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Post-2008 global food crisis the prices of perishable high-value food commodities, such as vegetables and fruits, in India have risen faster and become more volatile compared to that of cereals. The welfare consequences of price shocks though are well understood yet the policy responses to manage these remain blurred because of a lack of clarity on their causes. Focusing on onions that comprise an important constituent of the Indian diet, the purpose of this paper is to explore causes of high price volatility.

Using high-frequency time series data on wholesale prices and arrivals of onions in major markets and other relevant variables, this paper analyzes causes of price volatility from several angles, that is production shocks, seasonality in production and market arrivals, internal trade, export policies and market power of intermediaries on the supply chain.

Despite markets being integrated and no significant climatic shocks to production there exists a strong element of uncertainty in market arrivals of onions, pointing toward the market power immediate downstream the production or alternatively anti-competition trade practices in major markets as a cause of high price volatility. The measures to manage price volatility, such as an increase in minimum export prices and bans on exports, are also not found to have an immediate cooling effect on prices.

The agricultural policy should provide for a system of market intelligence to monitor anti-competitive trade practices along the supply chain, and to take proactive trade control measures to prevent frequent ups and downs in domestic prices. In addition, it should provide for incentives for developing efficient supply chains and for the cultivation of onions in the regions that have agronomic potential but it has remained underexploited due to one or the other constraint.

Excessive volatility in food prices impacts farmers, consumers, processors, and traders and even political system. It may distort production and investment decisions of farmers and intermediaries on the value chains, leading to inefficient allocation of resources. The poor consumers may be forced to reduce food and non-food productive expenditures. If persists for a longer period, it may lead to political instability too.

Several studies have analyzed volatility in food prices and causes thereof. However, rarely any of these has examined volatility in prices of perishable high-value food commodities. This paper is an attempt toward filling this gap.

The purpose of this paper is to examine the market response of apple growers to price and price risk along with weather factors and weather risk in the state of Jammu & Kashmir. In other words, it tries to find the both short-run and long-run price elasticities of apples' market arrival and also the elasticity with respect to price-risk and weather-risk variables.

This paper uses the bound test approach of “auto-regressive distributed lag” (ARDL) model. Monthly data on market arrival of apples and respective prices along with other nonprice factors are used.

The bound test approach of ARDL confirms the existence of long-run relationship between the market arrival of apples and price and nonprice factors. The market response to price is found to be inelastic both in shortrun and longrun. The risk coefficients are negative indicating that apple growers are risk averse. However, they do respond strongly to weather risk than price risk.

Weather insurance must be provided to the apple growers to safeguard their production loss due to weather risks. Proper infrastructure in the form of storage facilities, marketing information, transport and communication to local markets should be provided to them. Unavailability of data at the district level poses a great difficulty to have a panel data analysis. But future research can be initiated to bridge this gap.

This paper considers the market response of apple growers under both price risk and weather risk which is first in its nature. The authors have not found any other paper discussing this in the case of apple in India.

One of the main factors that can impact the cost of holidays to a particular destination is the exchange rate; exchange rate fluctuations impact the overall price of the holiday and should be expected to effect tourism demand. This paper aims to scrutinize the volatility of the real effective exchange rate between the source market relative to the holiday destination and tourism demand volatility, where the influence of disaggregated data is noted.

The study uses multivariate conditional volatility regressions to simulate the time-varying conditional variances of international visitor demand and exchange rates for the relatively mature Caribbean tourist destination of Barbados. Data on the country’s main source markets, the UK, the USA and Canada is used, where the decision to disaggregate the analysis by market allows the authors to contribute to policymaking, particularly the future of tourism marketing.

The volatility models used in the paper suggests that shocks to total arrivals, as well as the USA and UK markets tend to die out relatively quickly. Asymmetric effects were observed for total arrivals, mainly due to the combination of the different source markets and potential evidence of Butler’s (1980) concept of a tourist area’s cycle of growth. The results also highlight the significance of using disaggregated tourism demand models to simulate volatility, as aggregated models do not adequately capture source market specific shocks, due to the potential model misspecification. Exchange rate volatility is postulated to have resulted in the greater utilization of packaged tours in some markets, while the effects of the market’s online presence moderates the impact of exchange rate volatility on tourist arrivals. Markets should also explore the potential of attracting higher numbers of older tourist, as this group may have higher disposable incomes, thereby mitigating the influence of exchange rate volatility.

Some of the explanatory variables were not available on a high enough frequency and proxies had to be used. However, the approach used was consistent with other papers in the literature.

The results from the paper suggest that the effects of exchange rate volatility in key source markets were offset by non-price factors in some markets and the existence of the exchange rate peg in others. In particular, the online presence of the destination was one of those non-price factors highlighted as being important.

In most theoretical models of tourism demand, disaggregation is not normally considered a significant aspect of the model. This paper contributes to the literature by investigating the impact real effective exchange rate volatility has on tourism demand at a disaggregated source country level. The approach highlights the importance of modeling tourism demand at a disaggregated level and provides important perspective from a mature small island destination.

该研究采用多元条件波动回归来拟合相对成熟的加勒比海旅游目的地巴巴多斯的国际游客需求和汇率的时变条件方差。本研究逐一分析了该国主要客源市场（英国, 美国和加拿大）的数据, 从而为政策制定, 尤其是对今后的旅游营销做出贡献。

汇率是影响到特定目的地度假成本的主要因素之一。汇率波动会影响整体的度假成本, 并会影响旅游需求。基于按客源地分类的数据, 本文详细研究了客源市场相对于度假目的地的实际有效汇率的波动性以及旅游需求的波动性。

本文使用的波动模型表明, 汇率冲击对入境总人数以及美国和英国市场影响短暂。冲击对总入境人数产生的不对称效应, 主要是由于不同的客源市场加总和巴特勒（1980）关于旅游区增长周期概念所致。本文结论还凸显了使用基于客源地数据的旅游需求模型来模拟波动性的重要性, 因为加总数据不能充分捕获具体客源地市场的冲击从而产生模型设定作物。汇率波动会引起某些市场中团体游客的增加, 而目的地的线上热度影响会调节汇率波动对游客人数的影响。市场还应探索吸引更多老年游客的潜力, 因为该群体的可支配收入可能更高, 从而减轻了汇率波动的影响。

由于一些解释变量的数据频率不够高, 本文不得不使用一些替代指标。所使用的方法与文献中的其他论文一致。

该论文的结果表明, 在某些客源地市场, 汇率波动的影响会被某非价格因素所抵消, 而在另一些主要客源地市场, 固定汇率的存在刚好规避了汇率波动产生的影响。目的地的线上热度是重要的非价格因素之一。

在大多数旅游需求理论模型中, 按客源地拆分的数据通常不被视为模型的重要方面。本文的理论贡献则是通过研究实际有效汇率波动对不同客源国的旅游需求的影响强调了旅游需求建模中使用基于客源地数据的重要性, 并以一个成熟的小岛目的地为角度进行了阐述。

Uno de los principales factores que pueden afectar al costo de las vacaciones a un destino en particular es el tipo de cambio; Las fluctuaciones del tipo de cambio afectan a el precio general de las vacaciones y es normal que afecten a la demanda turística. Este documento analiza la volatilidad del tipo de cambio efectivo real entre el mercado de origen en relación con el destino de vacaciones y la volatilidad de la demanda turística, donde se observa la influencia de los datos desagregados.

El estudio emplea regresiones de volatilidad condicional multivariadas para simular las variaciones condicionales variables en el tiempo de la demanda de visitantes internacionales y los tipos de cambio para el destino turístico caribeño relativamente maduro de Barbados. Se emplean datos sobre los principales mercados de origen del país, el Reino Unido, los Estados Unidos de América y Canadá, donde la decisión de desagrerar el análisis por mercado permite a los autores contribuir a la formulación de políticas, en particular al futuro del marketing turístico.

Los modelos de volatilidad utilizados en el documento sugieren que los shocks en las llegadas totales, así como en los mercados de los Estados Unidos y el Reino Unido, tienden a desaparecer con relativa rapidez. Se observaron efectos asimétricos para las llegadas totales, principalmente debido a la combinación de los diferentes mercados de origen y la evidencia potencial del concepto de Butler (1980) del ciclo de crecimiento de un área turística. Los resultados también resaltan la importancia de utilizar modelos desagregados de demanda turística para simular la volatilidad, ya que los modelos agregados no capturan adecuadamente los shocks específicos del mercado de origen, debido a la posible especificación errónea del modelo. Se postula que la volatilidad del tipo de cambio influye en una mayor utilización de los paquetes turísticos en algunos mercados, mientras que los efectos de la presencia del mercado en linea (online) moderan el impacto de la volatilidad del tipo de cambio en las llegadas de turistas. Los mercados también deberían explorar el potencial de atraer un mayor número de turistas mayores, ya que este grupo puede tener mayores ingresos disponibles, mitigando así la influencia de la volatilidad del tipo de cambio.

Algunas de las variables explicativas no estaban disponibles en una frecuencia alta y se tuvieron que utilizar proxies. Sin embargo, el enfoque utilizado fue consistente con otros artículos en la literatura.

Los resultados del documento sugieren que los efectos de la volatilidad del tipo de cambio en los mercados de origen clave fueron compensados por factores no relacionados con los precios en algunos mercados y la existencia de la vinculación del tipo de cambio en otros. En particular, la presencia en línea (online) del destino fue uno de esos factores no relacionados con el precio destacados como importantes.

En la mayoría de los modelos teóricos de la demanda turística, la desagregación normalmente no se considera un aspecto significativo del modelo. Este documento contribuye a la literatura al investigar el impacto que la volatilidad efectiva del tipo de cambio real tiene sobre la demanda turística a nivel de país de origen desagregado. El enfoque resalta la importancia de modelar la demanda turística a un nivel desagregado y proporciona una perspectiva importante desde un destino insular pequeño y maduro.

This paper examines the impact of the Covid-19 induced lockdown on selected vegetables to confirm if the vegetable supply chain was disrupted during that period. It attempts to see if direct marketing via FPOs/FPCs helped Indian farmers to cope with adverse situations aroused in vegetable marketing.

This study opted for mixed methods research. First, a granular data set comprising daily observation on wholesale price and the market arrival of vegetables were analysed. Descriptive statistics and Kalmogorov-Smirnov test were used to understand the severity of disruptions in the vegetable supply chain in India during the lockdown. Then, qualitative information from different stakeholders engaged in the vegetable marketing was collected through a phone survey and assessed using content analysis to comprehend how FPOs have helped farmer’s during this crisis.

This paper confirms disruptions in the vegetable supply chain. Quantities of chosen vegetables arriving in the mandis were significantly lower than in the previous year for all phases of lockdown. Consequently, prices were much higher than in 2019–2020 for both the lockdown and subsequent phases unlock. Results further suggest that those farmers who are already in networks of FPOs/FPCs are able to get benefited. It was also observed that direct marketing through institutional supports is being more explored in the regions where FPOs/FPCs already exist.

Since it is an exploratory study involving a small sample, the research results may lack generalisability.

This study provides scope for direct marketing through FPOs/FPCs in improving the food supply chain.

This study focuses on accessing the impact of lockdown implemented to curb the pandemic of coronavirus disease 2019 (COVID-19) on prices of potato and onion crops using the time series analysis techniques.

The present study uses secondary price series data for both crops. Along with the study of percent increase or decrease, the time series analysis techniques of autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), as well as machine learning; neural network autoregressive (NNAR) models were used to model the prices. For the purpose of comparison, the data from past years were taken as the period of normalcy. The behaviour of the forecasts for the normal periods and during the pandemic based on respective datasets was compared.

The results show that there was an unprecedented rise in prices during the months of lockdown. It could be attributed to the decline in arrivals due to several reasons like issues with transportation and labour availability. Also, towards the end of lockdown (May 2020), the prices seemed to decrease. Such a drop could be attributed to the relaxations in lockdown and reduced demand. The study also discusses that how some unique approaches like e-marketing, localized resource development for attaining self-sufficiency and developing transport chain, especially, for agriculture could help in such a situation of emergency.

A more extensive study could be conducted to mark the factors specifically that caused the increase in price.

The study clearly marks that the prices of the crops increased more than expectations using time series methods. Also, it surveys the prevailing situation through available resources to link up the reasons behind it.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The purpose of this paper is to examine the market structure and value chain of pearl millet grain and fodder in India. There is a decline in demand for human consumption, with an increase in demand for non-food uses like cattle and poultry feed, raw material for starch and breweries industry. This paper explores alternative channels, uses and value chains of pearl millet grain and fodder. The paper examines in what ways small farmers can benefit from the evolving alternative uses for pearl millet grain in cattle and poultry feed industry, breweries and starch industry. The paper also analyses the impact of aggregators in increasing the efficiency of the value chain.

The study collected primary data from farmers, traders, commission agents and exporters and importers with innovative marketing channels with aggregators (Self-Help Groups) and without aggregators to analyze the prospects for improvements in marketing channels and value chain.

Given that the production of pearl millet is scattered and thin, there is a lot of scope for market aggregators to increase scale economies to reduce market costs to supply in bulk to food and industrial uses. Although there was some demand for human consumption high-quality grain, most of the future demand will come from cattle and poultry feed industry, breweries and starch industry. To tap these larger potentials, farmers need to aggregate their produce and ensure regular supply in bulk quantity at least to compete the cost with alternative grains like maize and broken rice.

The research is based on the field-level data collection and observations obtained from Western India. This paper provides insights how the value chain of pearl millet is working and what improvements are needed to make value chain more efficient and inclusive. Although the results are applicable to similar neglected crops and area, more caution is needed.

Through the formation of farmer aggregators, farmers can enhance their bargaining power vis-a-vis industry.

Till now, there is no study that explored the pearl millet value chain in detail in India, and the paper tries to fill this literature gap.

Information and communication technologies can work wonders for the social as well as economic empowerment of rural people, when implemented successfully. This paper aims to explore and compare the functioning of two ICT models, one each in the public as well as the private sector, in a rural area of MP state in India, identify the usage of various services by stakeholders, i.e. farmers, study the existing need‐service gap and highlight constraints and challenges in the implementation of these models at the field level.

The study is based on both secondary and primary data. The case study method has been used for describing the two models and has been supplemented with the primary data collected in the form of structured questionnaires and interviews from respondents comprising both users/non‐users and kiosk operators respectively.

The study finds that there exists a need‐service gap in service delivery. The information services related to agriculture, i.e. agri input prices, markets arrivals and prices, weather information are valued most by the farmers followed by e‐governance services, health and education. Barriers to adoption of these technologies have also been discussed.

The research is case study‐based and also gives the results of a survey of beneficiaries; therefore it is very useful for managers, policymakers, implementers as well as academicians. The research provides implications for managers as well as policy makers to successfully implement the ICT models in India. Implications for future research are also discussed.

The novelty of the research lies in the context that no effort has been made so far to effect a comparison of ICT models. There is also very little literature available to identify the need‐service gap in this area. The study adds value to the knowledge in the field of ICTs for development and also opens new vistas of research.

This study aims to better understand tourists’ revisit intention from their perspectives of past and expected experience and to investigate whether the formation of revisit intention differs between tourists from different nationalities.

The sample consisted of 250 Indonesian tourists who have visited Australia and 275 Australian tourists who have visited Indonesia. The data were analysed by conducting structural equation modelling and multi-group analysis with group comparisons.

Indonesian tourists’ intention to revisit Australia was influenced by past feeling experience and expected experience, while Australian tourists’ intention to revisit Indonesia was influenced by past feeling and relating experiences and expected experience. Both samples differ significantly in terms of relations between experiences and revisit intention. The relationship between past thinking experience and revisit intention was positive for Indonesians but negative for Australians. The relationship between past relating and expected experience was positive for Australians but negative for Indonesians. In addition, the influence of expected experience on revisit intention was stronger for Australian than Indonesian tourists.

To the best of the authors’ knowledge, this study, for the first time, enriches tourism literature in understanding revisit intention by investigating revisit intention in relation to both past and expected experiences, along with examining nationality differences in the revisit intention formation.

This chapter explores the data exchange relationships between stakeholders in a tourism domain as a means of assessing the potential application of open data initiatives. Social network analysis is utilized to analyze network relationships and explain the pattern and consequences of these relationships. Based on centrality and other network attributes, the analysis highlights the key influencers in the tourism data ecosystem examined, and suggests that initial steps towards implementing a tourism open data policy should focus on opening up tourism asset data, and relaxing current restrictive data exchange practices. The agency with responsibility for collecting and disseminating tourism asset data, is well positioned to become the data broker in an emergent tourism open data ecosystem.