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Singh, K. N.

The Impact of Crop Diversification Towards High-Value Crops on Economic Welfare of Agricultural Households in Eastern India

Abstract Views :300 | PDF Views:88

Authors

A. R. Anuja ¹, Anjani Kumar ², Sunil Saroj ², K. N. Singh ¹

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 South Asia Office of the International Food Policy Research Institute, New Delhi 110 012, IN

Source

Current Science, Vol 118, No 10 (2020), Pagination: 1575-1582

Abstract

Eastern India is among the most backward regions of the country with underutilizedagricultural potential. Diversification towards high-value crops can be a promising strategy to enhance farmers’ economic welfare in the region. The present study analyses the major determinants and impact of crop diversification towards high-value crops on farmers’ economic welfare in the region using large farm household-level data and advanced matching estimation methods. The findings reveal that cultivation of high-value crops plays a significant role in enhancing farm income, consumption expenditure and reducing poverty. Growers need to allocate at least 40% area for high-value crops to have significant income enhancement and poverty reduction.

Keywords

Agricultural Households, Diversification, Economic Welfare, High-Value Crops.

Full Text

References

GoI, Report of the expert group to review the methodology for measurement of poverty. Planning Commission, Government of India, 2014.

Joshi, P. K. and Kumar, A., In Transforming Agriculture in Eastern India: Challenges and Opportunities(eds Ramasamy, C. and Ashok, K. R.), Academic Foundation, New Delhi, 2016.

Chand, R., Doubling farmers’ income rationale, strategy, pro-spects and action plan. NITI Policy Paper No. 1, GoI, 2017.

Joshi, P. K., Laxmi, T. and Birthal, P. S., Diversification and its impact on smallholders: evidence from a study on vegetable production. Agric. Econ. Res. Rev., 2006, 19(2), 219–236.

Ryan, J. G. and Spencer, D. C., Future challenges and opportunities for agricultural R&D in the semi-arid tropics. International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 2001.

Van den Berg, M., Hengsdijk, H., Wolf, J., Ittersum, M. V., Guanghuo, W. and Roetter, R., The impact of increasing farm size and mechanization on rural income and rice production in Zhejiang Province, China. Agric. Syst., 2007, 94, 841–850.

Kahan, D., Managing risk in farming. In Farm Management Extension Guide 3, Food and Agriculture Organization of the United Nations, Rome, Italy, 2008, pp. 29–87.

Sharma, H. R., Crop diversification in Himachal Pradesh: patterns, determinants and challenges. Indian J. Agric. Econ., 2011, 66(1), 97–114.

Birthal, P. S., Roy, D. and Negi, D. S., Assessing the impact of crop diversification on farm poverty in India. World Dev., 2015, 72, 70–92.

Thapa, G., Kumar, A. and Joshi, P. K., Agricultural diversification in Nepal: Status, determinants, and its impact on rural poverty. Discussion Paper 01634, IFPRI – South Asia Office, New Delhi, 2017.

Kumar, A., Rural employment diversification in eastern India: trends and determinants. Agric. Econ. Res. Rev., 2009, 22, 47–60.

Dasgupta, S. and Bhaumik, S. K., Crop diversification and agricultural growth in West Bengal. Indian J. Agric. Econ., 2014, 69(1), 107–124.

Bhatt, B. P. and Mishra, J. S., Prospects of bringing second green revolution in eastern India. In Souvenir of the 4th International Agronomy Congress, 2016, pp. 22–36.

Nayak, D. K., Changing cropping pattern, agricultural diversification and productivity in Odisha – a district-wise study. Agric. Econ. Res. Rev., 2016, 29(1), 93–104.

National Sample Survey Organization (NSSO), Unit-level data of NSSO. 59th Round Situation Assessment Survey of Farmers, Ministry of Statisticsand Programme Implementation, GoI, 2003.

NSSO, Unit-level data of NSSO. 70th Round Situation Assessment Survey of Farmers, Ministry of Statistics and Programme Implementation, GoI, 2013.

Ali, A. and Abdulai, A., The adoption of genetically modified cotton and poverty reduction in Pakistan. J. Agric. Econ., 2010, 61(1), 175–192.

Heckman, J., Ichimura, H. and Todd, P., Matching as an econometric evaluation estimator: evidence from evaluating a job training programme. Rev. Econ. Stud., 1997, 64, 605–654.

Iacus, S., King, G. and Porro, G., Causal inference without balance checking: coarsened exact matching. Pol. Anal., 2001, 1–24.

Hirano, K. and Imbens, G. W., The propensity score with continuous treatments. In Applied Bayesian Modeling and Causal Inference from Incomplete – Data Perspectives(eds Gelman, A. and Meng, X. L.), Wiley Inter Science, West Sussex, England, UK, 2004, pp. 73–84.

Anwarul, H., Rajkhowa, P. and Gulati, A., Transforming Agriculture in Odisha: Sources and Drivers of Agriculture Growth, Working Paper No. 337, Indian Council for Research on International Economic Relations, New Delhi, 2017.

Haque, T., Bhattacharya, M., Sinha, G., Kalra, P. and Thomas, S., Constraints and Potentials of Diversified Agricultural Development in Eastern India, Project Report, Council for Social Development, New Delhi, 2010.

GoI, Report of the Working Group on Agricultural Development in Eastern and Northeastern India for the Formulation of the Tenth Five Year Plan, Planning Commission, Government of India, 2001.

Smriti, V., Gulati, A. and Hussain, S., Doubling agricultural growth in Uttar Pradesh: sources and drivers of agricultural growth and policy lessons. Working Paper No. 335, Indian Council for Research on International Economic Relations, New Delhi, 2017.

Birthal, P. S., Joshi, P. K., Roy, D. and Thorat, A., Diversification in Indian agriculture towards high-value crops – the role of smallholders, Discussion Paper No. 00727, Markets, Trade and Institutions Division, International Food Policy Research Institute, Washington, DC, USA, 2007.

Kumar, A., Kumar, P. and. Sharma, A. N., Crop diversification in eastern India: status and determinants. Indian J. Agric. Econ., 2012, 67(4), 600–616.

Thapa, G., Kumar, A., Roy, D. and Joshi, P. K., Impact of crop diversification on rural poverty in Nepal. Can. J. Agric. Econ., 2018, 66, 379–413.

Modelling and forecasting cotton production using tuned-support vector regression

Abstract Views :213 | PDF Views:81

Authors

Amit Saha ¹, K. N. Singh ², Mrinmoy Ray ², Santosha Rathod ³, Sharani Choudhury ⁴

Affiliations
1 Central Sericultural Research and Training Institute, Central Silk Board, Srirampura, Mysuru 570 008, India, IN
2 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India, IN
3 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India, IN
4 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India, IN

Source

Current Science, Vol 121, No 8 (2021), Pagination: 1090-1098

Abstract

India is the largest producer of cotton in the world. For proper planning and designing of policies related to cotton, robust forecast of future production is utmost necessary. In this study, an effort has been made to model and forecast the cotton production of India using tuned-support vector regression (Tuned-SVR) model, and the importance of tuning has also been pointed out through this study. The Tuned-SVR performed better in both modelling and forecasting of cotton production compared to auto regressive integrated moving average and classical SVR models

Keywords

ARIMA, cotton production forecasting, SVR, time series, tuned-SVR.

Full Text

References

Box, G. E. P. and Jenkins, G., Time series analysis, forecasting and control. Holden-Day, San Francisco, CA, 1970.

Ariyo, A. A., Adewumi, A. O. and Ayo, C. K., Stock price prediction using the ARIMA model. In UK Sim-AMSS 16th International Conference on Computer Modelling and Simulation, IEEE, 2014, pp. 106–112.

Badmus, M. A. and Ariyo, O. S., Forecasting cultivated areas and production of maize in Nigerian using ARIMA Model. Asian J. Agric. Sci., 2011, 3(3), 171–176.

Bari, S. H., Rahman, M. T., Hussain, M. M. and Ray, S., Forecasting monthly precipitation in Sylhet city using ARIMA model. Civil Environ. Res., 2015, 7(1), 69–77.

Suresh, K. K. and Priya, S. K., Forecasting sugarcane yield of Tamil Nadu using ARIMA models. Sugar Tech., 2011, 13(1), 23–26.

Padhan, P. C., Application of ARIMA model for forecasting agricultural productivity in India. J. Agric. Soc. Sci., 2012, 8(2), 50– 56.

Prabakaran, K. and Sivapragasam, C., Forecasting areas and production of rice in India using ARIMA model. Int. J. Farm Sci., 2014, 4(1), 99–106.

Sarika, Iquebal, M. A. and Chattopadhyay, C., Modelling and forecasting of pigeonpea (Cajanuscajan) production using autoregressive integrated moving average methodology. Indian J. Agric. Sci., 2011, 81(6), 520–523.

Cortes, C. and Vapnik, V., Support-vector network. Mach. Learn., 1995, 20, 1–25.

Vapnik, V., Golowich, S. and Smola, A., Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems (eds Mozer, M., Jordan, M. and Petsche, T.), MIT Press, Cambridge, USA, 1997, vol. 9, pp. 281–287.

Mattera, D. and Haykin, S., Support vector machines for dynamic reconstruction of achaotic system. In Advances in Kernel Methods – Support Vector Learning (eds Schölkopf, B. et al.), MIT Press, Cambridge, USA, 1999, pp. 211–242.

Muller, K. R., Smola, A., R¨atsch, G., Schölkopf, B., Kohlmorgen, J. and Vapnik, V., Predicting time series with support vector machines. In Artificial Neural Networks (eds Gerstner, W. et al.), ICANN 1997, Lecture Notes in Computer Science, Springer, Berlin, Germany, 1997, vol. 1327, pp. 999–1004.

Niu, D., Wang, Y. and Wu, D. D., Power load forecasting using support vector machine and ant colony optimization. Exp. Syst. Appl., 2010, 37, 2531–2539.

Saha, A., Singh, K. N., Ray, M. and Rathod, S., A hybrid spatiotemporal modelling: an application to space-time rainfall forecasting. Theor. Appl. Climatol., 2020, 142, 1271–1282.

Saha, A. and Bhattacharyya, S., Artificial insemination for milk production in India: a statistical insight. Indian J. Anim. Sci., 2021, 90, 1186–1190.

Stitson, M., Gammerman, A., Vapnik, V., Vovk, V., Watkins, C. and Weston, J., Support vector regression with ANOVA decomposition kernels. In Advances in Kernel Methods – Support Vector Learning (eds Schölkopf, B., Burges, C. J. C. and Smola, A. J.), MIT Press, Cambridge, USA, 1999, pp. 285–292.

Ortiz-Garcia, E. G., Salcedo-Sanz, S. and Casanova-Mateom, C., Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data. Atmos. Res., 2014, 139, 128–136.

Kumar, T. L. M. and Prajneshu, Development of hybrid models for forecasting time-series data using nonlinear SVR enhanced by PSO. J. Stat. Theory Prac., 2015, 9(4), 699–711.

Rathod, S., Singh, K. N., Patil, S. G., Naik, R. H., Ray, M. and Meena, V. S., Modeling and forecasting of oilseed production of India through artificial intelligence techniques. Indian J. Agric. Sci., 2018, 88(1), 22–27.

De Giorgi, M. G., Campilongo, S., Ficarella, A. and Congedo, P. M., Comparison between wind power rediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energy, 2014, 7, 5251–5272.

Balasundaram, S. and Gupta, D., Lagrangian support vector regression via unconstrained convex minimization. Neural Networks, 2014, 51, 67–79.

Balasundaram, S. and Gupta, D., On implicit Lagrangian twin support vector regression by Newton method. Int. J. Comput. Intel. Syst., 2014, 7(1), 50–64.

Balasundaram, S. and Gupta, D., Training Lagrangian twin support vector regression via unconstrained convex minimization. Knowl.-Based Syst., 2014, 59, 85–96.

Balasundaram, S. and Gupta, D., On optimization based extreme learning machine in primal for regression and classification by functional iterative method. Int. J. Mach. Learn. Cybernet., 2016, 7(5), 707–728.

Gupta, D., Richhariya, B. and Borah, P., A fuzzy twin support vector machine based on information entropy for class imbalance learning. Neural. Comput. Appl., 2019, 31(11), 7153–7164.

Gupta, U. and Gupta, D., An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function. Appl. Intell., 2019, 49(10), 3606–3627.

Hou, Q., Zhang, J., Liu, L., Wang, Y. and Jing, L., Discriminative information-based nonparallel support vector machine. Signal. Process., 2019, 162, 169–179.

Meyer, D. et al., Package ‘e1071’. The R Journal, 2019.

Pattern of crop diversification and its implications on undernutrition in India

Abstract Views :198 | PDF Views:84

Authors

A. R. Anuja ¹, G. P. Shivaswamy ¹, Mrinmoy Ray ¹, K. N. Singh ¹

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN

Source

Current Science, Vol 122, No 10 (2022), Pagination: 1154-1160

Abstract

The present study explores the pattern and extent of food-crop diversification and its implications on nutritional indicators in India using district-level data for the most recent period. It relied on data from land-use statistics and the National Family Health Survey 2015–16. We estimated the Simpson index for food-crop diversification and undernutrition index for nutritional status. The association of crop diversification and nutritional status was analysed employing bivariate copula function. The findings show striking regional differences in the extent of food-crop diversification and nutritional outcomes. The results of the copula function indicate a significant inverse relationship between crop diversification and undernutrition

Keywords

Bivariate copula, crop diversification, land use, nutritional status, undernutrition index.

Full Text

References

GoI, State of Indian agriculture. Ministry of Agriculture and Farmer’s Welfare, Government of India, 2016; http://agricoop.nic.in/otherreports/state-indian-agriculture-2017

Chand, R., Doubling farmers’ income: rationale, strategy, prospects and action plan. Policy Paper No. 1, NITI Aayog, GoI, 2017; https://niti.gov.in/writereaddata/files/document_publication/DOUBLING%20FARMERS%20INCOME.pdf

GoI, The Economic Survey 2017–18, Ministry of Finance, Government of India, 2018; http://mofapp.nic.in:8080/economicsurvey/

UN, #Envision2030 Goal 2: Zero Hunger, United Nations, New York, USA, 2015; https://www.un.org/development/desa/disabilities/envision2030-goal2.html

NITI Aayog, The SDG India Index 2019–20, GoI, 2019; https:// niti.gov.in/sdg-india-index-dashboard-2019-20

International Institute for Population Sciences (IIPS) and ICF, National Family Health Survey (NFHS-4), 2015–16, IIPS, Mumbai, 2017; http://rchiips.org/nfhs/NFHS-4Report.shtml

Radhakrishna, R. and Panda, M., Macroeconomics of poverty reduction: India case study, Indira Gandhi Institute of Development Research, Mumbai, 2006; http://oii.igidr.ac.in:8080/xmlui/handle/ 2275/180

Bobojonov, I. et al., Crop diversification in support of sustainable agriculture in Khorezm. In Cotton, Water, Salts and Soums Economic and Ecological Restructuring in Khorezm, Uzbekistan (eds Martius, C. et al.), Springer, Dordrecht, The Netherlands, 2012, pp. 219–233; https://link.springer.com/chapter/10.1007/978-94-0071963-7_14

Pingali, P. L. and Rosegrant, M. W., Agricultural commercialization and diversification: processes and policies. Food Policy, 1995, 20(3), 644–651; https://doi.org/10.1016/0306-9192(95)00012-4

Guvele, C. A., Gains from crop diversification in the Sudan Gezira scheme. Agric. Syst., 2001, 70, 319–333; https://doi.org/10.1016/ S0308-521X(01)00030-0.

Ryan, J. G. and Spencer, D. C., Future challenges and opportunities for agricultural R&D in the semi-arid tropics. International Crops Research Institute for the Semi-Arid Tropics, Patancheru, 2001; http://www.icrisat.org/PDF/Outlook%20rep%20Future%20Challenges%20in%20SAT-594.pdf

Joshi, P. K., Tewari, L. and Birthal, P. S., Diversification and its impact on smallholders: evidence from a study on vegetable production. Agric. Econ. Res. Rev., 2006, 19(2), 219–236; https://ageconsearch.umn.edu/record/57759.

Van den Berg, M. M., Hengsdijk, H., Wolf, J., Ittersum, M. K. V., Guanghuo, W., and Roetter, R. P., The impact of increasing farm size and mechanization on rural income and rice production in Zhejiang province, China. Agric. Syst., 2007, 94, 841–850; https://doi.org/10.1016/j.agsy.2006.11.010.

Kahan, D., Managing risk in farming. In Farm Management Extension Guide 3, Food and Agriculture Organization of the United Nations, Rome, Italy, 2008, pp. 29–87; 3-ManagingRiskInternLores.pdf (fao.org)

Sharma, H. R., Crop diversification in Himachal Pradesh; patterns, determinants and challenges. Indian J. Agric. Econ., 2011, 66(1), 97–114; https://ageconsearch.umn.edu/record/204738/files/11H.%20R%20Sharma.pdf

Feliciano, D., A review on the contribution of crop diversification to Sustainable Development Goal 1 ‘No poverty’ in different world regions. Sustain. Dev., 2019, 27(4), 795–808; https://doi.org/ 10.1002/sd.1923.

Anuja, A. R., Anjani Kumar, Saroj, S. and Singh, K. N., The impact of crop diversification towards high-value crops on economic welfare of agricultural households in eastern India. Curr. Sci., 2020, 118(10), 1575–1582; https://doi.org/10.18520/cs/v118/i10/ 1575-1582.

Pandey, S., Bhandari, H., Ding, S., Prapertchob, P., Sharan, R., Naik, D. and Taunk, S. K., Coping with drought in rice farming in Asia: insights from a cross-country comparative study. Agric. Econ., 2007, 37, 213–224; doi:10.1111/j.1574-0862.2007.00246.x

Lin, B. B., Resilience in agriculture through crop diversification: adaptive management for environmental change. BioScience, 2011, 61(3), 183–193; https://academic.oup.com/bioscience/article/ 61/3/183/238071

Barghouti, S., Kane, S., Sorby, K. and Ali, M., Agricultural diversification for the door: Guidelines for practitioners. Agriculture and Rural Development Discussion Paper 1. World Bank, Washington DC, USA, 2004; https://agris.fao.org/agris-search/search.do?recordID=GB2013203761

Birthal, P. S., Roy, D. and Negi, D. S., Assessing the impact of crop diversification on farm poverty in India. World Dev., 2015, 72, 70–92; https://doi.org/10.1016/j.worlddev.2015.02.015

Thapa, G., Kumar, A. and Joshi, P. K., Agricultural diversification in Nepal: status, determinants, and its impact on rural poverty. Discussion Paper No. 01634, International Food Policy Research Institute (IFPRI) – South Asia Office, New Delhi, 2017; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2972291

McCord, P. F., Cox, M., Schmitt-Harsh, M. and Evans, T., Crop diversification as a small holder livelihood strategy within semiarid agricultural systems near Mount Kenya. Land Use Policy, 2015, 42, 738–750; https://doi.org/10.1016/j.landusepol.2014.10.012.

Jones, A. D., Shrivinas, A. and Bezner‐Kerr, R., Farm production diversity is associated with greater household dietary diversity in Malawi: findings from nationally representative data. Food Policy, 2014, 46, 1–12; https://doi.org/10.1016/j.foodpol.2014.02.001

Njeru, E. M., Crop diversification: a potential strategy to mitigate food insecurity by smallholders in sub-Saharan Africa. J. Agric. Food Syst. Community Dev., 2013, 3(4), 63–69; https://doi.org/10.5304/jafscd.2013.034.006.

Davis, K. F. et al., Assessing the sustainability of post-Green Revolution cereals in India. Proc. Natl. Acad. Sci. USA, 2019, 116(50), 25034–25041; doi:10.1073/pnas.1910935116.

Ecker, O., Mabiso, A., Kennedy, A. and Diao, X., Making agriculture pro-nutrition: opportunities in Tanzania. IFPRI Discussion Papers, 1124, International Food Policy Research Institute, Washington DC, 2011; https://www.ifpri.org/publication/making-agriculture-pro-nutrition

Makate, C., Wang, R., Makate, M. and Mango, N., Crop diversification and livelihoods of smallholder farmers in Zimbabwe: adaptive management for environmental change. SpringerPlus, 2016, 5(1), 1135; https://springerplus.springeropen.com/articles/10.1186/ s40064-016-2802-4

Census, Provisional Population Totals Paper 1 of 2011 (India & States/UTs). 2011; https://censusindia.gov.in/2011-prov-results/ census2011_ppt_paper1.html

Gulati, A., Ganesh Kumar, A., Shreedhar, G. and Nandakumar, T., Agriculture and malnutrition in India. Food Nutr. Bull., 2012, 33(1), 74–86; https://journals.sagepub.com/doi/pdf/10.1177/156482651203300108

Li, D., Zhang, L., Tang, X., Zhou, W., Li, J., Zhou, C. and Phoon, K., Bivariate distribution of shear strength parameters using copulas and its impact on geotechnical system reliability. Comput. Geotech., 2015, 68, 184–195; https://doi.org/10.1016/j.compgeo.2015.04.002.

Fan, L. and Qian, Z., Probabilistic modelling of flood events using the entropy copula. Adv. Water Resour., 2016, 97, 233–240; https://doi.org/10.1016/j.advwatres.2016.09.016.

Mazdiyasni, O. et al., Increasing probability of mortality during Indian heat waves. Sci. Adv., 2017, 3, 1–5; doi:10.1126/sciadv.1700066.

Nguyen-Huy, T., Deo, R. C., An-Vo, D., Mushtaq, S. and Khan, S., Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones. Agric. Water Manage, 2017, 191, 153–172;

https://doi.org/10.1016/j.agwat.2017.06.010.

Gill, S., Water crisis in Punjab and Haryana. Econ. Polit. Wkly, 2016, 51(50), 37–41; https://www.epw.in/journal/2016/50/insight/water-crisis-punjab-and-haryana.html

Ghuman, R. S. and Sharma, R., Green revolution, cropping pattern and water scarcity in India: evidence from Punjab. Man Dev., 2016, XXXVIII(2), 1–20; http://esaharyana.gov.in/Portals/0/agriculture.pdf

Harzana, J., Birtal, P. S., Negi, D. S., Mani, G. and Pandey, G., Spatial spill-overs, structural transformation and economic growth in India. Agric. Econ. Res. Rev., 2019, 32(2), 145–158; https://10.5958/0974-0279.2019.00028.4.

Meera, S., Nutrition and agriculture: bridging the gap, 2015; https://blogs.worldbank.org/health/nutritionandagriculturebridginggap#:~:text=Physical%20and%20economic%20access%20to,costs-%20which%20can%20lower%20food

Chinnadurai, M., Karunakaran, K. R., Chandrasekaran, R., Balasubramanian, R. and Umanath, M., Examining linkage between dietary pattern and crop diversification: an evidence from Tamil Nadu. Agric. Econ. Res. Rev., 2016, 29, 149–160; doi:10.5958/0974-0279.2016.00042.2.

Mango, N., Makate, C., Mapemba, L. and Sopo, M., The role of crop diversification in improving household food security in central Malawi. Agric. Food Policy, 2018, 7, 7; https://doi.org/10.1186/s40066-018-0160-x.

Adjimoti, G. O. and Kwadzo, G. T., Crop diversification and household food security status: evidence from rural Benin. Agric. Food Secur., 2018, 7, 82; https://doi.org/10.1186/s40066-018-0233-x.

Johns, T. and Sthapit, B. R., Biocultural diversity in the sustainability of developing-country food systems. Food Nutr. Bull., 2004, 25(2), 143–155; https://doi.org/10.1177/156482650402500207.

Mofya-Mukuka, R. and Kuhlgatz, C. H., Child malnutrition, agricultural diversification and commercialization among smallholders in Eastern Zambia. Working Paper No. 90, Indaba Agricultural Policy Research Institute, Lusaka, Zambia, 2015; https://ageconsearch.umn.edu/record/198189/files/wp90_rev.pdf

Hydrological Assessment of Haveli-Based Traditional Water Harvesting System for the Bundelkhand Region, Uttar Pradesh, India

Abstract Views :87 | PDF Views:60

Authors

Liansangpuii ¹, Ramesh Singh ², R. M. Singh ¹, K. N. Singh ³, S. K. Kar ⁴

Affiliations
1 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221 005, IN
2 ICRISAT Development Centre, International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, IN
3 Department of Soil and Water Engineering, IGKV, Raipur 492 012, IN
4 Indian Council of Agricultural Research-Indian Institute of Soil and Water Conservation, Dehradun 248 001, IN

Source

Current Science, Vol 125, No 1 (2023), Pagination: 43-51

Abstract

Water harvesting is a critical component of any approach to alleviating India’s water crisis. Traditional rainwater harvesting systems are found in every region of the country. Haveli is one such system found in almost every village in the Bundelkhand region, Uttar Pradesh, India. A defunct Haveli in the Parasai–Sindh watershed of Jhansi district, Uttar Pradesh, was rejuvenated by providing a cement concrete core wall to the earthen embankment to address the problem of breaching, and the existing outlet was also expanded. This study was conducted from 2013 to 2019 to analyse the hydrology of the rejuvenated Haveli and to understand its impact on surface-water availability and recharging groundwater. The study period was divided based on long-term southwest monsoon (SWM) as wet (SWM > 20%), normal (SWM ± 20%) and dry (SWM < 20%) years. It was found that the Haveli could harvest about 1.91–2.0 times, 1.13–1.72 times and 0.2 times its capacity during a wet, normal and dry year, respectively. There was a 1.41 m difference in hydraulic head between pre- and post-Haveli rejuvenation in a wet year, whereas, a normal year, the difference was 2.71 m.

Keywords

Groundwater Resources, Hydrological Assessment, Southwest Monsoon, Traditional Rainwater Harvesting Structure, Water Scarcity.

Full Text

References

Mooley, D. A. and Parthasarathy, B., Indian summer monsoon and El Nino. Pure Appl. Geophys., 1983, 121(2), 339–352.

Prabhakar, S. V. R. K. and Shaw, R., Climate change adaptation implications for drought risk mitigation: a perspective for India. Climate Change, 2008, 88(2), 113–130.

Department of Agriculture and Cooperation, Drought 2002: A Report, Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India (GoI), 2004, p. 190.

IGES, Water availability for sustainable energy policy: assessing cases in South and South Asia, Institute for Global Environmental Strategies, Hayama, Japan, 2013.

Rosegrant, M., Ringler, C., Zhu, T., Tokgoz, S. and Bhandary, P., Water and food in the bioeconomy: challenges and opportunities for development. J. Agric. Econ., 2013, 44(1), 139–150.

Jain, S. K. and Kumar, V., Trend analysis of rainfall and temperature data for India. Curr. Sci., 2012, 102(1), 37–49.

Kundu, S., Khare, D., Mondal, A. and Mishra, P. K., Analysis of spatial and temporal variation in rainfall trend of Madhya Pradesh, India (1901–2011). Environ. Earth Sci., 2015, 73(12), 8197–8216.

Mondal, A., Khare, D. and Kundu, S., Spatial and temporal analysis of rainfall and temperature trend of India. Theor. Appl. Climatol., 2015, 122(1), 143–158.

Dash, S. K., Nair, A. A., Kulkarni, M. A. and Mohanty, U. C., Characteristic changes in the long and short spells of different rain intensities in India. Theor. Appl. Climatol., 2011, 105, 563–570.

Mishra, A. and Liu, S. C., Changes in precipitation pattern and risk of drought over India in the context of global warming. J. Geophys. Res. Atmos., 2014, 119(13), 7833–7841.

Gautam, R. C. and Bana, R. S., Drought in India: its impact and mitigation strategies – a review. Indian J. Agron., 2014, 59(2), 179–190.

Kulkarni, A., Gadgil, S. and Patwardhan, S., Monsoon variability, the 2015 Marathwada drought and rainfed agriculture. Curr. Sci., 2016, 111(7), 1182–1193.

Garg, N. K. and Hassan, Q., Alarming scarcity of water in India. Curr. Sci., 2007, 93(7), 932–941.

Gupta, S. K. and Deshpande, R. D., Water for India in 2050: first-order assessment of available options. Curr. Sci., 2004, 86(9), 1216–1224.

Luo, T., Young, R. and Reig, P., Aqueduct projected water stress country rankings. World Resources Institute, Washington, DC, USA, 2015.

Kar, S. K., Singh, R. M. and Thomas, T., Spatio-temporal evaluation of drought characteristics in the Dhasan basin. MAUSAM, 2018, 69(4), 589–598.

Kar, S. K., Thomas, T., Singh, R. M. and Patel, L., Integrated assessment of drought vulnerability using indicators for Dhasan basin in Bundelkhand region, Madhya Pradesh, India. Curr. Sci., 2018, 115(2), 338–346.

Anantha, K. H., Garg, K. K., Barron, J., Dixit, S., Venkataradha, A., Singh, R. and Whitbread, A. M., Impact of best management practices on sustainable crop production and climate resilience in smallholder farming systems of South Asia. Agric. Syst., 2021, 194, 103276.

Pandey, D. N., Gupta, A. K. and Anderson, D. M., Rainwater harvesting as an adaptation to climate change. Curr. Sci., 2003, 85(1), 46–59.

Mane, S. P. and Shinde, A. S., A study of changing pattern of rain water harvesting management an ancient to modern age. In India – geographical analysis. Rev. Res., 2014, 3(10), 1–6.

Glendenning, C. J., Van Ogtrop, F. F., Mishra, A. K. and Vervoort, R. W., Balancing watershed and local scale impacts of rain water harvesting in India – a review. Agric. Water Manage., 2012, 107, 1–13.

Agarwal, A. and Narain, S., Dying wisdom: the decline and revival of traditional water harvesting systems in India. Ecologist, 1997, 27(3), 112–117.

Bhattacharya, S., Dasgupta, A., Mahansaria, R., Ghosh, S., Chattopadhyay, D. and Mukhopadhyay, A., In Traditional Rainwater Harvesting in India: Historical Perspectives, Present Scenario and Future Prospects, World Archaeological Congress, Dakar, Senegal, 2011.

Balooni, K., Kalro, A. H. and Kamalamma, A. G., Community initiatives in building and managing temporary check-dams across seasonal streams for water harvesting in South India. Agric. Water Manage., 2008, 95(12), 1314–1322.

Borthakur, S., Traditional rain water harvesting techniques and its applicability. Indian J. Tradit. Knowl., 2008, 8(4), 525–530.

MoWR, National Water Policy-2002; Ministry of Water Resources, GoI, 2002; http://wrmin.nic.in/policy/nwp2002.pdf

Nair, G. K., Kerala: focus on lift irrigation, big projects out. In Business Line, 22 March 2002.

Paranjpye, V. (ed.), High Dams on the Narmada: A Holistic Analysis of the River Valley Projects, Indian National Trust for Art and Cultural Heritage, Delhi, 1990.

Sharma, T. C., Technological Change in Indian Agriculture, Rawat Publications, Jaipur, 1999.

Meter, K. J. V., Basu, N. B., Tate, E. and Wyckoff, J., Monsoon harvests: the living legacies of rainwater harvesting systems in South India. Environ. Sci. Technol., 2014, 48, 4217–4225.

Sahu, R. K., Rawat, A. K. and Rao, D. L. N., Traditional rainwater management system (‘Haveli’) in Vertisols of central India improves carbon sequestration and biological soil fertility. Agric. Ecosyst. Environ., 2015, 200(1), 94–101.

Shah, T., Who should manage Chandeli tanks? International Water Management Institute, Sri Lanka, 2003, p. 7.

Garg, K. K., Singh, R., Anantha, K. H., Singh, A. K., Akuraju, V. R., Barron, J. and Dixit, S., Building climate resilience in degraded agricultural landscapes through water management: a case study of Bundelkhand region, Central India. J. Hydrol., 2020, 591, 125592.

Niti Ayog, Bundelkhand Human Development Report 2012. Prepared under NITI Aayog–UNDP Project on Human Development: towards bridging inequalities, GoI, 2012, p. 258.

Shah, T., Climate change and groundwater: India’s opportunities for mitigation and adaptation. Environ. Res. Lett., 2009, 4(3), 035005.

TERI, Study of impact of special package for drought mitigation implemented in Bundelkhand region New Delhi, The Energy and Resources Institute, Project Report No. 2017HE02 (support from NITI Aayog, GoI), 2018.

Kumari, R., Singh, R., Singh, R. M., Tewari, R. K., Dhyani, S. K., Dev, I. and Singh, A. K., Impact of rainwater harvesting structures on water table behavior and groundwater recharge in Parasai–Sindh watershed of Central India. Indian J. Agrofor., 2014, 16(2), 47–52.

Rao, A. V. R. K., Wani, S. P., Singh, K. K., Ahmed, M. I., Srinivas, K., Bairagi, S. D. and Ramadevi, O., Increased arid and semi-arid areas in India with associated shifts during 1971–2004. J. Agrometeorol., 2013, 15(1), 11–18.

Singh, R., Garg, K. K., Wani, S. P., Tewari, R. K. and Dhyani, S. K., Impact of water management interventions on hydrology and ecosystem services in Garhkundar–Dabar watershed of Bundelkhand region, Central India. J. Hydrol., 2014, 509, 132–149.

Singh, R., Garg, K. K., Anantha, K. H., Akuraju, V., Dev, I., Dixit, S. and Dhyani, S. K., Building resilient agricultural system through groundwater management interventions in degraded landscapes of Bundelkhand region, Central India. J. Hydrol. Reg. Stud., 2021, 37, 100929.

Anantha, K. H., Garg, K. K., Barron, J., Dixit, S., Venkataradha, A., Singh, R. and Whitbread, A. M., Impact of best management practices on sustainable crop production and climate resilience in smallholder farming systems of South Asia. Agric. Syst., 2021, 194, 103276.

Glendenning, C. J., Van Ogtrop, F. F., Mishra, A. K. and Vervoort, R. W., Balancing watershed and local scale impacts of rain water harvesting in India – a review. Agric. Water Manage., 2012, 107, 1–13.

Sakthivadivel, R., The groundwater recharge movement in India. In The Agricultural Groundwater Revolution: Opportunities and Threats to Development (eds Giordano, M. and Villholth, K. G.), CAB Int, UK, 2007, pp. 195–210.

Rockström, J. and Karlberg, L., Zooming in on the global hotspots of rainfed agriculture in water constrained environment. Rainfed Agriculture: Unlocking the Potential, CABI, Wallingford, UK, 2009, pp. 36–42.

Genetic Algorithms-Based Fuzzy Analytical Hierarchical Process (GA-FAHP) for Evaluating Biofortified Crop Promotion Strategies

Abstract Views :50 | PDF Views:38

Authors

K. N. Singh ¹, Mrinmoy Ray ¹, Satyapriya ², Shashi Dahiya ¹, Jaya Pandey ³, Rajeev R. Kumar ¹

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
3 Indian Council of Medical Research, New Delhi 110 029, IN

Source

Current Science, Vol 125, No 3 (2023), Pagination: 317-320

Abstract

In developing nations such as India, malnutrition is a major nutritional and health challenge. Biofortification has the potential to be an effective instrument in India’s attempts to combat malnutrition. Expert opinion must be used to evaluate the factors related to the promotion, distribution and adoption of biofortified crops. The analytical hierarchy process (AHP) is one of the most often employed decision-making methods. However, conventional AHP is incapable of identifying ambiguity in human judgements. Fuzzy AHP has already been devised to overcome this limitation. Fuzzy AHP necessitates information in pairwise comparisons, which is not always easy to gather. In this context, the Fuzzy AHP technique based on the genetic algorithm has been proposed, which can compute the priority weight without using a pairwise comparison matrix by directly dealing with expert-provided data. The proposed approach has been illustrated using the opinions of 1600 farmers from Odisha, India.

Keywords

Biofortified Crops, Fuzzy AHP, Genetic Algorithm, Malnutrition.

Full Text

References

Food Insecurity in the World, The State of Food and Agriculture, Food and Agriculture Organization (FAO) of the United Nations, n.d., 2013.

Saaty, R. W., The analytic hierarchy process – what it is and how it is used. Math. Model., 1987, 9, 161–176.

Carlucci, D. and Schiuma, G., Knowledge assets value creation map. Assessing knowledge assets value drivers using AHP. Expert Syst. Appl., 2007, 32, 814–821.

Daǧdeviren, M., Yavuz, S. and Kilinç, N., Weapon selection using the AHP and TOPSIS methods under fuzzy environment. Expert Syst. Appl., 2009, 36, 8143–8151.

Ghosh, A. and Kar, S. K., Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Nat. Hazards, 2018, 94, 349–368.

Panchal, S. and Shrivastava, A. K., Landslide hazard assessment using analytic hierarchy process (AHP): a case study of National Highway 5 in India. Ain Shams Eng. J., 2022, 13, 101626.

Hamidah, M. et al., Development of a protocol for Malaysian important plant areas criterion weights using multi-criteria decision making – analytical hierarchy process (MCDM-AHP). Glob. Ecol. Conserv., 2022, 34, e02033.

Ly, P. T. M., Lai, W. H., Hsu, C. W. and Shih, F. Y., Fuzzy AHP analysis of internet of things (IoT) in enterprises. Technol. Forecast. Soc. Change, 2018, 136, 1–13.

Yucesan, M. and Gul, M., Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS. Soft Comput., 2019, 24, 3237–3255.

Kumar, R., Dwivedi, S. B. and Gaur, S., A comparative study of machine learning and Fuzzy-AHP technique to groundwater potential mapping in the data-scarce region. Comput. Geosci., 2021, 155, 104855.

Khan, A. A., Shameem, M., Nadeem, M. and Akbar, M. A., Agile trends in Chinese global software development industry: fuzzy AHP based conceptual mapping. Appl. Soft Comput., 2021, 102, 107090.

Paul, S. and Ghosh, S., Identification of solid waste dumping site suitability of Kolkata metropolitan area using fuzzy-AHP model. Clean. Logist. Supply Chain, 2022, 3, 100030.

Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison Wesley, Boston, USA, 1989, 13th edn.

Wittkowski, K. M., Lee, E., Nussbaum, R., Chamian, F. N. and Krueger, J. G., Combining several ordinal measures in clinical studies. Stat. Med., 2004, 23, 1579–1592.

Evaluating the Performance of Crop Yield Forecasting Models Coupled with Feature Selection in Regression Framework

Abstract Views :46 | PDF Views:42

Authors

Manoj Varma ¹, Achal Lama ¹, K. N. Singh ¹, Bishal Gurung ¹

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN

Source

Current Science, Vol 125, No 6 (2023), Pagination: 649-654

Abstract

As crop yield is determined by numerous input parameters, it is important to identify the most important variables/parameters and eliminate those that may reduce the accuracy of the prediction models. The feature selection algorithms assist in selecting only those relevant features for the prediction algorithms. Instead of a complete set of features, feature subsets give better results for the same algorithm with less computational time. Feature selection has the potential to play an important role in the agriculture domain, with the crop yield depending on multiple factors such as land use, water management, fertilizer application, other management practices and weather parameters. In the present study, feature selection algorithms such as forward selection, backward selection, random forest (RF) and least absolute shrinkage and selection operator (LASSO) have been applied to three different datasets. Regression forecasting models have been developed with selected features for all the algorithms. The forecasting performance of the proposed models was compared using statistical measures such as root mean square error, mean absolute prediction error and mean absolute deviation. A comparison was made among all the feature selection algorithms. The regression models developed with LASSO, RF and backward selection algorithms were the best for different datasets.

Keywords

Crop Yield, Feature Selection, Prediction Models, Regression Framework, Statistical Measures, Weather Indices.

Full Text

References

Singh, K. N., Singh, K. K., Kumar, S., Panwar, S. and Gurung, B., Forecasting crop yield through weather indices through LASSO. Indian J. Agric. Sci., 2019, 89, 540–544.

Agrawal, R., Jain, R. C. and Jha, M. P., Joint effects of weather variables on wheat yields. Mausam, 1983, 34, 189–194.

Agrawal, R., Has, C. and Aditya, K., Use of discriminant function analysis for forecasting crop yield. Mausam, 2012, 63(3), 455–458.

Springenberg, J., Dosovitskiy, A., Brox, T. and Riedmiller, M., Striving for simplicity: the all convolutional net. In 2nd International Conference on Learning Representations, ICLR2014, Banff, AB, Canada, 14–16 April 2014, pp. 1–14; https://arxiv.org/abs/1412.6806.

Oreski, D., Oreskib, S. and Klicek, B., Effects of dataset characteristics on the performance of feature selection techniques. Appl. Soft Comput., 2017, 52, 109–119.

Gopal, P. M. and Bhargavi, R., Optimum feature subset for optimizing crop yield prediction using filter and wrapper approaches. Appl. Eng. Agric., 2019, 35, 9–14.

Balogun, A. O., Basri, S., Abdulkadir, S. J. and Hashim, A. S., Performance analysis of feature selection methods in software defect prediction: a search method approach. Appl. Sci., 2019, 9(13), 2764.

Suruliandi, A., Mariammal, G. and Raja, S. P., Crop prediction based on soil and environmental characteristics using feature selection techniques. Math. Comput. Modell. Dyn. Syst., 2021, 27(1), 117–140.

Huang, J. Z., An Introduction to Statistical Learning: With Applications in R, Springer, New York, 2014, pp. 225–282.

Tibshirani, R., Regression shrinkage and selection via the LASSO. J. R. Stat. Soc., Ser. B, 1996, 58(1), 267–288.

Breiman, L., Random forests. Mach. Learn., 2001, 45, 5–32.

Whitmire, C. D., Vance, J. M., Rasheed, H. K., Missaoui, A., Rasheed, K. M. and Maier, F. W., Using machine learning and feature selection for alfalfa yield prediction. AI, 2021, 2, 71–88.

Bocca, F. F. and Rodrigues, L. H. A., The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling. Comput. Electron. Agric., 2016, 128, 67–76.

Agrawal, R. and Mehta, S., Weather based forecasting of crop yields, pests and diseases – IASRI models. J. Indian Soc. Agric. Stat., 2007, 61, 255–263.

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