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An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques

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  • Nahry, A.H. El
  • Ali, R.R.
  • Baroudy, A.A. El

Abstract

The current work is aimed to realizing land and water use efficiency and determining the profitability of precision farming economically and environmentally. The studied area is represented by an experimental pivot irrigation field cultivated with maize in Ismailia province, Egypt. Two field practices were carried out during the successive summer growing seasons (2008 and 2009) to study the response of maize plants single hybrid 10 (S.H.10) to traditional and precision farming practices. Traditional farming (TF) as handled by the farm workers were observed and noted carefully. On the other hand precision farming (PF) practices included field scouting, grid soil sampling, variable rate technology and its applications. After applying PF a dramatic change in management zones was noticed and three management zones (of total four) were merged to be more homogenous representing 84.3% of the pivot irrigation field. Under PF Remote Sensing and Geographic Information System techniques have played a vital role in the variable rate applications that were defined due to management zones requirements. Fertilizers were added in variable rates, so that rationalization of fertilizers saved 23.566 tonnes/experimental pivot area. Natural drainage system was improved by designing vertical holes to break down massive soil layers and to leach excessive salts. Crop water requirements were determined in variable rate according to the actual plant requirements using SEBAL model with the aid of FAO Cropwat model. Irrigation schedule of maize was adopted considering soil water retention, depletion, gross and net irrigation saving an amount of water equal to 93,718Â m3 in the pivot irrigation field (153.79Â acre). However costs of applying PF were much higher than TF, the economic profitability (returns-costs) achieved remarkable increase of 29.89% as a result of crop yield increment by 1000, 2100, 800 and 200Â kg/acre in the management zones 1, 2, 3 and 4, respectively. Finally applying adequate amounts of fertilizers beside water control the environmental hazards was reduced to the acceptable limits.

Suggested Citation

  • Nahry, A.H. El & Ali, R.R. & Baroudy, A.A. El, 2011. "An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques," Agricultural Water Management, Elsevier, vol. 98(4), pages 517-531, February.
    • Handle: RePEc:eee:agiwat:v:98:y:2011:i:4:p:517-531
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    References listed on IDEAS

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    1. Bastiaanssen, Wim G. M. & Molden, David J. & Makin, Ian W., 2000. "Remote sensing for irrigated agriculture: examples from research and possible applications," Agricultural Water Management, Elsevier, vol. 46(2), pages 137-155, December.
    2. Murat Isik & Madhu Khanna, 2003. "Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 305-317.
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    Cited by:

    1. Chen, Assaf & Orlov-Levin, Valerie & Meron, Moshe, 2019. "Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management," Agricultural Water Management, Elsevier, vol. 216(C), pages 196-205.
    2. Mwehe Mathenge & Ben G. J. S. Sonneveld & Jacqueline E. W. Broerse, 2022. "Application of GIS in Agriculture in Promoting Evidence-Informed Decision Making for Improving Agriculture Sustainability: A Systematic Review," Sustainability, MDPI, vol. 14(16), pages 1-15, August.
    3. Hasan Mirzakhaninafchi & Manjeet Singh & Anoop Kumar Dixit & Apoorv Prakash & Shikha Sharda & Jugminder Kaur & Ali Mirzakhani Nafchi, 2022. "Performance Assessment of a Sensor-Based Variable-Rate Real-Time Fertilizer Applicator for Rice Crop," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    4. Abid Ali & Valda Rondelli & Roberta Martelli & Gloria Falsone & Flavio Lupia & Lorenzo Barbanti, 2022. "Management Zones Delineation through Clustering Techniques Based on Soils Traits, NDVI Data, and Multiple Year Crop Yields," Agriculture, MDPI, vol. 12(2), pages 1-20, February.
    5. Mwinuka, Paul Reuben & Mourice, Sixbert K. & Mbungu, Winfred B. & Mbilinyi, Boniphace P. & Tumbo, Siza D. & Schmitter, Petra, 2022. "UAV-based multispectral vegetation indices for assessing the interactive effects of water and nitrogen in irrigated horticultural crops production under tropical sub-humid conditions: A case of Africa," Agricultural Water Management, Elsevier, vol. 266(C).
    6. Sebastian Lieder & Christoph Schröter-Schlaack, 2021. "Smart Farming Technologies in Arable Farming: Towards a Holistic Assessment of Opportunities and Risks," Sustainability, MDPI, vol. 13(12), pages 1-20, June.

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