Skip to main content

Abstract

The process for agriculture planning starts by delineating the field into site-specific rectangular management zones to face within-field variability. We propose a bi-objective model that minimizes the number of these zones and maximizes their homogeneity with respect to a soil property. Then we use a method to assign the crops to the different plots to obtain the best profit at the end of the production cycle subject to water forecasts for the period, humidity sensors, and the chemical and physical properties of the zones within the plot. With this crop planning model we can identify the best management zones of the previous bi-objective model. Finally, we show a real-time irrigation method to decide the amount of water for each plot, at each irrigation turn, in order to maximize the total final yield. This is a critical decision in countries where water shortages are frequent. In this study we integrate these stages in a hierarchical process for the agriculture planning and empirically prove its efficiency.

An erratum to this chapter is available at http://dx.doi.org/10.1007/978-1-4939-2483-7_6

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-1-4939-2483-7_20

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The Mexican national water commission.

  2. 2.

    Mexican ministry of agriculture, livestock, rural development, fisheries, and food.

  3. 3.

    The Mexican national institute for forestry, agriculture, and livestock. There is a research center INIFAP at every state, and therefore producers can get specific information depending on the geographic location of their fields.

References

  • Adeyemo J, Otieno F (2010) Differential evolution algorithm for solving multi-objective crop planning model. Agric Water Manag 97(6):848–856

    Article  Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (2006) Evapotranspiración del cultivo: guías para la determinación de los requerimientos de agua de los cultivos, vol 56. Food & Agriculture Organization, Rome

    Google Scholar 

  • Alminana M, Escudero LF, Landete M, Monge JF, Rabasa A, Sánchez-Soriano J (2010) WISCHE: A DSS for water irrigation scheduling. Omega 38(6):492–500

    Article  Google Scholar 

  • Bhatti AU, Mulla DJ, Frazier BE (1991) Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images. Remote Sens Environ 37(3):181–191

    Article  Google Scholar 

  • Bitran GR, Hax AC (1977) On the design of hierarchical production planning systems. Decis Sci 8(1):28–55

    Article  Google Scholar 

  • Blackmore S (2000) The interpretation of trends from multiple yield maps. Comput Electron Agric 26(1):37–51

    Article  Google Scholar 

  • Carr PM, Carlson GR, Jacobsen JS, Nielsen GA, Skogley EO (1991) Farming soils, not fields: a strategy for increasing fertilizer profitability. J Prod Agric 4(1):57–61

    Article  Google Scholar 

  • Casadesús J, Mata M, Marsal J, Girona J (2012) A general algorithm for automated scheduling of drip irrigation in tree crops. Comput Electron Agric 83:11–20

    Article  Google Scholar 

  • Cid-Garcia NM, Albornoz V, Rios-Solis YA, Ortega R (2013) Rectangular shape management zone delineation using integer linear programming. Comput Electron Agric 93:1–9

    Article  Google Scholar 

  • 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 Agri 107:20–28

    Google Scholar 

  • Diker K, Heermann DF, Brodahl MK (2004) Frequency analysis of yield for delineating yield response zones. Precis Agric 5(5):435–444

    Article  Google Scholar 

  • Doorenbos J, Kassam AH, Bentvelsen CLM (1986) Efectos del agua sobre el rendimiento de los cultivos, Vol 33. FAO, Roma

    Google Scholar 

  • Ehrgott M (2005) Multicriteria optimization, vol 2. Springer, Berlin

    Google Scholar 

  • Fraisse CW, Sudduth KA, Kitchen NR (2001) Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Trans ASAE 44(1):155–166

    Article  Google Scholar 

  • Franzen DW, Nanna TN (2003) Management zone delineation methods. In: Robert PC (ed) Proceedings of the 6th international conference on precision agriculture and other precision resources management, Minneapolis, 14–17 July, 2002. American Society of Agronomy, Minneapolis, pp 443–457

    Google Scholar 

  • Frogbrook ZL, Oliver MA (2007) Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data. Soil Use Manag 23(1):40–51

    Article  Google Scholar 

  • Hassanli AM, Ebrahimizadeh MA, Beecham S (2009) The effects of irrigation methods with effluent and irrigation scheduling on water use efficiency and corn yields in an arid region. Agric Water Manag 96(1):93–99

    Article  Google Scholar 

  • Hedley CB, Yule IJ (2009) A method for spatial prediction of daily soil water status for precise irrigation scheduling. Agric Water Manag 96(12):1737–1745

    Article  Google Scholar 

  • Hornung A, Khosla R, Reich R, Westfall DG (2003) Evaluation of site-specific management zones: grain yield and nitrogen use efficiency. In: Proceedings of the 4th European conference on precision agriculture. Wageningen Academic Publishers, Wageningen, pp 297–302

    Google Scholar 

  • Hornung A, Khosla R, Reich R, Inman D, Westfall DG (2006) Comparison of site-specific management zones: soil-color-based and yield-based. Agron J 98(2):407–415

    Article  Google Scholar 

  • INIA (2006) Métodos de análisis recomendados para los suelos de Chile. Instituto de Investigaciones Agropecuarias, Santiago de Chile

    Google Scholar 

  • Jaynes DB, Colvin TS, Kaspar TC (2005) Identifying potential soybean management zones from multi-year yield data. Comput Electron Agric 46(1):309–327

    Article  Google Scholar 

  • Jiang Q, Fu Q, Wang Z (2011) Study on delineation of irrigation management zones based on management zone analyst software. In: Computer and computing technologies in agriculture IV. Springer, New York, pp 419–427

    Google Scholar 

  • Li X, Pan Y, Zhang C, Liu L, Wang J (2005) A new algorithm on delineation of management zone. In: Proceedings of IEEE international geoscience and remote sensing symposium, IGARSS’05 2005, vol 1, p 4

    Google Scholar 

  • Mainuddin M, Gupta AD, Onta PR (1997) Optimal crop planning model for an existing groundwater irrigation project in Thailand. Agric Water Manag 33(1):43–62

    Article  Google Scholar 

  • Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26(6):369–395

    Article  Google Scholar 

  • McCauley A, Jones C, Jacobsen J (2005) soil and water management module 1: Basic soil properties. In: A self-study course from the MSU extension service continuing education series 4481–1. Montana State University, Bozeman

    Google Scholar 

  • Mortensen DA, Johnson CK, Doran JW, Shanahan JF, Wienhold BJ (2003) Site-specific management zones based on soil electrical conductivity in a semiarid cropping system. Agron J 95(2):303–315

    Article  Google Scholar 

  • Mulla DJ (1991) Using geostatistics and GIS to manage spatial patterns in soil fertility. In: Proceedings of the automated agricultural for the 21st century. American Society of Agricultural Engineers, St. Joseph, pp 336–345

    Google Scholar 

  • Ortega JA, Foster W, Ortega R (2002) Definición de sub-rodales para una silvicultura de precisión: una aplicación del método Fuzzy K-means. Ciencia e Investigación Agraria 29(1):35–44

    Google Scholar 

  • Ortega R, Flores L (1999) Introducción al manejo sitio-específico. In: Ortega R, Flores L (eds) Agricultura de Precisión, Ministerio de Agricultura, Instituto de Investigaciones Agropecuarias. Centro Regional de Investigación Quilamapu, pp 13–46

    Google Scholar 

  • Ortega RA, Santibáñez OA (2007) Determination of management zones in corn (Z ea mays L.) based on soil fertility. Comput Electron Agric 58(1):49–59

    Article  Google Scholar 

  • Ortega Álvarez JF, Juan Valero JA, Tarjuelo Martín-Benito JM, López Mata E (2004) MOPECO: an economic optimization model for irrigation water management. Irrig Sci 23:61–75

    Article  Google Scholar 

  • Pedroso M, Taylor J, Tisseyre B, Charnomordic B, Guillaume S (2010) A segmentation algorithm for the delineation of agricultural management zones. Comput Electron Agric 70(1):199–208

    Article  Google Scholar 

  • Reddy MJ, Kumar DN (2008) Evolving strategies for crop planning and operation of irrigation reservoir system using multi-objective differential evolution. Irrig Sci 26(2):177–190

    Article  Google Scholar 

  • Roudier P, Tisseyre B, Poilvé H, Roger JM (2008) Management zone delineation using a modified watershed algorithm. Precis Agric 9(5):233–250

    Article  Google Scholar 

  • Sahoo B, Lohani A, Sahu R (2006) Fuzzy multiobjective and linear programming based management models for optimal land-water-crop system planning. Water Resour Manag 20:931–948

    Article  Google Scholar 

  • Sarker R, Ray T (2009) An improved evolutionary algorithm for solving multi-objective crop planning models. Comput Electron Agric 68(2):191–199

    Article  Google Scholar 

  • Sarker RA, Talukdar S, Haque AFM (1997) Determination of optimum crop mix for crop cultivation in Bangladesh. Appl Math Model 21(10):621–632

    Article  Google Scholar 

  • Schepers JS, Luchiari A, Johnson SH, Liebig MA, Shanahan JF, Schepers AR (2004) Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agron J 96(1):195–203

    Article  Google Scholar 

  • Simbahan GC, Dobermann A (2006) An algorithm for spatially constrained classification of categorical and continuous soil properties. Geoderma 136(3):504–523

    Article  Google Scholar 

  • Whelan BM, Cupitt J, McBratney AB (2003) Practical definition and interpretation of potential management zones in Australian dryland cropping. In: Robert PC (ed) Proceedings of the 6th international conference on precision agriculture and other precision resources management, Minneapolis, 14–17 July 2002. American Society of Agronomy, Minneapolis, pp 395–409

    Google Scholar 

  • Xu L, Chen L, Chen T, Gao Y (2011) SOA-based precision irrigation decision support system. Math Comput Model 54(3):944–949

    Article  Google Scholar 

Download references

Acknowledgements

This study was partially supported by CONACYT (Grant 101857), by DGIP (Grant USM 28.13.69), CATA and CIDIEN of the Universidad Técnica Federico Santa María. Nestor M. Cid-Garcia wishes to acknowledge graduate scholarship from CONACYT. We are grateful to INIFAP for much of the data that we used in this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Víctor M. Albornoz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science + Business Media New York

About this chapter

Cite this chapter

Albornoz, V.M., Cid-García, N.M., Ortega, R., Ríos-Solís, Y.A. (2015). A Hierarchical Planning Scheme Based on Precision Agriculture. In: Plà-Aragonés, L. (eds) Handbook of Operations Research in Agriculture and the Agri-Food Industry. International Series in Operations Research & Management Science, vol 224. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2483-7_6

Download citation

Publish with us

Policies and ethics