SavedAdd your journal

Journals / Turkish Journal Of Field Crops / 2026 / Volume: 31 Issue: 2

Improving Selection Efficiency at the Head Rows Stage of Winter Wheat Breeding by Using the Normalized Difference Vegetation Index

Emrah Koç(International Maize and Wheat Improvement Center (CIMMYT)-Türkiye)0000-0003-3024-8691
Beyhan Akın(International Maize and Wheat Improvement Center (CIMMYT)-Türkiye)0000-0002-2721-9577
Savaş Belen(Transitional Zone Agricultural Research Institute)0000-0001-7357-8127
Emel Ozer(Bahri Dağdaş International Agricultural Research Institute)0000-0002-2320-3105
Kemal Subasi(Agricultural Credit Seed Company (TAREKS))0000-0003-3087-1678
Fatih Özdemir(General Directorate of Agricultural Research and Policies)0000-0001-7934-2844
Mesut Keser(International Center for Agricultural Research in the Dry Areas (ICARDA)-Türkiye)0000-0001-6316-7981
Alexey Morgounov(Al Farabi Kazakh National University)0000-0001-7082-5655
Pages
—
Publication Date
2026-09-07
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Considering the higher proportion of lines advanced by NDVI+ and Breeder & NDVI+ protocols, and the correlation between mean NDVI and grain yield at the HR and PYT stages, revealed that NDVI can improve selection efficiency.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Visual selection in early stage of wheat breeding is usually implemented method.
📊
Key Findings & Data:Performances of HRs selected by each protocol were studied for grain yield and relationship of NDVI with yield in yield trials. 403 HRs of total 1246 HRs derived from 38 populations were selected by protocols at varying rates: 13.2% by Breeder, 10% by NDVI+, 3% by NDVI-, and 6.1% by Breeder & NDVI+.
💡
Conclusion & Impact:Considering the higher proportion of lines advanced by NDVI+ and Breeder & NDVI+ protocols, and the correlation between mean NDVI and grain yield at the HR and PYT stages, revealed that NDVI can improve selection efficiency.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Head rows stage · NDVI · Selection efficiency

Abstract

Visual selection in early stage of wheat breeding is usually implemented method. This study was conducted to compare breeders’ visual selection with normalized difference vegetation index (NDVI) selection. Three different selection protocols were used at Head Rows (HR) stage: Breeder visual, NDVI+ (high mean NDVI), and NDVI- (low mean NDVI). Some HRs were selected by both Breeder and NDVI+ (Breeder & NDVI+). Performances of HRs selected by each protocol were studied for grain yield and relationship of NDVI with yield in yield trials. 403 HRs of total 1246 HRs derived from 38 populations were selected by protocols at varying rates: 13.2% by Breeder, 10% by NDVI+, 3% by NDVI-, and 6.1% by Breeder & NDVI+. NDVI values measured at the pre-heading stage in the HR and early grain filling in the Preliminary Yield Trial (PYT) showed strong correlations with grain yield (r= 0.55** and r= 0.73**, respectively; p<0.01). Lines selected by NDVI+ and Breeder & NDVI+ protocols had higher mean NDVI and Area Under NDVI Curve (AUNDVIC) values than selected by Breeder and NDVI- protocols at both stages. The lines selected by Breeder & NDVI+ protocol had the highest mean grain yields, while NDVI- had the lowest at both stages. Thirty-four lines were advanced to Advanced Yield Trial (AYT) stage: 14 by Breeder, 13 by NDVI+, 1 by NDVI-, and 6 by Breeder & NDVI+. Considering the higher proportion of lines advanced by NDVI+ and Breeder & NDVI+ protocols, and the correlation between mean NDVI and grain yield at the HR and PYT stages, revealed that NDVI can improve selection efficiency.

🔖

Cite This Article

Koç, E., Akın, B., Belen, S., Ozer, E., Subasi, K., Özdemir, F., Keser, M., Morgounov, A. (2026). Improving Selection Efficiency at the Head Rows Stage of Winter Wheat Breeding by Using the Normalized Difference Vegetation Index. Turkish Journal Of Field Crops, Volume: 31 Issue: 2. https://doi.org/10.17557/tjfc.1860713

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (28)

  1. Alam, M., Baenziger, P.S., and Frels, K. (2024). Emerging Trends in Wheat (Triticum spp.) Breeding: Implications for the Future. Front. Biosci. (Elite Ed) 2024; 16(1): 2 https://doi.org/10.31083/j.fbe1601002
  2. Alves, K.S., & Del Ponte E.M. (2021). Analysis and simulation of plant disease progress curves in R: introducing the epifitter package. Phytopathol Res. 3, 22. https://doi.org/10.1186/s42483-021-00098-7
  3. Araus J.L., Casadesus, J., & Borg, J. (2001). Recent tools for the screening of physiological traits determining yield. In: Reynolds M, Ortiz Monaterio I, McNab A, editors. Application of Physiology in Wheat Breeding. Mexico City, Mexico: CIMMYT, pp. 59–77.
  4. Bacianowski, J., Tratwal, A., & Nowosad, K. (2020). Genotype by environment interaction for area under the disease-progress curve (AUDPC) value in spring barley using additive main effects and multiplicative interaction model. Australasian Plant Pathology. 49:525–529. https://doi.org/10.1007/s13313-020-00723-7
  5. Casadesus, J., Kaya, Y., Bort, J., Nachit, M., Araus, L., Amor, S., Ferrazzano, G., Maalouf, F., Maccaferri, M., Martos, V., Ouabbou, H., & Villeges, D. (2006). Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Annals of Applied Biology. Volume 150, Issue 2, Pages 227-236. https://doi.org/10.1111/j.1744-7348.2007.00116.x
  6. Feng, M.C., & Yang, W.D. (2011). Changes in NDVI and yield of winter wheat cultivars with different plant types. Chinese J Eco-Agr. 19: 87–92. DOI: 10.3724/SP.J.1011.2011.00087
  7. Girma, K., Martin, K.L., Anderson, R.H., Arnall, D.B., Brixey, K.D., Casillas, M.A., Chung, B., Dobey, B.C., Kamenidou, S.K., Kariuki, S.K., Katsalirou, E.E., Morris, J.C., Moss, J.Q., Rohla, C.T., Sudbury, B.J., Tubana, B.S., & Raun, W.R. (2006). Mid-Season Prediction of Wheat-Grain Yield Potential Using Plant, Soil, and Sensor Measurements. Journal of Plant Nutrition. 29:873-897. https://doi.org/10.1080/01904160600649187
  8. Gutiérrez-Rodríguez, M., Reynolds, M.P., Escalante-Estrada, J.A., & Rodríguez-González, M.T. (2004). Association between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions. Australian Journal of Agricultural Research. 55(11) 1139-1147 https://doi.org/10.1071/AR04214
  9. Hazratkulova, S., Sharma, R.C., Alikulov, S., Islomov, S., Yuldashev, T., Ziyaev, Z., Kahlikulov, Z., Ziyadullaev, Z., & Turok, J. (2012). Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winter wheat under terminal heat stress. Plant Breeding. 131, 716-721. https://doi.org/10.1111/pbr.12003
  10. Huang, S., Tang, L., Hupy, J.P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. (2021) 32(1):1–6. https://doi.org/10.1007/s11676-020-01155-1
  11. Kimball, B.A., White, J.W., Wall, G.W., & Ottman, M.J. (2012). Infrared-Warmed and Unwarmed Wheat Vegetation Indices Coalesce Using Canopy-Temperature–Based Growing Degree Days. Agronomy Journal. Volume 104: Pages 114-118. https://doi.org/10.2134/agronj2011.0144
  12. Lei, W., YouLu, B., YanLi, L., Wang, H., & Liping, Y. (2012). NDVI analysis and yield estimation in winter wheat based on green-seeker. Acta Agronomica Sinica. Vol. 38 No. 4 pp. 747-753. doi: 10.3724/SP.J.1006.2012.00747
  13. Li, H., Lou, Y., Xue, X., Zhao, Y., Zhao, H., & Li, F. (2011a). Estimating harvest index of winter wheat from canopy spectral information. Journal of Food, Agriculture & Environment. 9, 3/4 part 1, pp 420-425.
  14. Li, H.L., Luo, Y., & Ma, J.H. (2011b). Radiation-use efficiency and the harvest index of winter wheat at different nitrogen levels and their relationships to canopy spectral reflectance. Crop & Pasture Science. 62(3) 208-217. https://doi.org/10.1071/CP10315
  15. Lopes, M.S., & Reynolds, M.P. (2012). Stay-green in spring wheat can be determined by spectral reflectance measurements (normalized difference vegetation index) independently from phenology. Journal of Experimental Botany. Vol. 63 no. 10 pp. 3789-3798. https://doi.org/10.1093/jxb/ers071
  16. Lukina, E.V., Stone, M.L., & Raun, W.R. (1999). Estimating vegetation coverage in wheat using digital images. J Plant Nutr. 22: 341-350. https://doi.org/10.1080/01904169909365631
  17. Marti, J., Bort, J., Slafer, G.A., & Araus, J.L. (2007). Can wheat yield be assessed by early measurements of Normalized Difference Vegetation Index? Annals of Applied Science. 150: 253-257. https://doi.org/10.1111/j.1744-7348.2007.00126.x
  18. MeiChen, F., Xiao, L., Yang, W., & Guangwei, D. (2010). Predicting grain yield of irrigation-land and dry-land winter wheat based on remote sensing data and meteorological data. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering. 26(11):183-188 10.3969/j.issn.1002-6819.2010.11.032
  19. MeiChen, F., & WuDe, Y. (2011). Changes in NDVI and yield of winter wheat cultivars with different plant types. Zhongguo Shengtai Nongye Xuebao / Chinese Journal of Eco-Agriculture. Vol. 19 No. 1 pp. 87-92. DOI:10.3724/SP.J.1011.2011.00087
  20. Moges, S.M., Raun, W.R., Mullen, R.W., Freeman, K.W., Johnson, G.V., & Solie, J.B. (2004). Evaluation of green, red, and near infrared bands for predicting winter wheat biomass, nitrogen uptake, and final grain yield. Marcel Dekker, Inc., Monticello, USA. Journal of Plant Nutrition. 27, 8 pp. 1431-1441, 14 ref. https://doi.org/10.1081/PLN-200025858
  21. Morgounov, A., Gummadov, N., Belen, S., Kaya, Y., Keser, M., & Mursalova, J. (2014). Association of digital photo parameters and NDVI with winter wheat grain yield in variable environments. Turkish Journal of Agriculture and Forestry. Vol. 38: No. 5, Article 5. https://doi.org/10.3906/tar-1312-90
  22. Okada, T., Sugahara, K., Yoshida, T., Hirafuji, M., Fuchiyama, R., Ota, T., Matsuo, K., Asai, M., Miura, S., Nakatani, K., Shibuya, T., Nakazono, K., Ohshita, Y., & Watanable, Y. (2011). Serial crop growth status diagnosis by using image data obtained with different sensors in different seasons: case study for the diagnosis of wheat growth status. Agricultural Information Research. Vol. 20 No. 4 pp. 148-159. DOI:10.3173/air.20.148
  23. Paraschivu, M., Cotuna, O., & Paraschivu, M. (2013). The use of the area under the disease progress curve (AUDPC) to assess the epidemics of Septoria tritici in winter wheat. Res. J. Agric. Sci. 2013, 45, 193–201. [Google Scholar]
  24. Prakash, B., McConkey, B., Lafond, GP., Moulin, A., & Pelcat, Y. (2004). Optimal time for remote sensing to relate to crop grain yield on the Canadian praries. Canadian Journal of Plant Science. 2004, 84, 1, pp 97-103, 41 ref. https://doi.org/10.4141/P03-070
  25. Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Lukina, E.V., Thomason, W.E., & Schepers, J.S. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal. 93, 1, pp 131-138, 30 ref. https://doi.org/10.2134/agronj2001.931131x
  26. Sultana, S.R., Ali, A., Ahmad, A., Mubeen, M., Ul-Haq, M.Z., Ahmad, S., Ercisli, S., & Jaafar, H.Z.E. (2014). Normalized Difference Vegetation Index as a tool for wheat yield estimation: a case study from Faisalabad, Pakistan. Scientific World Journal. 2014:725326. DOI: 10.1155/2014/725326
  27. Verhulst, N., & Govaerts, B. (2010). The normalized difference vegetation index (NDVI) GreenSeeker TM handheld sensor: Toward the integrated evaluation of crop management. Part A: Concepts and case studies. Mexico, D.F.; CIMMYT.
  28. Wang, X., Mao, W., Wang, Y., Lou, H., Guan, P., Chen, Y., Peng, H., & Wang, J. (2023). Breeding design in wheat by combining the QTL information in a GWAS panel with a general genetic map and computer simulation. The Crop Journal. 11 (2023) 1816–1827. https://doi.org/10.1016/j.cj.2023.10.001