Refine your search
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sirohi, Smita
- Sustainability of Dairy Breeding Practices : Empirical Evidences of Semi-arid Eastern Zone of Rajasthan
Abstract Views :300 |
PDF Views:0
Authors
Prem Chand
1,
Smita Sirohi
1
Affiliations
1 Dairy Economics, Statistics and Management Division, National Dairy Research Institute, Karnal, Haryana, IN
1 Dairy Economics, Statistics and Management Division, National Dairy Research Institute, Karnal, Haryana, IN
Source
Research Journal of Animal Husbandry & Dairy Science, Vol 4, No 2 (2013), Pagination: 47-50Abstract
The breeding practices followed by the farmers affect the genetic potential of the animals and hence, have long-run implications for sustainability of dairy farming. With the use of low quality germplasm, the productive breeds are getting progressively diluted and face degeneration. Such a loss of animal genetic diversity puts in jeopardy the sustainability of animal agriculture and the ability of the sector to respond to changing environmental conditions. The present study therefore examines the sustainability of dairy breeding practices followed by farmers in semi-arid eastern plain zone of Rajasthan based on primary data collected from 120 sample households of the zone. A composite index on 100 point scale was developed based on standardized scores assigned to each breeding practice as per their implications on sustainability. The results of study showed that the livestock breeding practices were low sustainable in the zone as the average value of index was only 51 on the scale. The sustainability was comparatively higher on large farmers (value of index 55.47) as compared to small farmers (value of index 48.16). The breeding infrastructural facilities were poor in the study area. Study suggests for strengthening the infrastructural facilities, extension facilities, vocational trainings particularly for farm women, etc. for improving the sustainability of breeding practices in the zone.Keywords
Sustainability, Dairy Breeding, Indexing, Livestock Support Services, Genetic Diversity
- Technical Efficiency of Cooperative Member Vis-A-Vis Non-Member Dairy Farms in Gujarat-Application of Data Envelopment Analysis
Abstract Views :317 |
PDF Views:133
Authors
Darshnaben Mahida
1,
R. Sendhil
1,
Smita Sirohi
1,
B. S. Chandel
1,
K. Ponnusamy
1,
Gopal Sankhala
1
Affiliations
1 Division of Dairy Economics, Statistics and Management, ICAR-National Dairy Research Institute, Karnal - 132 001, Haryana, IN
1 Division of Dairy Economics, Statistics and Management, ICAR-National Dairy Research Institute, Karnal - 132 001, Haryana, IN
Source
Indian Journal of Economics and Development, Vol 6, No 2 (2018), Pagination: 1-9Abstract
Objectives: To compare the technical efficiency scores of dairy cooperative member and non-member farms across the districts in Gujarat selected from regions having different level of dairy development.
Methods/Statistical Analysis: The present study has analyzed and compared the technical efficiency of 180 dairy farmers using a non-parametric approach i.e., Data Envelopment Analysis (DEA). The study is based on the primary data collected during 2016-17 using a well-structured, comprehensive and pre-tested interview schedule. Apart from conventional analysis, box-plot and scatter-plot were used to compare the efficiency scores.
Findings: The investigation identified regional disparities in efficiency scores based on dairy development. The DEA results showed that member farmers of the district selected from low (Tapi), moderate (Bharuch) and highly (Anand) dairy developed regions were more efficient than their respective non-member counterpart. Similarly, the overall comparison between dairy cooperative members and non-members showed that the efficiency of member farmers was 83.27% while for non-members it was 75.31%. Further, the results revealed that small herd size farmers were most efficient in both member (87.21%) and non-member (81.59%) categories. The paper established that membership in dairy cooperatives; herd size as well as status of dairy development in a region greatly influences the technical efficiency of farmers.
Application/Improvements: Overall, the study concludes that 24.69% and 16.73% inefficiency exist respectively in dairy cooperatives non-member and member farms indicating the scope for increasing the realized output with same level of resources and production technology.
Keywords
Gujarat, Technical Efficiency, Dairy Cooperatives, Member, Non-Member, Data Envelopment Analysis.Full Text
References
- F. Karanja, D. Gilmour,I. Fraser. Dairy productivity growth, efficiency change and technological progress in Victoria. Agricultural and Resource Economics Society. 2012; 1-27.
- D. Bardhan, M.L. Sharma. Technical efficiency in milk production in underdeveloped production environment of India. Springer Plus. 2013, 2(65).
- K.B. Kale, K. Ponnusamy, A.K. Chakravaty, R. Sendhil, A. Mohmmad. Assessing resource and infrastructure disparities to strengthen Indian dairy sector. Indian Journal of Animal Sciences. 2016, 86, 720-725.
- I. Fraser, D. Cordina. An application of data envelopment analysis to irrigated dairy farms in Northern Victoria, Australia. Agricultural Systems. 1999, 59(3), 267-282.
- I. Fraser, P. Hones. Farm level efficiency and productivity measurement using panel data. The Australian Journal of Agricultural and Resource Economics.2001, 215-232.
- J.R. Stokes, P.R. Tozer, J. Hyde. Identifying efficient dairy producers using data envelopment analysis. Journal of Dairy Science. 2007, 90(5), 2555-2562.
- D. Mahida, R. Sendhil. Data analysis tools and approaches (data) in agricultural sciences. ICAR-Indian Institute of Wheat and Barley Research. 2017, 54-56.
- A. Gelan, B. Muriithi. Measuring and explaining technical efficiency of dairy farms: a case study of smallholder farms in East Africa. 2010.
- Data Envelopment Analysis. https://www.researchgate.net/publication/321824482_Data_Envelopment_Analysis. Date accessed: 16/12/2017.
- E. Silva, A. Arzubi, J. Berbel. An application of data envelopment analysis (Dea) in Azores dairy farms. New Medit. 2004, 3, 39-43.
- V. Saravanakumar. Factor demand output supply and technical efficiency of milk production in Tamil Nadu. National Dairy Research Institute, Karnal. 2005, 1-246.
- S. Sirohi, D. Bardhan. Costs and returns in milk production: developing standardized methodology and estimates for various production systems. Project Report submitted to Department of Animal Husbandry. 2015.
- S. Mor, S. Sharma. Technical efficiency and supply chain practices in dairying: the case of India. Agricultural Economics (CZECH).2012, 8(2), 85–91.
- Tracking the Disparities in Gujarat Dairy Development – An Application of Biplot Analysis
Abstract Views :206 |
PDF Views:66
Authors
Darshnaben P. Mahida
1,
R. Sendhil
2,
Smita Sirohi
1,
B. S. Chandel
1,
K. Ponnusamy
1,
Gopal Sankhala
1
Affiliations
1 ICAR-National Dairy Research Institute, Karnal 132 001, IN
2 ICAR-Indian Institute of Wheat and Barley Research, Karnal 132 001, IN
1 ICAR-National Dairy Research Institute, Karnal 132 001, IN
2 ICAR-Indian Institute of Wheat and Barley Research, Karnal 132 001, IN
Source
Current Science, Vol 114, No 10 (2018), Pagination: 2151-2155Abstract
Gujarat, despite being a highly progressive state of India in terms of dairying, has great potential to enhance its milk production and make dairying a more lucrative enterprise. It is home to many high quality dairy animal breeds and has a very active milk cooperative structure which can accelerate the possibilities of uplifting the level of milk production further, if proper and balanced micro level development policies are promoted. The present study depicts the causes for disparities besides analysing the strengths and weaknesses in dairying across 26 districts in Gujarat. To identify and capture the variation in resource use which causes disparities in dairy development, the principal component analysis-based biplot technique was employed. Data on different variables like resource availability, infrastructure and veterinary facilities, and, milking animals and their yields have been sourced from 26 districts of Gujarat for tracing the disparity. The conclusions drawn from the biplot imply that promoting the quality of animal breeds and increasing the population of high yielding cattle breeds in low-developed districts can lead to high milk production. In the setting of increased milk production, the cooperative milk marketing structure will become more dynamic and result in enhanced income for dairy producers.Keywords
Biplot, Eigen Value, Gujarat Dairy Development, PCA, Regional Disparity.Full Text
References
- NDDB, 2015–16; http://dahd.nic.in/sites/default/files/NDDB%-20AR%202015-16.pdf (accessed on 18 May 2017).
- Belhekar, S. and Dash, S., Role of dairy industry in rural development in India. Indian J. Res., 2016, 5(11), 509–510.
- Chakraborty, S., On world milk day, a look at how India became the largest producer and why it continues to be so. Financial Express, 2017; http://www.financialexpress.com/economy/on-world-milk-day-a-look-at-how-india-became-the-largest-producer-and-why-it-continues-to-be-so/695991/
- Statistics Times, 2015; http://statisticstimes.com/economy/gdp-of-indian-states.php (accessed on 26 April 2017).
- Times of India, 2014; http://timesofindia.indiatimes.com/india/Centre-studies-Modis-animal-hostel-scheme/articleshow/37878661.cms (accessed on 18 May 2017).
- Kale, R. B., Ponnusamy, K., Chakravarty, A. K., Sendhil, R. and Mohammad, A., Assessing resource and infrastructure disparities to strengthen Indian dairy sector. Indian J. Anim. Sci., 2016, 86(6), 720–725.
- Gabriel, K. R., The biplot graphic display of matrices with application to principal component analysis. Biometrika, 1971, 58(3), 453–467.
- Torres-Salinas, D., Robinson-Garcia, N., Jimenez-Contreras, E., Herrera, F. and Lopez-Cozar, E. D., On the use of Biplot analysis for multivariate bibliometric and scientific indicators. J. Am. Soc. Infor. Sci. Technol., 2013, 64, 1468–1479; doi:10.1002/asi.22837.
- Jacoby, W. G., Statistical Graphics for Visualizing Multivariate Data, Sage publication, New Delhi, 1998.
- Kroonenberg, P. M., Applied Multiway Data Analysis, John Wiley & Sons, Inc. Publication, 2007.
- Rana, V., Ram, S., Sendhil, R., Nehra, K. and Sharma, I., Physio-logical, biochemical and morphological study in wheat (Triticum aestivum L.) RILs population for salinity tolerance. J. Agric. Sci., 2015, 7, 119–128; doi:0.5539/jas.v7n10p119.
- Yan, W. and Rajcan, I., Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci., 2002, 42, 11–20.
- Rubio, J., Cubero, J. I., Martın, L. M. and Suso, M. J., Flores, F., Biplot analysis of trait relations of white lupin in Spain. Euphytica, 2004, 135, 217–224; doi:10.1023/B:EUPH.0000014911.70355.c9
- Shah, J. and Dave, D., Regional trends and pattern in milk production and drivers for future growth in Gujarat state. Agric. Econ. Res. Rev., 2010, 23, 259–302.
- Addressing Agricultural Income Risks in India: Efficacy of Risk Management Options in Hazard-Prone Regions
Abstract Views :161 |
PDF Views:85
Authors
Affiliations
1 Punjab Agricultural University, Ludhiana 141 00, India, IN
2 Embassy of India to the European Union, Belgium, and Luxembourg, 217 Chaussee de Vleurgat, 1050 Brussels, BE
1 Punjab Agricultural University, Ludhiana 141 00, India, IN
2 Embassy of India to the European Union, Belgium, and Luxembourg, 217 Chaussee de Vleurgat, 1050 Brussels, BE
Source
Current Science, Vol 122, No 2 (2022), Pagination: 178-186Abstract
The present study discusses how formal risk management options (RMOs) have evolved in reducing agricultural income loss. It also considers the dependency of farm households on informal RMOs. Based on our analysis of a representative sample of 180 rural households, we conclude that the formal RMOs such as insurance and commercial credit markets need to improve. Addressing the complexity of claim assessment and the problem of information lop-sidedness in the insurance market should be a top priority for reducing inefficiency. Ascertaining the adequacy of claim amount in comparison to the loss is also important. The absence or underdevelopment of the formal market leads to efficient utilization of informal RMOs to reduce income risk. In this regard, informal ex-ante RMOs may be advantageous to the stable rise of farmers’ income and development of the rural financial system in the long run.Keywords
Addressing Risk, Finance Markets, Finance Service, Income Risk.Full Text
References
- Lesk, C., Rowhani, P. and Ramankutty, N., Influence of extreme weather disasters on global crop production. Nature, 2016, 529, 84–87.
- Pelling, M. et al., Reducing Disaster Risk: A Challenge for Development, United Nations, New York, 2004, 32; http://archive-ouverte.unige.ch/unige:77685
- Scott-Smith, T., Paradoxes of resilience: a review of the world disasters report 2016. Dev. Change, 2018, 49, 662–677.
- Cannon, T., World Disasters Report 2014 – focus on culture and risk, 2014; https://www.ifrc.org/world-disasters-report-2014
- Field, C. B. et al. (eds), Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2012.
- Diersen, M. and Taylor, G., Examining economic impact and recovery in South Dakota from the 2002 drought, Economic Staff Paper Series, 2003, 173; https://openprairie.sdstate.edu/cgi/viewcontent.cgi?article=1172&context=econ_staffpaper
- Bazza, M., Kay, M. and Knutson, C., Drought characteristics and management in North Africa and the Near East. FAO Water Rep., 2018, 45, 1020–1023.
- Mohanty, S., Wassmann, R., Nelson, A., Moya, P. and Jagadish, S. V. K., Rice and Climate Change: Significance for Food Security and Vulnerability, International Rice Research Institute, 2013, vol. 14, pp. 1–14.
- Food and Agriculture Organization of the United Nations, Disaster risk reduction: strengthening livelihood resilience, 2013; http:// www.fao.org/docrep/018/i3325e/i3325e15.pdf
- Singh, N. P., Anand, B. and Khan, M. A., Micro-level perception to climate change and adaptation issues: a prelude to mainstreaming climate adaptation into developmental landscape in India. Nat. Hazards, 2018, 92, 1287–1304.
- Shiferaw, B., Tesfaye, K., Kassie, M., Abate, T., Prasanna, B. M. and Menkir, A., Managing vulnerability to drought and enhancing livelihood resilience in sub-Saharan Africa: technological, institutional and policy options. Weather Climate Extrem., 2014, 3, 67–79.
- Yang, X., Liu, Y., Bai, W. and Liu, B., Evaluation of the crop insurance management for soybean risk of natural disasters in Jilin Province, China. Nat. Hazards, 2015, 76, 587–599.
- Marichamy, K. and Aananthi, N., Kisan Credit Card – a boon to small farmers in India. Tactful Manage. Res. J., 2014, 8, 1–6.
- Clarke, D. J., Mahul, O., Rao, K. N. and Verma, N., Weather based crop insurance in India. World Bank Policy Research Working Paper 5985, 2012.
- Kanwal, V., Sirohi, S. and Chand, P., Farmers’ perception on climate extremes and their coping mechanism: evidences from disaster prone regions of India. Indian J. Tradit. Knowl., 2021, 20(2), 512– 519.
- Thorat, V. S. and Sirohi, S., Income risk and management strategies of rural households: evidence from distressed regions of Maharashtra. Agric. Econ. Res. Rev., 2018, 31, 101.
- Abbas, A. et al., Sustainable survival under climatic extremes: linking flood risk mitigation and coping with flood damages in rural Pakistan. Environ. Sci. Pollut. Res., 2018, 25, 32491–32505.
- Lyu, K. and Barré, T. J., Risk aversion in crop insurance program purchase decisions: evidence from maize production areas in China. China Agric. Econ. Rev., 2017, 9, 62–80.
- Bhuiyan, C., Singh, R. P. and Kogan, F. N., Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. Int. J. Appl. Earth Obs. Geoinf., 2006, 8, 289–302.
- Shinde, S. S. and Modak, P., Vulnerability of Indian agriculture to climate change. In Climate Vulnerability: Understanding and Addressing Threats to Essential Resources, Elsevier, 2013, pp. 139–152.
- Wilk, J., Jonsson, A. C., Rydhagen, B., Rani, A. and Kumar, A., The perspectives of the urban poor in climate vulnerability assessments – the case of Kota, India. Urban Climate, 2018, 24, 633–642.
- Upadhyaya, H., Vulnerability and Adaptation to Climate Change in the Context of Water Resource with Reference to Rajasthan, Ph.D. thesis, IIS University, Jaipur.
- Prasad, A. K., Vinay Kumar, K., Singh, S. and Singh, R. P., Potentiality of multi-sensor satellite data in mapping flood Hazard. J. Indian Soc. Remote Sensing, 2006, 34, 219–231.
- Wang, Y., Income uncertainty, risk coping mechanism and farmer production and management decision: an empirical study from Sichuan province. In Agriculture and Agricultural Science Procedia, Elsevier B.V., 2010, pp. 230–240.
- Press Information Bureau, 2021, 5; https://static.pib.gov.in/ WriteReadData/specificdocs/documents/2021/mar/doc202131981.pdf 2
- Rajeev, M. and Nagendran, P., Where do we stand? Crop insurance in India. Rev. Rural Aff., 2019, 54, 26–27.
- Rai, R., Pradhan Mantri Fasal Bima Yojana: an assessment of India’s crop insurance scheme. ORF Issue Brief, No. 296, Observer Research Foundation, India, 2019.
- Krishna, V. V., Aravalath, L. M. and Vikraman, S., Does caste determine farmer access to quality information? PLOS ONE, 2019, 14(1), e0210721.
- Padmaja, S. S. and Ali, J., Correlates of agrarian indebtedness in rural India. J. Agribus. Dev. Emerg. Econ., 2019, 9, 125–138.
- Kumar, A., Singh, R. K. P., Jee, S., Chand, S., Tripathi, G. and Saroj, S., Dynamics of access to rural credit in India: patterns and determinants. Agric. Econ. Res. Rev., 2015, 28, 151.
- Golait, R., Current issues in agriculture credit in India: an assessment. Reserv. Bank India Occas. Pap., 2007, 28, 79–99.
- Singh, S., Kaur, M. and Kingra, H. S., Inadequacies of institutional agricultural credit system in Punjab state 1. Agric. Econ. Res. Rev., 2009, 22, 309–318.
- Cariappa, A. A. and Sendhil, R., Does institutional credit induce on-farm investments? Evidence from India. In 31st International Conference of Agricultural Economists, International Association of Agricultural Economists, 2021.
- Cole, S., Stein, D. and Tobacman, J., Dynamics of demand for index insurance: evidence from a long-run field experiment. Am. Econ. Rev., 2014, 104, 284–290.
- Matul, M., Dalal, A., de Bock, O. and April, W. G., Why people do not buy microinsurance and what we can do about it. Briefing note 17, Microinsurance Innovation Facility, International Labour Office, 2013; http://www.impactinsurance.org/sites/default/files/brnote17_en.pdf
- Drèze, J., Lanjouw, P. and Sharma, N., Credit in Rural India: a case study. Development Economics Research Paper 6. Development Economics Research Programme, Suntory and Toyota International Centres for Economics and Related Disciplines, London, 1997; https://sticerd.lse.ac.uk/dps/de/dedps6.pdf
- Guirkinger, C., Understanding the coexistence of formal and informal credit markets in Piura, Peru. World Dev., 2008, 36, 1436–1452.
- Boucher, S. and Guirkinger, C., Risk, wealth, and sectoral choice in rural credit markets. Am. J. Agric. Econ., 2007, 89, 991–1004.