IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v277y2023ics037837742200645x.html
   My bibliography  Save this article

Field evaluation of automated site-specific irrigation for cotton and perennial ryegrass using soil-water sensors and Model Predictive Control

Author

Listed:
  • McCarthy, Alison
  • Foley, Joseph
  • Raedts, Pieter
  • Hills, James

Abstract

Variable-rate irrigation technology can reduce water use in centre pivot and lateral move irrigation systems through application of irrigation according to spatially varied soil-water profiles. However, filling the profile may not maximise yield because of variations in crop response and water requirements with crop stage. For example, cotton crops produce optimal yield under slight water stress during early stages. An irrigation strategy ‘Model Predictive Control’ has been implemented that accounts for changes in crop water requirements at different growth stages using biophysical crop models. This strategy involves automatically and iteratively executing the biophysical crop model APSIM parameterised with local soil and weather information, with different irrigation depths, to identify which combination maximises yield with the minimum depth of water application. This strategy has potential to address spatial and temporal variations in crop water requirements but has not previously been evaluated for variable-rate irrigation in the field. This paper reports field trials conducted over four cotton (Gossypium hirsutum L.) seasons and two perennial ryegrass (Lolium. perenne L.) seasons to evaluate the accuracy of the yield prediction of the biophysical model and compare field performance of irrigation strategies: uniform irrigation and variable-rate irrigation using a fixed underlying map, soil-water sensors and Model Predictive Control. Yield was most accurately predicted using on-site weather data and field soil core information, with R² = 0.733 and RMSE = 153.9 kg/ha for cotton, and R² = 0.336 and RMSE = 295.3 kg/ha for ryegrass. For cotton, Model Predictive Control led to 4.9% more yield with 5.6% reduction in water application, mainly through reduced water after peak bloom and/or open boll physiological stages. For grazed ryegrass, the Model Predictive Control strategy led to 8.5% more yield with 5.4% reduction in water application, potentially caused by reduced applications after grazing events. Further work includes evaluating the Model Predictive Control strategy with control of irrigation event timing under a broader range of field conditions to identify parameters to provide greatest economic return, and to refine biophysical models for improved performance of optimisation in the strategy.

Suggested Citation

  • McCarthy, Alison & Foley, Joseph & Raedts, Pieter & Hills, James, 2023. "Field evaluation of automated site-specific irrigation for cotton and perennial ryegrass using soil-water sensors and Model Predictive Control," Agricultural Water Management, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:agiwat:v:277:y:2023:i:c:s037837742200645x
    DOI: 10.1016/j.agwat.2022.108098
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037837742200645X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.108098?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. O’Shaughnessy, Susan A. & Kim, Minyoung & Andrade, Manuel A. & Colaizzi, Paul D. & Evett, Steven R., 2020. "Site-specific irrigation of grain sorghum using plant and soil water sensing feedback - Texas High Plains," Agricultural Water Management, Elsevier, vol. 240(C).
    2. 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).
    3. Sharma, Vasudha & Irmak, Suat, 2020. "Economic comparisons of variable rate irrigation and fertigation with fixed (uniform) rate irrigation and fertigation and pre-plant fertilizer management for maize in three soils," Agricultural Water Management, Elsevier, vol. 240(C).
    4. Veysi, Shadman & Naseri, Abd Ali & Hamzeh, Saeid & Bartholomeus, Harm, 2017. "A satellite based crop water stress index for irrigation scheduling in sugarcane fields," Agricultural Water Management, Elsevier, vol. 189(C), pages 70-86.
    5. Kelly, T.D. & Foster, T. & Schultz, David M., 2023. "Assessing the value of adapting irrigation strategies within the season," Agricultural Water Management, Elsevier, vol. 275(C).
    6. Wells, A.T. & Hearn, A.B., 1992. "OZCOT: A cotton crop simulation model for management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 33(5), pages 433-438.
    7. Milroy, S. P. & Bange, M. P. & Hearn, A. B., 2004. "Row configuration in rainfed cotton systems: modification of the OZCOT simulation model," Agricultural Systems, Elsevier, vol. 82(1), pages 1-16, October.
    8. Hedley, Carolyn & Bradbury, Stu & Watson, Eric & Dalrymple, Hew & Wright, John, 2011. "Farm scale trials of variable rate irrigation to assess the benefits of modifying existing sprinkler systems for precision application," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 1(2), pages 1-5.
    9. Barker, J. Burdette & Heeren, Derek M. & Neale, Christopher M.U. & Rudnick, Daran R., 2018. "Evaluation of variable rate irrigation using a remote-sensing-based model," Agricultural Water Management, Elsevier, vol. 203(C), pages 63-74.
    10. Vogeler, Iris & Thomas, Steve & van der Weerden, Tony, 2019. "Effect of irrigation management on pasture yield and nitrogen losses," Agricultural Water Management, Elsevier, vol. 216(C), pages 60-69.
    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. Zhang, Junwei & Xiang, Lingxiao & Liu, Yuxin & Jing, Dan & Zhang, Lili & Liu, Yong & Li, Wuqiang & Wang, Xiaoyan & Li, Tianlai & Li, Jianming, 2024. "Optimizing irrigation schedules of greenhouse tomato based on a comprehensive evaluation model," Agricultural Water Management, Elsevier, vol. 295(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. Bhatti, Sandeep & Heeren, Derek M. & Evett, Steven R. & O’Shaughnessy, Susan A. & Rudnick, Daran R. & Franz, Trenton E. & Ge, Yufeng & Neale, Christopher M.U., 2022. "Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Hui, Xin & Lin, Xueji & Zhao, Yue & Xue, Mengyun & Zhuo, Yue & Guo, Hui & Xu, Yuncheng & Yan, Haijun, 2022. "Assessing water distribution characteristics of a variable-rate irrigation system," Agricultural Water Management, Elsevier, vol. 260(C).
    3. O’Shaughnessy, Susan A. & Kim, Minyoung & Andrade, Manuel A. & Colaizzi, Paul D. & Evett, Steven R., 2020. "Site-specific irrigation of grain sorghum using plant and soil water sensing feedback - Texas High Plains," Agricultural Water Management, Elsevier, vol. 240(C).
    4. 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).
    5. Nicole Meinusch & Susanne Kramer & Oliver Körner & Jürgen Wiese & Ingolf Seick & Anita Beblek & Regine Berges & Bernhard Illenberger & Marco Illenberger & Jennifer Uebbing & Maximilian Wolf & Gunter S, 2021. "Integrated Cycles for Urban Biomass as a Strategy to Promote a CO 2 -Neutral Society—A Feasibility Study," Sustainability, MDPI, vol. 13(17), pages 1-22, August.
    6. França, Ana Carolina Ferreira & Coelho, Rubens Duarte & da Silva Gundim, Alice & de Oliveira Costa, Jéfferson & Quiloango-Chimarro, Carlos Alberto, 2024. "Effects of different irrigation scheduling methods on physiology, yield, and irrigation water productivity of soybean varieties," Agricultural Water Management, Elsevier, vol. 293(C).
    7. Srinivasan, M.S. & Measures, Richard & Muller, Carla & Neal, Mark & Rajanayaka, Channa & Shankar, Ude & Elley, Graham, 2021. "Comparing the water use metrics of just-in-case, just-in-time and justified irrigation strategies using a scenario-based tool," Agricultural Water Management, Elsevier, vol. 258(C).
    8. Katimbo, Abia & Rudnick, Daran R. & Liang, Wei-zhen & DeJonge, Kendall C. & Lo, Tsz Him & Franz, Trenton E. & Ge, Yufeng & Qiao, Xin & Kabenge, Isa & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions," Agricultural Water Management, Elsevier, vol. 274(C).
    9. Luo, Qunying & Bange, Michael & Braunack, Michael & Johnston, David, 2016. "Effectiveness of agronomic practices in dealing with climate change impacts in the Australian cotton industry — A simulation study," Agricultural Systems, Elsevier, vol. 147(C), pages 1-9.
    10. Lalehzari, Reza & Kerachian, Reza, 2020. "Developing a framework for daily common pool groundwater allocation to demands in agricultural regions," Agricultural Water Management, Elsevier, vol. 241(C).
    11. Lo, Tsz Him & Rudnick, Daran R. & Singh, Jasreman & Nakabuye, Hope Njuki & Katimbo, Abia & Heeren, Derek M. & Ge, Yufeng, 2020. "Field assessment of interreplicate variability from eight electromagnetic soil moisture sensors," Agricultural Water Management, Elsevier, vol. 231(C).
    12. Camille Rousset & Timothy J. Clough & Peter R. Grace & David W. Rowlings & Clemens Scheer, 2021. "Irrigation Scheduling with Soil Gas Diffusivity as a Decision Tool to Mitigate N 2 O Emissions from a Urine-Affected Pasture," Agriculture, MDPI, vol. 11(5), pages 1-15, May.
    13. Srinivasagan N. Subhashree & C. Igathinathane & Adnan Akyuz & Md. Borhan & John Hendrickson & David Archer & Mark Liebig & David Toledo & Kevin Sedivec & Scott Kronberg & Jonathan Halvorson, 2023. "Tools for Predicting Forage Growth in Rangelands and Economic Analyses—A Systematic Review," Agriculture, MDPI, vol. 13(2), pages 1-30, February.
    14. Li, Maona & Wang, Yunling & Guo, Hui & Ding, Feng & Yan, Haijun, 2023. "Evaluation of variable rate irrigation management in forage crops: Saving water and increasing water productivity," Agricultural Water Management, Elsevier, vol. 275(C).
    15. Mwinuka, Paul Reuben & Mbilinyi, Boniface P. & Mbungu, Winfred B. & Mourice, Sixbert K. & Mahoo, H.F. & Schmitter, Petra, 2021. "The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)," Agricultural Water Management, Elsevier, vol. 245(C).
    16. Umut Erdem & K. Mert Cubukcu & Ayyoob Sharifi, 2021. "An analysis of urban form factors driving Urban Heat Island: the case of Izmir," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 7835-7859, May.
    17. Lena, Bruno Patias & Bondesan, Luca & Pinheiro, Everton Alves Rodrigues & Ortiz, Brenda V. & Morata, Guilherme Trimer & Kumar, Hemendra, 2022. "Determination of irrigation scheduling thresholds based on HYDRUS-1D simulations of field capacity for multilayered agronomic soils in Alabama, USA," Agricultural Water Management, Elsevier, vol. 259(C).
    18. 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).
    19. Pôças, I. & Calera, A. & Campos, I. & Cunha, M., 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    20. Said A. Hamido & Kelly T. Morgan, 2021. "The Effect of Irrigation Rate on the Water Relations of Young Citrus Trees in High-Density Planting," Sustainability, MDPI, vol. 13(4), pages 1-18, February.

    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:277:y:2023:i:c:s037837742200645x. 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.