Agronomy Science, przyrodniczy lublin, czasopisma up, czasopisma uniwersytet przyrodniczy lublin
Skip to main navigation menu Skip to main content Skip to site footer
Agronomy Science Baner text

Vol. 81 No. 1 (2026)

Articles

Understanding the impact of cultivar characteristics and environmental conditions on grain protein content and yield in wheat

DOI: https://doi.org/10.24326/as.2026.5529
Submitted: April 15, 2025
Published: 16.02.2026

Abstract

In the face of changing climatic conditions, there is a growing need to better understand the mechanisms influencing wheat yield and grain quality, particularly grain protein content (GPC). While genotype-by-environment interactions (GEI) have been widely studied, few investigations have focused on how specific environmental and varietal traits contribute to these interactions.

In this study, we applied the classification and regression tree (CART) method and a linear mixed model (LMM) to analyze field trial data across different wheat cultivars and varying environmental conditions. The analysis included factors such as soil nutrient content, rainfall distribution during the growing season, and varietal characteristics including plant height and growth duration. Our results revealed that GPC was primarily determined by rainfall during the grain-filling phase and the level of available nitrogen in the soil, while grain yield (GY) was strongly influenced by total rainfall during stem elongation and certain morphological traits. The variable “falling number” was included in the initial analysis but was excluded by the model due to its lack of predictive significance.

This study provides detailed insights into which environmental and varietal traits are most influential in shaping GEI effects on GPC and GY. The use of CART modelling enabled the identification of key predictors affecting cultivar responses under diverse growing conditions. These findings can support breeding and agronomic decision-making by offering predictive tools to select cultivars with improved stability in yield and grain quality under variable climatic conditions.

Our research fills a gap in existing studies by providing new insights into GEI interactions and their impact on yield and grain quality. CART-based models can serve as predictive tools, helping breeders forecast how varieties will respond to future climatic changes and environmental conditions. This approach can significantly contribute to optimizing breeding practices and improving yield stability and quality, supporting sustainable agricultural development.

References

  1. Bustos-Korts D., Romagosa I., Borràs-Gelonch G. et al., 2019. The nutritional value of wheat grain: impact on health. Nutrients 11(8), 1835. https://doi.org/10.3390/nu11081835 DOI: https://doi.org/10.3390/nu11081835
  2. Breiman L., Friedman J.H., Olshen R.A. et al., 2017. Classification and regression trees, 1st ed. Routledge, London. DOI: https://doi.org/10.1201/9781315139470-1
  3. Derejko A., Studnicki M., Wójcik-Gront E., 2021. Grain yield performance and stability of winter wheat and triticale cultivars in a temperate climate. Crop Sci. 61(6), 3962–3971. https://doi.org/10.1002/csc2.20594 DOI: https://doi.org/10.1002/csc2.20594
  4. FAO, 2019. The state of food and agriculture. Moving forward on food loss and waste reduction. FAO, Rome. https://openknowledge.fao.org/server/api/core/bitstreams/11f9288f-dc78-4171-8d02-92235b8d7dc7/content [access: 15.03.2025].
  5. Mladenov N., Przulj N., Hristov N. et al., 2001. Cultivar-by-environment interactions for wheat quality traits in semiarid conditions. Cereal Chem. 78(3), 363–367. https://doi.org/10.1094/CCHEM.2001.78.3.363 DOI: https://doi.org/10.1094/CCHEM.2001.78.3.363
  6. Hatfield J.L., Boote K.J., Kimball B.A. et al., 2011. Climate impacts on agriculture: Implications for crop production. Agron J. 103(2), 351–370. https://doi.org/10.2134/agronj2010.0303 DOI: https://doi.org/10.2134/agronj2010.0303
  7. Iwańska M., Oleksy A., Dacko M. et al., 2018. Use of classification and regression trees (CART) for analyzing determinants of winter wheat yield variation among fields in Poland. Biom. Lett. 55(2), 197–214. https://doi.org/10.2478/bile-2018-0013 DOI: https://doi.org/10.2478/bile-2018-0013
  8. Johansson E., Branlard G., Cuniberti M. et al., 2020. Genotypic and environmental effects on wheat technological and nutritional quality. In: G. Igrejas, T.M. Ikeda, C. Guzmán (eds). Wheat Quality for Improving Processing and Human Health. Springer, 171–204. https://doi.org/10.1007/978-3-030-34163-3_8 DOI: https://doi.org/10.1007/978-3-030-34163-3_8
  9. Oury F., Godin C., 2007. Yield and grain protein concentration in bread wheat. How to use the negative relationship between the two characters to identify favorable genotypes. Euphytica 157(1–2), 45–57. https://doi.org/10.1007/s10681-007-9395-5 DOI: https://doi.org/10.1007/s10681-007-9395-5
  10. Sanchez-Garcia M., Alvaro F., Peremarti A. et al., 2015. Changes in bread-making quality attributes of bread wheat varieties cultivated in Spain during the 20th century. Eur. J. Agron. 63, 79–88. https://doi.org/10.1016/j.eja.2014.11.006 DOI: https://doi.org/10.1016/j.eja.2014.11.006
  11. Savary S., Willocquet L., Pethybridge S.J. et al., 2019. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 3(3), 430–439. https://doi.org/10.1038/s41559-018-0793-y DOI: https://doi.org/10.1038/s41559-018-0793-y
  12. Shewry P.R., Hey S.J., 2015. The contribution of wheat to human diet and health. Food Energy Secur. 4(3), 178–202. https://doi.org/10.1002/fes3.64 DOI: https://doi.org/10.1002/fes3.64
  13. Wójcik-Gront E., Iwańska M., Wnuk A. et al., 2022. The analysis of wheat yield variability based on experimental data from 2008-2018 to understand the yield gap. Agriculture 12(1), 32. https://doi.org/10.3390/agriculture12010032 DOI: https://doi.org/10.3390/agriculture12010032
  14. Zhang M., Ma D., Ma G. et al., 2017. Responses of glutamine synthetase activity and gene expression to nitrogen levels in winter wheat cultivars with different grain protein content. J. Cer. Sci. 74, 187–193. https://doi.org/10.1016/j.jsc.2017.01.012 DOI: https://doi.org/10.1016/j.jcs.2017.01.012
  15. Zheng H., Chen L., Han X. et al., 2010. Effectiveness of phosphorus application in improving regional soybean yields under drought stress. A multivariate regression tree analysis. Afr. J. Agric. Res. 5(23), 3251–3258.
  16. Zheng H., Chen L., Han X. et al., 2009. Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China. The importance of phosphorus application rates under drought conditions. Agric. Ecosyst. Environ. 132, 98–105. Htpps://doi.org/10.1016/j.agee.2009.03.004 DOI: https://doi.org/10.1016/j.agee.2009.03.004

Downloads

Download data is not yet available.

Similar Articles

<< < 62 63 64 65 66 67 68 69 70 71 > >> 

You may also start an advanced similarity search for this article.