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Irrigation Science

, Volume 34, Issue 4, pp 287–296 | Cite as

Evapotranspiration and crop coefficients of irrigated biomass sorghum for energy production

  • R. López-UrreaEmail author
  • L. Martínez-Molina
  • F. de la Cruz
  • A. Montoro
  • J. González-Piqueras
  • M. Odi-Lara
  • J. M. Sánchez
Original Paper

Abstract

New cultivars of sorghum for biomass energy production are currently available. This crop has a positive energy balance being irrigation water the largest energy consumer during the growing cycle. Thence, it is important to know the biomass sorghum water requirements, in order to minimize irrigation losses, thus saving water and energy. The objective of this study was to quantify the water use and crop coefficients of irrigated biomass sorghum without soil water limitations during two growing seasons. A weighing lysimeter located in Albacete (Central Spain) was used to measure the daily biomass sorghum evapotranspiration (ETc) throughout the growing season under sprinkler irrigation. Seasonal lysimeter ETc was 721 mm in 2007 and 691 mm in 2010. The 4 % higher ETc value in 2007 was due to an 8 % higher evaporative demand in that year. Maximum average K c values of 1.17 in 2007 and 1.21 in 2010 were reached during the mid-season stage. The average K c values for the 2 years of study were K c-ini: 0.64 and K c-mid: 1.19. The seasonal evaporation component was estimated to be about 18 % of ETc. The average basal K c (K cb) values for the two study years were K cb-ini: 0.11 and K cb-mid: 1.17. The good linear relationship found between K cb values and the fraction of ground cover (f c) and the excellent agreement found between Normalized Difference Vegetation Index and different biophysical parameters, such as K cb and f c, will allow monitoring and estimating the spatially distributed water requirements of biomass sorghum at field and regional scales.

Keywords

Root Mean Square Error Normalize Difference Vegetation Index Ground Cover Sweet Sorghum Crop Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors gratefully acknowledge the helpful comments and suggestions from the anonymous reviewers. This work has been funded by the Spanish Science and Innovation Ministry (Project AGL2009-13124), the Spanish Ministry of Economy and Competitiveness (CGL2013-46862-C2-2-P and AGL2014-54201-C4-4-R), and the Education and Science Council (JCCM, Spain) (Projects PAI07-0058-5569 and PPII10-0319-8732). M. Odi-Lara acknowledges the support of the Chilean Government through the project FONDECYT (No. 3130319).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • R. López-Urrea
    • 1
    Email author
  • L. Martínez-Molina
    • 1
  • F. de la Cruz
    • 1
  • A. Montoro
    • 1
  • J. González-Piqueras
    • 2
  • M. Odi-Lara
    • 3
  • J. M. Sánchez
    • 4
  1. 1.Instituto Técnico Agronómico Provincial (ITAP) and FUNDESCAMPolígono Industrial CampollanoAlbaceteSpain
  2. 2.Applied Physics Department, School of Agronomic Engineering and IDRUniversity of Castilla-La ManchaAlbaceteSpain
  3. 3.Centro de Investigación y Transferencia en Riego y AgroclimatologíaUniversidad de TalcaTalcaChile
  4. 4.Applied Physics Department, School of Mining and Industrial EngineeringUniversity of Castilla-La ManchaAlmadénSpain

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