A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain

Abstract

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.

A decision supporting algorithm for improving sunflower production amidst changing weather in the Singida Region, Tanzania

“…The study's conclusions showed that farmers who use feature phones or smartphones have advantages while utilizing DSA rural ~ 7 ~ areas. Nhenio et al (2019) [14] and Tripath et al (2022) [19] have concurred with the study's findings, stating that most farmers own feature phones and are proficient in using the DSA.…”

“…According to the findings of this study, the database as part of the DSA is crucial since it stores a predetermined set of faming activities as well as the classified state of the amount of rain. Similarly, the findings of Soyemi and Bolaji (2018) [20] , Churi et al (2013) [1] , and Tripath et al (2022) [19] , the data base serves as the component that facilitates queries of stored information by making comparisons for decision making. According to previous research, it is adequate for DSA to use SMS-based mechanisms to convey information from weather sources by contextualizing it to farmers' everyday activities.…”

“…The findings of this study suggest that the usage of AEOs is critical since they are specialists who can use the knowledge and aid fathers in making appropriate decisions. The TABD (2019) [19] reports back up the study's findings by applying AEOs to aid farmers in reinforcing the usage of DSA information. The algorithm identifies the type of activity to be conducted at a certain ward by comparing the amount of rain observed and classified for a specific ward with the predefined daily activities, and then conveys information for use and subsequent action.…”

“…The Russian Federation is the world leader in sunflower production with 13,314,418 tonnes, closely followed by Ukraine with 13,110,430 tonnes. With 390,000 metric tonnes or 55.8% of the entire commercial production, South Africa is the continent's top producer (TABD, 2019; Chacha, 2021) [19,4] . Tanzania produces 35% of the total sunflower production in Africa, making the nation the second-largest producer of the crop (FAOSTAT, 2017) [9] .…”

“…Therefore, to address the issues raised by the existing systems and algorithms, this study designed a feasible support algorithm for sunflower production among farmers in the Singida region. Even though sunflower products in the Singida region significantly contribute to household income, small-scale farmers are facing low crop yields, attributable to ineffective decision-making processes (TABD, 2019 and Tripath et al, 2022 and Zilihona et al, 2013) [18][19] . The current decisionmaking tools, such as algorithms, rely on farmers having smartphones and internet connection, yet the majority of farmers possess feature phones and lack internet access.…”