Determining the Importance of Quinoa Growth Stages in Irrigation Management Using Multi-Criteria Decision-Making Theory

Document Type : Research Paper

Author

Researcher, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO)

Abstract

This study was conducted over two consecutive growing seasons at the research farm of the Soil and Water Research Institute in Karaj, Iran, to identify the most water-sensitive growth stages of quinoa for irrigation management. Crop evapotranspiration (ETc) was determined using a drainage lysimeter and the water balance method, while the aboveground biomass was measured at the end of each growth stage. Four growth stages (initial, development, mid, and final) were considered as decision alternatives, and two evaluation criteria including biomass water productivity and the proportion of biomass accumulated during each stage were selected. The sensitivity of each growth stage to irrigation management was assessed using two multi-criteria decision-making (MCDM) methods, TOPSIS and VIKOR. In addition, sensitivity analyses were performed to evaluate the effects of changes in criterion weights and the VIKOR compromise parameter (v). The results showed that total seasonal evapotranspiration was 413 mm, with the highest water consumption occurring during the mid-growth stage. Biomass accumulation followed a sigmoidal growth pattern, with the mid-growth stage accounting for the largest share (57.7%) of the total biomass production. Both TOPSIS and VIKOR consistently identified the mid-growth stage as the most water-sensitive period, followed by the development stage. Sensitivity analysis demonstrated that the ranking of the mid-growth stage remained unchanged across a wide range of criterion weights (0.1–0.8) and VIKOR parameter values (v = 0.1–0.9), confirming the robustness of the results. Overall, prioritizing irrigation during the mid-growth stage, and to a lesser extent during the development stage, can maximize biomass production and improve water productivity in quinoa cultivation under semi-arid conditions. The proposed MCDM-based framework provides a robust and practical tool for optimizing irrigation management and enhancing the sustainability of agricultural production in water-limited environments.

Keywords

References

  1. متکان، عبدالرضا، درویش‌زاده، رضا، حسینی‌اصل، احمد، ابراهیمی‌خفسی، محمد و ابراهیمی‌خفسی، زهرا، ۱۳۹۱. خشکسالی ‌پهنه‌بندی در مناطق خشک با استفاده از الگوریتم‌های مبتنی بر دانش در محیط GIS (مطالعه موردی: شیتور، یزد). فصلنامه پژوهش‌های اقلیم‌شناسی، ۲(۵–۶)، ۱۰۳–۱۱۶.
  2. سازمان هواشناسی کشور، ۱۴۰۰. داده‌های اقلیم‌شناسی ایستگاه کرج (1399–1370). تهران، سازمان هواشناسی کشور
  3. Aboukhaled, A., Alfaro, J. F., and Smith, M. 1982. Lysimeters. FAO Irrigation and Drainage Paper No. 39. Food and Agriculture Organization of the United Nations.
  4. Akram, M. Z., Libutti, A., and Rivelli, A. R. 2024. Drought stress in quinoa: Effects, responsive mechanisms, and management through biochar amended soil: A review. Agriculture, 14(8), 1418. DOI: https://doi.org/10.3390/agriculture14081418
  5. Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. Food and Agriculture Organization of the United Nations.
  6. Awa, M., Zhao, J., and Tumaerbai, H. 2024. Deficit irrigation-based improvement in growth and yield of quinoa in the northwestern arid region in China. Sustainability, 16(10), 4136. DOI: https://doi.org/10.3390/su16104136
  7. Bahraseman, S. E., Firoozzare, A., Boccia, F., Pourmohammad, F., and Ameri, A. H. 2025. Determining the best strategies to improve agricultural water productivity in arid and semi-arid regions: An ordinal priority approach. Journal of Arid Environments, 231, 105458. DOI: https://doi.org/10.1016/j.jaridenv.2025.105458
  8. Bazile, D., Jacobsen, S.-E., and Verniau, A. 2016. The global expansion of quinoa: Trends and limits. Frontiers in Plant Science, 7, 622.

DOI: https://doi.org/10.3389/fpls.2016.00622

  1. Biswas, T., Sah, R. P., Kumar, R., Kumar, V., and Nath, C. P. 2024. Evaluation of management practices in rice–wheat cropping system using multicriteria decision-making methods in conservation agriculture. Scientific Reports, 14, 8600.

DOI: https://doi.org/10.1038/s41598-024-58022-w

  1. Borg, H., and Grimes, D. W. 1986. Depth development of roots with time: An empirical description. Transactions of the ASAE, 29(1), 194–197.

DOI: https://doi.org/10.13031/2013.30125

  1. 2025. Quinoa production statistics (2010–2023). https://faostat.fao.org
  2. Gámez, A. L., Soba, D., Zamarreño, Á. M., García-Mina, J. M., Aranjuelo, I., and Morales, F. 2019. Effect of water stress during grain filling on yield, quality and physiological traits of Illpa and Rainbow quinoa (Chenopodium quinoa) cultivars. Plants, 8(6), 173. DOI: https://doi.org/10.3390/plants8060173
  3. Garcia, M., Raes, D., and Jacobsen, S.-E. 2003. Evapotranspiration analysis and irrigation requirements of quinoa (Chenopodium quinoa). Agricultural Water Management, 60(2), 119–134. DOI: https://doi.org/10.1016/S0378-3774(02)00162-2
  4. Geerts, S., and Raes, D. 2008. Crop water use indicators to quantify the flexible phenology of quinoa (Chenopodium quinoa) in response to drought stress. Field Crops Research, 108(2), 150–156. DOI: https://doi.org/10.1016/j.fcr.2008.04.008
  5. Geerts, S., Jacobsen, S.-E., and Raes, D. 2008. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management, 95(6), 1525–1539. DOI: https://doi.org/10.1016/j.agwat.2008.04.009
  6. Geerts, S., Raes, D., Garcia, M., Miranda, R., Cusicanqui, J. A., Taboada, C., Mendoza, J., Huanca, R., Mamani, A., Condori, O., Mamani, J., Morales, B., Osco, V., and Steduto, P. 2009. Simulating yield response of quinoa to water availability with AquaCrop. Agronomy Journal, 101(3), 499–508. DOI: https://doi.org/10.2134/agronj2008.0137s
  7. Gómez, J., García-Parra, L. A., Roa-Acosta, D., and Bravo-Gómez, J. E. 2022. Growth and water use efficiency of quinoa in Colombia: Stage-specific irrigation management. Agricultural Water Management, 263, 107488.

DOI: https://doi.org/10.1016/j.agwat.2022.107488

  1. Hinojosa, L., Kumar, N., Gill, K. S., and Murphy, K. M. 2019. Spectral reflectance indices and physiological parameters in quinoa under contrasting irrigation regimes. Crop Science, 59(5), 1927–1944. DOI: https://doi.org/10.2135/cropsci2018.11.0711
  2. Howell, T. A., Schneider, A. D., and Jensen, M. E. 1991. History of lysimeter design and use for evapotranspiration measurements. In R. G. Allen et al. (Eds.), Advances in Evapotranspiration (pp. 1–9). ASAE.
  3. 2022. Climate Change 2022: Impacts, Adaptation and Vulnerability. Intergovernmental Panel on Climate Change.
  4. Li, H., Wang, Y., Zhang, X., Zhang, J., and Liu, X. 2023. Water use efficiency of quinoa during flowering and grain filling under deficit irrigation in China. Agronomy, 13(2), 390. DOI: https://doi.org/10.3390/agronomy13020390
  5. Liu, F., Song, C., and Feng, Z. 2021. Growth, biomass accumulation and water use efficiency of quinoa under different irrigation regimes in Northwest China. Grassland Science, 67(3), 207–216. DOI: https://doi.org/10.1111/grs.12307
  6. Mardani, A., Jusoh, A., Md Nor, K., Khalifah, Z., Zakwan, N., and Valipour, A. 2015. Multiple criteria decision-making techniques and their applications – A review of the literature from 2000 to 2014. Economic Research-Ekonomska Istraživanja, 28(1), 516–571. DOI: https://doi.org/10.1080/1331677X.2015.1075139
  7. Mendoza-Márquez, B., Chuchon-Remon, R., Ramos-Fernández, L., Falconí-Palomino, J., Garcia-Lopez, Y., and Gómez-Pando, L. 2026. Water stress thresholds and irrigation management via infrared thermography and AquaCrop model in quinoa cultivation in arid zones. Agricultural Research, 15, 1038–1048. DOI: https://doi.org/10.1007/s40003-025-00869-0
  8. Mirsafi, S. M., Sepaskhah, A. R., and Ahmadi, S. H. 2024. Physiological traits, crop growth, and grain quality of quinoa in response to deficit irrigation and planting methods. BMC Plant Biology, 24, 809. DOI: https://doi.org/10.1186/s12870-024-05523-5
  9. Opricovic, S., and Tzeng, G. H. 2004. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156, 445–455.
  10. Oustani, M., Mehda, S., Halilat, M. T., et al. 2023. Yield, growth development and grain characteristics of seven quinoa genotypes grown under hot-arid conditions. Scientific Reports, 13, 1991. DOI: https://doi.org/10.1038/s41598-023-28974-4
  11. Pereira, L. S., Paredes, P., Hunsaker, D. J., López-Urrea, R., and Jovanovic, N. 2021. Updates and advances to the FAO56 crop water requirements method. Agricultural Water Management, 248, 106697. DOI: https://doi.org/10.1016/j.agwat.2020.106697
  12. Pradhan, A., Rane, J., Basavaraj, P. S., Kumar, N., Shid, D., Paul, N. C., Pal, K. K., and Reddy, K. S. 2025. Optimizing crop management strategies for improved yield, water productivity, and sustainability of quinoa in shallow basaltic semi-arid regions. Frontiers in Plant Science, 16, 1522995. DOI: https://doi.org/10.3389/fpls.2025.1522995
  13. Qureshi, M. R. N., Singh, R. K., and Hasan, M. A. 2018. Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM. Environment, Development and Sustainability, 20(2), 641–659. DOI: https://doi.org/10.1007/s10668-016-9903-7
  14. Razzaghi, F., Bahadori-Ghasroldashti, M. R., Henriksen, S., Sepaskhah, A. R., and Jacobsen, S.-E. 2020. Physiological characteristics and irrigation water productivity of quinoa (Chenopodium quinoa) in response to deficit irrigation imposed at different growing stages. Journal of Agronomy and Crop Science, 206(3), 390–404. DOI: https://doi.org/10.1111/jac.12392
  15. Salim, S. A. A. R., Al-Hadithi, I. K. H., and Al-Hadithi, A. G. M. 2022. Effect of water stress at different growth stages on yield and its components of quinoa. The Iraqi Journal of Agricultural Sciences, 53(6), 1241–1251.
  16. Salim, S. A., Rasheed, F. T., Al-Deen, U. H., and Muneem, R. A. A. 2024. Water requirements, soil moisture availability, and their effects on quinoa (Chenopodium quinoa) development and yield. Asian Journal of Science and Applied Technology, 13(2), 25–33. DOI: https://doi.org/10.70112/ajsat-2024.13.2.4250
  17. Tuncel, G., and Gunturk, B. 2024. A fuzzy multi-criteria decision-making approach for agricultural land selection. Sustainability, 16(23), 10509.

DOI: https://doi.org/10.3390/su162310509

  1. Tzeng, G. H., and Huang, J. J. 2011. Multiple Attribute Decision Making: Methods and Applications. CRC Press.
  2. Velasquez, M., and Hester, P. T. 2013. An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56–66.
  3. Vilcacundo, R., and Hernández-Ledesma, B. 2017. Nutritional and biological value of quinoa. Current Opinion in Food Science, 14, 1–6.

DOI: https://doi.org/10.1016/j.cofs.2016.11.007

Journal of Water Research in Agriculture
Volume 40, Issue 1
October 2026
Pages 55-71

Files

Share

How to cite

Statistics