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Methodology for obtaining prediction models of the root depth of lettuce for its application in irrigation automation

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  • Escarabajal-Henarejos, D.
  • Molina-Martínez, J.M.
  • Fernández-Pacheco, D.G.
  • García-Mateos, G.

Abstract

Irrigation scheduling and automation are usually conducted using models that are based on the measurement of the soil water content. In this sense, water balance has established itself as a good indicator of the growth and development of crops and is currently used in several automatic programming systems, primarily in intensive farming and microirrigation systems. This method analyses the gains and losses of water in a limited volume of soil to determine the water availability for crops and the soil water status. A parameter of great importance for the application of this method is the root depth, which limits the soil volume to be considered in the water balance. In most cases, the actual evolution of this parameter during crop development is not considered, using instead fixed tabulated values or values that have been proposed in the literature. However, during some periods of crop development, the soil profile that is considered for the water balance does not correspond to the profile that is actually explored by the root system, resulting in a mismatch in the water balance. A good relationship between the root depth and the percentage of ground cover in lettuce has been observed, the latter of which is associated with crop development and the evapotranspirative demand. Therefore, this paper presents a methodology for obtaining prediction models of the root depth of the ‘Little Gem’ lettuce crop from the percentage of ground cover. The implementation of this prediction model in an automated irrigation management system will permit the optimisation of water resources due to the adjustment of the water content to the actual volume that is explored by the roots.

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  • Escarabajal-Henarejos, D. & Molina-Martínez, J.M. & Fernández-Pacheco, D.G. & García-Mateos, G., 2015. "Methodology for obtaining prediction models of the root depth of lettuce for its application in irrigation automation," Agricultural Water Management, Elsevier, vol. 151(C), pages 167-173.
    • Handle: RePEc:eee:agiwat:v:151:y:2015:i:c:p:167-173
    DOI: 10.1016/j.agwat.2014.10.012
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    1. García-Mateos, G. & Hernández-Hernández, J.L. & Escarabajal-Henarejos, D. & Jaén-Terrones, S. & Molina-Martínez, J.M., 2015. "Study and comparison of color models for automatic image analysis in irrigation management applications," Agricultural Water Management, Elsevier, vol. 151(C), pages 158-166.
    2. Hernández-Hernández, J.L. & Ruiz-Hernández, J. & García-Mateos, G. & González-Esquiva, J.M. & Ruiz-Canales, A. & Molina-Martínez, J.M., 2017. "A new portable application for automatic segmentation of plants in agriculture," Agricultural Water Management, Elsevier, vol. 183(C), pages 146-157.
    3. González-Esquiva, J.M. & García-Mateos, G. & Hernández-Hernández, J.L. & Ruiz-Canales, A. & Escarabajal-Henerajos, D. & Molina-Martínez, J.M., 2017. "Web application for analysis of digital photography in the estimation of irrigation requirements for lettuce crops," Agricultural Water Management, Elsevier, vol. 183(C), pages 136-145.
    4. González-Esquiva, J.M. & García-Mateos, G. & Escarabajal-Henarejos, D. & Hernández-Hernández, J.L. & Ruiz-Canales, A. & Molina-Martínez, J.M., 2017. "A new model for water balance estimation on lettuce crops using effective diameter obtained with image analysis," Agricultural Water Management, Elsevier, vol. 183(C), pages 116-122.

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