Abstract
Agri-food production must increase while food waste needs to be reduced for improving the position of farmers. To do so it is necessary to sustainably manage agri-food supply chains beginning with the crop planning decisions. Although the centralized approach has usually been adopted for this purpose, it can lead to unfair solutions due to inequitable distribution of profits among farmers causing their unwillingness to collaborate in the implementation of decisions made. To solve this, in this paper a novel centralized multi-objective mathematical programming model is proposed to support the sustainable crop planning definition for a region that jointly optimize three objectives aligned to the sustainability aspects: supply chain profits maximization (economic objective), waste minimization (environmental objective) and unfairness among farmers minimization (social objective), being the last two objectives novel in the crop planning literature. It has also shown the conflicting nature of the three objectives finding trade-offs among them. Other novelties of this proposal are: (1) anticipation of operative decisions (such as harvest, transport, sale, clearance sale, waste and unmet demand) when defining the crop planning, (2) possibility of clearing the oversupply of crops as a means of increasing the farmers’ profits and reducing waste, and (3) the modelling of a agri-food supply chain characterized by the lack of intermediaries between farmers and retailers, fostering the freshest product delivery and farmers’ power position. The model is solved by applying the weighted sum method concluding that the crop waste generated along the chain and the unfairness among farmers can be considerably reduced by little decreasing the optimal SC profits.
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References
Adekanmbi O, Olugbara O (2015) Multiobjective optimization of crop-mix planning using generalized differential evolution algorithm. J Agric Sci Technol 17:1103–1114
Ahumada O, Villalobos JR (2011a) Operational model for planning the harvest and distribution of perishable agricultural products. Int J Prod Econ 133:677–687. https://doi.org/10.1016/j.ijpe.2011.05.015
Ahumada O, Villalobos JR (2011b) A tactical model for planning the production and distribution of fresh produce. Ann Oper Res 190:339–358. https://doi.org/10.1007/s10479-009-0614-4
Ahumada O, Villalobos JR, Mason AN (2012) Tactical planning of the production and distribution of fresh agricultural products under uncertainty. Agric Syst 112:17–26. https://doi.org/10.1016/j.agsy.2012.06.002
Ajmal MM, Khan M, Hussain M, Helo P (2018) Conceptualizing and incorporating social sustainability in the business world. Int J Sustain Dev World Ecol 25:327–339. https://doi.org/10.1080/13504509.2017.1408714
Albornoz VM, Sáez JL, Véliz MI (2017) Delineation of rectangular management zones and crop planning under uncertainty in the soil properties. Commun Comput Inf Sci 695:117–131. https://doi.org/10.1007/978-3-319-53982-9_7
Albornoz VM, Véliz MI, Ortega R, Ortíz-Araya V (2020) Integrated versus hierarchical approach for zone delineation and crop planning under uncertainty. Ann Oper Res 286:617–634. https://doi.org/10.1007/s10479-019-03198-y
Alfandari L, Lemalade JL, Nagih A, Plateau G (2011) A MIP flow model for crop-rotation planning in a context of forest sustainable development. Ann Oper Res 190:149–164. https://doi.org/10.1007/s10479-009-0553-0
Alfandari L, Plateau A, Schepler X (2015) A branch-and-price-and-cut approach for sustainable crop rotation planning. Eur J Oper Res 241:872–879. https://doi.org/10.1016/j.ejor.2014.09.066
Anastasiadis F, Tsolakis N, Srai J (2018) Digital technologies towards resource efficiency in the agrifood sector: key challenges in developing countries. Sustainability 10:4850. https://doi.org/10.3390/su10124850
Azevedo S, Silva M, Matias J, Dias G (2018) The influence of collaboration initiatives on the sustainability of the cashew supply chain. Sustainability 10:2075. https://doi.org/10.3390/su10062075
Banasik A, Bloemhof-Ruwaard JM, Kanellopoulos A et al (2018) Multi-criteria decision making approaches for green supply chains: a review. Flex Serv Manuf J 30:366–396. https://doi.org/10.1007/s10696-016-9263-5
Blanco V, Carpente L, Hinojosa Y, Puerto J (2010) Planning for agricultural forage harvesters and trucks: model, heuristics, and case study. Netw Spat Econ 10:321–343. https://doi.org/10.1007/s11067-009-9120-0
Catalá LP, Durand GA, Blanco AM, Bandoni JA (2013) Mathematical model for strategic planning optimization in the pome fruit industry. Agric Syst 115:63–71. https://doi.org/10.1016/j.agsy.2012.09.010
Cid-Garcia NM, Ibarra-Rojas OJ (2019) An integrated approach for the rectangular delineation of management zones and the crop planning problems. Comput Electron Agric 164:104925. https://doi.org/10.1016/j.compag.2019.104925
Cid-Garcia NM, Bravo-Lozano AG, Rios-Solis YA (2014) A crop planning and real-time irrigation method based on site-specific management zones and linear programming. Comput Electron Agric 107:20–28. https://doi.org/10.1016/j.compag.2014.06.002
Darby-Dowman K, Barker S, Audsley E, Parsons D (2000) A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture. J Oper Res Soc 51:83–89. https://doi.org/10.1057/palgrave.jors.2600858
Djekic I, Sanjuán N, Clemente G et al (2018) Review on environmental models in the food chain—current status and future perspectives. J Clean Prod 176:1012–1025. https://doi.org/10.1016/j.jclepro.2017.11.241
dos Santos LMR, Costa AM, Arenales MN, Santos RHS (2010) Sustainable vegetable crop supply problem. Eur J Oper Res 204:639–647. https://doi.org/10.1016/j.ejor.2009.11.026
Dury J, Schaller N, Garcia F et al (2012) Models to support cropping plan and crop rotation decisions: a review. Agron Sustain Dev 32:567–580. https://doi.org/10.1007/s13593-011-0037-x
Ertogral K, Wu SD (2000) Auction-theoretic coordination of production planning in the supply chain. IIE Trans 32:931–940. https://doi.org/10.1080/07408170008967451
Esteso A, Alemany MME, Ortiz A (2018a) Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. Int J Prod Res 56:4418–4446. https://doi.org/10.1080/00207543.2018.1447706
Esteso A, Alemany MME, Ortiz Á, Peidro D (2018b) A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements. Comput Ind Eng 124:180–194. https://doi.org/10.1016/j.cie.2018.07.025
European Commission (2018) Key policy objectives of the future CAP. https://ec.europa.eu/info/food-farming-fisheries/key-policies/common-agricultural-policy/future-cap/key-policy-objectives-future-cap_en#nineobjectives. Accessed 18 July 2020
Fang Y, Jiang Y, Sun L, Han X (2018) Design of green cold chain networks for imported fresh agri-products in belt and road development. Sustainability 10:1572. https://doi.org/10.3390/su10051572
FAO Sustainability Pathways. http://www.fao.org/nr/sustainability/food-loss-and-waste/en/. Accessed 29 Jan 2020
Filippi C, Mansini R, Stevanato E (2017) Mixed integer linear programming models for optimal crop selection. Comput Oper Res 81:26–39. https://doi.org/10.1016/j.cor.2016.12.004
Flores H, Villalobos JR (2018) A modeling framework for the strategic design of local fresh-food systems. Agric Syst 161:1–15. https://doi.org/10.1016/j.agsy.2017.12.001
Flores H, Villalobos JR, Ahumada O et al (2019) Use of supply chain planning tools for efficiently placing small farmers into high-value, vegetable markets. Comput Electron Agric 157:205–217. https://doi.org/10.1016/j.compag.2018.12.050
Forrester RJ, Rodriguez M, Forrester R, Rodriguez M (2018) An integer programming approach to crop rotation planning at an organic farm. UMAP J 38:5–25
Hasuike T, Kashima T, Matsumoto S (2018) Multiobjective crop planning considering optimal matching between retailers and farmers with contract. J Adv Mech Des Syst Manuf 12:1–16. https://doi.org/10.1299/jamdsm.2018jamdsm0071
Hong Y, Berentsen P, Heerink N et al (2019) The future of intercropping under growing resource scarcity and declining grain prices—a model analysis based on a case study in Northwest China. Agric Syst 176:102661. https://doi.org/10.1016/j.agsy.2019.102661
Jarin S, Khatun MK, Shafie AA (2016) Multi-objective constrained algorithm (MCA) and non-dominated sorting genetic algorithm (NSGA-ii) for solving multi-objective crop planning problem. ARPN J Eng Appl Sci 11:4079–4086
Jaya Brindha G, Gopi ES (2019) Maximizing profits in crop planning using socio evolution and learning optimization. Stud Comput Intell 828:151–174. https://doi.org/10.1007/978-981-13-6569-0_8
Jonkman J, Barbosa-Póvoa AP, Bloemhof JM (2019) Integrating harvesting decisions in the design of agro-food supply chains. Eur J Oper Res 276:247–258. https://doi.org/10.1016/j.ejor.2018.12.024
Li J, Rodriguez D, Zhang D, Ma K (2015) Crop rotation model for contract farming with constraints on similar profits. Comput Electron Agric 119:12–18. https://doi.org/10.1016/j.compag.2015.10.002
Mason AN, Villalobos JR (2015) Coordination of perishable crop production using auction mechanisms. Agric Syst 138:18–30. https://doi.org/10.1016/j.agsy.2015.04.008
Mavrotas G (2009) Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl Math Comput 213:455–465. https://doi.org/10.1016/j.amc.2009.03.037
Mellaku MT, Reynolds TW, Woldeamanuel T (2018) Linear programming-based cropland allocation to enhance performance of smallholder crop production: a pilot study in Abaro Kebele, Ethiopia. Resources. https://doi.org/10.3390/resources7040076
Moon I, Jeong YJ, Saha S (2018) Investment and coordination decisions in a supply chain of fresh agricultural products. Oper Res. https://doi.org/10.1007/s12351-018-0411-4
Najafabadi MM, Ziaee S, Nikouei A, Ahmadpour Borazjani M (2019) Mathematical programming model (MMP) for optimization of regional cropping patterns decisions: a case study. Agric Syst 173:218–232. https://doi.org/10.1016/j.agsy.2019.02.006
Nguyen T-D, Venkatadri U, Nguyen-Quang T et al (2019) Optimization model for fresh fruit supply chains: case-study of dragon fruit in Vietnam. AgriEngineering 2:1–26. https://doi.org/10.3390/agriengineering2010001
Pérez-Mesa JC, Piedra-Muñoz L, García-Barranco MC, Giagnocavo C (2019) Response of fresh food suppliers to sustainable supply chain management of large European retailers. Sustainability 11:3885. https://doi.org/10.3390/su11143885
Pourhejazy P, Kwon O (2016) The new generation of operations research methods in supply chain optimization: a review. Sustainability 8:1033. https://doi.org/10.3390/su8101033
Prima Dania WA, Xing K, Amer Y (2018) Collaboration behavioural factors for sustainable agri-food supply chains: a systematic review. J Clean Prod 186:851–864. https://doi.org/10.1016/j.jclepro.2018.03.148
Radulescu M, Radulescu CZ (2013) Simulation and optimization for crop planning under risk. In: Proceedings—8th EUROSIM congr model simulation, EUROSIM 2013, pp 409–414. https://doi.org/10.1109/EUROSIM.2013.117
Rǎdulescu M, Zbǎganu G, Rǎdulescu CZ (2008) Crop planning in the presence of production quotas (invited paper). In: Proceedings—UKSim 10th Int Conf Comput Model Simulation, EUROSIM/UKSim2008, pp 549–554. https://doi.org/10.1109/UKSIM.2008.40
Rǎdulescu M, Rǎdulescu CZ, Zbǎganu G (2014) A portfolio theory approach to crop planning under environmental constraints. Ann Oper Res 219:243–264. https://doi.org/10.1007/s10479-011-0902-7
Ren C, Li Z, Zhang H (2019) Integrated multi-objective stochastic fuzzy programming and AHP method for agricultural water and land optimization allocation under multiple uncertainties. J Clean Prod 210:12–24. https://doi.org/10.1016/j.jclepro.2018.10.348
RUC-APS (2016) Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems. In: Proj. 691249 funded by Eur. Union’s Res. Innov. Program. under H2020 Marie Skłodowska-Curie Actions. www.ruc-aps.eu
Santos LMR, Munari P, Costa AM, Santos RHS (2015) A branch-price-and-cut method for the vegetable crop rotation scheduling problem with minimal plot sizes. Eur J Oper Res 245:581–590. https://doi.org/10.1016/j.ejor.2015.03.035
Sarker RA, Quaddus MA (2002) Modelling a nationwide crop planning problem using a multiple criteria decision making tool. Comput Ind Eng 42:541–553. https://doi.org/10.1016/S0360-8352(02)00022-0
Sarker R, Ray T (2009) An improved evolutionary algorithm for solving multi-objective crop planning models. Comput Electron Agric 68:191–199. https://doi.org/10.1016/j.compag.2009.06.002
Sebatjane M, Adetunji O (2021) Optimal lot-sizing and shipment decisions in a three-echelon supply chain for growing items with inventory level- and expiration date-dependent demand. Appl Math Model 90:1204–1225. https://doi.org/10.1016/j.apm.2020.10.021
Seuring S, Müller M (2008) From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod 16:1699–1710. https://doi.org/10.1016/j.jclepro.2008.04.020
Stadtler H (2009) A framework for collaborative planning and state-of-the-art. Or Spectr 31:5–30. https://doi.org/10.1007/s00291-007-0104-5
Suthar RG, Barrera JI, Judge J et al (2019) Modeling postharvest loss and water and energy use in Florida tomato operations. Postharvest Biol Technol 153:61–68. https://doi.org/10.1016/j.postharvbio.2019.03.004
Tan Q, Zhang S, Li R (2017) Optimal use of agricultural water and land resources through reconfiguring crop planting structure under socioeconomic and ecological objectives. Water (switzerland). https://doi.org/10.3390/w9070488
United Nations (2019) The sustainable development goals report 2019. United Nations Publ issued by Dep Econ Soc Aff 64
Villa G, Adenso-Díaz B, Lozano S (2019) An analysis of geographic and product diversification in crop planning strategy. Agric Syst 174:117–124. https://doi.org/10.1016/j.agsy.2019.05.006
Zaraté P, Alemany M, del Pino M, et al (2019) How to support group decision making in horticulture: an approach based on the combination of a centralized mathematical model and a group decision support system. In: Lecture Notes in Business Information Processing, pp 83–94
Acknowledgements
The authors also acknowledge the support of Mariana del Pino, farming expert from the Universidad de la Plata.
Funding
We acknowledge the support of the Project 691249, RUCAPS: “Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems”, funded by the European Union’s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions.
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AE: methodology, software, validation, writing-original draft, writing—review and editing, visualization, funding acquisition; MMEA: conceptualization, methodology, validation, writing—original draft, writing—review and editing, supervision, funding acquisition, funding administration; AO: conceptualization, methodology, validation, resources, writing—original draft, writing—review and editing, supervision, funding acquisition, funding administration; SL: investigation, validation, funding acquisition.
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Esteso, A., Alemany, M.M.E., Ortiz, A. et al. Optimization model to support sustainable crop planning for reducing unfairness among farmers. Cent Eur J Oper Res 30, 1101–1127 (2022). https://doi.org/10.1007/s10100-021-00751-8
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DOI: https://doi.org/10.1007/s10100-021-00751-8