Ashwini Darekar | National Institute of Agricultural Extension Management (MANAGE) (original) (raw)

Papers by Ashwini Darekar

Outbreak of the COVID-19 pandemic has not only disrupted the agriculture supply chain but also af... more Outbreak of the COVID-19 pandemic has not only disrupted the agriculture supply chain but also affected consumer behaviour towards agriculture goods and services. Though there have been many studies on consumer behaviour in the past, its study during such an extraordinary situation may yield some unique outcomes. To assess consumer awareness, sentiments and behavioural changes, an online survey was conducted and data were collected from 156 urban residents of Maharashtra. The results show that consumers' sentiments and behaviour in Maharashtra has been changed over the period of the lockdown. The results show that consumer behaviour towards agriculture goods and services was affected by various demographic, social and psychological factors.This study can help researchers, agri-business enterprises and policy makers to understand consumer behaviour during a pandemic so that they can take appropriate measures.

The ongoing crisis around COVID-19 has rapidly affected all walks of our life. It is evident that... more The ongoing crisis around COVID-19 has rapidly affected all walks of our life. It is evident that the pandemic has shaken agriculture in many ways. The global efforts to control the COVID-19 through restrictive human movement has certainly affected the functioning of agricultural and food systems worldwide. The impact is massive and has far-reaching consequences. In the meantime, agripreneurship is emerging as a prerequisite for improving the profitability in agriculture and allied sectors as well as to address the distress in the wake of pandemics like COVID-19. The Agri-Clinics and Agri-Business Centres (AC&ABC), a flagship scheme of the Ministry of Agriculture and Farmers Welfare, Government of India is aimed at training educated youth having an academic background in agriculture and science to start a own agri-enterprises and thus helping the farmers. Agripreneurship has been linked to augmented growth and amplified quality of life and its importance has been increased during the pandemic. The research study was undertaken by the MANAGE-Centre for Agricultural Extension Innovations, Reforms and Agripreneurship (CAEIRA), entitled "Agricultural Extension and Advisory Services: Serving Farming Community by Agripreneurship Amid COVID 19" noted that agripreneurs have played a significant role to reduce farmer's distress by not only creating awareness about COVID-19 but also providing counselling, inputs and crop-based advisory services during the lockdown. I appreciate all agripreneurs for their hard work and innovation which made their enterprises worth helping the farming community during the pandemic. The important dynamic that policymakers need to consider in preserving the role of entrepreneurship in agriculture. Post pandemic, there is a dire need to proactively engage with more farmers, youth in agripreneurship and migrants at the local level by improving their skillset in the utilization of online media tools. I appreciate and congratulate Dr. Saravanan Raj, Director (Agricultural Extension) and his team for taking up this study during the COVID-19. The findings and recommendations that emerged from the study are providing good insights and policy input to understand the potential role of agripreneurs during crisis management. MANAGE, Hyderabad (P. Chandra Shekara) 20. 12. 2020 Agricultural Extension and Advisory Services: Serving Farming Community by Agripreneurship Amid COVID-19 v Glossary Entrepreneur: A person who starts a business and assumes the risk of that business in order to make money (USAID, 2016). Entrepreneurship: The craft or skill of starting, developing, organizing and managing a business and assuming the associated risk in order to make a profit (USAID, 2016). Agripreneurs: Like all entrepreneurs, agripreneurs are risk-takers who deliberately allocate resources to a business venture, in this case, an agribusiness, to exploit opportunities in return for profit; they are the primary decision-makers, responsible for the businesses' success or failure (FAO, 2019). Agripreneurship: Agripreneurship is an adaptive process of business development in the agriculture sector which brings innovation as well as value addition and helps rural people not only raising their livelihood options but also providing new job opportunities (FAO, 2019). Agri-Clinics (ACs): Agri-Clinics are envisaged to provide expert advice and services to farmers on various technologies including soil health, cropping practices, plant protection, crop insurance, postharvest technology and clinical services for animals, feed and fodder management, prices of various crops in the market etc. which would enhance the productivity of crops/animals and ensure increased income to farmers (AC&ABC Guidelines, 2018). Agri-Business Centres (ABCs): Agri-Business Centres are commercial units of agriventures established by trained agriculture professionals. Such ventures may include maintenance and custom hiring of farm equipment, sale of inputs and other services in agriculture and allied areas, including post-harvest management and market linkages for income generation and entrepreneurship development (AC&ABC Guidelines, 2018).

International Journal of Management, Technology And Engineering, 2018

Agriculture is the source of livelihood of the majority of its population and being one of the mo... more Agriculture is the source of livelihood of the majority of its population and being one of the most disaster prone
countries, crop insurance occupies an important place in India. Hence, an attempt has been made to understand
and assess the successful implementation of Prime Minister Fasal Bima Yojana (PMFBY) in Datia district of
Madhya Pradesh in order to propose a suitable strategy for India. The required information culled out from the
Department of Agriculture, Collector office and private insurance agencies to understand the issues in
implementation of the crop insurance programs. Few farmers from the districts in were interviewed to explore
the perception of the farmers regarding crop insurance. It was noted that Datia district of Madhya Pradesh
became the first district of India to provide Mid Season Adversity benefit to 16293 Farmers of INR 9.41 crore
from November 2016. Claim settlement increased by paying full insurance claim of INR. 62.37 /- crore amongst
the 28739 farmers for Kharif season in 2016.Most of the surveyed farmers were aware about the relief payments
and prefer relief to crop insurance.They achieved this success by developing confidence among the farmers and mobilizing the entire District Administrative functionaries.

Oilseed crops contribute 13 % of the country's gross cropped area and 10 per cent of the value of... more Oilseed crops contribute 13 % of the country's gross cropped area and 10 per cent of the value of all agricultural produce. Groundnut is the major oilseed crop accounting about 30 per cent of the total oilseeds cropped area in the country with a production share of near about 36 per cent. Prices of groundnuts are highly volatile, hence farmers need a reasonable forecasting of harvest period price to decide on the acreage under groundnut. Hence, the present study aimed to build a model to forecast the groundnut prices and applied to forecast kharif harvesting season prices in major producing states viz., Gujarat, Andhra Pradesh, Tamil Nadu, Karnataka and Maharashtra. The prices were forecasted by using the time series data of monthly average prices for the period of 11 years (January 2006 to December 2016). ARIMA model introduced by Box and Jenkins (1970) which is the most widely used amongst time series models was used for predictions. R2, RMSE, MAPE, MAE and normalized BIC these parameters were used to test the reliability of model. Model parameters were estimated by using the Statistical Packages for Social Sciences software. In India kharif season groundnut is harvested during the period of September to December. Forecast shows that market prices of groundnut, would be ruling in the range of`3,760 to 5,520 per quintal in kharif harvesting season (2017-18). Hence, using ARIMA model to forecast groundnut prices is very useful not only to farmers but in policy formulation and also in promoting efficiency of groundnut marketing. The farmers are advised to take marketing decision accordingly.

Wheat is the single most important crop after paddy, which plays vital role in the Indian economy... more Wheat is the single most important crop after paddy, which plays vital role in the Indian economy. In this paper, ARIMA model was used to predict the future harvest period prices of wheat before the sowing date to facilitate farmers to make decision on their acreage under wheat. For this, a monthly data of modal prices from January, 2006 to June, 2017 were used for model fitting and forecasting. The best fit ARIMA models were selected based on autocorrelation function and partial auto correlation function at various lags. Forecasting performance of this model was evaluated with 95% confidence interval using criterions like MAE, MAPE and RMSE. Model parameters were estimated by using the Statistical Packages for Social Sciences (SPSS) software. Empirical results showed that ARIMA (0,1,1) (0,1,1) model was found suitable to forecast the future prices of wheat in India during harvesting season with 95% accuracy level. This model can facilitate the farmers and wholesalers in effective decision making. In India, wheat is harvested during the month of February to May. Forecast shows that market prices of wheat, would be ruling in the range of Rs. 1,620 to 2,080 per quintal during harvesting season, 2017-18. The farmers are advised to take sowing and marketing decision accordingly..

Maize is one of the most important cereal crop of the world and contributes to food security in m... more Maize is one of the most important cereal crop of the world and contributes to food security in most of the developing countries. In India, maize is emerging as third most important crop after rice and wheat. Hence, it is important to have some idea about future harvest prices before sowing. Therefore, the present study was undertaken to build a model to forecast harvest prices of kharif season based on past monthly modal prices of maize in selected states viz; Madhya Pradesh, Andhra Pradesh, Karnataka, Bihar and Rajasthan. The time series data on monthly wholesale price data for a period of 11 years (January, 2006 to December, 2016) was used for this purpose. ARIMA analysis was employed to quantify the variation in prices and also to forecast maize prices for the harvesting period. To test the reliability of model MAPE, AIC, and BIC Criterion were used. The model was validated for the year 2016-17. Model parameters were estimated using the R programming software. In kharif season the crop is harvested during September – December and Forecast shows that market prices of maize, would be ruling in the range of Rs. 1,200-1,600 per quintal in this season, 2017-18. The forecasted results suggest that there is likely possibility of higher maize prices during the harvesting season of 2017-18 compared to the last year. Hence, farmers are advised to increase acreage under maize wherever suitable soil and agro-climatic conditions exists.

Mustard accounts nearly about one third of the oil production in India. It is mainly cultivated i... more Mustard accounts nearly about one third of the oil production in India. It is mainly cultivated in the rainfed and resource scarce regions of the country. Hence, it contributes to livelihood security of the small and marginal farmers in these regions. Therefore, accurate forecasting of the oil prices will help the farmer to plan the area under the crop and the traders to plan their decisions. In this paper, ARIMA model was carried out to predict the future prices of mustard in major producing states viz., Gujarat, Haryana, Madhya Pradesh, Rajasthan, and Uttar Pradesh during the harvesting season. For this purpose, time series data on monthly wholesale prices of mustard (from January, 2006 to June, 2017) was collected from AGMARKNET website. Different criterion's such as: MAE, MAPE and RMSE were used for evaluating and comparing the forecasting performance of this model. Parameters of the model were estimated by using the Statistical Packages for Social Sciences (SPSS) software. Empirical results showed that ARIMA (0,1,0)(0,1,1) model was most suitable to forecast the future prices of mustard in India during harvesting season. In India, mustard is harvested during the month of February to April. The forecasted prices of mustard were almost similar to actual prices with a good validation. Forecast shows that market prices of mustard would be ruling in the range of Rs. 2,640 to 4,250per quintal in Rabi harvesting season, 2017-18.

Soybean (Glycine max) is as an oilseed crop with inadvertent importance. It is a good source of p... more Soybean (Glycine max) is as an oilseed crop with inadvertent importance. It is a good source of protein both for the human beings and livestock including pieces. The production and demand for soybean have been many traits increased in India during the last decade resolving in its winder adoption among farmers in Madhya Pradesh, Maharashtra, Rajasthan, Karnataka and Gujarat. This necessitates the need for reliable information on futures prices for soybean. Therefore, the present study was undertaken by collecting monthly prices of soybean in major soybean states of India for a period of 11 years (January 2006 to December 2016) by using ARIMA (Box-Jenkins model) so as to predict the future prices of soybean.The performance of fitted model was examined by computing various measures of goodness of fit viz., AIC, SBC and MAPE. ARIMA was the most representative model for the price forecast of soybean among states and the country as a while. The developed model can be used as a policy instrument for the farmers, processors and traders. The harvest of crop during September to October. The production and market prices of soybean, would be ruling in the range of INR 2,6000-3,6000 per tonne in kharif harvesting season, 2017-18. Average price of soybean ruled at INR 2, 6930 per tonne, compared to its MSP at INR 2,7750 per tonne during the last year. INR may recover for the coming kharif season. Since India is the largest importer of edible oils, there in a need to encourage soybean cultivation where ever climate is suitable for its cultivation.

Paddy is an important food crop in India and second most in the world. About 35% of net cropped a... more Paddy is an important food crop in India and second most in the world. About 35% of net cropped area under paddy and about 50% of the farmers cultivate paddy every year. Farmer’s decision making on acreage under paddy depends on the future prices to be realized during harvest period. Hence this paper presents a methodology to forecast prices during harvest period and applied the method to forecast for the kharif 2017-18. The time series data on monthly average prices of paddy from January, 2006 to December, 2016 collected from AGMARK was used. ARIMA (Box-Jenkins) model was employed to predict the future prices of paddy. Model parameters were estimated using the R programming
software. The performance of fitted model was examined by computing various measures of goodness of fit viz., AIC, BIC and MAPE. The ARIMA model was the most representative model for the price forecast of paddy in overall India. In kharif season the paddy is harvested during September – November. The forecast shows that market prices of paddy, would be ruling in the range of Rs. 1,600 – 2,200 per quintal in kharif harvesting season, 2017-18.

India is the largest cotton producing and second largest cotton exporting country. India accounti... more India is the largest cotton producing and second largest cotton exporting country. India accounting about 26% of the world cotton production. It has the distinction of having the largest area under cotton cultivation in the world with about 11-12 million hectares and constituting about 40% of the world area under cotton cultivation. Cotton is a global crop with high price fluctuation, which depends on the global business cycles. It is a mostly used as raw material for apparel and cloth industry. In addition to production risk cotton farmers encounter high price risk. Thus, it is important to forecast the cotton prices for the benefit of farmers as well as millers who purchase the cotton. The present study is aimed to forecast the prices of cotton of major producing states of India. The time series data on monthly price of cotton required for the study was collected from the AGMARKNET website from January, 2006 to December, 2016 to forecast prices for kharif 2017-18 year harvest months. ARIMA model was employed to predict the future prices of cotton. Model parameters were estimated using the R programming software. The performance of fitted model was examined by computing various measures of goodness of fit viz., AIC, SBC and MAPE. In Kharif season the cotton crop is harvested during December to January. Forecast shows that market prices of cotton, would be ruling in the range of`4,600 – 4,900 per quintal (medium staple cotton) in kharif harvesting season, 2017-18.

The present study is an attempt to forecast the prices of onion at Yeola market of Western Mahara... more The present study is an attempt to forecast the prices of onion at Yeola market of Western Maharashtra, as being a primary market the arrivals of Onion were found to be maximum in this market. The time series data on monthly price of onion required for the study was collected from the registers maintained in the Yeola APMC from year 2004 to 2013. The ARIMA model forecasted prices revealed an increase in the prices of onion in the future years and also demand for onion. Hence, farmers need to plan the production process in such a way that good price for the produce could be expected. ARIMA model is an extrapolation method that requires only historical time series data on the variable under study. The Box-Jenkins model provides a verified approach for identifying and filtering most appropriate variations for the series being analyzed, for diagnosing the accuracy and the reliability of the models that have been estimated and lastly, for forecasting the price. Similar model was used by Almemaychu Amera (2002), Punitha (2007) and Jalikatti and Patil (2015) to forecast the prices and arrivals of agricultural commodities and drawn conclusions. INTRODUCTION Auto Regressive Integrated Moving Average (ARIMA) models are extensively used to study market fluctuations particularly of agricultural commodities. The main advantage of this class of models lies in its ability to quantify random variations present in any economic time series. Hence the data on prices of onion in the selected markets were subjected to ARIMA analysis to quantify the variation and also to predict the future prices of onion. Since ARIMA model used only stationary series, there was also a need to change the non-stationary series into stationary series by applying appropriate order of differencing to the series. Thereafter, the autocorrelation and partial autocorrelation coefficients of the working series were computed and confirmed the absence of trend component in the series. An examination of such tables revealed that this is justified by the autocorrelation function of the series dropping to zero after second or third lag. The present study is an attempt to study the forecasting of prices of onion at Yeola market of Western Maharashtra.

ARTICLE INFO ABSTRACT The present study is an attempt to forecast the prices of onion at Kolhapur... more ARTICLE INFO ABSTRACT The present study is an attempt to forecast the prices of onion at Kolhapur market of Western Maharashtra, as being a primary market the arrivals of Onion were found to be maximum in this market. The time series data on monthly price of onion required for the study was collected from the registers maintained in the Kolhapur APMC from year 2004 to 2013. The ARIMA model forecasted prices revealed an increase in the prices of onion in the future years and also demand for onion. Hence, farmers need to plan the production process in such a way that good price for the produce could be expected. ARIMA model is an extrapolation method that requires only historical time series data on the variable under study. The Box-Jenkins model provides a verified approach for identifying and filtering most appropriate variations for the series being analyzed, for diagnosing the accuracy and the reliability of the models that have been estimated and lastly, for forecasting the price. Similar model was used by AlmemaychuAmera (2002), Punitha (2007) and Jalikatti and Patil (2015) to forecast the prices and arrivals of agricultural commodities and drawn conclusions.

Soybean [Glycine max] is the world's natural source of protein. Soybean is the most important oil... more Soybean [Glycine max] is the world's natural source of protein. Soybean is the most important oilseed crop of the world. It is grown successfully in various agro-climatic conditions. Investigation was carried out for the year 2013-14 in order to study the marginal productivity and economic efficiency in soybean production in all regions of Maharashtra. The data were collected from 144 soybean growers from all three regions of Maharashtra state. Cobb-Douglas production function was fitted to the data of soybean production. Results revealed that, regression coefficients of human labour (0.083) and irrigation (0.023) were positive and significant at 10 per cent level of significance. Similarly regression coefficients of manures (0.016) and Technology Adoption Index (0.112) were positive and significant at 1 per cent level of significance. It could be inferred that, if one per cent increased in use of human labour, irrigation, manures and Technology Adoption Index, it would lead to increase the soybean production by 0.083, 0.023, 0.016 and 0.112 per cent, respectively. Thus, it implied that, there was scope to increase these resources in soybean production. The value of coefficient of multiple determination (R 2 ) was turned out to 0.65.

Information and communication support for cotton farmers during last 65 years has mainly been con... more Information and communication support for cotton farmers during last 65 years has mainly been conventional through extension personnel of Department of Agriculture and that was mostly been manual. This approach has not been able to reach majority of the cotton farmers spread across the Western Maharashtra region. To reach over 12 million hectare farms spread over ten states is an uphill task. Further, the needs of cotton farmers in these states are much more diversified and the knowledge required to them is beyond the capacity of the grass root level extension functionaries. Hence in order to speed up the dissemination of cotton production technologies from research system to end users, a novel extension mechanism of 'e-kapas' networking of farmers has been initiated by Central Institute for Cotton Research, Nagpur aiming to empower cotton farmers with knowledge. CICR has thus designed programme to cover maximum farmers across the cotton growing states. Under this programme Cotton Improvement Project, MPKV, Rahuri (Maharashtra) is catering to the farmer's needs in local regional languages. By using modern ICTs and establishing a strong linkages between research and technology 'e-kapas' system provides an excellent opportunity to reach far and wide spread clientele very quickly with advance viable information and helps in creating and sustaining significant changes in the productivity and profitability.

The present study is an attempt to forecast the prices of onion at Lasalgaon and Pimpalgaon marke... more The present study is an attempt to forecast the prices of onion at Lasalgaon and Pimpalgaon market of Western Maharashtra. The forecasted values revealed an increasing or decreasing trend for the future years. The results showed the exante and ex-post forecast of monthly prices of onion in selected market from 2004 to 2013. In these markets, it was observed that there was sudden increase or decrease in the prices during 2011, 2012 and 2013. The year-wise alternate decrease in production and adequate storage facilities might be the reasons for the sudden increase in the price. The forecasted price values revealed an increasing or decreasing trend in the next ensuing years. Hence, farmers need to plan the production process in such a way that a good price for the produce would be expected.

This study mainly focused on district wise growth and instability of onion in Maharashtra. Errati... more This study mainly focused on district wise growth and instability of onion in Maharashtra. Erratic weather, volatile market price and lack of adequate storage and market infrastructure caused instability in production through preventing the farmers in taking the optimal decision on allocation of area and raising farm productivity. Study categorized period as follows; Period I: 1975-76 to 1984-85, Period II: 1985-86 to 1994-95, Period III: 1995-96 to 2004-05, Period IV: 2005-06 to 2012-13 and overall period 1975-76 to 2012 revealed that onion production in Maharashtra is mainly driven by acreage allocation. But in the long-run increasing area under onion may not be feasible without reducing the area of other important crops. Hence, solution lies in by bridging the yield gap or increasing the yield potential. The major reason for the instability of onion production after period II was mainly due to area instability and partly due to yield instability.

International Journal of Management, Technology And Engineering, 2018