Assessment of smart irrigation controllers under subsurface and drip-irrigation systems for tomato yield in arid regions
H. M. Al-Ghobari A , F. S. Mohammad A and M. S. A. El Marazky A B
+ Author Affiliations
- Author Affiliations
A Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Kingdom of Saudi Arabia.
B Corresponding author. Email: melmarazky@ksu.edu.sa
Crop and Pasture Science 66(10) 1086-1095 https://doi.org/10.1071/CP15065
Submitted: 23 February 2015 Accepted: 24 April 2015 Published: 22 September 2015
Abstract
Here, two types of smart irrigation controllers intended to reduce irrigation water are investigated under Saudi Arabia’s present water crisis scenario. These controllers are specially made for scheduling irrigation and management of landscaping. Consequently, the aim of this study is to adapt the efficient automated controllers to tomato crops, and for extension to other similar agricultural crops. The controllers are based on evapotranspiration and have been shown to be promising tools for scheduling irrigation and quantifying the water required by plants to achieve water savings. In particular, the study aims to evaluate the effectiveness of these technologies (SmartLine SL 1600and Hunter Pro-C) in terms of the amount of irrigation applied and compare them with conventional irrigation scheduling methods. The smart irrigation systems were implemented and tested under drip irrigation and subsurface irrigation for tomato (cv. Nema) in an arid region. The results revealed significant differences between the three irrigation-scheduling methods in both the amount of applied water and yield. For example, each 1 mm water depth applied to the tomato crop via subsurface (or drip) irrigation by SmartLine, Hunter Pro-C, and the control system yielded 129.70 kg (70.33 kg), 161.50 kg (93.47 kg), and 109.78 kg (108.32 kg), respectively. Generally, the data analysis indicates that the Hunter Pro-C system saves water and produces a higher yield with the greatest irrigation water-use efficiency (IWUE) of the irrigation scheduling methods considered. Moreover, the results indicate that the subsurface irrigation system produced a higher yield and IWUE than the drip system.
Additional keywords: arid region, drip irrigation, evapotranspiration controllers, irrigation water-use efficiency, smart irrigation, subsurface drip irrigation systems, tomato yields.
References
Acar B, Topak R, Direk M (2010) Impacts of pressurized irrigation technologies on efficient water resources uses in semi-arid climate of Konya Basin of Turkey. International Journal of Sustainable Water & Environmental Systems 1, 1–4.| Impacts of pressurized irrigation technologies on efficient water resources uses in semi-arid climate of Konya Basin of Turkey.Crossref | GoogleScholarGoogle Scholar |
Al-Ghobari HM, Mohammad FS (2011) Intelligent irrigation performance: evaluation and quantifying its ability for conserving water in arid region. Journal of Applied Water Science 1, 73–83.
| Intelligent irrigation performance: evaluation and quantifying its ability for conserving water in arid region.Crossref | GoogleScholarGoogle Scholar |
Al-Omran AM, AL-Harbi AR, Wahb-Allah MA, Nadeem M, AL-Eter A (2010) Impact of irrigation water quality, irrigation systems, irrigation rates and soil amendments on tomato production in sandy calcareous soil. Turkish Journal of Agriculture and Forestry 34, 59–73.
Allen RG, Pereia LS, Raes D, Smith M (1998) ‘Crop evapotranspiration—guidelines for computing crop water requirements.’ FAO Irrigation and Drainage Paper No. 56. pp. 7–28. (FAO: Rome)
Almarshadi MH, Ismail SM (2011) Effects of precision irrigation on productivity and water use efficiency of alfalfa under different irrigation methods in arid climates. Journal of Applied Sciences Research 7, 299–308.
ASABE (2007) ASABE Standard S436.1 Test procedure for determining the uniformity of water distribution of center pivot and lateral move irrigation machines equipped with spray or sprinkler nozzles. American Society of Agricultural and Biological Engineers, St. Joseph, MI, USA.
Cornelis WM, Khlosi M, Hartmann R, Van Meirvenne M, De Vos B (2005) Comparison of unimodal analytical expressions for the soil-water retention curve. Soil Science Society of America Journal 69, 1902–1911.
| Comparison of unimodal analytical expressions for the soil-water retention curve.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXht1WrurnE&md5=e3efc632288ca3cffda7447e3a03d0bfCAS |
Dabach S, Lazarovitch N, Šimůnek J, Shani U (2013) Numerical investigation of irrigation scheduling based on soil water status. Irrigation Science 31, 27–36.
| Numerical investigation of irrigation scheduling based on soil water status.Crossref | GoogleScholarGoogle Scholar |
Davis SL, Dukes MD (2012) Landscape irrigation with evapotranspiration controller in a humid climate. Transactions of the ASABE 55, 571–580.
| Landscape irrigation with evapotranspiration controller in a humid climate.Crossref | GoogleScholarGoogle Scholar |
Davis S, Dukes MD, Miller GL (2009) Landscape irrigation by evapotranspiration-based irrigation controllers under dry conditions in Southwest Florida. Agricultural Water Management 96, 1828–1836.
| Landscape irrigation by evapotranspiration-based irrigation controllers under dry conditions in Southwest Florida.Crossref | GoogleScholarGoogle Scholar |
Davis SL, Dukes MD, Miller GL (2010) Irrigation scheduling performance by evapotranspiration-based controllers. Agricultural Water Management 98, 19–28.
| Irrigation scheduling performance by evapotranspiration-based controllers.Crossref | GoogleScholarGoogle Scholar |
Dukes MD (2012) Water conservation potential of landscape irrigation smart controllers. Transactions of the ASABE 55, 563–569.
| Water conservation potential of landscape irrigation smart controllers.Crossref | GoogleScholarGoogle Scholar |
Evett SR (2008) Neutron moisture meters. In ‘Field estimation of soil water content: A practical guide to methods, instrumentation, and sensor technology’. Ch. 3. (Eds SR Evett, LK Heng, P Moutonnet, ML Nguyen) pp. 39–54. (International Atomic Energy Agency: Vienna) Available at: www.pub.iaea.org/mtcd/publications/PubDetails.asp?pubId=7801 (accessed 20 August 2008)
Fernández MD, Bonachela S, Orgaz F, Thompson R, López JC, Granados MR, Gallardo M, Fereres E (2010) Measurements and estimation of plastic green-house reference evaportranspiration in a Mediterranean climate. Irrigation Science 28, 497–509.
| Measurements and estimation of plastic green-house reference evaportranspiration in a Mediterranean climate.Crossref | GoogleScholarGoogle Scholar |
Green G, Sunding D, Zilberman D, Parker D (1996) Explaining irrigation technology choices: a microparameter approach. American Journal of Agricultural Economics 78, 1064–1072.
| Explaining irrigation technology choices: a microparameter approach.Crossref | GoogleScholarGoogle 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. Agricultural Water Management 96, 93–99.
| The effects of irrigation methods with effluent and irrigation scheduling on water use efficiency and corn yields in an arid region.Crossref | GoogleScholarGoogle Scholar |
Irrigation Association (2011) Smart Water Application Technologies (SWAT) program. Turfgrass and Landscape Irrigation System Smart Controllers, Falls Church, VA, USA. Available at: www.irrigation.org/
Jones H (2004) Irrigation scheduling: advantages and pitfalls of plant based methods. Journal of Experimental Botany 55, 2427–2436.
| Irrigation scheduling: advantages and pitfalls of plant based methods.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXovVOisr4%3D&md5=ed0366b4368e087869105d22814c0cf1CAS | 15286143PubMed |
Khairy MFA, Elmeseery AA, Abdel-Aziz AA (2009) Trickle irrigation utilization for tomato in sandy soil. Misr Journal of Agricultural Engineering 26, 170–185.
Mayer PW, DeOreo WB, Hayden M, Davis R, Caldwell E, Miller T, Bickel PJ (2009) Evaluation of California weather-based “smart” irrigation controller programs. Aquacraft, Inc., Boulder, CO, USA. Available at: www.aquacraft.com (accessed 15 August 2009)
McCready MS, Dukes MD, Miller GL (2009) Water conservation potential of smart irrigation controllers on St. Augustine grass. Agricultural Water Management 96, 1623–1632.
| Water conservation potential of smart irrigation controllers on St. Augustine grass.Crossref | GoogleScholarGoogle Scholar |
Michael A (1978) ‘Irrigation and theory practice.’ (Vikas Publishing House Pvt Ltd: New Delhi, India)
MOA (Ministry of Agriculture) (2012) Agriculture Statistical Year Book. Agricultural Research and Development Affairs, Department of Studies Planning and Statistics.
Mohammad FS, Al-Ghobari HM, El Marazky MSA (2013) Adoption of intelligent irrigation scheduling technique and its effect on water use efficiency of tomato crops in arid regions. Australian Journal of Crop Science 3, 305–313.
Mun S, Sassenrath GF, Schmidt AM, Lee N, Wadsworth MC, Rice B, Corbitt JQ, Schneider JM, Tagert ML, Pote J, Prabhu R (2015) Uncertainty analysis of an irrigation scheduling model for water management in crop production. Agricultural Water Management 155, 100–112.
| Uncertainty analysis of an irrigation scheduling model for water management in crop production.Crossref | GoogleScholarGoogle Scholar |
Muñoz-Carpena R, Dukes MD (2005) Automatic irrigation based on soil moisture for vegetable crops. Fact Sheet ABE 356 of the Department of Agriculture and Biological Engineering, University of Florida, FL, USA.
Muñoz-Carpena R, Bryan H, Klassen W, Dispenza TT, Dukes MD (2003) Evaluation of an automatic soil moisture-based drip irrigation system for row tomatoes. In ‘ASABE Annual Meeting’. Paper No. 032093, 27–30 July, Las Vegas, NV, USA. (American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA)
Muñoz-Carpena R, Dukes MD, Li VC, Klassen W (2005) Field comparison of tensiometer and granular matrix sensor automatic drip irrigation on tomato. HortTechnology 15, 584–590.
Norum MN, Adhikari D (2009) Smart irrigation system controllers. In ‘Proceedings 7th World Congress on Computers in Agriculture Conference’. Reno, Nevada. (American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA)
Oktem A, Simsek M, Oktem AG (2003) Deficit irrigation effects on sweet corn (Zea mays saccharata Sturt) with drip irrigation system in a semi-arid region I. water-yield relationship. Agricultural Water Management 61, 63–74.
| Deficit irrigation effects on sweet corn (Zea mays saccharata Sturt) with drip irrigation system in a semi-arid region I. water-yield relationship.Crossref | GoogleScholarGoogle Scholar |
SAS (2008) ‘The SAS System for Windows. Release 9.0.’ (SAS Institute: Cary, NC, USA)
Steel RG, Torrie JH (1980) ‘Principles and procedures of statistics.’ (McGraw-Hill: New York)
Vellidis G, Tucker M, Perry C, Wen C, Bednarz C (2008) A real-time wireless smart sensor array for scheduling irrigation. Computers and Electronics in Agriculture 61, 44–50.
| A real-time wireless smart sensor array for scheduling irrigation.Crossref | GoogleScholarGoogle Scholar |
Wan S, Kang Y (2006) Effect of drip irrigation frequency on radish (Raphanus sativus L.) growth and water use. Irrigation Science 24, 161–174.
| Effect of drip irrigation frequency on radish (Raphanus sativus L.) growth and water use.Crossref | GoogleScholarGoogle Scholar |
Wang JD, Gong S, Gao Z (2009) Effects of drip irrigation mode on spatial distribution of soil water and nitrate and winter tomato yield. Transactions of CSAE 25, 67–72.
