IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v98y2011i4p517-531.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378-3774(10)00318-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    3. 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.
    4. 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.
    5. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hermann, Daniel & Musshoff, Oliver & Agethen, Katrin, 2014. "I will never switch sides: an experimental approach to determine drivers for investment decisions of conventional and organic hog farmers," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 183084, European Association of Agricultural Economists.
    2. Anna‐Katharina Hornidge & Lisa Oberkircher & Bernhard Tischbein & Gunther Schorcht & Anik Bhaduri & Ahmad M. Manschadi, 2011. "Reconceptualizing water management in Khorezm, Uzbekistan," Natural Resources Forum, Blackwell Publishing, vol. 35(4), pages 251-268, November.
    3. Corbari, Chiara & Paciolla, Nicola & Rossi, Greta & Mancini, Marco, 2023. "A double two-sources energy-water balance model for improving evapotranspiration estimates and irrigation management in fruit trees fields," Agricultural Water Management, Elsevier, vol. 289(C).
    4. Martin de Santa Olalla, F. & Calera, A. & Dominguez, A., 2003. "Monitoring irrigation water use by combining Irrigation Advisory Service, and remotely sensed data with a geographic information system," Agricultural Water Management, Elsevier, vol. 61(2), pages 111-124, June.
    5. Bastiaanssen, W. G. M. & Chandrapala, L., 2003. "Water balance variability across Sri Lanka for assessing agricultural and environmental water use," Agricultural Water Management, Elsevier, vol. 58(2), pages 171-192, February.
    6. Syster C. Maart-Noelck & Oliver Musshoff, 2014. "Measuring the risk attitude of decision-makers: are there differences between groups of methods and persons?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 58(3), pages 336-352, July.
    7. Kimhi, Ayal & Rubin, Ofir D., 2006. "Assessing The Response Of Farm Households To Dairy Policy Reform In Israel," Discussion Papers 7134, Hebrew University of Jerusalem, Department of Agricultural Economics and Management.
    8. Muhammad Usman & Talha Mahmood & Christopher Conrad & Habib Ullah Bodla, 2020. "Remote Sensing and Modelling Based Framework for Valuing Irrigation System Efficiency and Steering Indicators of Consumptive Water Use in an Irrigated Region," Sustainability, MDPI, vol. 12(22), pages 1-33, November.
    9. Mao, Hui & Zhou, Li & Ifft, Jennifer & Ying, RuiYao, 2019. "Risk preferences, production contracts and technology adoption by broiler farmers in China," China Economic Review, Elsevier, vol. 54(C), pages 147-159.
    10. Isik, Murat, 2003. "Environmental Regulation And The Optimal Location Of The Firm Under Uncertainty," 2003 Annual meeting, July 27-30, Montreal, Canada 22066, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Yongqing Zhao & Rendong Li & Juan Qiu & Xiangdong Sun & Lu Gao & Mingquan Wu, 2019. "Prediction of Human Brucellosis in China Based on Temperature and NDVI," IJERPH, MDPI, vol. 16(21), pages 1-15, November.
    12. Isik, Murat & Coble, Keith H. & Hudson, Darren & House, Lisa O., 2003. "A model of entry-exit decisions and capacity choice under demand uncertainty," Agricultural Economics, Blackwell, vol. 28(3), pages 215-224, May.
    13. Durbach, Ian, 2009. "On the estimation of a satisficing model of choice using stochastic multicriteria acceptability analysis," Omega, Elsevier, vol. 37(3), pages 497-509, June.
    14. Finger, R. & Gerwig, C.N., 2008. "The Impact of Climate Change on the Profitability of Site Specific Technologies," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 43, March.
    15. Marc Baudry & Edouard Civel & Camille Tévenart, 2023. "Land allocation and the adoption of innovative practices in agriculture: a real option modelling of the underlying hidden costs," Working Papers hal-04159839, HAL.
    16. Praveen Koovalamkadu Velayudhan & Alka Singh & Girish Kumar Jha & Pramod Kumar & Kingsly Immanuelraj Thanaraj & Aditya Korekallu Srinivasa, 2021. "What Drives the Use of Organic Fertilizers? Evidence from Rice Farmers in Indo-Gangetic Plains, India," Sustainability, MDPI, vol. 13(17), pages 1-13, August.
    17. Xiaoxiao Li & Man Yu & Jing Ma & Zhanbin Luo & Fu Chen & Yongjun Yang, 2018. "Identifying the Relationship between Soil Properties and Rice Growth for Improving Consolidated Land in the Yangtze River Delta, China," Sustainability, MDPI, vol. 10(9), pages 1-14, August.
    18. Yotsaphat Kittichotsatsawat & Varattaya Jangkrajarng & Korrakot Yaibuathet Tippayawong, 2021. "Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies," Sustainability, MDPI, vol. 13(8), pages 1-20, April.
    19. Ihli, Hanna Julia & Gassner, Anja & Musshoff, Oliver, 2018. "Experimental insights on the investment behavior of small-scale coffee farmers in central Uganda under risk and uncertainty," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 75(C), pages 31-44.
    20. Robert Finger & Stéphanie Schmid, 2008. "Modeling agricultural production risk and the adaptation to climate change," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 68(1), pages 25-41, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:98:y:2011:i:4:p:517-531. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.