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ThesisItem Open Access DYNAMICS AND PATTERNS OF EGG PRODUCTION, PRICES AND TRADE IN INDIA: A SPATIO-TEMPORAL ANALYSIS 3757(JAU JUNGADH, 2023-09) ABINAYA M C; Dr. B. SWAMINATHAN; 2010121071India is the second largest producer of poultry eggs with a share of nearly 6 per cent of global egg production. The present study was conducted to analyse the growth rate, instability, growth pattern and forecasting of egg production, price and export and also market integration and price transmission among the major egg markets. The major egg producing states were selected based on the egg production volume and the state of Gujarat was also selected. Ten major egg markets across the country were selected based on the egg transaction volume. Time series data was collected on all-over India egg production from 1961-62 to 2021-22, major state-wise egg production from 1997-98 to 2021-22, average monthly wholesale egg prices from January 2009 to December 2022 and egg exports from India from 1986-87 to 2021-22 from authenticated sources. To fulfil the objectives of the present study, the best econometric and statistical tools such as Compound Growth Rate, Cuddy-Della Valle Index, centered twelve month moving average method, Engle-Granger and Johansen cointegration analysis, Granger causality test, VECM, Impulse response function, Variance decomposition, ARIMA and SARIMA models were employed in this study. The overall period growth rate and instability of egg production in India were 6.23 and 38.83 per cent respectively. Among the major egg producing states, the highest growth rate and instability in egg production was achieved in Haryana (10.87%) and West Bengal (25.29%) respectively in the study period. The overall month-wise growth rate of nominal egg prices in all the selected markets in India was positive and highly significant at 1 per cent level where the highest and lowest growth rate was achieved in Barwala (2.41%) and Chennai market (1.27%) respectively. The overall instability shows that the price volatility was high in Barwala market and low in Namakkal market. The overall month-wise growth rate of real egg prices was highest in Barwala (2.44 %) and lowest in Chennai market (1.31 %) whereas instability was highest in Barwala (10.38 %) and lowest in Namakkal market (5.76 %). The market-wise growth rate of egg prices in selected markets showed that a very high growth rate happened in Barwala market in 2020 (8.54%) and a very low growth rate in Chennai market in 2010 (0.60%) whereas the instability was highest in Barwala (5.78%) and lowest in Mumbai (3.78%) market. The growth rate and instability of egg export quantity, nominal and real value of egg exports and unit price of egg exports were also analysed in this study. The overall period growth rate and instability in the quantity of eggs exported from India were 10.34 and 65.84 per cent respectively. The growth rate and instability in the nominal value of egg export in the study period as a whole were at 18.70 and 32.26 per cent respectively whereas the overall growth rate and instability in terms of the real value of egg export were at 12.5 and 57.97 per cent respectively. The growth rate and instability of unit price of egg exports (Nominal) in the study period as a whole were 7.57 and 27.59 per cent respectively whereas the growth rate and instability in real unit price of egg export in the overall period of the study were at 1.95 and 30.69 per cent respectively. The seasonal indices showed that in all the markets the highest egg price indices were seen in November or December month and the lowest indices were seen in April month. The stationarity test showed that all the selected egg markets attained stationarity at their first difference. The optimum lag order was selected as 3 which is devoid of autocorrelation. The Engle and Granger test showed that all 45 market pairs were integrated. The Johansen cointegration method shows that all the selected 10 major markets were integrated in the long run. The VECM shows that the speed of convergence of short-run disequilibrium towards long-run equilibrium will be faster in Barwala market (7.18 %). The LOOP does not hold for the selected egg markets. The Granger causality test revealed that the Barwala market granger causes all other markets except Bangalore market and it is not granger caused by any other markets. Barwala market acts as a price leader among the selected egg markets, exhibiting a strong exogenous effect. The impulse response function showed that the response of the markets to one-unit standard deviation shock given to other markets disappeared mostly after period 9 or 10. The variance decomposition analysis showed that the Barwala market is the major influencer of egg price in other markets both in the short run as well as in the long run and it is the major influencer of its own price. The ARIMA (2,1,4) model is the most suitable model for onward forecasting of egg production in India. It predicts a rising trend in egg production in India, projecting an increase from 73.75 lakh tonnes in 2022 to 97.69 lakh tonnes in 2026. The SARIMA (4,1,4) (2,1,1), (4,0,4) (3,0,1), (6,1,4) (1,1,1), (6,1,4) (1,1,1), (3,1,4) (2,1,1), (4,1,3) (1,1,1), (5,1,4) (1,1,1), (6,1,4) (1,1,1), (5,1,3) (2,1,1) and (6,1,4) (2,1,1) models were selected as the best models for forecasting egg prices Ahmedabad, Barwala, Bangalore, Chennai, Delhi, Hyderabad, Kolkata, Mumbai, Namakkal and Visakhapatnam markets respectively where it forecasted that the egg price in 2023 will be higher in November or December month and lower in March or April month. The ARIMA (4,1,5) model is best for onward forecasting of egg exports from India which forecasted that the egg exports from India will be in increasing trend up to 2023 and then it will decrease in 2024 and after that it will start to increaseThesisItem Open Access PERFORMANCE AND STABILITY ANALYSIS OF CITRUS FRUITS EXPORTS FROM INDIA 3700(JAU,JUNAGADH, 2023-07) DUDHATRA PRITKUMAR RAJESHBHAI; Dr. PUSHPA YADAV; 2010121017India ranks first in the world in terms of lime/lemon production and ranks 2nd in orange production. India has 26th rank in exports of lime/lemon and orange secured 58th rank in the world exporting countries. The changes in macroeconomic policies and trade liberalization across the world have given more importance to the export. Thereby, it is pertinent to emphasize the export through recognition of potential markets of lime/lemon and orange exports in context of increasing income of farmer through exports. In this connection, the study on “Performance and Stability Analysis of Citrus Fruits Exports from India” was undertaken with the main objectives to assess the growth trend, instability, sources of growth and variability, direction of trade and forecasting of quantity of future lime/lemon and orange exports to major importing countries. The secondary data on quantity and value of lime/lemon and orange exports from India was collected for 21 years from the year 2001 to 2021. It was analyzed using compound growth rate, coefficient of variation, Cuddy Della Vella Index, Hazell’s decomposition analysis and first order Markov chain model.ThesisItem Open Access EXPORT PERFORMANCE AND DIRECTION OF TRADE OF GUAVA AND ITS PRODUCT FROM INDIA 3737(JAU JUNAGADH, 2023-08) PANSARA TRUPTIBEN DEVAJIBHAI; Dr. N. J. Ardeshna; 2010120095As India occupies top position in total production among guava growing countries, it is pertinent to recognize potential markets for guava and guava product exports from India for increasing farmers’ income. The study on “Export Performance and Direction of Trade of Guava and Its Product from India” was undertaken with the specific objectives to assess the growth dimensions, instability, sources of growth and variability, direction of trade, export competitiveness, factors influencing the exports of major vegetable products and forecasting of future exports. The secondary data on volume and value of guava and guava product exports from India were collected for 20 years from the year 2002-03 o 2021-22. The data were analyzed using compound growth rate, coefficient of variation, Cuddy Della Valle index, Hazell’s decomposition analysis, first order Markov chain model. Among export volume, value and price the highest compound growth rate was noticed in export value in both guava (76.59%) and guavas fresh or dried (125.34%) while the highest instability was observed in export price in case of guava (316.98%) and guavas fresh or dried (331.94%). Risk assessment revealed that none of the countries were recorded in high growth rate and low risk category which is highly preferable category in guava and guavas fresh or dried requires more concentrated efforts towards shifting them from less desirable category. The results of decomposition analysis revealed that for guava exports, the contribution of change in mean export price (161.65%) in Period I, while the change in mean export quantity (144.72%) in Period II and for guavas fresh or dried the contribution of interaction between changes in mean export quantity and mean export price covariance (278.7%) in Period I, while the change in mean export quantity (112.54%) in Period II was the highest among the components of change in the average export value. The results of Markov chain analysis revealed that Other Countries (Indonesia, Sudan, Yemen Republc, Kuwait, Tanzania, Malasia, etc.), Saudi Arabia, UK, USA and UAE were found the most stable market and Netherland remained the most unstable market for guava export. Nepal was found the most stable market, Kuwait was the moderately stable market and UAE and Netherland was the most unstable market in case of guavas fresh or dried. The predicted shares of major importers of guava and guavas fresh or dried reveal that, quantity of guava export for countries like Netherland, UAE and Other Countries are expected to increase while quantity exported to Saudi Arab, USA and UK are expected to decline from the year 2021-22. On the other hand for guavas fresh or dried forecasting reveal that quantity of guavas fresh or dried export to UAE and Other Countries will rise and quantity exported to Nepal, Saudi Arab, Netherland and Kuwait will declineThesisItem Open Access SPATIAL INTEGRATION AND PRICE TRANSMISSION ANALYSIS OF DOMESTIC AND INTERNATIONAL COTTON MARKETS 3764(jau junagadh, 2023-09) BHOPALA UPASANA DEVAYATBHAI; Dr. M. G. Dhandhalya; 1010120003Cotton is one of the most important fiber crops playing essential role in the history of mankind and civilization. The Indian economy is influenced by cotton through its export performances, textile industry and processing sectors. The nature and extent of price fluctuations provides necessary guidance to farmers for marketing the products efficiently. Looking this importance of cotton price behaviour, the present study entitled “Spatial Integration and Price Transmission Analysis of Domestic and International Cotton Markets” was undertaken with the main objectives to assess the price growth dimensions, price variability and interrelation among the major national and international cotton markets. The study was carried out using the secondary data from various public sources. The monthly time series data of cotton wholesale prices of six selected national and two international markets were collected from January 2001 to December 2021. Besides, the data on area, production, and productivity of cotton in eight major producing states of India and two overseas countries were collected for the period from 2001-02 to 2020-21 for this study purpose. The data was analyzed using techniques of compound growth rate, linear and quadratic trends, seasonal and cyclical variations, and inter-relationship by correlations co-efficient and co-integration analysis. The investigation revealed that, during last two decades, only India has achieved positively higher growth rate in cotton area, production and productivity, but USA, China and Egypt shown declining trend, and at world level, it remained almost stagnant, with lower growth in production and yield. In India from 2001-02 to 2020-21, Andhra Pradesh achieved the highest growth in area, Rajasthan in production and Madhya Pradesh in yield. Besides, on an average, Gujarat contributed the highest in mean cotton production and Maharashtra in mean area. Linear nominal and real price trend was found more or less similar in USA and Egypt markets. Among the selected markets, the annual rate of real price increase was the highest i.e. 5.24 per cent in USA market followed by Parbhani and Budalada markets (2.58 %) and the lowest was 1.47 per cent in India S-6 market. A considerable amount of variation in the real price could be explained by the linear trend. The co-efficient of quadratic term (i.e., T 2 ) in nominal prices, found to be significant in Adoni, Sendhawa, Rajkot, and Gondal markets had increasing growth pattern in 2001 to 2021 indicating the rise in raw cotton price was not only due to inflation, but it was real and benefitted to farmers to some extent. The nominal prices of cotton in Indian markets increased at the compound growth rate of 6 to 7 per cent per annum during last two decades, but its real prices increased at lower growth rate of around 2 per cent and in USA it was slight more (3.22 %). The seasonal indices indicated the maximum cotton prices in months of July August and minimum in September to December. Fourier analysis indicated that the USA and India S-6 cotton lint markets followed generally 7 to 8 years cyclical variation. The cotton prices found to be non-stationary in their levels for all the markets, but become stationary in the first differences i.e. I(1). The cotton prices in domestic markets had co integrated with each other and it transmitted from one market to the other, and moving together in the long-run equilibrium. SARIMA/ARIMA model found the best fit to respective markets for cotton price forecasting giving minimum error, compared to ARCH-GARCH and VAR model. This study suggest that there is large scope for achieving higher production of cotton in India by developing new high yielding varieties replacing Bt. cotton. As cotton price are highly volatile, the Government should take immediate action to enhance export, when price falls drastically and allow the import, when price increase beyond certain level. The short-run price distortions, needs to be corrected by improving market conducts. As all markets are co-integrated, farmers may sell cotton in nearby marketThesisItem Open Access UNIVARIATE AND MULTIVARIATE ANALYSIS IN BREAD WHEAT (Triticum aestivum L.) 3611(2022) UNIVARIATE AND MULTIVARIATE ANALYSIS IN BREAD WHEAT (Triticum aestivum L.) 3611ThesisItem Open Access GROWTH PERFORMANCE AND PRICE BEHAVIOUR OF ONION IN MAJOR STATES OF INDIA 3365(JAU JUNAGADH, 2021-09) PARMAR HETAL BHIKHABHAI; M. G. Dhandhalya; 2010119080Onion, growth rate, instability, contribution, price behavior Onion is one of the important vegetable crop widely used in all households all the year round in India and throughout world. It is commonly known as “Queen of the kitchen” due to its highly valued flavour, aroma, and unique taste, and the medicinal properties of its flavour compounds. Onion is an important commercial crop earning sizable foreign currencies and it has received greater attention because of extreme price volatility. Keeping in view of this potentialities, the present study, “Growth performance and price behaviour of onion in major states of India” was undertaken with the main objectives to assess the growth dimensions, instability, sources of growth, price variability and interrelation among the major markets in India. The secondary data on area, production and productivity of onion in seven major producing states of India were collected for the period from 1990-91 to 2019-20. Besides, nine wholesale market prices of onion, one each from major states, representing consumption and two major producing locations markets were also collected for the period from 2001-02 to 2019-20 from various public sources. The data was analyzed using techniques of compound growth rate, linear and quadratic trends, Cuddy Della Valle index, decomposition analysis, seasonal and irregular variations, and inter-relationship by correlations co-efficient and co-integration analysis. The investigation revealed that, among the major onion growing states, Madhya Pradesh recorded the highest increase in onion area about 9.23 per cent per annum followed by Maharashtra (8.04%), Rajasthan (6.31%) during 1990-91 to 2019-20. Madhya Pradesh also registered significantly the highest growth rate of onion production about 12.69 per cent per annum, while Bihar registered significantly the highest growth rate of productivity about 4.59 per cent per annum. During 2000s, significantly the highest increase in area (19.89%) and production (20.96) of onion was reported in Gujarat. At all India level shown significantly positive growth in onion area (5.89%), production (8.33) and yield (2.30) during overall period. At country level as well as in all the major states, the yield effect was found as the main component of production growth followed by area and interaction effects. Rajasthan noticed the highest instability in onion area (39.27%), Madhya Pradesh production (52.33%) and Karnataka in yield (40.93%). The co-efficient of quadratic trend (T2 ) found non-significant in all markets noticed that there was no change in growth pattern of onion price in last two decades, which indicates that farmers were not benefited by rise in price, it is the effect of inflation factors only. On an average the onion prices in major markets of India was increased annually at the rate of 8.50 per cent during last two decades. The seasonal indices indicated the minimum onion price in the months of April-May and the maximum in the months of October-November in most of the markets, besides revealed high seasonal and irregular fluctuations. The monthly (>0.87) and yearly (>0.90) onion prices were found highly correlated among all the markets. The Augmented Dickey-Fuller (ADF) unit root test for onion showed the data are level stationary I (0). Moreover, all he onion markets were shown spatially integrated, that is there is a co-movement in prices across markets in long run and the short-run disequilibrium was found existing, but it found get corrected at larger speed. Most of the markets found bidirectional influence on prices. This study suggest that there is large scope for achieving higher production of onion in India, development of irrigations facilities and proper agronomical practices to stabilize onion acreage. As onion price are highly volatile, the Government should take immediate action to enhance export, when price falls drastically and allow the import, when price increase beyond certain level.ThesisItem Open Access COMPARATIVE ECONOMICS OF ORGANIC AND INORGANIC FARMING OF MANGO IN SOUTH SAURASHTRA REGION OF GUJA 3353(JAU JUNAGADH, 2021-09) VIRANI ABHISHEK DHIRAJLAL; S. B. Vekariya; 2010119118Organic mango farming, Socio-economic, adoption, Costs-Returns, Resources use efficiency and Constraints. The present investigation was undertaken with a view to analyze the socio economic status, factors that determine the adoption of organic farming, costs and returns, efficiency of resources and constrains in organic and inorganic mango farming. Hence, the present investigation was carried out under the title, “Comparative economics of organic and inorganic farming of mango in South-Saurashtra region of Gujarat.’’ A multistage sampling was adopted as appropriate sampling procedure for the study. The study covered 2 district, 4 talukas, in all 120 respondents, of which 60 for organic, and 60 for inorganic mango growers of (Junagadh and Gir-Somnath district) South Saurashtra region. The year of data collection was 2020-21. The collected data was systematically compiled and analyzed through tabular and statistical tools. The finding revealed the organic farming, 43.3 per cent of respondents’ falls under 20-40 years age group, while inorganic farming it was in 40-60 years age group. Educational status of organic farming has higher proportion of respondents who have studied upto middle school and above, which was leading to better awareness on adoption of organic farming. Organic farming mainly depends on organic manures from cattle, hence, more number of respondents have their own livestock for manure purpose. Organic mango farming were found to have a stronger association with the organization compared to inorganic mango farming. Furthermore, association with organization which were directly related to adoption of organic farming. The total average per hectare cost of cultivation was Rs. 76,995 under organic farming and Rs. 1,07,624 under inorganic farming. Zero cost incurred on fertilizer, pesticides and growth regulators under organic farming, gives cost advantage to organic mango farming. The total per hectare average yield of organic and inorganic mango were 92.83 quintal and 100.51 quintal respectively in the study area. Farm harvest price per quintal received by organic mango farming was Rs. 6,624. The gross income per hectare of organic mango farming was higher i.e. Rs. 6,18,395 on organic farming. While inorganic mango farming it was Rs. 4,13,669. In order to find efficiency of resources used by organic and inorganic mango farming, Cobb Douglas production function was employed. Among all the variables included in the production function, human labour was found positively significant for organic farming. While number of plant and human labour found positively significant for inorganic farming. However, quantity of fertilizers found negatively significant for inorganic farming. The value of coefficient of multiple determinations (R2 ) showed higher about 89 per cent in organic farming and slightly lower 87 per cent for inorganic farming. The high returns was observed from the organic mango farming, even though the production constraints faced by the farmers to get better returns were marketing difficulty followed by irregular bearing, fruit drop, severity of pest and disease, lack of awareness about organic produces among the customers, damaged by birds, difficulties in access to inputs, to obtain premium price, lack of training and lack of finance. The common major problems in both organic and inorganic farming were irregular bearing, marketing difficulty and severity of pest and disease.ThesisItem Open Access EXPORT PERFORMANCE AND DIRECTION OF TRADE OF MAJOR VEGETABLE PRODUCTS FROM INDIA 3248(JAU, 2021-05) oducts and forecasting of future exports. The secondary data on volume and value of major vegetable products exports from India were collected for 24 years from the year 1996-2019. The data were analyzed using compound growth rate, coefficient of variation, Cuddy Della Valle index, Hazell’s decomposition analysis, first order Markov chain model, Nominal protection coefficient, Revealed comparative advantage, Gravity model and ARIMA model. The findings revealed that the highest compound growth rate was noticed in export volume in tomato fresh or chilled while the highest instability was in export volume (120.62%)and export value (131.42%) in case of tomato fresh or chilled during overall period from year 1996-2019. The results of decomposition analysis revealed that in majority of the vegetable products,the share of mean export quantity contributed the highest in change in variance of e; Dr. R. L. Shiyanioducts and forecasting of future exports. The secondary data on volume and value of major vegetable products exports from India were collected for 24 years from the year 1996-2019. The data were analyzed using compound growth rate, coefficient of variation, Cuddy Della Valle index, Hazell’s decomposition analysis, first order Markov chain model, Nominal protection coefficient, Revealed comparative advantage, Gravity model and ARIMA model. The findings revealed that the highest compound growth rate was noticed in export volume in tomato fresh or chilled while the highest instability was in export volume (120.62%)and export value (131.42%) in case of tomato fresh or chilled during overall period from year 1996-2019. The results of decomposition analysis revealed that in majority of the vegetable products,the share of mean export quantity contributed the highest in change in variance of eThesisItem Open Access SPATIAL INTEGRATION AND PRICE FORECASTING ANALYSIS OF MAJOR TOMATO WHOLESALE MARKETS IN INDIA: AN APPLICATION OF SARIMA MODEL 3541(JAU JUNAGADH, 2022-09) 2010120073; B. SWAMINATHAN; 2010120073Tomato, Growth rate, instability, Trends, Seasonality, Market integration, Granger causality, Price shocks, forecasting Tomato is the second most important vegetable crop under cultivation next only to potatoes in the world. The present study has been undertaken to analyze the growth rate, instability, the behaviour of arrivals and prices, market integration, market behaviour to price shocks among the tomato markets. Major eight tomato markets of India were selected based on the largest average volume of arrivals during the study period. Time series data regarding monthly tomato arrivals and prices in selected markets were collected for the period 2010 to 2021 from secondary sources. Appropriate econometric tools like CGR, CDVI, trend analysis, seasonal indices, johansen cointegration, granger causality test, VECM, impulse response function and variance decomposition besides, SARIMA model were employed to meet the objectives of the study. In overall, growth rate of arrivals showed that Kolar market recorded a highest positive significant value of 0.11 per cent and in case of prices Coochbehar registered non-significant highest growth rate of 0.21 per cent. In month wise also Kolar market showed higher growth rate of arrivals compared to other markets and in case of prices Coochbehar showed higher growth rate with more than 10 per cent in all the months. Instability analysis of arrivals and prices showed that the variability of arrivals is higher than the prices. The trend component of arrivals showed negative trend except in Azadpur and Kolar market while in case of prices all the study markets showed highly significant positive values over the study period the with the highest annual increase in the prices was observed in the Coochbehar market (Rs.305.04/q). The seasonal indices of tomato arrivals had not shown a unique seasonal pattern while prices of study markets showed a uniform pattern with highest during July. Correlation coefficient of market arrivals and prices over the months and years showed the inverse relationship between them with some exceptions. The stationarity tests showed that the all the markets were stationary at level itself. Engle-granger and Johansen cointegration test confirmed the presence of long run relationship with each other and thus are integrated so that there is a smooth transmission of prices across the spatially separated markets. To ascertain the speed of adjustment among the markets for long run equilibrium vector error correction model was employed the coefficient of error correction term of Azadpur market revealed that about 56 per cent adjustment towards the long run equilibrium occurs in the period of one month. Granger causality test revealed the presence of bidirectional relationship between the pair’s viz., Azadpur– Ahmedabad, Kanpur-Azadpur, Coochbehar-Bargarh, Kanpur-Bargarh and Kanpur Coochbehar. Kolar market was the lead tomato market as it was influencing prices of other selected tomato markets of India except Coochbehar market. Impulse response function showed that the one-unit standard deviation shock to any of the market result in the immediate decline in all the other selected markets during the past 4-5 months thereafter the prices getting stabilized with exception in Coochbehar market the shock originating from Coochbehar is less transmitted to the other selected tomato markets. Variance decomposition analysis results showed that impact of variable on self is greater than the impacted caused by other variables in all the selected tomato markets of India. In terms of price forecasting, SARIMA models were found to be the best fit among different models to forecast tomato prices in all the markets. The SARIMA model (1,0,3) (1,0,2)12 was selected for predicting onward tomato prices in Ahmedabad (Gujarat) market while (2,0,1) (1,0,1)12, (3,0,2) (2,1,2)12, (2,0,3) (1,0,1)12, (3,0,3) (2,0,1)12, (2,0,6) (1,1,3)12, (2,0,4) (1,0,1)12, and (2,0,2) (1,1,0)12 were selected for Azadpur (New Delhi), Bargarh (Odisha) and Coochbehar (West Bengal) markets, Kanpur (Uttar Pradesh), Kolar (Karnataka), Mulakalacheruvu (Andhra Pradesh) and Nashik (Maharashtra). Similarly, SARIMA models were found useful for forecasting of tomato prices in all the markets with MAPE values ranging from 17.7% in Azadpur (New Delhi) market to 36.3% in Mulakalacheruvu (Andhra Pradesh) market
