IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v159y2015icp209-224.html
   My bibliography  Save this article

Comparison of traditional and ET-based irrigation scheduling of surface-irrigated cotton in the arid southwestern USA

Author

Listed:
  • Hunsaker, D.J.
  • French, A.N.
  • Waller, P.M.
  • Bautista, E.
  • Thorp, K.R.
  • Bronson, K.F.
  • Andrade-Sanchez, P.

Abstract

The use of irrigation scheduling tools to produce cotton under-surface irrigation in the arid southwestern USA is minimal. In the State of Arizona, where traditional irrigation scheduling is the norm, producers use an average of 1460mm annually to grow a cotton crop. The purpose of this paper was to determine whether or not the use of ET-based irrigation scheduling methods could improve lint yield and irrigation water use productivity over traditional cotton border irrigation scheduling practices in the region. A field study with four irrigation scheduling treatments replicated in 4 blocks was conducted for two cotton seasons (2009 and 2011) in 16, 12-m×168-m cotton borders at the Maricopa Agricultural Center (MAC), in Arizona, USA. Remotely-sensed vegetation indices (VI) were used to estimate basal crop coefficients (Kcb) at 40, 4-m×8-m zones within borders for two treatments, denoted as VI_A and VI_B, whereas a single Kcb curve was applied to all zones in borders for a third treatment (FAO). Daily ETc for these three treatments was estimated using FAO-56 dual crop coefficient procedures with local weather data and irrigation scheduling for the three treatments were based on soil water balance predictions of soil water depletion (SWD). For the VI_A and FAO treatments, irrigations were given when predicted SWD of all 160 zones in the treatment averaged 45% of total available water (TAW). For the VI_B treatment, irrigations were given when 5% of the 160 zones in the treatment were predicted to be at 65% SWD. A fourth treatment (MAC) represented the traditional irrigation scheduling treatment and was scheduled solely by the MAC farm irrigation manager using only experience as a guide. The study showed that the lint yields attained under the MAC farm manager’s irrigation scheduling equaled or exceeded the yields for the three ET-based irrigation scheduling treatments. Although the MAC irrigation scheduling resulted in somewhat higher irrigation input than for the other treatments, the MAC treatment maintained or exceeded the irrigation water productivity attained for other treatments that had lower irrigation inputs. A major conclusion of the study was that present-day irrigation water use for cotton in surface-irrigated fields could be substantially reduced. When compared to Arizona state cotton averages, any of the four treatments presented in the study could potentially offer methods to significantly reduce cotton irrigation water use while maintaining or increasing current lint yields levels.

Suggested Citation

  • Hunsaker, D.J. & French, A.N. & Waller, P.M. & Bautista, E. & Thorp, K.R. & Bronson, K.F. & Andrade-Sanchez, P., 2015. "Comparison of traditional and ET-based irrigation scheduling of surface-irrigated cotton in the arid southwestern USA," Agricultural Water Management, Elsevier, vol. 159(C), pages 209-224.
  • Handle: RePEc:eee:agiwat:v:159:y:2015:i:c:p:209-224
    DOI: 10.1016/j.agwat.2015.06.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377415300317
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2015.06.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Bautista, E. & Clemmens, A.J. & Strelkoff, T.S. & Schlegel, J., 2009. "Modern analysis of surface irrigation systems with WinSRFR," Agricultural Water Management, Elsevier, vol. 96(7), pages 1146-1154, July.
    2. Schaible, Glenn D. & Aillery, Marcel P., 2012. "Water Conservation in Irrigated Agriculture: Trends and Challenges in the Face of Emerging Demands," Economic Information Bulletin 134692, United States Department of Agriculture, Economic Research Service.
    3. Jayanthi, Harikishan & Neale, Christopher M.U. & Wright, James L., 2007. "Development and validation of canopy reflectance-based crop coefficient for potato," Agricultural Water Management, Elsevier, vol. 88(1-3), pages 235-246, March.
    4. Pereira, Luis S. & Cordery, Ian & Iacovides, Iacovos, 2012. "Improved indicators of water use performance and productivity for sustainable water conservation and saving," Agricultural Water Management, Elsevier, vol. 108(C), pages 39-51.
    5. Hedley, C.B. & Yule, I.J., 2009. "A method for spatial prediction of daily soil water status for precise irrigation scheduling," Agricultural Water Management, Elsevier, vol. 96(12), pages 1737-1745, December.
    6. Hunsaker, D. J. & Clemmens, A. J. & Fangmeier, D. D., 1998. "Cotton response to high frequency surface irrigation," Agricultural Water Management, Elsevier, vol. 37(1), pages 55-74, June.
    7. Pereira, L.S. & Paredes, P. & Sholpankulov, E.D. & Inchenkova, O.P. & Teodoro, P.R. & Horst, M.G., 2009. "Irrigation scheduling strategies for cotton to cope with water scarcity in the Fergana Valley, Central Asia," Agricultural Water Management, Elsevier, vol. 96(5), pages 723-735, May.
    8. Evett, Steven R. & Schwartz, Robert C. & Casanova, Joaquin J. & Heng, Lee K., 2012. "Soil water sensing for water balance, ET and WUE," Agricultural Water Management, Elsevier, vol. 104(C), pages 1-9.
    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. Jamali, Mohsen & Soufizadeh, Saeid & Yeganeh, Bijan & Emam, Yahya, 2021. "A comparative study of irrigation techniques for energy flow and greenhouse gas (GHG) emissions in wheat agroecosystems under contrasting environments in south of Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    2. Brar, Harjeet Singh & Singh, Pritpal, 2022. "Pre-and post-sowing irrigation scheduling impacts on crop phenology and water productivity of cotton (Gossypium hirsutum L.) in sub-tropical north-western India," Agricultural Water Management, Elsevier, vol. 274(C).
    3. Galioto, Francesco & Battilani, Adriano, 2021. "Agro-economic simulation for day by day irrigation scheduling optimisation," Agricultural Water Management, Elsevier, vol. 248(C).
    4. Fan, Yubing & Himanshu, Sushil K. & Ale, Srinivasulu & DeLaune, Paul B. & Zhang, Tian & Park, Seong C. & Colaizzi, Paul D. & Evett, Steven R. & Baumhardt, R. Louis, 2022. "The synergy between water conservation and economic profitability of adopting alternative irrigation systems for cotton production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 262(C).
    5. Liu, Qi & Niu, Jun & Wood, Jeffrey D. & Kang, Shaozhong, 2022. "Spatial optimization of cropping pattern in the upper-middle reaches of the Heihe River basin, Northwest China," Agricultural Water Management, Elsevier, vol. 264(C).
    6. Thorp, K.R. & Thompson, A.L. & Bronson, K.F., 2020. "Irrigation rate and timing effects on Arizona cotton yield, water productivity, and fiber quality," Agricultural Water Management, Elsevier, vol. 234(C).
    7. Fan, Yubing & McCann, Laura M., 2017. "Farmers’ Adoption of Pressure Irrigation Systems and Scientific Scheduling Practices: An Application of Multilevel Models," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258458, Agricultural and Applied Economics Association.
    8. Himanshu, Sushil Kumar & Fan, Yubing & Ale, Srinivasulu & Bordovsky, James, 2021. "Simulated efficient growth-stage-based deficit irrigation strategies for maximizing cotton yield, crop water productivity and net returns," Agricultural Water Management, Elsevier, vol. 250(C).
    9. Hunsaker, D.J. & Bronson, K.F., 2021. "FAO56 crop and water stress coefficients for cotton using subsurface drip irrigation in an arid US climate," Agricultural Water Management, Elsevier, vol. 252(C).

    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. Darouich, Hanaa & Gonçalves, José M. & Muga, André & Pereira, Luis S., 2012. "Water saving vs. farm economics in cotton surface irrigation: An application of multicriteria analysis," Agricultural Water Management, Elsevier, vol. 115(C), pages 223-231.
    2. Darouich, Hanaa & Karfoul, Razan & Ramos, Tiago B. & Moustafa, Ali & Shaheen, Baraa & Pereira, Luis S., 2021. "Crop water requirements and crop coefficients for jute mallow (Corchorus olitorius L.) using the SIMDualKc model and assessing irrigation strategies for the Syrian Akkar region," Agricultural Water Management, Elsevier, vol. 255(C).
    3. Manel Ben Hassen & Federica Monaco & Arianna Facchi & Marco Romani & Giampiero Valè & Guido Sali, 2017. "Economic Performance of Traditional and Modern Rice Varieties under Different Water Management Systems," Sustainability, MDPI, vol. 9(3), pages 1-10, February.
    4. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).
    5. Brar, Harjeet Singh & Singh, Pritpal, 2022. "Pre-and post-sowing irrigation scheduling impacts on crop phenology and water productivity of cotton (Gossypium hirsutum L.) in sub-tropical north-western India," Agricultural Water Management, Elsevier, vol. 274(C).
    6. Himanshu, Sushil Kumar & Fan, Yubing & Ale, Srinivasulu & Bordovsky, James, 2021. "Simulated efficient growth-stage-based deficit irrigation strategies for maximizing cotton yield, crop water productivity and net returns," Agricultural Water Management, Elsevier, vol. 250(C).
    7. Mohammadi, Adel & Besharat, Sina & Abbasi, Fariborz, 2019. "Effects of irrigation and fertilization management on reducing nitrogen losses and increasing corn yield under furrow irrigation," Agricultural Water Management, Elsevier, vol. 213(C), pages 1116-1129.
    8. Fan, Yubing & Massey, Raymond E. & Park, Seong C., 2017. "Multicrop Production Decisions and Economic Irrigation Water Use Efficiency: Effects of Water Costs, Pressure Irrigation Adoption and Climate Determinants," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258561, Agricultural and Applied Economics Association.
    9. Serra-Wittling, Claire & Molle, Bruno & Cheviron, Bruno, 2019. "Plot level assessment of irrigation water savings due to the shift from sprinkler to localized irrigation systems or to the use of soil hydric status probes. Application in the French context," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    10. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Quanzhong & Xiong, Yunwu & Huo, Zailin & Huang, Guanhua, 2021. "A comprehensive analysis of water productivity in natural vegetation and various crops coexistent agro-ecosystems," Agricultural Water Management, Elsevier, vol. 243(C).
    11. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).
    12. Asci, Serhat & Borisova, Tatiana & VanSickle, John J., 2015. "Role of economics in developing fertilizer best management practices," Agricultural Water Management, Elsevier, vol. 152(C), pages 251-261.
    13. Al Zayed, Islam Sabry & Elagib, Nadir Ahmed & Ribbe, Lars & Heinrich, Jürgen, 2016. "Satellite-based evapotranspiration over Gezira Irrigation Scheme, Sudan: A comparative study," Agricultural Water Management, Elsevier, vol. 177(C), pages 66-76.
    14. Andarzian, B. & Bannayan, M. & Steduto, P. & Mazraeh, H. & Barati, M.E. & Barati, M.A. & Rahnama, A., 2011. "Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran," Agricultural Water Management, Elsevier, vol. 100(1), pages 1-8.
    15. Himanshu, Sushil Kumar & Ale, Srinivasulu & Bordovsky, James & Darapuneni, Murali, 2019. "Evaluation of crop-growth-stage-based deficit irrigation strategies for cotton production in the Southern High Plains," Agricultural Water Management, Elsevier, vol. 225(C).
    16. Cameira, Maria do Rosário & Rodrigo, Isabel & Garção, Andreia & Neves, Manuela & Ferreira, Antónia & Paredes, Paula, 2024. "Linking participatory approach and rapid appraisal methods to select potential innovations in collective irrigation systems," Agricultural Water Management, Elsevier, vol. 299(C).
    17. Zwart, Sander J. & Bastiaanssen, Wim G. M., 2004. "Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize," Agricultural Water Management, Elsevier, vol. 69(2), pages 115-133, September.
    18. Gonçalves, Ivo Zution & Mekonnen, Mesfin M. & Neale, Christopher M.U. & Campos, Isidro & Neale, Michael R., 2020. "Temporal and spatial variations of irrigation water use for commercial corn fields in Central Nebraska," Agricultural Water Management, Elsevier, vol. 228(C).
    19. Gao, Yang & Yang, Linlin & Shen, Xiaojun & Li, Xinqiang & Sun, Jingsheng & Duan, Aiwang & Wu, Laosheng, 2014. "Winter wheat with subsurface drip irrigation (SDI): Crop coefficients, water-use estimates, and effects of SDI on grain yield and water use efficiency," Agricultural Water Management, Elsevier, vol. 146(C), pages 1-10.
    20. El-Naggar, A.G. & Hedley, C.B. & Horne, D. & Roudier, P. & Clothier, B.E., 2020. "Soil sensing technology improves application of irrigation water," Agricultural Water Management, Elsevier, vol. 228(C).

    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:159:y:2015:i:c:p:209-224. 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.