IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v280y2023ics0378377423000872.html
   My bibliography  Save this article

Evaluation of growth-stage-based variable deficit irrigation strategies for cotton production in the Texas High Plains

Author

Listed:
  • Himanshu, Sushil K.
  • Ale, Srinivasulu
  • Bell, Jourdan
  • Fan, Yubing
  • Samanta, Sayantan
  • Bordovsky, James P.
  • Gitz III, Dennis C.
  • Lascano, Robert J.
  • Brauer, David K.

Abstract

Irrigated agriculture in the Texas High Plains (THP) region faces severe challenges due to rapidly declining groundwater levels in the underlying Ogallala Aquifer, recurring droughts, and projected warmer and drier future climatic conditions. Scheduling irrigation with appropriate deficits in different crop growth stages could improve irrigation water use efficiency (IWUE), and thereby enable additional savings in valuable groundwater without severely compromising the crop yield. Our objective was to identify efficient growth-stage-based variable deficit-irrigation (GS-VDI) strategies for cotton production in the THP region. For this purpose, we used an evaluated Decision Support System Agrotechnology Transfer (DSSAT) CROPGRO-Cotton model based on measured data from a cotton IWUE field experiment conducted at Texas A&M AgriLife Research Center at Halfway, TX, in the THP region. This study considered four growth stages: (i) first leaf to first square (GS1), (ii) flower initiation/ early bloom (GS2), (iii) peak bloom (GS3), and (iv) cutout, late bloom, and boll opening stage (GS4). Long-term (1977 – 2019) simulations were conducted with four deficit levels (30%, 50%, 70%, and 90% evapotranspiration [ET] replacements) implemented in the above described four different growth stages, resulting in 256 combinations of deficit-irrigation scenarios. Based on the results of simulated seed cotton yield and IWUE, efficient GS-VDI strategies were suggested for dry, normal, and wet years. For example, a strategy of 90% ET-replacement in GS1 to GS3 and of 30% ET-replacement in GS4 was found to be an ideal strategy in normal years to achieve higher seed cotton yield (∼ 8% less than that for the baseline scenario with 100% ET-replacement implemented in all growth stages) while saving 65 mm of irrigation water. Results from this modeling study provide useful recommendations on appropriate irrigation management strategies for sustaining cotton production under different weather conditions while conserving valuable groundwater resources of the Ogallala Aquifer.

Suggested Citation

  • Himanshu, Sushil K. & Ale, Srinivasulu & Bell, Jourdan & Fan, Yubing & Samanta, Sayantan & Bordovsky, James P. & Gitz III, Dennis C. & Lascano, Robert J. & Brauer, David K., 2023. "Evaluation of growth-stage-based variable deficit irrigation strategies for cotton production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:agiwat:v:280:y:2023:i:c:s0378377423000872
    DOI: 10.1016/j.agwat.2023.108222
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377423000872
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2023.108222?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. 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).
    2. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2022. "Application of the WRF-DSSAT Modeling System for Assessment of the Nitrogen Fertilizer Used for Improving Rice Production in Northern Thailand," Agriculture, MDPI, vol. 12(8), pages 1-15, August.
    3. Guerra, L.C. & Garcia y Garcia, A. & Hook, J.E. & Harrison, K.A. & Thomas, D.L. & Stooksbury, D.E. & Hoogenboom, G., 2007. "Irrigation water use estimates based on crop simulation models and kriging," Agricultural Water Management, Elsevier, vol. 89(3), pages 199-207, May.
    4. Fan, Yubing & Wang, Chenggang & Nan, Zhibiao, 2018. "Determining water use efficiency of wheat and cotton: A meta-regression analysis," Agricultural Water Management, Elsevier, vol. 199(C), pages 48-60.
    5. Chen, Yong & Marek, Gary W. & Marek, Thomas H. & Porter, Dana O. & Brauer, David K. & Srinivasan, Raghavan, 2021. "Simulating the effects of agricultural production practices on water conservation and crop yields using an improved SWAT model in the Texas High Plains, USA," Agricultural Water Management, Elsevier, vol. 244(C).
    6. Zou, Yufeng & Saddique, Qaisar & Ali, Ajaz & Xu, Jiatun & Khan, Muhammad Imran & Qing, Mu & Azmat, Muhammad & Cai, Huanjie & Siddique, Kadambot H.M., 2021. "Deficit irrigation improves maize yield and water use efficiency in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 243(C).
    7. 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).
    8. Jalota, S.K. & Sood, Anil & Chahal, G.B.S. & Choudhury, B.U., 2006. "Crop water productivity of cotton (Gossypium hirsutum L.)-wheat (Triticum aestivum L.) system as influenced by deficit irrigation, soil texture and precipitation," Agricultural Water Management, Elsevier, vol. 84(1-2), pages 137-146, July.
    9. Cheng, Minghui & Wang, Haidong & Fan, Junliang & Zhang, Shaohui & Wang, Yanli & Li, Yuepeng & Sun, Xin & Yang, Ling & Zhang, Fucang, 2021. "Water productivity and seed cotton yield in response to deficit irrigation: A global meta-analysis," Agricultural Water Management, Elsevier, vol. 255(C).
    10. Li, Meng & Du, Yingji & Zhang, Fucang & Bai, Yungang & Fan, Junliang & Zhang, Jianghui & Chen, Shaoming, 2019. "Simulation of cotton growth and soil water content under film-mulched drip irrigation using modified CSM-CROPGRO-cotton model," Agricultural Water Management, Elsevier, vol. 218(C), pages 124-138.
    11. Islam, Adlul & Ahuja, Lajpat R. & Garcia, Luis A. & Ma, Liwang & Saseendran, Anapalli S. & Trout, Thomas J., 2012. "Modeling the impacts of climate change on irrigated corn production in the Central Great Plains," Agricultural Water Management, Elsevier, vol. 110(C), pages 94-108.
    12. Geneille E. Greaves & Yu-Min Wang, 2017. "Identifying Irrigation Strategies for Improved Agricultural Water Productivity in Irrigated Maize Production through Crop Simulation Modelling," Sustainability, MDPI, vol. 9(4), pages 1-17, April.
    13. 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).
    14. Aujla, M.S. & Thind, H.S. & Buttar, G.S., 2005. "Cotton yield and water use efficiency at various levels of water and N through drip irrigation under two methods of planting," Agricultural Water Management, Elsevier, vol. 71(2), pages 167-179, February.
    15. Kothari, Kritika & Ale, Srinivasulu & Bordovsky, James P. & Thorp, Kelly R. & Porter, Dana O. & Munster, Clyde L., 2019. "Simulation of efficient irrigation management strategies for grain sorghum production over different climate variability classes," Agricultural Systems, Elsevier, vol. 170(C), pages 49-62.
    16. Adhikari, Pradip & Ale, Srinivasulu & Bordovsky, James P. & Thorp, Kelly R. & Modala, Naga R. & Rajan, Nithya & Barnes, Edward M., 2016. "Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-Cotton model," Agricultural Water Management, Elsevier, vol. 164(P2), pages 317-330.
    17. Garibay, Victoria M. & Kothari, Kritika & Ale, Srinivasulu & Gitz, Dennis C. & Morgan, Gaylon D. & Munster, Clyde L., 2019. "Determining water-use-efficient irrigation strategies for cotton using the DSSAT CSM CROPGRO-cotton model evaluated with in-season data," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Cheng, Minghui & Wang, Haidong & Fan, Junliang & Zhang, Shaohui & Wang, Yanli & Li, Yuepeng & Sun, Xin & Yang, Ling & Zhang, Fucang, 2021. "Water productivity and seed cotton yield in response to deficit irrigation: A global meta-analysis," Agricultural Water Management, Elsevier, vol. 255(C).
    3. 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).
    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. 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. Desheng Wang & Chengkun Wang & Lichao Xu & Tiecheng Bai & Guozheng Yang, 2022. "Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang," Agriculture, MDPI, vol. 12(7), pages 1-20, June.
    7. Xiao, Chao & Ji, Qingyuan & Zhang, Fucang & Li, Yi & Fan, Junliang & Hou, Xianghao & Yan, Fulai & Liu, Xiaoqiang & Gong, Kaiyuan, 2023. "Effects of various soil water potential thresholds for drip irrigation on soil salinity, seed cotton yield and water productivity of cotton in northwest China," Agricultural Water Management, Elsevier, vol. 279(C).
    8. Wang, Haidong & Cheng, Minghui & Liao, Zhenqi & Guo, Jinjin & Zhang, Fucang & Fan, Junliang & Feng, Hao & Yang, Qiliang & Wu, Lifeng & Wang, Xiukang, 2023. "Performance evaluation of AquaCrop and DSSAT-SUBSTOR-Potato models in simulating potato growth, yield and water productivity under various drip fertigation regimes," Agricultural Water Management, Elsevier, vol. 276(C).
    9. Wang, Haidong & Wu, Lifeng & Wang, Xiukang & Zhang, Shaohui & Cheng, Minghui & Feng, Hao & Fan, Junliang & Zhang, Fucang & Xiang, Youzhen, 2021. "Optimization of water and fertilizer management improves yield, water, nitrogen, phosphorus and potassium uptake and use efficiency of cotton under drip fertigation," Agricultural Water Management, Elsevier, vol. 245(C).
    10. Garibay, Victoria M. & Kothari, Kritika & Ale, Srinivasulu & Gitz, Dennis C. & Morgan, Gaylon D. & Munster, Clyde L., 2019. "Determining water-use-efficient irrigation strategies for cotton using the DSSAT CSM CROPGRO-cotton model evaluated with in-season data," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    11. Chen, Xiaoping & Qi, Zhiming & Gui, Dongwei & Gu, Zhe & Ma, Liwang & Zeng, Fanjiang & Li, Lanhai, 2019. "Simulating impacts of climate change on cotton yield and water requirement using RZWQM2," Agricultural Water Management, Elsevier, vol. 222(C), pages 231-241.
    12. Ünlü, Mustafa & Kanber, RIza & Koç, D. Levent & Tekin, Servet & Kapur, Burçak, 2011. "Effects of deficit irrigation on the yield and yield components of drip irrigated cotton in a mediterranean environment," Agricultural Water Management, Elsevier, vol. 98(4), pages 597-605, February.
    13. Ko, Jonghan & Piccinni, Giovanni & Steglich, Evelyn, 2009. "Using EPIC model to manage irrigated cotton and maize," Agricultural Water Management, Elsevier, vol. 96(9), pages 1323-1331, September.
    14. Chen, Ning & Li, Xianyue & Shi, Haibin & Zhang, Yuehong & Hu, Qi & Sun, Ya’nan, 2023. "Modeling effects of biodegradable film mulching on evapotranspiration and crop yields in Inner Mongolia," Agricultural Water Management, Elsevier, vol. 275(C).
    15. Hou, Xianghao & Xiang, Youzhen & Fan, Junliang & Zhang, Fucang & Hu, Wenhui & Yan, Fulai & Guo, Jinjin & Xiao, Chao & Li, Yuepeng & Cheng, Houliang & Li, Zhijun, 2021. "Evaluation of cotton N nutrition status based on critical N dilution curve, N uptake and residual under different drip fertigation regimes in Southern Xinjiang of China," Agricultural Water Management, Elsevier, vol. 256(C).
    16. Komlan Koudahe & Aleksey Y. Sheshukov & Jonathan Aguilar & Koffi Djaman, 2021. "Irrigation-Water Management and Productivity of Cotton: A Review," Sustainability, MDPI, vol. 13(18), pages 1-21, September.
    17. Wang, Xiao-Ling & Duan, Pei-Ling & Yang, Shen-Jiao & Liu, Yu-Hua & Qi, Lin & Shi, Jiang & Li, Xue-Lin & Song, Peng & Zhang, Li-Xia, 2020. "Corn compensatory growth upon post-drought rewatering based on the effects of rhizosphere soil nitrification on cytokinin," Agricultural Water Management, Elsevier, vol. 241(C).
    18. Yunfeng Li & Quanqing Feng & Dongwei Li & Mingfa Li & Huifeng Ning & Qisheng Han & Abdoul Kader Mounkaila Hamani & Yang Gao & Jingsheng Sun, 2022. "Water-Salt Thresholds of Cotton ( Gossypium hirsutum L.) under Film Drip Irrigation in Arid Saline-Alkali Area," Agriculture, MDPI, vol. 12(11), pages 1-21, October.
    19. Brar, S.K. & Mahal, S.S. & Brar, A.S. & Vashist, K.K. & Sharma, Neerja & Buttar, G.S., 2012. "Transplanting time and seedling age affect water productivity, rice yield and quality in north-west India," Agricultural Water Management, Elsevier, vol. 115(C), pages 217-222.
    20. Liu, Yi & Zeng, Wenzhi & Ao, Chang & Lei, Guoqing & Wu, Jingwei & Huang, Jiesheng & Gaiser, Thomas & Srivastava, Amit Kumar, 2022. "Optimization of winter irrigation management for salinized farmland using a coupled model of soil water flow and crop growth," Agricultural Water Management, Elsevier, vol. 270(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:280:y:2023:i:c:s0378377423000872. 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.