abstract Views :261 |
PDF Views:131
Authors
Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 124, No 5 (2023), Pagination: 632-635Abstract
For the modelling of time series data having long memory properties, we generally use the autoregressive fractionally integrated moving average (ARFIMA) model. This model performs well compared to the autoregressive integrated moving average (ARIMA) model. However, it cannot capture the nonlinear property of the data. In order to achieve the desired and accurate forecasts, hybridizing the existing forecasting models is an important technique. The hybrid time-series model combines the strength of individual models. Accordingly, this study proposes a hybrid model based on ARFIMA and extreme learning machine (ELM) for agricultural time-series data with long memory properties. For evaluation of the proposed model, the daily mustard price (₹/q) of Agra and Bharatpur markets from 1 January 2016 to 31 January 2020 was used. Empirical results show that the forecasting performance of the proposed hybrid model based on ARFIMA and ELM is better than the existing models.Keywords
Hybrid Model, Long Memory, Mustard, Price Forecasting, Time-Series Data.References
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