Modelling the dynamics of economic development driven by agricultural growth in Patna Region, India

Kumari, Rashmi; Devadas, V. Article Modelling the dynamics of economic development driven by agricultural growth in Patna Region, India Journal of Economic Structures Provided in Cooperation with: Pan-Pacific Association of Input-Output Studies (PAPAIOS) Suggested Citation: Kumari, Rashmi; Devadas, V. (2017) : Modelling the dynamics of economic development driven by agricultural growth in Patna Region, India, Journal of Economic Structures, ISSN 2193-2409, Springer, Heidelberg, Vol. 6, Iss. 15, pp. 1-27, https://doi.org/10.1186/s40008-017-0075-x This Version is available at: https://hdl.handle.net/10419/194882 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Modelling the dynamics ofeconomic development driven byagricultural growth inPatna Region, India Rashmi Kumari1* and V. Devadas2 1 Background Agriculture sector occupies the centre stage in shaping India’s socio-economic status. The economy of the second most populous country in the world is inextricably linked to the pulse of its agricultural success or failure. India has witnessed a significant increase in food grain production after green revolution, oilseeds production after yellow revolution, milk production after white revolution, fish production after blue revolution, and fruits and vegetables production after golden revolution, since independence. The agricultural development has been achieved due to the application of science and technology coupled with the positive policy implementation, and hard work of Indian farmers (Ahuja 2000). However, after nearly three and half decades in the post-green revolution period, the country still faces crisis each year in trying to meet the burgeoning demand for food by its growing population. Thus, the need of research and development in the field of agriculture for increasing the application of frontier technologies (i.e. information technology application in agriculture, precision farming, post-harvest technologies, mechanized farming and organic farming) cannot be denied, which is a feasible Abstract Regional planning primarily aims at reducing the disparity in socio-economic status of the people, and the wide gulf between urban and rural life. A planning region or an area comprises of many complex structures and behavioural phenomena which more or less change with time due to some external forces acting upon or the internal forces acting within the regional system. While planning for the development of a region, the well-known phenomena and structures within the regional system can be considered. Further, the sustainable economic development of a region can only be achieved by ensuring the optimum utilization of resources. This requires plausible policy planning guidelines and feasible development plan, for which thorough grassroots level investigation is essential. In this present investigation, an attempt has been made to evolve a set of plausible policy guideline and recommendations for the sustainable economic development of Patna region, by considering it as a system, and considering the important control parameters of its subsystems. Keywords: Agriculture, Regional development, Forecasting, System dynamics, GDP, Simulation Open Access © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. RESEARCH Kumari and Devadas Economic Structures (2017) 6:15 DOI 10.1186/s40008-017-0075-x *Correspondence: [email protected] 1 Department of Architecture and Landscape Design, SMVD University, Kakryal, Katra, Jammu and Kashmir 182320, India Full list of author information is available at the end of the article Page 2 of 27 Kumari and Devadas Economic Structures (2017) 6:15 approach to achieve sustainable agriculture (Rao and Jeromi 2000; Ray etal. 2001; Srinivasan 2001). The best way to meet the increasing food demand for the rapidly growing population is to increase the production of crops using new agricultural technologies which can increase productivity of limited land resource and preserving its soil quality as well (Ray etal. 2001; Sarkar etal. 2013; Spiertz 2013; Wilson and Scot 1982). Studies suggest that agricultural productivity plays a key role in the process of industrialization and development, and there is a linkage between increase in agricultural productivity and poverty reduction (Ludena 2010; Prasad 1971; Schneider and Gugerty 2011; Lindh and Malmberg 2007; Weintraub 1976). Thus, regional development, which primarily aims at reducing the disparity in socio-economic status of the people, can be achieved by agricultural development (Mandal and Peters 1990; Mcloughlin 1970; Miller etal. 2009). The aim of the present research is to characterize the role of agriculture in economic development and identifying ways in which this role can be enhanced. In India, agriculture is the main source of resources that can be utilized in the emerging activities. Hence, successful industrialization requires a solution to the problems associated with the generation, transfer, and use of an agricultural resource surplus (Griffin and Enos 1970; Grabowski 2015; Kachru 2006). Generation of growing surplus demands a rising productivity of resource use in agriculture (Dasgupta 1974). This is achieved by successful agricultural and rural development, more specifically through technological and institutional development (Chand and Puri 2009; Winters and Stamoulis 1997). The use of modern varieties of irrigation and fertilizers is important aspects of higher growth in crop production. Studies suggest that modern inputs, such as improved seeds (HYVs), mechanized farm inputs, and organic manures, play an important role in agricultural development in a region (Kannan and Sundaram 2011; Letourneau and Goldstein 2001; Wilson and Scot 1982). The crop diversification with the adoption of modern technologies has boosted Indian agriculture to move from subsistence farming to intensive and technology-based cultivation for enhancing the productivity (Chattopadhayay and Roy 2011; Fontes etal. 2009). 2 Trend ofagricultural production inIndia Despite all the natural advantages, India’s productivity of food grains per hectare is no more than three-fourths of the world average and less than half of that in agriculturally advanced countries. The per capita food grain availability has been less than two-thirds of the world average, even after the green revolution. Only five states in India, namely Himachal Pradesh, Punjab, Haryana, Uttar Pradesh, and Madhya Pradesh, produce more grain than their populations can consume (Bhide 2015; Rao and Jeromi 2000). The combined population of these five states is less than one-third of the total of the country. More than two-thirds of the population live in states that are still food deficit (Rao and Jeromi 2000). This requires transport of millions of tonnes of food grain, involving high costs and time. The effort should have been to make all the states self-sufficient with respect to food grains, and if some disturbances occurred due to unnoticed natural calamities, the nation must be in an ever ready position to mitigate such challenging tasks. The Indian green revolution is also associated with negative ecological and environmental consequences. Besides, India shares 17% of world’s population with two and half per cent of geographical area, 1% of gross world product, 4% of world carbon Page 3 of 27 Kumari and Devadas Economic Structures (2017) 6:15 emissions, and hardly 2% of world forest area. The Indian status of environment, though not alarming when compared to developed countries, gives an early warning to take appropriate precautionary measures. The growth rate of grain production has been higher than the population growth rate in the post-independence era, but still the growth is much less to fulfil the domestic need (Table1). Per capita availability of grain and per capita calorie intake, which were less than the minimum required for adequate nutrition, have further declined. According to Human Development Report 2003, the percentage of the undernourished in India, which was twenty-one a few years ago, has now reached twenty-four (Rao and Jeromi 2000). The value of agricultural output in India has grown significantly during past few decades (Table2; Fig.1). The government claims that India has emerged as the seventh largest exporter of food grains in the world. However, we take into account that the total Table 1 Population growth versusagricultural growth. Source: Agricultural Statistics at a Glance, GOI, New Delhi, 1993 Census of India, 2000 Year Population growth Period Agricultural growth (annual compound growth rate) Total population Annual compound growth rate (%) Area Yield Production 1951 361.1 1.25 1949–1950 to 1964–1965 1.61 1.50 3.13 1961 439.2 1.96 1971 548.2 2.20 1967–1968 to 1980–1981 0.54 1.83 2.38 1981 685.2 2.22 1991 844.3 2.11 1980–1981 to 1991–1992 0.05 3.16 3.21 2000 987.3 1.09 Table 2 Value ofagricultural output (in Rs. millions) 44 crops: India. Source: Government of India, Area and Production of Principal Crops in India (various issues), Ministry of Agriculture Year 1963 1973 1983 1993 2003 Value 560,923 662,358 831,846 1,167,785 1,450,096 Fig. 1 Agricultural output in India. Graph showing decadal growth of agricultural output from the year 1963 to 2003, of crops in India Page 4 of 27 Kumari and Devadas Economic Structures (2017) 6:15 Indian grain export in 2002–2003 did not add up to even 4% of the total world exports, and the value of our grain exports did not add up to even the value of our imports of vegetable oils and pulses. The more crucial question, however, is whether it is morally justifiable to export grain when 24% of the population remains undernourished (Rao and Jeromi 2000). The main reason for the poor performance of the farm sector has been the long persisting adverse terms of trade policies for agriculturists; in addition, the mismanagement of natural resources leads to never-ending crisis. Even though India has shown remarkable progress in recent years and has attained selfsufficiency in food staples, the productivity of Indian farms for the same crop is very low compared to farms in Brazil, the USA, France, and other nations. It implies there is a vast scope of agriculture growth. Indian wheat farms, for example, produce about a third of wheat per hectare per year in contrast to wheat farms in France (Tables2, 3; Figs.1, 2). Similarly, at forty-four million hectares, India had the largest farm area under rice production in 2009; yet, the rice farm productivity in India was less than half the rice farm productivity in China (Rao and Jeromi 2000). Other food staples productivity in India is similarly low, suggesting a major opportunity for growth and future agricultural prosperity potential in India. Indian total factor productivity growth remains below 2% per annum; in contrast, China has shown total factor productivity growths of about 6% per annum, even though China too has a small land holding farmers (Table4) (Rao and Jeromi 2000). If India could adopt technologies and improve its infrastructure, several studies suggest India could eradicate hunger and malnutrition within India, and be a major source of food in the world. 3 About the study area The study area of Patna region, India, has been selected for the present research, keeping in view the potential of agricultural growth in the region as explained in the above section. The study area has an area of 16,873km2 (according to Directorate of Economics Table 3 Crop yield (value output Rs. per hectare of gross cropped area): India. Source: Government of India, Area and Production of Principal Crops in India (various issues), Ministry of Agriculture Year 1963 1973 1983 1993 2003 Crop yield 3738 4257 5090 6957 8460 Fig. 2 Crop yield in India. Graph shows the value output of crops per hectare of gross cropped area in India, from the year 1963 to 2003 Page 5 of 27 Kumari and Devadas Economic Structures (2017) 6:15 and Statistics Bihar 2007 and Directorate of Statistics and Evaluation Bihar 2007). The study area of Patna region, an administrative geographical unit of Bihar state, India, is located in the eastern part of the country (between latitude 24°30′50″N to 25°44′00″N and longitude 83°19′30″E to 86°00′00″E) (Fig.3). The region is least prone to flood hazard among all the regions of Bihar and has same soil type as Gangetic alluvial plane; agro-climatic conditions are same as it comes under one zone (zone iii) among three agro-climatic zones of Bihar, India. Similar kind of agricultural production, similar language, socio-economic condition, and demographic condition persists in the region (according to Economic Survey 2014). The above-mentioned homogeneous characteristics were taken into consideration while delineating the Patna region. The region consists of Patna District, Nalanda District, Bhojpur District, Rohtas District, Buxar District, and Kaimur District, also called Bhabhua District (according to Directorate of Statistics and Evaluation Bihar 2007). Table 4 Agriculture productivity in India, growth in average yields from1970 to 2010. Source: Government of India, Area and Production of Principal Crops in India (various issues), Ministry of Agriculture Crop Average yield, 1970–1971 (kilogram perhectare) Average yield, 1990–1991 (kilogram perhectare) Average yield, 2010–2011 (kilogram perhectare) Rice 1123 1740 2240 Wheat 1307 2281 2938 Pulses 524 578 689 Oilseeds 579 771 1325 Sugar cane 48,322 65,395 68,596 Tea 1182 1652 1669 Cotton 106 225 510 Fig. 3 Map of Patna region. Existing base map of the study area, Patna region, India Page 6 of 27 Kumari and Devadas Economic Structures (2017) 6:15 4 Justification ofthe study area The study area of Patna region, India, is facing higher degree of poverty, unemployment, and overall deprivation in the region. According to studies, the agricultural productivity potential of the region is very high; thus, there is a need to harness the region’s agricultural resources judiciously, not only to liberate the region from its socio-economic and ecological glooms, but also to trigger the process of overall regional development, as agriculture sector has been called as the precursor of economic growth process. The percentage of working population employed in agricultural operations in the region is estimated to be 85%, which is much higher than the national average (according to Directorate of Economics and Statistics Bihar 2007). Two-third of the total land area of Patna region is used for agricultural purpose (according to Directorate of Economics and Statistics Bihar 2007). High concentration of population, largely dependent on agriculture coupled with low infrastructure and technological development, is main reason for high poverty ratio in the region. Further, the study area, with abundance of water bodies, has very high potential for fisheries and aquaculture, but it has not been fully realized till today. Livestock is also a major resource in this region (according to Directorate of Economics and Statistics Bihar 2007 and Directorate of Statistics and Evaluation Bihar 2007). Despite the strength of the agriculture sector, it is a paradox that this sector is much below the potential. The region’s development is much needed, by reducing un-utilization and/or underutilization of rural resources, in turn reducing unemployment, regional imbalances and disparities, inequalities in the distribution of income and wealth, for the development of whole nation. There is need of sustainable development of the region ensuring the proper utilization of region’s resources including human resource to help in minimizing the pressure on other urban centres outside the region, which have their own acute problems of traffic congestion, in migration, housing shortage, slum formation, water scarcity, etc. Having above knowledge in mind, the study area has been selected for the present investigation to evolve a set of policy guidelines for the sustainable economic development of the region. 5 Methods In this present investigation, an attempt has been made to evolve plausible policy guidelines and recommendations for the sustainable economic development of Patna region, by considering it as a system. It has been observed that lack of comprehensive plan and integrated approach became a deterrent in the development process in the study area. Therefore, a thorough grassroots level investigation has been carried out through conducting primary survey at the household level and exploration and analysis of available literature and collected data from secondary sources, to understand the important control parameters, which influence the function of the system. Further, by considering the important control parameters of various subsystems of the system, system dynamic model for sustainable development in the system has been evolved. The projections, for the year 2031, from the base year 2011, have been made to understand the behaviour of the system. The functions of the system under various alternative conditions are closely examined by developing various scenarios and tested to arrive at alternative policy option for taking decisions. Finally, a set of plausible policy guidelines has been prepared screening phasewise requirements and achievements in alternative conditions. Page 7 of 27 Kumari and Devadas Economic Structures (2017) 6:15 It is anticipated that if the proposed planning model is implemented successfully in the study area, it will ensure sustainable development in the system, definitely. 6 Application ofsystem dynamics model System dynamics (SD) is one of the numerical simulation techniques. The fundamentals of System dynamics were defined by Forrester (1958) as a method for the modelling of industrial dynamics which was later, in the early 1980, renamed as system dynamics (SD) (Rozman etal. 2013). System dynamics is an effective tool for simulating and analysing complex systems in which various factors are interrelated. Sterman (2000) writes ‘System Dynamics is a perspective and set of conceptual tools that enable us to understand the structure and dynamics of complex systems. System dynamics is also a rigorous modeling method that enables us to build formal computer simulations of complex systems and use them to design more effective policies and organizations’. According to Tang and Vijay (2001), ‘Simply stated, system dynamics is a method. A method, that permits the analyst to decompose a complex social or behavioural system into its constituent components and then integrate them into a whole that can be easily visualized and simulated’. The literature suggests that system dynamics (SD) is considered to be an appropriate approach for predicting results of the dynamic interactions and analysing the effects of different policies for given complex systems. The method can effectively incorporate individual components of a system within a general framework and then comprehensively analyse their interactions (Guan etal. 2011). These interactions are complex because they simultaneously involve various system components and they dynamically change over time (Guan etal. 2011). The SD approach revealed considerable advantages over other approaches. One of the major advantages is that there is possibility of experimenting with model scenarios even though limited data are available to find out system’s potential response to different policies prior to making any decision (Rozman etal. 2013). In system dynamics, simulation is completely governed by the passage of time and is referred to as ‘time-step’ simulation (Coyle 1999). The available literature describes the steps involved in system dynamic modelling process (Fig.4). The thorough study of available literature suggests that the steps involved in the system dynamics modelling process can be taken as follows: In the present investigation, system dynamics theory has been implemented to develop system dynamics model consisting of various submodels, for plausible policy planning for sustainable economic development of the study area. The model has been validated to generate policy scenarios in alternate conditions. The forecast has been done for the year 2031. The system dynamics model has been developed for the present investigation, considering region as a system consisting of seven subsystems, which are physical subsystem, social subsystem, economic subsystem, ecological subsystem, environmental subsystem, infrastructure subsystem, and institutional subsystem (Devadas etal. 2008; Kumari and Devadas 2014) (Fig.5). Attempts have been made to develop system dynamics model based on the survey data and historical data, to understand the influence of the most important controlling parameters that decide the function of the system for evolving optimal strategies for integrated development of the system. The simulation results have been generated from the model to evolve set of plausible policy guidelines for the sustainable development of the study area. Page 8 of 27 Kumari and Devadas Economic Structures (2017) 6:15 6.1 System dynamics model forGDP inAgriculture andAllied Sector The GDP in Agriculture and Allied Sector has been considered as an important parameter, which influence the system. A system dynamic model is built to calculate GDP in Agriculture and Allied Sector by considering the influential variables, cereals production, pulses production, vegetables production, fruits production, flowers production, milk production, egg production, fish production, and meat production. A functional flow diagram is developed and presented in Fig.6. The GDP in Agriculture and Allied Sector have been considered as the level variables, whereas GDP AAL rate has been taken as rate variable. The model equation used for the above purpose is presented as follows. The definitions of each variable and mathematical (algebraic) equation are described in the model equations; they are: Fig. 4 Model development as an iterative process. The flow chart shows the system dynamics model development process Fig. 5 Regional system. The chart explains the region as a system, comprising of seven subsystems, which comprise of various sub-subsystems Page 15 of 27 Kumari and Devadas Economic Structures (2017) 6:15 9.2 Projected percapita income The projected per capita income in the study area is presented in Table10 and Fig.12. The model results reveal that the per capita annual income in the study area would increase from Rs. 32,101 in the year 2011 to Rs. 2,49,576 in the year 2031. 9.3 Projected yield ofcereals, pulses, fruits, vegetables, andflowers The projected yield of cereals, pulses, fruits, vegetables, and flowers in the study area is presented in Table11 and Fig.13. The model results reveal that the yield of cereals in Table 8 Model validation using percentage difference andregression analysis method Sl. no. Name ofparameters Percentage difference between real value andmodel result (%) R value R2 1 Population ≤1.19 1.00 1.00 2 Sex ratio ≤0.10 0.99 0.99 3 Birth rate ≤2.08 0.97 0.94 4 Literacy rate ≤4.78 1.00 1.00 5 IMR ≤1.72 0.91 0.82 6 GDP ≤5.25 0.99 0.98 7 Percentage of villages electrified ≤4.17 0.99 0.98 8 Road length ≤4.24 1.00 0.99 Table 9 Projected population Sl. no. Year Population (in numbers) 1. 2011 1,76,62,618 2. 2016 1,95,18,975 3. 2021 2,14,08,368 4. 2026 2,32,63,270 5. 2031 2,49,74,770 18:27 16 Jul 2015Page 1 2011.00 2016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 17500000 21500000 25500000 16872 16873 16874 0 1500 3000 1: Population 2: Area 3: Population Density 1 1 1 1 2 2 2 2 333 3 Fig. 11 Projected population and population density up to 2031 Page 16 of 27 Kumari and Devadas Economic Structures (2017) 6:15 the study area would increase from 302,000kg/km2 in the year 2011 to 667,000kg/km2 in the year 2031; yield of pulses would increase from 124,000 to 220,000kg/km2; yield of fruits would increase from 2,021,000 to 3,219,000kg/km2; yield of vegetables would increase from 1,691,000 to 2,173,000kg/km2; and yield of flowers would increase from 1,291,000 to 1,976,000kg/km2 during the same period of time. 9.4 Projected production ofmilk, egg, fish, andmeat The projected production of milk, egg, fish, and meat in the study area is presented in Table12 and Fig.14. The model results reveal that the annual production of milk in the study area would increase from 812 million kg in the year 2011 to 9530 million kg in the year 2031; the annual production of eggs would increase from 103 to 291 million; the Table 10 Projected percapita income Sl. no. Year Per capita income (in Rs./year) 1. 2011 32,101 2. 2016 53,372 3. 2021 88,659 4. 2026 1,47,899 5. 2031 2,48,981 18:32 16 Jul 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 0 150000 300000 1: Per Capita Income 1 1 1 1 Fig. 12 Projected per capita income up to 2031 Table 11 Projected production ofcereals, pulses, fruits, vegetables, andflowers Sl. no. Year Yield ofcereals (in 000’ kg/km2)Yield ofpulses (in 000’ kg/km2)Yield offruits (in 000’ kg/km2)Yield ofvegetables (in 000’ kg/ km2) Yield offlowers (in 000’ kg/km2) 1. 2011 302 124 2021 1691 1291 2. 2016 381 144 2289 1841 1441 3. 2021 464 166 2568 1999 1599 4. 2026 557 191 2871 2173 1773 5. 2031 667 220 3219 2376 1976 Page 17 of 27 Kumari and Devadas Economic Structures (2017) 6:15 18:34 16 Jul 2015 Productivity (in 000' kg/ sq km) Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 4: 4: 4: 5: 5: 5: 0 375 750 0 125 250 0 1750 3500 0 1250 2500 0 1000 2000 1: Cereals Productivity 2: Pulses Productivity 3: Fruits Productivity 4: Vegetab…roductivity 5: Flowers Productivity 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 Fig. 13 Projected production of cereals, pulses, fruits, vegetables, and flowers up to year 2031 Table 12 Projected production ofmilk, egg, fish, andmeat Sl. no. Year Milk production (in million kg perannum) Egg production (millions innumber perannum) Meat production (million kg perannum) Fish production (million kg perannum) 1. 2011 812 103 20 41 2. 2016 1438 116 28 46 3. 2021 2688 143 45 55 4. 2026 5076 195 76 73 5. 2031 9530 291 134 106 18:37 16 Jul 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 4: 4: 4: 500 5500 10500 100 200 300 0 100 200 40 75 110 1: Milk Production 2: Egg Production 3: Meat Production 4: Fish Production 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 Fig. 14 Projected production of milk, egg, fish, and meat up to 2031 Page 18 of 27 Kumari and Devadas Economic Structures (2017) 6:15 annual production of meat would increase from 20 to 134 million kg; and the annual production of fish would increase from 41 to 106 million kg during the year 2011 to year 2031. 9.5 Projected GDP, GDP inPrimary Sector, GDP inSecondary Sector, GDP inTertiary Sector, GDP inAgriculture andAllied Sector, andGDP inIndustrial Sector The projected GDP, GDP in Primary Sector, GDP in Secondary Sector, GDP in Tertiary Sector, GDP in Agriculture and Allied Sector, and GDP in Industrial Sector in the study area are presented in Table13 and Figs.15 and 16. The model results reveal that the GDP in the study area would increase from Rs. 5,66,990 million in the year 2011 to Rs. 62,18,231 million in the year 2031; GDP in Primary Sector would increase from Rs. 71,449 million to Rs. 2,32,471 million; GDP in Secondary Sector would increase from Rs. 56,778 million to Rs. 3,93,155 million; GDP in Tertiary Sector would increase from Rs. 4,60,269 million to Rs. 53,75,907 million; GDP in Agriculture and Allied Sector would increase from Rs. 49,943 million to Rs. 1,21,966 million; and GDP in Industrial Sector would increase from Rs. 55,023 million to Rs. 7,40,066 million during the year 2011 to year 2031. Table 13 Projected GDP, GDP inPrimary Sector, GDP inSecondary Sector, GDP inTertiary Sector, GDP inAgriculture andAllied Sector, andGDP inIndustrial Sector Sl. no. Year GDP (Rs. inmillions) GDP inPrimary Sector (Rs. inmillions) GDP inSecondary Sector (Rs. inmillions) GDP inTertiary Sector (Rs. inmillions) GDP inAgriculture andAllied Sector (Rs. inmillions) GDP inIndustrial Sector (Rs. inmillions) 1. 2011 5,66,990 71,449 56,778 4,60,269 49,943 55,023 2. 2016 10,41,767 1,01,385 1,11,854 8,69,609 60,304 1,11,920 3. 2021 18,98,036 1,50,235 2,11,958 16,12,042 74,037 2,15,291 4. 2026 34,40,608 2,32,471 3,93,155 29,54,182 93,271 4,02,348 5. 2031 62,18,231 3,74,194 7,20,358 53,75,907 1,21,966 7,40,066 12:24 13 Aug 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 4: 4: 4: 0 3500000 7000000 0 200000 400000 0 400000 800000 0 2750000 5500000 1: GDP Planned 2: GDP PS 3: GDP SS 4: GDP TS 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 Fig. 15 Projected GDP, GDP in Primary Sector, GDP in Secondary Sector, and GDP in Tertiary Sector up to year 2031 Page 19 of 27 Kumari and Devadas Economic Structures (2017) 6:15 10 Recommendation On the basis of detailed analysis of the evolved policies and their perceived outcomes, a policy would be more suitable for overall development of the regional system. The policy is developed on the basis of composite scenario, according to which there are 2.00% addition in the percentage expenditure on agriculture and allied sector; 2.00% exclusion from the percentage expenditure on rural development; 6.00% addition in the percentage expenditure on transport and communication; 4.00% exclusion from the percentage expenditure on irrigation and flood control; 6.00% exclusion from the percentage expenditure on social services; 2.00% addition in the percentage expenditure on energy; and 2.0% addition in percentage expenditure on science, technology and environment. This policy has been considered for recommendation and phasewise development of all the subsystems of the regional system. Four phases has been considered for the recommended policy analysis, between 2011 and 2031, which are Phase 1 (2011–2016), Phase 2 (2016–2021), Phase 3 (2021–2026), and Phase 4 (2026–2031), and are presented in Table14; Figs.17, 18, 19, 20, 21, and 22. These tables and figures reveal that the achievements and thrust areas at different phases vary considerably in the study area. The phasewise socio-economic development have been presented as follows. They are: 10.1 Phase 1: (2011–2016) It has been observed that the population would increase from 1,76,62,618 to 1,95,09,939 during this phase, the population density would increase from 1047 to 1156 per km2, the birth rate would decrease from 29 to 27 per 1000 of mid-year population, the death rate would be 7 per 1000 of mid-year population, sex ratio would increase from 902 to 909 female/1000 males, the literacy rate would increase from 69.04 to 72.32%, the infant mortality rate would decrease from 59 to 56 per 1000 of mid-year population, and the per capita income would increase from Rs. 34,979 to 61,978 per annum. It has been observed in the production analysis that the production of cereals would increase from 18:39 16 Jul 2015 GDP (Rs. in Millions) Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 0 3500000 7000000 0 75000 150000 0 400000 800000 1: GDP Planned 2: GDP Agriculture and Allied 3: GDP Industry 1 1 1 1 2 2 2 2 3 3 3 3 Fig. 16 Projected GDP, GDP in Agriculture and Allied Sector, and GDP in Industrial Sector up to year 2031 Page 20 of 27 Kumari and Devadas Economic Structures (2017) 6:15 Table 14 Perceived phasewise development Sl. no. Year Birth rate (per 1000 ofmidyear population) Death rate (per 1000 ofmidyear population) Sex ratio (female/1000 males) Literacy rate (%) Infant mortality rate (per 1000 ofmid-year population) 1. 2011 29 7 902 69.04 59 2. 2011–2016 27 7 909 72.32 56 3. 2016–2021 26 6 920 75.45 53 4. 2021–2026 24 6 940 78.51 46 5. 2026–2031 23 5 978 81.55 33 Sl. no. Year Population (in numbers) Population density (numbers/km2)Per capita income (in Rs./year) 1. 2011 1,76,62,618 1047 34,979 2. 2016 1,95,09,939 1156 61,978 3. 2021 2,13,72,375 1267 1,09,943 4. 2026 2,31,58,074 1372 1,96,438 5. 2031 2,47,05,275 1464 3,55,883 Sl. no. Year Production ofcereals (in 000’ kg/km2) Production ofpulses (in 000’ kg/km2) Production offruits (in 000’ kg/km2) Production ofvegetables (in 000’ kg/ km2) Production offlowers (in 000’ kg/km2) 1. 2011 302 124 2021 1691 1291 2. 2016 381 145 2289 1842 1442 3. 2021 467 167 2570 2003 1603 4. 2026 567 193 2880 2185 1785 5. 2031 695 227 3247 2409 2009 Sl. no. Year Milk production (in million kg perannum) Egg production (million innumber perannum) Meat production (million kg perannum) Fish production (million kg perannum) 1. 2011 812 103 20 41 2. 2016 1838 125 33 49 3. 2021 4011 172 62 65 4. 2026 8416 267 119 98 5. 2031 17,142 455 234 163 Sl. no. Year GDP (Rs. inmillions) GDP inPrimary Sector (Rs. inmillions) GDP inSecondary Sector (Rs. inmillions) GDP inTertiary Sector (Rs. inmillions) 1. 2011 6,17,828 71,449 62,917 5,04,968 2. 2016 12,09,188 1,01,562 1,32,047 10,16,660 3. 2021 23,49,738 1,51,431 2,66,326 20,08,179 4. 2026 45,49,114 2,36,785 5,26,366 39,25,164 5. 2031 87,92,197 3,86,724 10,29,287 76,28,414 Sl. no. Year GDP inAgriculture andAllied Sector (Rs. inmillions) GDP inIndustrial Sector (Rs. inmillions) 1. 2011 49,943 61,163 2. 2016 60,482 1,32,113 3. 2021 75,232 2,69,659 4. 2026 97,585 5,35,559 5. 2031 1,34,497 10,48,994 Page 21 of 27 Kumari and Devadas Economic Structures (2017) 6:15 302 thousand to 381 thousand kg/km2, the production of pulses would increase from 124 to 145 thousand kg/km2, the production of fruits would increase from 2021 thousand to 2289 thousand kg/km2, the production of vegetables would increase from 1691 thousand to 1842 thousand kg/km2, the production of flowers would increase from 1291 thousand to 1442 thousand kg/km2, the production of milk would increase from 812 million to 1838 million kg per annum, the production of eggs would increase from 103 million to 125 million per annum, the production of meat would increase from 20 million to 33 million kg per annum, the production of fish would increase from 41 million to 49 million kg per annum. It has been further observed in the economic analysis that the GDP would increase from Rs. 6,17,828 million to 12,09,188 million, the GDP in Primary Sector would increase from Rs. 71,449 million to 1,01,562 million, the GDP in Secondary Sector would increase from Rs. 62,917 million to 1,32,047 million, the GDP in Tertiary Sector would increase from Rs. 5,04,968 million to 10,16,660 million, the GDP in 18:25 24 Jul 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 4: 4: 4: 17500000 21500000 25500000 0 1500 3000 0 15 30 5 7 8 1: Population 2: Population Density 3: Birth rate 4: Death rate 1 1 1 1 22 22 3 3 3 3 4 4 4 4 Fig. 17 Perceived projected population, population density, birth rate, and death rate 21:10 30 Jul 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 4: 4: 4: 900 940 980 0 50 100 0 200000 400000 1: Sex ratio 2: Literacy rate 3: Infant Mortality Rate 4: Per Capita Income 1 1 1 1 2222 33 3 3 4 4 4 4 Fig. 18 Perceived projected sex ratio, literacy rate, infant mortality rate, and per capita income Page 22 of 27 Kumari and Devadas Economic Structures (2017) 6:15 Agriculture and Allied Sector would increase from Rs. 49,943 million to 60,482 million, and the GDP in Industrial Sector would increase from Rs. 61,163 million to 1,32,113 million.. 10.2 Phase 2: (2016–2021) It has been observed that the population would increase from 1,95,09,939 to 2,13,72,375 during this phase, the population density would increase from 1156 to 1267 per km2, the birth rate would decrease from 27 to 26 per 1000 of mid-year population, the death rate would decrease from 7 to 6 per 1000 of mid-year population, sex ratio would increase from 909 to 920 female/1000 males, the literacy rate would increase from 72.32 to 75.45%, the infant mortality rate would decrease from 56 to 53 per 1000 of mid-year population, and the per capita income would increase from Rs. 61,978 to 1,09,943 per annum. It has been observed in the production analysis that the production of cereals 18:27 24 Jul 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 4: 4: 4: 5: 5: 5: 300 500 700 100 200 300 2000 3000 4000 1650 2050 2450 1000 1550 2100 1: Cereals Productivity 2: Pulses Productivity 3: Fruits Productivity 4: Vegetab…roductivity 5: Flowers Productivity 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 Fig. 19 Perceived projected production of cereals, pulses, fruits, vegetables, and flowers 18:28 24 Jul 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 4: 4: 4: 0 10000 20000 100 300 500 0 150 300 0 100 200 1: Milk Production 2: Egg Production 3: Meat Production 4: Fish Production 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 Fig. 20 Perceived projected production of milk, egg, meat, and fish Page 23 of 27 Kumari and Devadas Economic Structures (2017) 6:15 would increase from 381 thousand to 467 thousand kg/km2, the production of pulses would increase from 145 thousand to 167 thousand kg/km2, the production of fruits would increase from 2289 thousand to 2570 thousand kg/km2, the production of vegetables would increase from 1842 thousand to 2003 thousand kg/km2, the production of flowers would increase from 1442 thousand to 1603 thousand kg/km2, the production of milk would increase from 1832 million to 4011 million kg per annum, the production of eggs would increase from 125 million to 172 million per annum, the production of meat would increase from 33 million to 62 million kg per annum, the production of fish would increase from 49 million to 65 million kg per annum. It has been further observed in the economic analysis that the GDP would increase from Rs. 12,09,188 million to 23,49,738 million, the GDP in Primary Sector would increase from Rs. 1,01,562 million to 1,51,431 million, the GDP in Secondary Sector would increase from Rs. 1,32,047 million to 2,66,326 million, the GDP in Tertiary Sector would increase from Rs. 10,16,660 million 18:29 24 Jul 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 500000 5000000 9500000 0 75000 150000 0 524500 1049000 1: GDP Planned 2: GDP Agriculture and Allied 3: GDP Industry 1 1 1 1 2 2 2 2 3 3 3 3 Fig. 21 Perceived projected GDP, GDP in Agriculture and Allied Sector, and GDP in Industrial Sector 18:30 24 Jul 2015Page 1 2011.002016.00 2021.00 2026.00 2031.00 Years 1: 1: 1: 2: 2: 2: 3: 3: 3: 4: 4: 4: 500000 5000000 9500000 0 200000 400000 50000 550000 1050000 0 3814000 7628000 1: GDP Planned 2: GDP PS 3: GDP SS 4: GDP TS 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 Fig. 22 Perceived projected GDP, GDP in Primary Sector, GDP in Secondary Sector, and GDP in Tertiary Sector Page 24 of 27 Kumari and Devadas Economic Structures (2017) 6:15 to 20,08,179 million, the GDP in Agriculture and Allied Sector would increase from Rs. 60,482 million to 75,232 million, and the GDP in Industrial Sector would increase from Rs. 1,32,113 million to 2,69,659 million. 10.3 Phase 3: (2021–2026) It has been observed that the population would increase from 2,13,72,375 to 2,31,58,074 during this phase, the population density would increase from 1267 to 1372 per km2, the birth rate would decrease from 26 to 24 per 1000 of mid-year population, the death rate would be same as 6 per 1000 of mid-year population, sex ratio would increase from 920 to 940 female/1000 males, the literacy rate would increase from 75.45 to 78.51%, the infant mortality rate would decrease from 53 to 46 per 1000 of mid-year population, and the per capita income would increase from Rs. 1,09,943 to 1,96,438 per annum. It has been observed in the production analysis that the production of cereals would increase from 467 thousand to 567 thousand kg/km2, the production of pulses would increase from 167 thousand to 193 thousand kg/km2, the production of fruits would increase from 2570 thousand to 2880 thousand kg/km2, the production of vegetables would increase from 2003 thousand to 2185 thousand kg/km2, the production of flowers would increase from 1603 thousand to 1785 thousand kg/km2, the production of milk would increase from 4011 million to 8416 million kg per annum, the production of eggs would increase from 172 million to 267 million per annum, the production of meat would increase from 62 million to 119 million kg per annum, the production of fish would increase from 65 million to 98 million kg per annum. It has been further observed in the economic analysis that the GDP would increase from Rs. 23,49,738 million to 45,49,114 million, the GDP in Primary Sector would increase from Rs. 1,51,431 million to 2,36,785 million, the GDP in Secondary Sector would increase from Rs. 2,66,326 million to 5,26,366 million, the GDP in Tertiary Sector would increase from Rs. 20,08,179 million to 39,25,164 million, the GDP in Agriculture and Allied Sector would increase from Rs. 75,232 million to 97,585 million, and the GDP in Industrial Sector would increase from Rs. 2,69,659 million to 5,35,559 million. 10.4 Phase 4: (2026–2031) It has been observed that the population would increase from 2,31,58,074 to 2,47,05,275 during this phase, the population density would increase from 1372 to 1464 per km2, the birth rate would decrease from 24 to 23 per 1000 of mid-year population, the death rate would decrease from 6 to 5 per 1000 of mid-year population, sex ratio would increase from 940 to 978 female/1000 males, the literacy rate would increase from 78.51 to 81.55%, the infant mortality rate would decrease from 46 to 33 per 1000 of mid-year population, and the per capita income would increase from Rs. 1,96,438 to 3,55,883 per annum. It has been observed in the production analysis that the production of cereals would increase from 567 thousand to 695 thousand kg/km2, the production of pulses would increase from 193 thousand to 227 thousand kg/km2, the production of fruits would increase from 2880 thousand to 3247 thousand kg/km2, the production of vegetables would increase from 2185 thousand to 2409 thousand kg/km2, the production of flowers would increase from 1785 thousand to 2009 thousand kg/km2, the production of milk would increase from 8416 million to 17,142 million kg per annum, the production of eggs would increase from 267 million to 455 million per annum, the production