Drought stress is a major global constraint limiting wheat productivity, necessitating the identification of stable genotypes and reliable selection criteria for breeding programs. The present study was conducted at the Nuclear Institute of Agriculture (NIA), Tandojam, during Rabi 2017–18 using sixteen spring wheat genotypes, including thirteen coded lines and three local checks (Kiran-95, NIA-Sunhari, and Chakwal), evaluated under three irrigation regimes (T1 = zero, T2 = two, and T3 = four irrigations). Significant genetic variability was observed among genotypes and treatments for most agronomic traits, including relative water content, tillers per meter², plant height, peduncle length, spike length, spikelets per spike, grains per spike, 1000-grain weight, biological yield, grain yield, and harvest index.
Correlation analysis revealed that grain yield was consistently and strongly associated with biological yield (r = 0.87** under T1; r = 0.87** under T2; r = 0.88** under T3) and with harvest index under higher irrigation (r = 0.65**). The overall correlation across environments confirmed significant positive associations of grain yield with biological yield (r = 0.89**) and harvest index (r = 0.66**). Genotypes such as CIM-04-10, C5-13-4a, and C2-98-8 demonstrated superior performance across multiple traits, while NIA-Sunhari excelled among local checks.
These findings suggest that spike length, peduncle length, grains per spike, biological yield, and early heading can serve as effective selection criteria for improving spring wheat productivity under drought stress. The study underscores the potential of correlation and regression analysis in identifying key yield components to guide future wheat breeding strategies under water-limited conditions.
Table of Contents
CHAPTER 1
INTRODUCTION
OBJECTIVES
CHAPTER II
REVIEW OF LITERATURE
2.1. Impact of Drought Stress on Wheat Physiology and Yield
2.2. Interrelationships among Yield Components and Their Contribution to Grain Yield
2.3. Application of Statistical Tools: Correlation and Regression
2.4. Screening and Identification of Drought-Tolerant Genotypes
2.5. Conclusion from the Literature Review
CHAPTER III
MATERIALS AND METHODS
3.1 Experimental Site and Plant Material
3.2 Experimental Design and Irrigation Treatments
3.3 Recorded Traits
3.5 Experimental Layout
Table 3.1: Irrigation Schedule and Growth Stages
CHAPTER-IV
RESULTS
Correlation Coefficient
4.1.1 Correlation Coefficient of Polled data.
4.1.2 Correlation Coefficient in T1 (zero irrigation)
4.1.3 Correlation Coefficient in T2 (Two irrigations)
4.1.4 Correlation coefficient in T3 (four irrigations)
4.2 Correlation (r) Coefficient of determination (r2) and regression coefficient (b)
4.2.1 Correlation (r) Coefficient of determination (r2) and regression coefficient (b) of pooled data
4.2.2 Correlation (r) Coefficient of determination (r2) and regression coefficient (b) of T1 (zero irrigation)
4.2.3 Correlation (r) Coefficient of determination (r2) and regression coefficient (b) of T2 (two irrigations)
4.2.4 Correlation (r) Coefficient of determination (r2) and regression coefficient (b) of T3 (four irrigations)
CHAPTER-V
DISCUSSIONS
CHAPTER-VI
SUMMARY AND CONCLUSION
LITERATURE CITED
Research Objectives and Themes
The primary aim of this study is to evaluate the influence of drought stress on the morpho-yield traits of spring wheat, utilizing correlation and regression analysis to identify key contributing factors and improve breeding strategies for water-limited environments.
- Assessment of associations between grain yield and contributing traits under varying irrigation conditions.
- Statistical partitioning of yield components to determine their specific influence on grain production.
- Identification of superior, climate-resilient wheat genotypes through integrated selection indices.
- Validation of biometrical tools like correlation and regression models for predicting yield variability.
- Development of effective selection criteria for sustainable wheat improvement in stress-prone regions.
Excerpt from the Book
Correlation and regression analysis
To advance wheat breeding under such constraint conditions, it is crucial not only to identify stress-tolerant genotypes but also to statistically quantify the contribution of individual yield components to the final output. In this pursuit, correlation and regression analysis serve as powerful and indispensable biometrical tools.
Correlation analysis measures the degree and direction of the mutual association between pairs of traits at phenotypic, genotypic, and environmental levels (Bhutto et al., 2016). A strong positive correlation between a yield component (e.g., number of grains per spike) and grain yield suggests that selection for that component will likely lead to yield improvement. Recent applications of this analysis have been vital for identifying key traits under water-deficit conditions, such as chlorophyll content and canopy temperature (Sallam et al., 2024). Conversely, understanding undesirable negative correlations is vital for designing effective breeding strategies that avoid compromising one trait for another, such as the trade-off between high yield and protein content (Guttieri et al., 2021).
While correlation identifies associated traits, regression analysis takes a step further by defining the functional relationship between a dependent variable (e.g., grain yield) and one or more independent variables (yield components). This technique partitions the influence of each independent variable, allowing researchers to develop predictive models that explain the variability in yield (El-Mohsen et al., 2014). Modern approaches, including path analysis and multiple regression, are extensively used to disentangle the complex web of cause-and-effect relationships between yield and its attributes, helping breeders pinpoint the most effective direct selection criteria (El-Hashash et al., 2020; Tiwari et al., 2023).
Therefore, this study aims to employ these robust statistical techniques to unravel the inter-character relationships and quantify the direct and indirect effects of various agronomic traits on grain yield in wheat. The insights gained will be instrumental in selecting superior, climate-resilient genotypes and formulating effective selection indices for enhancing and stabilizing wheat productivity under stress-prone environments.
Summary of Chapters
CHAPTER 1: Provides an overview of wheat's importance to global food security and discusses the challenges posed by biotic and abiotic stresses, particularly drought.
CHAPTER II: Reviews existing literature regarding the impact of drought on wheat physiology, established yield components, and the application of statistical modeling in breeding.
CHAPTER III: Details the experimental site, plant material (sixteen genotypes), randomized complete block design, and the specific methodology used for data collection and analysis.
CHAPTER-IV: Presents the comprehensive results of correlation coefficients, coefficients of determination, and regression analysis across different irrigation treatments.
CHAPTER-V: Discusses the findings by interpreting the statistical relationships between yield-related traits and comparing them with established agricultural research.
CHAPTER-VI: Summarizes the study’s findings and provides final conclusions regarding the identification of drought-tolerant wheat lines and future breeding recommendations.
Keywords
Spring wheat, Triticum aestivum L., drought stress, grain yield, correlation analysis, regression analysis, yield components, plant breeding, climate change, genotype selection, irrigation management, biometrical tools, physiological traits, harvest index, crop productivity.
Frequently Asked Questions
What is the core focus of this research?
The study investigates the impact of drought stress on sixteen different spring wheat genotypes, specifically examining how various morphological and physiological traits influence grain yield.
What are the primary themes addressed in the work?
The research centers on yield components, drought tolerance, statistical biometrics (correlation and regression), and the identification of resilient wheat cultivars for breeding.
What is the main objective of the study?
The goal is to quantify how different yield traits contribute to final grain production under drought stress and to establish effective selection criteria for breeding improved varieties.
Which statistical methods were employed?
The researcher used analysis of variance (ANOVA), Pearson correlation coefficients, coefficients of determination (r²), and linear regression analysis to analyze trait relationships.
What is covered in the main body of the work?
The main body covers a literature review of drought impacts, a detailed description of the experimental materials and methods, and an extensive reporting of statistical results segmented by irrigation treatments.
What characterize the keywords of this paper?
The keywords highlight the intersection of plant breeding, abiotic stress management (specifically drought), and the statistical modeling of complex quantitative traits in cereal crops.
How were the different irrigation treatments classified?
The experiments were divided into T1 (zero irrigation control), T2 (two irrigations at three-leaf and tillering stages), and T3 (four irrigations through the grain-filling stage).
Why is path analysis considered important by the author?
The author notes that path analysis is a crucial tool because it partitions correlation coefficients into direct and indirect causal effects, which simplifies the identification of true selection criteria.
What conclusion did the author reach regarding the coded lines?
The study concluded that specific coded lines, such as C5-13-41, CIM-04-10, and C2-98-8, showed superior performance and are highly recommended for future breeding programs.
- Quote paper
- Muzamil Hussain Memon (Author), 2025, Yield and Yield Components of Spring Wheat under Drought Stress, Munich, GRIN Verlag, https://www.grin.com/document/1611532
