- R. Kaushal
- K. S. Verma
- Gopichand
- R.d. Singh
- Ramjee Lal Meena
- Amit Kumar
- M. S. Jolly
- G. K. Shukla
- G. S. Tiwari
- R. N. Tiwari
- M. C. Gupta
- B. K. Singh
- R. K. Mittal
- K. C. Barthwal
- G. C. Agrawal
- N. R. Yadav
- S. R. Pandey
- U . R. Bharadwaj
- Arun Kumar
- Deepak Kumar
- Poonam
- Gaurav Kumar
- A. R. Anuja
- Anjani Kumar
- Sunil Saroj
- Amit Saha
- Mrinmoy Ray
- Santosha Rathod
- Sharani Choudhury
- G. P. Shivaswamy
- Liansangpuii
- Ramesh Singh
- R. M. Singh
- S. K. Kar
- Satyapriya
- Shashi Dahiya
- Jaya Pandey
- Rajeev R. Kumar
- Manoj Varma
- Achal Lama
- Bishal Gurung
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
Singh, K. N.
- A Strategy for Conservation of the Habitat of North-western Population of Indian Elephants
Authors
Source
Indian Forester, Vol 128, No 10 (2002), Pagination: 1061-1077Abstract
The North-Western Himalayan population of the Indian elephant , Elephss maximus , used to be distributed contigually from the River Yamuna to the River Sharda in the past. This population was studied in the last decade of the twentieth century. The past migration patterns of elephants were compared with current movements in this region. The present day migration of elephants is localized and seems chiefly influenced by fodder and water requirements. The causes behind the fragmentation of elephant habitat mainly river valley projects and major construction works taken up after independence in the wake of development drive have been discussed in detail. The viability and ways means of restoring the possible corridors are discussed and a strategy for the long-term conservation of this population is suggested.- Decomposition Pattern and Nutrient Dynamics in Leaf Litter of Populus deltoides Marsh. In Himachal Pradesh
Authors
Source
Indian Forester, Vol 132, No 4 (2006), Pagination: 456-466Abstract
Decomposition rates and nutrient dynamics of Populus deltoides litter was investigated in three year old coppiced plantation situated in the mid-hills of Himachal Pradesh using the nylon net bags. Complete loss of Populus leaf litter was achieved in 20 months. The decomposition constant (k) was worked out to 1.27. Significant and a positive correlation was observed between decomposition and climatic parameters viz., rainfall and temperature (R2 = 0.61), whereas temperature did not exhibit any significant influence on the decomposition rate. Nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg) dynamics in decomposing litter revealed that concentration of nitrogen, phosphorus and calcium did not follow any specific trend during the decomposition process. Potassium and magnesium concentration, however, revealed a decreasing trend throughout the study period. Changes in absolute amount, on the other hand, followed a release pattern through the study period for N, K and Mg. P and Ca, however, depicted a three-phase pattern i.e. leaching, immobilization and release during the entire course of investigations.- North Indian Asiatic Elephant Population Conflict Wrrh Man with Reference to Crops Damage
Authors
Source
Indian Forester, Vol 137, No 8 (2011), Pagination: 941-947Abstract
The problem of raiding of agricultural crops by elephants in the foothills of Himalayas in Uttarakhand has been studied during the period- 1995-2000. The damage to the crops is classified in three categories viz. trampling, grazing and browsing. Maize is the crop most favoured by elephants followed by sugarcane, mustard and wheat. The damage to the crops varied from 21.63% to 12.47% in the villages studied. The damage remained nearly constant over the period. Seasonal migration, competition for water, reduction or fragmentation of natural habitat, replacement of crops by sumptuous crops etc. are the main reasons behind elephant raids. Forest fires infirmity, heavy biotic pressure resulting in increased competition for fodder and the element of chance are some other reasons for elephant raids. Some of the solutions suggested for the problem are making noise by beating canisters, shouting in groups, lighting fire and torches, digging trenches and using flashlights etc. Other measures suggested are immediate stoppage of all non forestry works within the elephant habitat and absolute ban on human encroachment in elephant habitat. Control over grazing by cattle in the habitat areas, provision of a buffer belt of thorny trees at the forest boundary, ban on cultivation of tempting crops such as sugarcane, elimination of disturbances in and around the natural corridor used by elephants for movement, promotion of eco-development activities in the adjoining villages , digging of elephant proof trenche s, and providing water inside elephant habitat during lean period etc. are also suggested. Traditional electric fences are found fatal and are not recommended while high voltage non fatal electric fences are suggested which can be powered by solar batteries. Anchored mela shikar, where trained domestic elephants are used to chase away rowdy wild elephants and capture and translocation of such rowdy wild elephants are also suggested as a solution.Keywords
Elephant Raids, Agricultural Crops, Crop Damage, Grazing, Biotic Pressure- Effect of Plant Growth Regulators on Germination of Seed of Rheum australe D. Don
Authors
Source
Indian Forester, Vol 136, No 11 (2010), Pagination: 1503-1507Abstract
Rheum australe D. Don. Syn R. emodi Wall ex Meissan, locally known as Himalayan rhubarb, rhubarb ischolar_main, belong to family Polygonaceae. R. australe is an endangered medicinal plant species in Indian Himalaya. Study was conducted to evaluate effect performance of IAA, IBA, NAA and GA3 at three concentrations viz., 50, 100 and 200 mg/l on in terms of rate of germination of R. australe seeds. Observations were also recorded on days taken for initiation of germination, seed germination percentage, shoot length of the seedlings and number of leaves per seedling. Significantly higher seed germination was recorded in seeds treated with IBA 200 mg/1 followed by NAA (200 mg/l) and GA3 (l00 and 200 mg/l).Keywords
Rheum austral, R. emodi, Seed Germination, Seedlings- Air Layering in Terminalia arjuna (Roxb.) W.&A.
Authors
Source
Indian Forester, Vol 95, No 8 (1969), Pagination: 539-540Abstract
no abstract- Prospects of Lemon Grass Industry in Uttar Pradesh
Authors
Source
Indian Forester, Vol 99, No 11 (1973), Pagination: 651-654Abstract
no abstract- Experimental Gum Tapping of Jhingan in Uttar Pradesh
Authors
Source
Indian Forester, Vol 101, No 9 (1975), Pagination: 517-522Abstract
Jhingan gum is mostly obtained from natural exudation and sometimes by tapping. The gum finds its use in Confectionary, Calico-Printing, preparation of interior varnishes, plastering, white washing etc. About 10,41,100 Jhingan trees of different diameter classes are estimated to spread over Tarai, Siwalik and Vindhyan tract of Uttar Pradesh. With a view to standardise the technique of tapping, Jhingan trees of different diameter classes were selected for tapping under local and notching methods. The trees were subjected to tapping under the two different methods and yield data were recorded Statistical analysis or the data reveal that there is no significant difference in yield of Jhingan gum whether the trees are tapped by local method or notching method. On combining the yield of gum for the two different methods and plotting average yield per tree against different diameter classes, a close scrutiny of the graph reveals that apart from a few random fluctuation, Jhingan trees of 40-50 cm diameter class yield more gum.- SEM Surface Microtextures of Quartz Grains from Ganga and Yamuna River Sediments, Allahabad, U.P.
Authors
1 Petrology Division, Geological Survey of India, Lucknow - 226 024, IN
2 Department of Geology, Banaras Hindu University, Varanasi - 221 005, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 63, No 5 (2004), Pagination: 515-521Abstract
Quartz grain surface features of recent sediments saniples collected from vertical profiles developed in channel bar, point bar and natural levee deposits around the confluence of the Ganga and Yamuna rivers at Allahabnd, UP, were examined under the Scanning Electron Microscope (SEM). The study reveals the existence of several surface features as a result of mechanical and chemical action on the quartz grains.In channel bar and point bar deposits, mechanicaf features like low relief, v-shaped impact pits of coalescing nature and conchoidal fracture are dominating surface textures, which indicate the high-energy fluviatile depositional environment. Features like surface pits and surface solution are common in quartz grains from natural levee deposits. These surface textures owe their origin to chemical dissolution, which is possible only in low energy conditions associated with the deposition of natural levee and flood plain deposits.
Keywords
SEM-Microtextures, Quartz Grains, Fluvial Sediments, Ganga-Yamuna, U.P.- Coal Bed Methane Potentiality - Case Studies from Umaria, Korba and Ib-Valley Coals, Son-Mahanadi Basin
Authors
1 School of Studies in Earth Science, Vikram University, Ujjain – 456 010, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 76, No 1 (2010), Pagination: 33-39Abstract
The Coal Bed Methane (CBM), a dreadful mining hazard, became eco-friendly clean gas for the third world country when humanity is facing challenges against pollution and energy crisis. It is observed from the studies that the Korba and Ib valley coals have potential for CBM exploration whereas Umaria coals have negligible potential. Though, the Sohagpur coalfields (east of Umaria coalfield) have ample scope of CBM exploration.
In the absence of enough desorption data on gas content, the in situ gas content has been determined here following the theoretical interpretation of adsorption curve, taking into account of the moisture, ash, volatile matter, fixed carbon, depth of coal bed and the geothermal gradient. Data generation on gas content, gas saturation and gas sorption characteristics is vital for better definition of high quality coal prospects.
Keywords
CBM Potentiality, Umaria, Korba And Ib Valley Coalfields, Son-Mahanadi Basin.- Engineering Geological Rock Mass Classification of Punasa Tunnel Site, Khandwa District, Madhya Pradesh
Authors
1 Water Resources Department, Narmada-Tapti, Indore - 452 001, IN
2 Department of Civil Engineering, Govt. Engg. College, Ujjain - 456 010, IN
3 School of Studies in Earth Science, Vikram University, Ujjain - 456 010, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 77, No 3 (2011), Pagination: 269-272Abstract
Systematic rock mass characterization is an integral part of rock engineering practices. In the present scenario several classifications are in used for rock mass characterization for tunnelling. The present paper discusses engineering geological investigations carried out for Punasa tunnel, a part of Narmada Sagar project. The horse shoe shaped tunnel is 3675.25 m long and 9 m in diameter. This straight and free flow tunnel has been constructed in basaltic lava flows erupted during Cretaceous to Eocene age, belonging to poor to fair rock mass rating (RMR) and extremely poor to good in tunnel quality (Q-system). The values of RMR and Q-system ranges from 29 to 74 and 0.0825 to 13.33 respectively.Keywords
Rock Mass Characterization, Rock Class, Tunnel Quality, RMR And Q-System, Madhya Pradesh.- Vertical Successions of Channel Bar, Point Bar and Natural Levee Deposits, Ganga and Yamuna River, Allahabad, U.P.
Authors
1 Petrology Division, Geological Survey of India, Lucknow - 226 024, IN
2 Department of Geology, Banaras Hindu University, Varanasi - 221 005, IN
Source
Journal of Geological Society of India (Online archive from Vol 1 to Vol 78), Vol 64, No 3 (2004), Pagination: 305-316Abstract
The modern sediments of Ganga and Yamuna rivers at Allahabad, Uttar Pradesh are studied in 17 vertical sections of channel bar, point bar and natural levee deposits. Based on this study nine lithofacies, i.e. St, Sp2, Sp1, Sr1, Sr2, SI, Sh, FI and Fm (8-Sand and 1-Mud sub-lithofacies) have been identified. An attempt has been made to evolve a generalised lithofacies succession for channel bar, point bar and natural levee deposits. The channel bar exhibits the lithofacies succession of Sp2, Sp1, Sr2, Sr1, Sh and FI; Sp2, Sp1, Sr1 Sh and FI succession in point bar-and Sp2, Sp1, Sr1, Sh, FI and Fm lithofacies succession in natural levee deposits. No major difference in channel bar and point bar lithofacies succession have been found. The point bars are characterised by trough cross-bedded lithofacies - St, which are formed by the migration of 3-D dunes. The Fm lithofacies is characteristics of natural levee, the muddy deposits often show desiccation cracks. Deposition of mud takes place in low energy conditions mostly during receding floods.Keywords
Quaternary Sediments, Facies Architecture, Lithofacies Code, Fluvial Geomorphic Units, Ganga and Yamuna Rivers, Allahabad.- Repletion Studies on Liver Protein, Glycogen Content and Liver Enzymes by Feeding Protein Isolates of Cassia Marginata and Cassia Renigera Wild Leguminous Seeds in Normal Albino Rats
Authors
1 Department of Physiology, M. L. N. Medical College, Allahabad, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 13, No 5 (1976), Pagination: 142-147Abstract
Dietary proteins influence the labile protein stores of the animal tissues and in the process enzyme systems are not spared. Methods based on the capacity of different proteins to replenish the liver protein stores have been standardized and much ground has been covered during the past years. Major emphasis was laid upon the changes in the amount of liver protein, since the quantity of this tissue component appeared to offer the most appropriate estimate of the functionally effective liver mass.- Biochemical Studies on Indian Wild Legumes: (7) Effect of Acacia Arabica and Acacia Catechu Seed Protein Isolates on Total Liver Protein, Glycogen and Albumin Content, Xanthine Oxidase and Active Phosphorylase Activity in Normal Young Albino Rats
Authors
1 Biochemistry Section, Department of Physiology, M. L. N. Medical College, Allahabad, IN
2 Department of Physiology, M. L. N. Medical College, Allahabad, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 7, No 4 (1970), Pagination: 223-227Abstract
While studying the nutritional characteristics' of some Indian wild legumes it was thought of utility and interest to investigate them with respect to some of their important effects on the body systems, from a medical standpoint.- Variation of Different Physiological and Yield Related Traits in Rice (Oryza sativa L.)
Authors
1 Department of P.M.B. and G.E., N.D. University of Agriculture and Technology, Kumarganj, Faizabad (U.P.), IN
Source
Asian Journal of Bio Science, Vol 11, No 1 (2016), Pagination: 238-240Abstract
Rice grain yield is a quantitative polygenic character and highly influenced by environment. Extent and significance of association of yield with yield components should be considered, while determining the selection criteria of germplasm on the basis of available genetic variation plant height ranged from 84.60 to 148.62, panical length ranged from 20.76 to 26.93, grain per panical ranged from 125 to 210 cm, yield per plant ranged from 13.36 to 22.77 g and test weight ranged from 26.83 to 28.64.
Keywords
Variation, Physiological, Yield.- The Impact of Crop Diversification Towards High-Value Crops on Economic Welfare of Agricultural Households in Eastern India
Authors
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: 1575-1582Abstract
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.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
Authors
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: 1090-1098Abstract
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 modelsKeywords
ARIMA, cotton production forecasting, SVR, time series, tuned-SVR.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
Authors
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 122, No 10 (2022), Pagination: 1154-1160Abstract
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 undernutritionKeywords
Bivariate copula, crop diversification, land use, nutritional status, undernutrition index.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
Authors
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: 43-51Abstract
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.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
Authors
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: 317-320Abstract
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.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
Authors
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 125, No 6 (2023), Pagination: 649-654Abstract
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.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.