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2020 | OriginalPaper | Chapter

A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios

Authors : Israel D. Herrera-Granda, Leandro L. Lorente-Leyva, Diego H. Peluffo-Ordóñez, M. M. E. Alemany

Published in: Machine Learning, Optimization, and Data Science

Publisher: Springer International Publishing

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Abstract

Ecuador is worldwide considered as one of the main natural flower producers and exporters –being roses the most salient ones. Such a fact has naturally led the emergence of small and medium sized companies devoted to the production of quality roses in the Ecuadorian highlands, which intrinsically entails resource usage optimization. One of the first steps towards optimizing the use of resources is to forecast demand, since it enables a fair perspective of the future, in such a manner that the in-advance raw materials supply can be previewed against eventualities, resources usage can be properly planned, as well as the misuse can be avoided. Within this approach, the problem of forecasting the supply of roses was solved into two phases: the first phase consists of the macro-forecast of the total amount to be exported by the Ecuadorian flower sector by the year 2020, using multi-layer neural networks. In the second phase, the monthly demand for the main rose varieties offered by the study company was micro-forecasted by testing seven models. In addition, a Bayesian network model is designed, which takes into consideration macroeconomic aspects, the level of employability in Ecuador and weather-related aspects. This Bayesian network provided satisfactory results without the need for a large amount of historical data and at a low-computational cost.

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Asociación de Productores y Exportadores de Flores: Inicio – Expoflores. https://expoflores.com/

Palacios, J., Rosero, D.: Análisis de las condiciones climáticas registradas en el Ecuador continental en el año 2013 y su impacto en el sector agrícola. Estud. e Investig. meteorológicas. Ina. Inst. Nac. Meteorol. e Hidrol. Ecuador, 28, p. (2014)

Hidalgo-Proaño, M.: Variabilidad climática interanual sobre el Ecuador asociada a ENOS. CienciAmérica 6, 42–47 (2017)

Ritchie, J.W., Abawi, G.Y., Dutta, S.C., Harris, T.R., Bange, M.: Risk management strategies using seasonal climate forecasting in irrigated cotton production: a tale of stochastic dominance. Aust. J. Agric. Resour. Econ. 48, 65–93 (2004). https://doi.org/10.1111/j.1467-8489.2004.t01-1-00230.xCrossRef

Letson, D., Podesta, G.P., Messina, C.D., Ferreyra, R.A.: The uncertain value of perfect ENSO phase forecasts: Stochastic agricultural prices and intra-phase climatic variations. Clim. Change 69, 163–196 (2005). https://doi.org/10.1007/s10584-005-1814-9CrossRef

Weber, E.U., Laciana, C., Bert, F., Letson, D.: Agricultural decision making in the argentine Pampas: Modeling the interaction between uncertain and complex environments and heterogeneous and complex decision makers (2008)

Loy, J.-P., Pieniadz, A.: Optimal grain marketing revisited a german and polish perspective. Outlook Agric. 38, 47–54 (2009). https://doi.org/10.5367/000000009787762761CrossRef

Wang, Q.J., Robertson, D.E., Haines, C.L.: A Bayesian network approach to knowledge integration and representation of farm irrigation: 1. Model development. WATER Resour. Res. 45 (2009). https://doi.org/10.1029/2006wr005419

Keesman, K.J., Doeswijk, T.: uncertainty analysis of weather controlled systems (2010). https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960073961&doi=10.1007%2F978-3-642-03735-1_12&partnerID=40&md5=210525584472097e996a9f124f96fddb

10.

Schnepf, R.: U.S. livestock and poultry feed use and availability: background and emerging issues. In: Feed Market Dynamics and U.S. Livestock Implications. pp. 1–36. Nova Science Publishers, Inc., CRS, United States (2012)

11.

Medellín-Azuara, J., Howitt, R.E., MacEwan, D.J., Lund, J.R.: Economic impacts of climate-related changes to California agriculture. Clim. Change 109, 387–405 (2011). https://doi.org/10.1007/s10584-011-0314-3CrossRef

12.

McCown, R.L., Carberry, P.S., Dalgliesh, N.P., Foale, M.A., Hochman, Z.: Farmers use intuition to reinvent analytic decision support for managing seasonal climatic variability. Agric. Syst. 106, 33–45 (2012). https://doi.org/10.1016/j.agsy.2011.10.005CrossRef

13.

Scott, S.L., Varian, H.R.: Predicting the present with bayesian structural time series. Available SSRN 2304426 (2013)

14.

Prudhomme, C., Shaffrey, L., Woollings, T., Jackson, C., Fowler, H., Anderson, B.: IMPETUS: Improving predictions of drought for user decision-making. International Conference on Drought: Research and Science-Policy Interfacing, 2015. pp. 273–278. CRC Press/Balkema, Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom (2015)

15.

Wiles, P., Enke, D.: A hybrid neuro-fuzzy model to forecast the Soybean complex. International Annual Conference of the American Society for Engineering Management 2015, ASEM 2015. pp. 1–5. American Society for Engineering Management, Missouri University of Science and Technology, Engineering Management and Systems Engineering Department, United States (2015)

16.

Hansen, B.G., Li, Y.: An analysis of past world market prices of feed and milk and predictions for the future. Agribusiness 33, 175–193 (2017). https://doi.org/10.1002/agr.21474CrossRef

17.

Johnson, M.D., Hsieh, W.W., Cannon, A.J., Davidson, A., Bedard, F.: Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agric. For. Meteorol. 218, 74–84 (2016). https://doi.org/10.1016/j.agrformet.2015.11.003CrossRef

18.

Chen, J., Yang, J., Zhao, J., Xu, F., Shen, Z., Zhang, L.: Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method. Neurocomputing 174, 1087–1100 (2016). https://doi.org/10.1016/j.neucom.2015.09.105CrossRef

19.

Fodor, N., et al.: Integrating plant science and crop modeling: assessment of the impact of climate change on soybean and maize production. Plant Cell Physiol. 58, 1833–1847 (2017). https://doi.org/10.1093/pcp/pcx141CrossRef

20.

Chapman, R., et al.: Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: a proof of concept analysis. Comput. Electron. Agric. 151, 338–348 (2018). https://doi.org/10.1016/j.compag.2018.06.006CrossRef

21.

Lara-Estrada, L., Rasche, L., Sucar, L.E., Schneider, U.A.: Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks. LAND. 7 (2018). https://doi.org/10.3390/land7010004

22.

Abdelaal, H.S.A., Thilmany, D.: Grains production prospects and long run food security in Egypt. Sustain. 11 (2019). https://doi.org/10.3390/su11164457

23.

Kusunose, Y., Ma, L., Van Sanford, D.: User responses to imperfect forecasts: findings from an experiment with Kentucky wheat farmers. Weather. Clim. Soc. 11, 791–808 (2019). https://doi.org/10.1175/wcas-d-18-0135.1

24.

Kadigi, I.L., et al.: Forecasting yields, prices and net returns for main cereal crops in Tanzania as probability distributions: a multivariate empirical (MVE) approach. Agric. Syst. 180 (2020). https://doi.org/10.1016/j.agsy.2019.102693

25.

McGrath, G., Rao, P.S.C., Mellander, P.-E., Kennedy, I., Rose, M., van Zwieten, L.: Real-time forecasting of pesticide concentrations in soil. Sci. Total Environ. 663, 709–717 (2019). https://doi.org/10.1016/j.scitotenv.2019.01.401CrossRef

26.

Yang, B., Xie, L.: Bayesian network modelling for “direct farm” mode based agricultural supply chain risk. Ekoloji 28, 2361–2368 (2019)

27.

Zaporozhtseva, L.A., Sabetova, T. V, Yu Fedulova, I.: Assessment of the uncertainty factors in computer modelling of an agricultural company operation. International Conference on Information Technologies in Business and Industries, ITBI 2019. Institute of Physics Publishing, Voronezh State Agrarian University, Michurina Str. 30, Voronezh, 394087, Russian Federation (2019)

28.

Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time series analysis: forecasting and control. Wiley (2015)

29.

Hanke, J., Wichern, D.: Business forecast. Pearson Educación (2010)

30.

Novagric: Invernaderos para Cultivo de Rosas. https://www.novagric.com/es/invernaderos-rosas

31.

Weather Spark: Clima promedio en Quito, Ecuador, durante todo el año - Weather Spark. https://es.weatherspark.com/y/20030/Clima-promedio-en-Quito-Ecuador-durante-todo-el-año

32.

Instituto Nacional de Estadísticas y Censos-INEC: Encuesta Nacional de Empleo, Desempleo y subempleo-ENEMDU. https://www.ecuadorencifras.gob.ec/empleo-diciembre-2019/

33.

Central Bank of Ecuador: Central Bank of Ecuador. www.bce.fin.ec

34.

Hyndman, R., Athnasopoulos, G.: Forecasting: Principles and Practice. OTexts, Australia (2018)

35.

Herrera-Granda, I.D., et al.: Artificial neural networks for bottled water demand forecasting: a small business case study. In: Rojas, I., Joya, G.C.A. (eds.) International Work-Conference on Artificial Neural Networks, pp. 362–373. Springer, Canaria (2019)

Title: A Forecasting Model to Predict the Demand of Roses in an Ecuadorian Small Business Under Uncertain Scenarios
Authors: Israel D. Herrera-Granda
Leandro L. Lorente-Leyva
Diego H. Peluffo-Ordóñez
M. M. E. Alemany
Publisher: Springer International Publishing
Book: Machine Learning, Optimization, and Data Science
Print ISBN: 978-3-030-64579-3

Electronic ISBN: 978-3-030-64580-9

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-64580-9_21

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