Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Vol. 12 No. sp4 (2025): Recent Advances in Agriculture by Young Minds - III

Quantitative assessment of cotton evapotranspiration, irrigation requirements and water productivity from weather-based estimations

DOI
https://doi.org/10.14719/pst.11843
Submitted
18 September 2025
Published
19-11-2025

Abstract

This study investigates the water requirements, evapotranspiration dynamics and water productivity of cotton (CO 17) under semi-arid climatic conditions across three cropping seasons (2023-2025) at Agricultural Research Station, Bhavanisagar. Field experiments were conducted on sandy loam soils with continuous monitoring of weather, soil moisture and crop parameters. The standardized FAO Penman-Monteith equation was used to compute daily reference evapotranspiration (ETo), while actual crop evapotranspiration (ETc) was estimated using both locally developed and FAO-based crop coefficient (Kc) curves derived as functions of thermal units. Seasonal ETc ranged from 386 to 607 mm and irrigation applied varied between 170 and 336 mm. Seed cotton yield ranged from 1997 to 2237 kg/ha and lint yield from 678.8 to 761.7 kg/ha, with corresponding water use efficiency (WUE) values between 0.107 and 0.155 kg/m³ and water productivity (WP) from 0.32 to 0.46 kg/m³. Statistical analyses, including paired t-tests and regression, confirmed no significant difference (p > 0.05) between local and FAO-based ETc estimates, while a strong linear relationship (R² = 0.93) was observed between adjusted ETc and irrigation applied. The locally calibrated Kc improved ETc estimation by better representing canopy development and climatic variability. Precisely scheduled irrigation and site-specific Kc calibration enhanced yield, water productivity and resource-use efficiency, emphasizing the importance of ET-based irrigation management for sustainable cotton production in water-limited environments.

References

  1. 1. Aslam S, Khan SH, Ahmed A, Dandekar AM. The tale of cotton plant: from wild type to domestication, leading to its improvement by genetic transformation. Am J Mol Biol. 2020;10(2):91-127. https://doi.org/10.4236/ajmb.2020.102008
  2. 2. Krifa M, Stevens SS. Cotton utilization in conventional and non-conventional textiles-a statistical review. Agric Sci. 2016;7(10):747-58. https://doi.org/10.4236/as.2016.710069
  3. 3. Angamuthu R. A study on growth of cotton industry in Tamil Nadu. J Bus Manag. 2018;20(6):1-6.
  4. 4. U.S. Department of Agriculture. Cotton & wool outlook: CWS-25h. Washington (DC): USDA; 2025.
  5. 5. U.S. Department of Agriculture, Foreign Agricultural Service. Production: Commodity - Cotton. Washington (DC): USDA; 2025.
  6. 6. Organisation for Economic Co-operation and Development, Food and Agriculture Organization of the United Nations. Agricultural outlook 2025-2034: Cotton (full report). Paris: OECD; Rome: FAO; 2025.
  7. 7. U.S. Department of Agriculture, Foreign Agricultural Service. Cotton: PSD online circular. Washington (DC): USDA; 2025.
  8. 8. IndiaStat. IndiaStat: data & statistics on India. 2023-2024.
  9. 9. Food and Agriculture Organization of the United Nations. FAOSTAT: Food and Agriculture Organization statistical database. Rome: FAO; 2025.
  10. 10. Madhavan G, Vigneshwari R, Hemalatha B, Veerasiva G. Sustainable water management practices. In: Agronomy vistas: emerging trends in crop science. Vol. 1. Coimbatore: Stella International Publication; 2024. p. 178-204.
  11. 11. Jans Y, von Bloh W, Schaphoff S, Müller C. Global cotton production under climate change - implications for yield and water consumption. Hydrol Earth Syst Sci. 2021;25(4):2027-44. https://doi.org/10.5194/hess-25-2027-2021
  12. 12. Madhavan G, Pazhanivelan S, Madhupriya SK, Akchaya K. The impact of climate change on agricultural productivity. In: Comprehensive insights into environmental science. Vol. 1. 1st ed. Coimbatore: Stella International Publication; 2024. p. 1-25.
  13. 13. Orke YA, Shanka AS. Analyzing the impact of climate change on crop water demand in the semi-arid Bilate watershed, Ethiopia. Adv Meteorol. 2025;2025(1):9466355. https://doi.org/10.1155/adme/9466355
  14. 14. Koudahe K, Sheshukov AY, Aguilar J, Djaman K. Irrigation-water management and productivity of cotton: a review. Sustainability. 2021;13:10070. https://doi.org/10.3390/su131810070
  15. 15. Xu M, Wu H, Chen X. Impacts of climate changes on spatiotemporal variation of cotton water requirement and irrigation in Tarim Basin, Central Asia. Plants. 2024;13(22):3234. https://doi.org/10.3390/plants13223234
  16. 16. Raguramakrishnan M, Pazhanivelan S, Raju M, Kumaraperumal R, Ravi KV, Senthil A. Assessment of maize evapotranspiration, water requirements and productivity using weather data in Coimbatore's semiarid climate. Plant Sci Today. 2024;11:5246. https://doi.org/10.14719/pst.5246
  17. 17. Hussain S, Mubeen M, Nasim W, Fahad S, Ali M, Ehsan MA, et al. Investigation of irrigation water requirement and evapotranspiration for water resource management in Southern Punjab, Pakistan. Sustainability. 2023;15(3):1768. https://doi.org/10.3390/su15031768
  18. 18. Wang T, Sun S, Yin Y, Zhao J, Tang Y, Wang Y, et al. Status of crop water use efficiency evaluation methods: a review. Agric For Meteorol. 2024;349:109961. https://doi.org/10.1016/j.agrformet.2024.109961
  19. 19. Niu Y, Zhang K, Khan KS, Fudjoe SK, Li L, Wang L, et al. Deficit irrigation as an effective way to increase potato water use efficiency in Northern China: a meta-analysis. Agronomy. 2024;14(7):1533.https://doi.org/10.3390/agronomy14071533
  20. 20. Djaman K, O'Neill M, Owen CK, Smeal D, Koudahe K, West M, et al. Crop evapotranspiration, irrigation water requirement and water productivity of maize from meteorological data under semiarid climate. Water. 2018;10(4):405. https://doi.org/10.3390/w10040405
  21. 21. Pazhanivelan S, Kumaraperumal R, Shanmugapriya P, Sudarmanian NS, Sivamurugan AP, Satheesh S. Quantification of biophysical parameters and economic yield in cotton and rice using drone technology. Agriculture. 2023;13(9):1668. https://doi.org/10.3390/agriculture13091668
  22. 22. Pavithran P, Pazhanivelan S, Sivamurugan A, Ragunath K, Selvakumar S, Vanitha K, et al. Long-term analysis of reference evapotranspiration variations in the lower Bhavani basin. Plant Sci Today. 2024;11(4).https://doi.org/10.14719/pst.5119
  23. 23. Leib B, Payero J, Pringle L, Bordovsky J, Porter W, Barnes E. Placement and interpretation of soil moisture sensors for irrigated cotton production in humid regions. Cotton Incorporated; 2015.
  24. 24. Environmental and Water Resources Institute (EWRI) of the American Society of Civil Engineers. The ASCE standardized reference evapotranspiration equation. In: Allen RG, Walter IA, Elliot RL, editors. Standardization of reference evapotranspiration task committee final report. Reston (VA): American Society of Civil Engineers; 2005. p. 213.
  25. 25. Allen RG, Pereira LS, Raes D, Smith M. Crop evapotranspiration-guidelines for computing crop water requirements. FAO irrigation and drainage paper 56. Rome: Food and Agriculture Organization of the United Nations; 1998. p. 300.
  26. 26. Awal R, Fares A, Bayabil H. Assessing potential climate change impacts on irrigation requirements of major crops in the Brazos headwaters basin, Texas. Water. 2018;10(11):1610. https://doi.org/10.3390/w10111610
  27. 27. Tennakoon SB, Milroy SP. Crop water use and water use efficiency on irrigated cotton farms in Australia. Agric Water Manag. 2003;61(3):179-94. https://doi.org/10.1016/S0378-3774(03)00023-4
  28. 28. Howell TA. Enhancing water use efficiency in irrigated agriculture. Agron J. 2001;93:281-9. https://doi.org/10.2134/agronj2001.932281x
  29. 29. Khalilian A, Han Y, Barnes EM, Henderson W. Water use efficiency of different cotton cultivars. In: Proceedings of the Beltwide Cotton Conferences; 2012 Jan; Nashville, TN. Memphis (TN): National Cotton Council of America; 2012. p. 3-6.
  30. 30. Gebremariam FT, Habtu S, Yazew E, Teklu B. The water footprint of irrigation-supplemented cotton and mung-bean crops in Northern Ethiopia. Heliyon. 2021;7(4):e06792. https://doi.org/10.1016/j.heliyon.2021.e06822
  31. 31. Kumar R, Mishra SK, Singh K, Al-Ashkar I, Iqbal MA, Muzamil MN, et al. Impact analysis of moisture stress on growth and yield of cotton using DSSAT-CROPGRO-cotton model under semi-arid climate. PeerJ. 2023;11:e16329. https://doi.org/10.7717/peerj.16329
  32. 32. Bhargavi B, Blaise D, Nalayini P. Water footprint, runoff and soil loss assessment of cotton production in central India. Clean Water. 2025;100119. https://doi.org/10.1016/j.clwat.2025.100119
  33. 33. Suleiman AA, Soler CMT, Hoogenboom G. Evaluation of FAO-56 crop coefficient procedures for deficit irrigation management of cotton in a humid climate. Agric Water Manag. 2007;91(1-3):33-42. https://doi.org/10.1016/j.agwat.2007.03.006
  34. 34. Datta A, Ullah H, Ferdous Z, Santiago-Arenas R, Attia A. Water management in cotton. In: Cotton production. 2019. p. 47-59. https://doi.org/10.1002/9781119385523.ch3
  35. 35. Kakani VG, Reddy KR, Zhao D. Deriving a simple spectral reflectance ratio to determine cotton leaf water potential. J New Seeds. 2006;8(3):11-27. https://doi.org/10.1300/J153v08n03_02
  36. 36. Teshome FT, Bayabil HK, Schaffer B, Ampatzidis Y, Hoogenboom G, Singh A. Crop growth model and machine learning approaches for simulating soil hydrology dynamics. SSRN [Preprint]. 2024. https://doi.org/10.2139/ssrn.4725156
  37. 37. Manibharathi S, Somasundaram S, Parasuraman P, Subramanian A, Ravichandran V, Manikanda Boopathi N. Exploring the impact of high density planting system and deficit irrigation in cotton (Gossypium hirsutum L.): a comprehensive review. J Cotton Res. 2024;7(1):28. https://doi.org/10.1186/s42397-024-00190-1
  38. 38. Sainath BR, Patil RS, Nayak SN, MP BP, Aishwarya B, Akshaya M. Correlation and path analysis studies for yield and yield attributes in recombinant inbred lines in cotton (Gossypium hirsutum L.). Biol Forum Int J. 2022;14(4):539-42.
  39. 39. Lope MF, Elam EW. Estimating seed yield using lint yield and lint percentage of seed cotton. In: Proceedings of the NCR-134 Conference on Applied Commodity Price Analysis, Forecasting and Market Risk Management; 1996; Chicago, IL. Chicago (IL): NCR-134 Conference; 1996. p. 486.
  40. 40. Reddy KR, Doma PR, Mearns LO, Boone MY, Hodges HF, Richardson AG, et al. Simulating the impacts of climate change on cotton production in the Mississippi Delta. Clim Res. 2002;22(3):271-81. https://doi.org/10.3354/cr022271
  41. 41. Pandya PA, Ghosiya SM, Pithiya VH, Dudhatra SP. Effect of rainfall on productivity of cotton. Emerg Life Sci Res. 2020;6:56-63.https://doi.org/10.31783/elsr.2020.615663
  42. 42. Sharma V, Bhambota S. Strategies to improve crop-water productivity. In: Ray C, Muddu S, Sharma S, editors. Food, energy and water nexus. Cham: Springer; 2022. p. 143-62. https://doi.org/10.1007/978-3-030-85728-8_8
  43. 43. Hatfield JL, Dold C. Water-use efficiency: advances and challenges in a changing climate. Front Plant Sci. 2019;10:103. https://doi.org/10.3389/fpls.2019.00103
  44. 44. Luo Q, Bange M, Johnston D, Braunack M. Cotton crop water use and water use efficiency in a changing climate. Agric Ecosyst Environ. 2015;202:126-34. https://doi.org/10.1016/j.agee.2015.01.006
  45. 45. Fan Y, Massey R, Park SC. Multi-crop production decisions and economic irrigation water use efficiency: the effects of water costs, pressure irrigation adoption and climatic determinants. Water. 2018;10(11):1637. https://doi.org/10.3390/w10111637
  46. 46. Zheng Z, Wei X, Wang L, Guo R, Xu H. Design of DEA water efficiency evaluation model for cotton field irrigation in Xinjiang. Water Supply. 2023;23(3):1519-30. https://doi.org/10.2166/ws.2023.077
  47. 47. Dong Z, Wan S, Ma Y, Wang J, Feng L, Zhai Y, et al. Productivity of water and heat resources and cotton yield response to cropping pattern and planting density in cotton fields in arid area. Agric Water Manag. 2025;307:109197. https://doi.org/10.1016/j.agwat.2024.109197
  48. 48. Li JH, Wang YY, Li NN, Zhao RH, Khan A, Wang J, et al. Cotton leaf photosynthetic characteristics, biomass production and their correlation analysis under different irrigation and phosphorus application. Photosynthetica. 2019;57(4):1030-40. https://doi.org/10.32615/ps.2019.118
  49. 49. Wang Y, Li G, Guo X, Sun R, Dong T, Yang Q, et al. Dissecting the genetic architecture of seed-cotton and lint yields in Upland cotton using genome-wide association mapping. Breed Sci. 2019;69(4):611-20. https://doi.org/10.1270/jsbbs.19057
  50. 50. Ahmad HS, Imran M, Ahmad F, Rukh S, Ikram RM, Rafique HM, et al. Improving water use efficiency through reduced irrigation for sustainable cotton production. Sustainability. 2021;13(7):4044. https://doi.org/10.3390/su13074044
  51. 51. Chen Z, Zhu Z, Jiang H, Sun S. Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. J Hydrol. 2020;591:125286.https://doi.org/10.1016/j.jhydrol.2020.125286
  52. 52. Santos PABD, Schwerz F, Carvalho LGD, Baptista VBDS, Marin DB, Ferraz GAES, et al. Machine learning and conventional methods for reference evapotranspiration estimation using limited-climatic-data scenarios. Agronomy. 2023;13(9):2366. https://doi.org/10.3390/agronomy13092366
  53. 53. Rahman MHU, Ahmad I, Ghaffar A, Haider G, Ahmad A, Ahmad B, et al. Climate resilient cotton production system: a case study in Pakistan. In: Cotton production and uses: agronomy, crop protection and postharvest technologies. Singapore: Springer Singapore; 2020. p. 447-84. https://doi.org/10.1007/978-981-15-1472-2_22
  54. 54. Shwetha MN, Devi IS, Lavanya T, Suhasini K, Meena A. Growth in area, production and productivity of cotton crop in India: a state wise analysis. Int J Environ Clim Change. 2022;12(11):51-7. https://doi.org/10.9734/ijecc/2022/v12i1130944
  55. 55. Ben-Gal A, Barski A, Bukris O, Yasuor H, O'Shaughnessy SA, Hansen NC, et al. Dynamic approaches to precision irrigation of cotton. Precis Agric. 2025;26(4):64. https://doi.org/10.1007/s11119-025-10261-1

Downloads

Download data is not yet available.