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Biomass Yield in Bread Wheat: The Effect of Sowing Density and Predicting Using NDVI

Year 2024, Volume: 27 Issue: 5, 1148 - 1158, 17.09.2024

Ali Cevat Sönmez

https://doi.org/10.18016/ksutarimdoga.vi.1226330

Abstract

This is due to approaching the upper limits of the harvest index. The study aimed to determine the effect of sowing density (SD) practices on BY in the Central Anatolian Region and the use of normalized vegetation index (NDVI) values in estimating BY easily. Field trials were carried out for three consecutive years in rainfed and irrigated conditions in Eskişehir between 2012 and 2015. Four different SDs (Sparse, Ordinary, High, and Very High) were applied to six winter wheat cultivars. Biomass samples were taken during flowering and harvesting periods. NDVI values were measured using the Green Seeker Handhold Sensor (GSHS) tool during the flowering period. In both conditions, the correlation between the NDVI values during the flowering period and the biomass yields obtained during the harvesting period was found to be positive and significant (in rainfed conditions (R = 0.837), in irrigated conditions (R = 0.786)). To obtain high BY, it is recommended that to be sowed with High SD in rainfed conditions, and Ordinary SD practices in irrigated conditions. Considering the varieties, Alpu 2001 and Harmankaya-99 stand out from the others in terms of getting high biomass yield in both conditions. In addition, it was determined that the GSHS is a reliable tool that can be used for the estimation of BY.

Keywords

Triticum aestivum L. Biomass estimation Seeding rate Vegetation Index Correlation

Supporting Institution

The Republic of Türkiye Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policy, Transitional Zone Agricultural Research Institute,

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Ekmeklik Buğdayda Biyokütle Verimi: Ekim Sıklığının Etkisi ve NDVI Kullanılarak Tahmini

Year 2024, Volume: 27 Issue: 5, 1148 - 1158, 17.09.2024

Ali Cevat Sönmez

https://doi.org/10.18016/ksutarimdoga.vi.1226330

Abstract

Bu çalışmanın amacı, İç Anadolu Bölgesi'nde ekim sıklığı(ES) uygulamalarının BV'ye etkisini ve BV'nin kolayca tahmin edilmesinde normalize edilmiş bitki indeksi (NDVI) değerlerinin kullanımını belirlemektir. Tarla denemeleri 2012-2015 yılları arasında arka arkaya üç yıl süreyle yağmura bağımlı ve sulu koşullarda Eskişehir'de yapılmıştır. Altı adet kışlık buğday çeşidine dört farklı ekim sıklığı (Seyrek, Sıradan, Yüksek ve Çok Yüksek) uygulanmıştır. Biyokütle örnekleri çiçeklenme ve hasat dönemlerinde alınmıştır. NDVI değerleri çiçeklenme döneminde Green Seeker Handhold Sensor (GSHS) cihazı kullanılarak ölçülmüştür. Her iki koşulda da çiçeklenme dönemindeki NDVI değerleri ile hasat döneminde elde edilen BV’leri arasındaki korelasyon pozitif ve istatistiksel olarak önemli bulunmuştur (yağmura bağımlı koşullarda (R = 0,837), sulu koşullarda (R = 0,786)). Yüksek BV elde etmek için çeşitlerin yağışlı koşullarda Yüksek ES uygulamasıyla, sulu koşullarda Normal ES uygulamasıyla ekilmesi önerilir. Çeşitler arasında Alpu 2001 ve Harmankaya-99 çeşitlerinden her iki koşulda da yüksek verim alınmıştır. Ayrıca GSHS'nin BV tahmini için kullanılabilecek güvenilir bir araç olduğu belirlenmiştir.

Keywords

Vejetasyon indeksi Korelasyon Triticum aestivum L. Biyokütle Tahmini Tohum sıklığı

References

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  • Aisawi, K. A. B., Reynolds, M. P., Singh, R. P., & Foulkes, M. J. (2015). The physiological basis of the genetic progress in yield potential of CIMMYT spring wheat cultivars from 1966 to 2009. Crop Science, 55(4), 1749-1764. doi:10.2135/ cropsci2014.09.0601
  • Amanullah, Khan, A., Hussain, & Z., Jan, D. (2010). Performance of wheat cultivars sown at different seeding rates under drought-stress conditions. Archives of Agronomy and Soil Science, 56(1), 99-105. doi:10.1080/03650340.902897641
  • Anonymous. (2022). The world population is projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. Retrieved from https://www.un.org/en/desa/ world population-projected-reach-98-billion-2050-and-112 billion-2100
  • Atkinson Amorim, J. G., Schreiber, L. V., de Souza, M. R. Q., Negreiros, M., Susin, A., Bredemeier, C., ... & Parraga, A. (2022). Biomass estimation of spring wheat with machine learning methods using UAV-based multispectral imaging. International Journal of Remote Sensing, 43(13), 4758-4773.
  • Babar, M. A., Reynolds, M. P., Van Ginkel, M., Klatt, A. R., Raun, W. R., & Stone, M. L. (2006). Spectral reflectance to estimate genetic variation for in‐season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Science, 46(3), 1046-1057. doi:10.2135/cropsci2005.0211
  • Bhatta, M., Eskridge, K. M., Rose, D. J., Santra, D. K., Baenziger, P. S., & Regassa, T. (2017). Seeding rate, genotype, and topdressed nitrogen effects on yield and agronomic characteristics of winter wheat. Crop Science, 57(2), 951-963. doi:10.2135/cropsci2016.02.0103
  • Cabrera-Bosquet, L., Molero, G., Stellacci, A., Bort, J., Nogués, S., & Araus, J. (2011). NDVI is a potential tool for predicting biomass, plant nitrogen content, and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Research Communications, 39(1), 147-159.
  • Carr, P. M., Horsley, R. D., & Poland, W. W. (2003). Tillage and seeding rate effects on wheat cultivars: I. Grain production. Crop Science, 43(1), 202-209.
  • Chandel, N. S., Tiwari, P. S., Singh, K. P., Jat, D., Gaikwad, B. B., Tripathi, H., & Golhani, K. (2019). Yield prediction in wheat (Triticum aestivum L.) using spectral reflectance indices. Curr. Sci, 116(2), 272.
  • Dai, J., Bean, B., Brown, B., Bruening, W., Edwards, J., Flowers, M., …& Wiersma, J. (2016). Harvest index and straw yield of five classes of wheat. Biomass and Bioenergy, 85, 223-227. doi:10.1016/j.biombioe.2015.12.023
  • Foulkes, M. J., Snape, J. W., Shearman, V. J., Reynolds, M. P., Gaju, O., & Sylvester-Bradley, R. (2007). Genetic progress in yield potential in wheat: recent advances and prospects. The Journal of Agricultural Science, 145(1), 17-29. doi:10.1017/S0021859607006740
  • Foulkes, M. J., Slafer, G. A., Davies, W. J., Berry, P. M., Sylvester-Bradley, R., Martre, P., ... & Reynolds, M. P. (2011). Raising yield potential of wheat. III. Optimizing partitioning to grain while maintaining lodging resistance. Journal of Experimental Botany, 62(2), 469-486. doi:10.1093/jxb/erq300
  • Gündoğdu, K. S. (2018). Buğday ekili parsellerde NDVI değerlerinin konumsal ve zamana bağlı değişiminin belirlenmesi. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 21(4), 492-499.
  • 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. doi:10.1071/AR04214
  • Hu, C., Zheng, C., Sadras, V. O., Ding, M., Yang, X., & Zhang, S. (2018). Effect of straw mulch and seeding rate on the harvest index, yield, and water use efficiency of winter wheat. Scientific reports, 8(1), 1-8. doi:10.1038/s41598-018-26615-x
  • JMP. (2016). JMP Users Guide, Version 13.0.0, SAS Institute Inc., Cary, NC, USA.
  • Kara, R., & Akkaya, A. (2009). Kahramanmaraş yöresine ait yerel ekmeklik buğday genotiplerinin verim ve fizyolojik özellikler yönünden incelenmesi. Türk Tarım ve Doğa Bilimleri Dergisi, 7(4), 1186-1204.
  • Khan, A., Ahmad, A., Ali, W., Hussain, S., Ajayo, B. S., Raza, M. A., ...& Yang, W. (2020). Optimization of plant density and nitrogen regimes to mitigate lodging risk in wheat. Agronomy Journal, 112(4), 2535-2551. doi:10.1002/agj2.20211
  • Khan, T. S., & Mubeen, U. (2012). Wheat straw: A pragmatic overview. Curr. Res. J. Biol. Sci, 4(6), 673-675.
  • Lapidus, D., Salem, M. E., Beach, R. H., Zayed, S., & Ortiz-Monasterio, I. (2022). Greenhouse gas mitigation benefits and profitability of the GreenSeeker Handheld NDVI sensor: evidence from Mexico. Precision Agriculture, 23(6), 2388-2406.
  • Lovegrove, A., Pellny, T. K., Hassall, K. L., Plummer, A., Wood, A., Bellisai, A., ... & Shewry, P. R. (2020). Historical changes in the contents and compositions of fiber components and polar metabolites in white wheat flour. Scientific reports, 10(1), 1-9. doi:10.1038/s41598-020-62777-3
  • Ma, S. C., Wang, T. C., Guan, X. K., & Zhang, X. (2018). Effect of sowing time and seeding rate on yield components and water use efficiency of winter wheat by regulating the growth redundancy and physiological traits of root and shoot. Field Crops Research, 221, 166-174. doi:10.1016/j.fcr.2018.02.028
  • Menegat, A., & Nilsson, A. T. (2019). Interaction of preventive, cultural, and direct methods for integrated weed management in winter wheat. Agronomy, 9(9), 564.
  • Meng, J., Du, X., & Wu, B. (2013). Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation. International Journal of Digital Earth, 6(3), 203-218. doi:10.1080/17538947.2011.623189
  • Mitchell, P. L., & Sheehy, J. E. (2018). Potential yield of wheat in the United Kingdom: How to reach 20 t ha-1. Field crops research, 224, 115-125. doi:10.1016/j.fcr.2018.05.008
  • Molero, G., Joynson, R., Pinera‐Chavez, F. J., Gardiner, L. J., Rivera‐Amado, C., Hall, A., & Reynolds, M. P. (2019). Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant biotechnology journal, 17(7), 1276-1288. doi:10.1111/pbi.13052
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Details

Primary Language English
Subjects Agricultural, Veterinary and Food Sciences
Journal Section RESEARCH ARTICLE
Authors

Ali Cevat Sönmez 0000-0002-9818-2660

Early Pub Date July 2, 2024
Publication Date September 17, 2024
Submission Date December 29, 2022
Acceptance Date December 12, 2023
Published in Issue Year 2024 Volume: 27 Issue: 5

Cite

APA Sönmez, A. C. (2024). Biomass Yield in Bread Wheat: The Effect of Sowing Density and Predicting Using NDVI. Kahramanmaraş Sütçü İmam Üniversitesi Tarım Ve Doğa Dergisi, 27(5), 1148-1158. https://doi.org/10.18016/ksutarimdoga.vi.1226330
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