Factors and scenarios affecting a farmer’s grain harvest logistics

Kingwell, Ross; Loxton, Ryan; Mardaneh, Elham

doi:10.1111/1467-8489.12355

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Currently, only large export organizations such as Alisur S.A.C, Exportadora Agrícola Orgánica S.A.C and Grupo Orgánico Nacional S.A. maintain direct communication between farmers and traders. In addition, the merchandise receives a value increase at the collection centers, where the grain is prepared for further processing by being cleaned, rinsed and dried, threshed and undergoes quality control to ensure a consistent final product (Kingwell, 2019).…”

Section: Quinoa Supply Chainmentioning

confidence: 99%

Distribution and Export Logistics: Commercial Development of the Millenary Product Chenopodium Quinoa

Carazas,

Cabe,

Soto

et al. 2023

ijmst

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The objective of this article was the commercial development of the millenary product Chenopodium Quinoa from a perspective of distribution and export logistics. Methodologically, it was a documentary research that used content analysis as a strategy for the review of the texts, which were retrieved through the following databases: a) Scopus, b) Scielo, c) Web of Science - WoS and, d) Google Scholar, and selected based on the criteria: (a) original or review scientific articles, (b) published in Spanish or English language, (c) temporal delimitation to the last eight years (2015-2022), (d) citable documents, resulting in a total of fourteen reviewed articles (n=14). The bibliographic analysis allowed discriminating from an analytical point of view the supply chain from the quinoa production source, markets and logistic corridors to its export point. This literature review demonstrated that the creation of a more efficient supply chain (distribution logistics) would be beneficial for all parties involved, as it would expedite the circulation of the product, improve responsiveness to customer needs and encourage creative problem solving in the service of distribution and export, validating it from a documentary and bibliographic perspective.

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Section: Quinoa Supply Chainmentioning

confidence: 99%

Distribution and Export Logistics: Commercial Development of the Millenary Product Chenopodium Quinoa

Carazas,

Cabe,

Soto

et al. 2023

ijmst

View full text Add to dashboard Cite

show abstract

“…If we consider each of the original variables, we can identify a number of factors that shape it. For example, the harvest duration depends on factors such as: area of grain crops, initial grain crops yield, number of days without reduced grain crops yield (such reduction usually occurs due to weather conditions, drying or damage by pests), harvest speed, technical and operational efficiency of vehicles, distance to the reception centre, as well as its working hours [10].…”

Section: Use Of Modern Harvesting Equipmentmentioning

confidence: 99%

Development of the grain crops supply chain model

Kotenko¹

2022

JMET

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The possibility of optimization of the grain crops supply chain taking into account the interests of the farm is investigated in the work. The main trends in the agricultural sector of Ukraine, which directly or indirectly affect the activities of the farm, are analyzed in the work. It is established that the choice of the most optimal means of transport for grain crops transportation allows the farm to reduce the cost of the logistics supply chain, which in turn will lead to increased profitability and expansion of the agricultural sector. The solution to this problem is possible through the modeling of the grain crops logistics supply chain. To form a model of the grain crops supply chain the process of grain delivery as a system that contains many acceptable alternatives for making the most rational decision. The proposed model is presented in form of a "black box" model. The objective function of this model for farms is to make a rational decision on the process of grain delivery in order to maximize profits by optimizing the costs of such supply chain and ensuring the development of farming as an activity. The original variables of this model are: grain crops yields and grain crops humidity; farm’s sown area; harvesting equipment efficiency; own granaries capacity; cost for grain storage in own granary or for elevator service; grain share depending on the storage place; sales price of grain crops: to the internal buyer, at the grain reception center, at the port terminal, and also weather and climate conditions during harvest (favorable / unfavorable conditions). The initial indicators that will characterize the success of the grain delivery process will be the following one: harvest duration, harvest amount, trucking cost, average daily harvest duration, additional costs obtained by managing grain humidity, magnitude of crop losses due to the harvest duration, magnitude of losses due to the grain quality reduction (with mixing, without mixing). The proposed model will consider all these factors and their impact on harvesting, storage and transportation of grain crops, and will help to take into account the costs for farms depending on the situation.

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“…It revealed significant gaps and connections among themes, finding no direct overlap between Farmers and Model themes indicating that research in GSC often limits farmers' roles to production and demand satisfaction, treating them as nodes rather than central figures in models and themes (Kingwell et al, 2020;Krejci & Beamon, 2015). The study emphasizes the need for more simulation-based methods in supply chain research due to their ability to capture complexity, stakeholder behaviour, and uncertainty, offering adaptability and facilitating scenario analysis (Higgins et al, 2018).…”

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confidence: 94%

Grain supply chain optimisation: A qualitative perspective

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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Grain supply chains (GSC) are complex systems with numerous stakeholders, dynamic interactions, and uncertainties, requiring sophisticated modelling and simulation for understanding and predicting decision-making (Behzadi et al., 2018). This abstract presents a systematic literature review using content analysis (using Leximancer) to identify gaps and interconnectedness in GSC research. A Boolean search string (agri* AND supply chain* AND (simulation* OR optimi?ation*)) in all fields (title, keywords, abstracts) published in English since 2000)) was used to obtain 1,681 records from SCOPUS and Web of Science databases, with 75 relevant studies (13 review articles, 7 conference proceedings, 49 Q1, and 6 Q2 articles) selected following PRISMA guidelines. Existing systematic literature reviews (SLRs) were separately examined to avoid duplication. The Leximancer analysis based on machine learning methods for understanding text data through word frequency relations of 62 selected studies revealed six primary themes: Supply Chain, Model, Farmers, Agents, Simulation, and Transport and Logistics.

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Factors and scenarios affecting a farmer’s grain harvest logistics

Cited by 6 publications

References 13 publications

Distribution and Export Logistics: Commercial Development of the Millenary Product Chenopodium Quinoa

Distribution and Export Logistics: Commercial Development of the Millenary Product Chenopodium Quinoa

Development of the grain crops supply chain model

Grain supply chain optimisation: A qualitative perspective

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