Use of supply chain planning tools for efficiently placing small farmers into high-value, vegetable markets

Flores, Héctor; Villalobos, J. René; Ahumada, Omar; Uchanski, Mark E.; Meneses, Cesar; Sánchez, Octavio

doi:10.1016/j.compag.2018.12.050

Cited by 42 publications

(8 citation statements)

References 21 publications

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“…However, to balance supply and demand it is necessary to take into account more operative decisions. However, few papers as well as this paper take into account additional more operative decisions such as the harvest, transport and sale of crops in order to anticipate the balance between supply and demand at markets (Ahumada et al, 2012;Villalobos, 2011a, 2011b;Flores et al, 2019;Flores and Villalobos, 2018;Mason and Villalobos, 2015;Najafabadi et al, 2019;Nguyen et al, 2019). In the particular case of transport decisions, analysed models do not take into account neither the capacity of vehicles nor the minimum cargo to be filled in order to use a vehicle, which determines the quantity of vehicles necessary to transport crops and limits the quantity of crops to be transported ready to satisfy demand.…”

Section: Introductionmentioning

confidence: 99%

Optimization model to support sustainable crop planning for reducing unfairness among farmers

Esteso

Alemany

Ortíz

et al. 2021

Cent Eur J Oper Res

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Inherent uncertainty surrounding the agri-food sector negatively impacts the supply chain's (SC) sustainability and performance. A main consequence of this uncertainty is the imbalance between supply and demand with volatility in prices and high quantities of waste and unmet demand. Usually, farmers are the most affected by the negative impact of uncertainty. To improve their competitive position, it is necessary to implement new business models that encourage the collaboration among farms, try to reduce the number of intermediaries between farms and markets, reduce the activities related to the management of perishable crops and their associated costs, and enable mechanisms to sell the oversupply of crops such as their settlement. In this paper, a novel multi-objective model is proposed to support the crop planning under uncertainty for the proposed business model. Three objectives aligned with the triple bottom lines are considered: SC profit maximization (economic), waste minimization (environmental) and unfairness minimization (social). The last objective reduces the unwillingness of farms to cooperate with the crop planning. The model is solved with the weighted sum method and compared to an equivalent model considering only economic objectives, concluding that environmental and social aspects can be highly improved by little decreasing profits.

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Section: Introductionmentioning

confidence: 99%

Optimization model to support sustainable crop planning for reducing unfairness among farmers

Esteso

Alemany

Ortíz

et al. 2021

Cent Eur J Oper Res

View full text Add to dashboard Cite

show abstract

“…10 Although solutions can be provided for a given situation, many crop planning models have difficulty to adapt to evolving environment. 11 The age of big-data brings new chances as the daily price information is open and accessible in web. The prediction of price for the following days, weeks, or months becomes possible, 12 which allows the corresponding adjustment of crop planning to adapt to evolving conditions.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to its complexity, the uncertainty in price was simply modeled as stochastic variable without temporal information 10 . Although solutions can be provided for a given situation, many crop planning models have difficulty to adapt to evolving environment 11 . The age of big‐data brings new chances as the daily price information is open and accessible in web.…”

Section: Introductionmentioning

confidence: 99%

Parallel crop planning based on price forecast

Fan

Kang

Wang

et al. 2021

Int J of Intelligent Sys

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Agrifood system actors operate within diverse sociocultural, economic, and biophysical settings. For growers, crop planning, usually a yearly business plan, is a key decision to make on when, what, and how many to plant. It is a challenging task as it deals with multiple constraints in volatile economic and/or climate environment. Most crop planning models have difficulty in adapting to changing situation. In this study, a parallel system of crop planning composed of the artificial system, computational experiment, and parallel execution is proposed. The farmers are described as agents, and the decision is made based on the heuristic searching of optimal plan; the adaption of plan is triggered autonomously given strong environment changes. Focus is given to economic environment, which is indicated as product price. In a case study, the economic environment of the artificial system is built based on the monthly and weekly price information for 13 products during 7 years. The computational experiment provides the initial cropping plan and harvest time, with social and ecological constraints. Result shows that the cropping plan can further adapt to price variation. This flexible cropping plan system can strengthen the capability of cooperatives serving small-scale farmers.

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“…Protected cropping offers independent climate control through automated cooling, heating, and fertigation mechanisms to maximise the yield potential and quality of crops [5]. One of the characteristics of protected cropping is the use of advanced greenhouse technologies for year-round production of high-quality produce with better utilization of various resources, particularly scarce resources including water, fertilizer, and energy, irrespective of climate and weather conditions [1,[7][8][9]. Main drivers for protected cropping include creating sustainable production and consumption patterns [3], meeting an increased demand for a wide range of products under constrained resources [9], promoting sustainable waste management strategies, and considering economic, environmental and social aspects [10].…”

Section: Introductionmentioning

confidence: 99%

Energy Minimisation in a Protected Cropping Facility Using Multi-Temperature Acquisition Points and Control of Ventilation Settings

et al. 2021

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Energy management in protected cropping is critical due to the high cost of energy use in high-tech greenhouse facilities. The main purpose of this research was to investigate the optimal strategy to reduce cooling energy consumption, by regulating the settings (opening/closing) of either vents or curtains during the day, at the protected cropping facility at Western Sydney University. We measured daily changes in air temperature and energy consumption under four treatments (open/closed combinations of vents and shade screens) and developed an optimal cooling strategy for energy management using multi-temperature acquisition points at different heights within a greenhouse compartment. The optimal treatment (vents open/curtains closed) reduced energy load at the rooftop, thereby maintaining a desirable plant canopy temperature profile, and reducing cooling energy. Daily energy consumption was lowest for vents open/curtains closed (70.5 kWh) and highest for vents closed/curtains open (121 kWh). It was also found that delaying the operation of opening and closing of vents and curtains until the plant canopy temperature reached 25 °C reduced cooling energy consumption and decreased heating energy consumption in the morning (e.g., 08:00 to 10:00). The estimated savings of 1.83 kWh per 1 °C cooling between the optimal (vents open/curtains closed) and least optimal (vents closed/curtains open) conditions had the potential for significant energy savings at 494 kWh per °C over a crop cycle of nine months in warm weather conditions. However, selection of the optimal cooling strategy utilising control of vents and curtains must also account for the impact from other greenhouse environmental factors, including light, humidity, and CO2 concentration, which may be crop specific.

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Use of supply chain planning tools for efficiently placing small farmers into high-value, vegetable markets

Cited by 42 publications

References 21 publications

Optimization model to support sustainable crop planning for reducing unfairness among farmers

Optimization model to support sustainable crop planning for reducing unfairness among farmers

Parallel crop planning based on price forecast

Energy Minimisation in a Protected Cropping Facility Using Multi-Temperature Acquisition Points and Control of Ventilation Settings

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