PurposeIn 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.Design/methodology/approachThe 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.FindingsEmpirical 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.Practical implicationsAs 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.Originality/valueBased 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.
Small and marginal farmers (SMFs) in developing countries, perennially struggle with low marketable surplus, inadequate storage facilities, poor market access and logistical constraints that in turn leave them with distressed sales of their produce to exploitative middlemen in the agricultural supply chain. Addressing such pressing concerns, the present study aims at proposing a market-facilitating demand-centric agricultural supply chain model where the income of the farmers is directly linked with risk-adjusted actual market movement. The price-sensitive model, being a facilitator for direct marketing, makes farm produce marketable by designing a cost-effective and scientifically managed shared warehouse, and minimizing market volatility risk through diversification among a group of contributing farmers. Empirical validation of this simulation-based modelling was tested on three essential year-round food staples – Tomato, Onion and Potato (TOP) – against the prevailing market settings. Interestingly, instead of immediately selling agricultural produce to market intermediaries due to a lack of storage options, if farmers shared the associated storage cost among themselves and distributed the market returns by proportionate crop sales to fulfil the demand, they could not only realize better returns during the season but also could turn off-season market unpredictability into their favour. The model is focused on enhancing SMFs’ income in the emerging economy context, and its empirical approach for risk minimization strategy is indeed proposed for the first time in the available literature. The finding, demonstrably, substantiates that the policy implication of the proposed model for SMFs in the fruits and vegetables (F&V) segment could improve market access and derive fair returns.
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