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Article

Yield, Protein, and Starch Equilibrium of Indigenous Varieties: An Open Door for Computational Breeding in Enhancing Selection Strategies

by
Gabriela Gorinoiu
1,
Cerasela Petolescu
2,
Alina Laura Agapie
1,
Ciprian Buzna
1,
Petru Rain
1,
Nicolae Marinel Horablaga
1,3,
Adina Horablaga
3,
Ionel Samfira
1,3,
Marius Valentin Boldea
4,
Irina Petrescu
2,
Ioan Sarac
2 and
Emilian Onisan
2,*
1
Agricultural Research and Development Station Lovrin, 307250 Lovrin, Romania
2
Faculty of Engineering and Applied Technologies, University of Life Sciences “King Mihai I”, 300645 Timisoara, Romania
3
Faculty of Agriculture, University of Life Sciences “King Mihai I”, 300645 Timisoara, Romania
4
Faculty of Veterinary Medicine, University of Life Sciences “King Mihai I”, 300641 Timisoara, Romania
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(6), 1280; https://doi.org/10.3390/agronomy15061280
Submission received: 19 March 2025 / Revised: 18 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025
(This article belongs to the Topic Advances in Crop Simulation Modelling)
Browse Figures
Versions Notes

Abstract

:
Given the increasing demand for wheat and the challenges of climate change, it is essential for breeding programs to adapt their strategies to reach the maximum biological potential of new varieties faster. Our study investigates the relationship between wheat yield, protein content, and starch accumulation over five years of Romanian winter wheat varieties. This study included a total of 25 wheat varieties, comprising 16 newly developed ones and 9 varieties registered and cultivated in Romania. The experiment was conducted in three replications over a period of five years. To monitor the equilibrium pattern, the Glosa variety was used as a reference, known for its optimal balance of yield and protein across Romania, as reported in several studies and farmers’ reports. Our research results indicate an inverse correlation between protein content and yield, whereas starch content exhibits a positive correlation with yield among the wheat varieties analyzed. K-means and Principal Component Analyses (PCA) identified Glosa, Lovrin02, Lovrin08, and Boema as the most balanced varieties regarding yield and grain quality stability. The equilibrium model revealed in the results offers information on trait inheritance and heritability, as similar equilibrium patterns were observed across the 25 analyzed varieties over a five-year testing period. Furthermore, integrating an equilibrium model into computational breeding could provide a framework for enabling breeding programs to optimize yield and grain composition while eliminating low-potential varieties.

1. Introduction

Wheat is one of the world’s most important cereal crops due to its significant role in the human diet. Romania is one of the leading countries in wheat grain yield, with cultivation covering more than 2 million hectares [1,2,3]. In Western Romania, wheat yields can exceed 6 tons per hectare annually, highlighting the importance of the region [4]. In 2021, the Ministry of Agriculture and Rural Development (MADR) reported a wheat yield of 11.33 million tons [5], one of the highest productions since Romania joined the European Union, followed by 9.17 million tons in 2022 [6] and slightly over 10 million tons in 2023 [7]. However, in recent years, climate change and soil pollution have led to a significant decrease in wheat production across the country [8,9,10].
Breeding programs for wheat aim to achieve the best possible balance between protein content and yield under various environmental conditions [11,12]. However, the interaction between genotype and environment has a major impact on wheat grain quality [13]. In this respect, it is necessary to monitor patterns of grain quality and yield new varieties in order to select and develop a genetic pool adapted to our current environmental challenges [14]. According to scientific reports, a positive correlation between wheat starch and yield and a negative correlation between protein content and yield were revealed [15,16]. Indeed, several authors reveal a strong connection between these three elements: yield, protein, and starch [17,18,19]. Furthermore, starch and seed storage protein (SSP) deposition in the endosperm are controlled by separate mechanisms that significantly influence the impact on wheat yield and quality [20].
According to studies, starch synthesis involves coordination between nuclear genes and plastid function. In contrast, SSP synthesis occurs within the endoplasmic reticulum and Golgi apparatus, relying on nuclear gene regulation [21]. Key proteins in starch synthesis in wheat are SUS3, UGP1, cAGPase, and Bt1, which form the main metabolic pathway for starch processing in amyloplasts [16]. On the other hand, the synthesis of SSP is influenced by transcription factors such as TaSPA, SHP, SPR, PBF, TaGAMyb, TaNAC019, and TaFUSCA3, which regulate the expression of genes involved in the production of glutenins and gliadins [20]. Studies reveal that protein percentage, on the other hand, reflects the capacity for nitrogen metabolization [22]. Numerous specific correlations in wheat could form a fundamental basis for more efficient genotype selection in wheat breeding, particularly by focusing on the relationships between the plant’s quality traits (starch and protein) and yield [23,24,25]. Therefore, patterns of starch and protein accumulation can provide significant insights into trait inheritance and heritability, greatly influencing the selection of new varieties in breeding programs.
Indigenous varieties, such as Glosa, have demonstrated their potential as a benchmark for maximum biological productivity. According to different research studies, Glosa recorded the highest average yield over a 15-year period, making it one of the most widely cultivated and stable varieties in Romania [26]. Thus, monitoring autochthonous varieties could serve as a foundational resource in integrating yield, protein, and starch equilibrium breeding models. Indeed, in this way, a further approach would be to integrate equilibrium-based quality and yield traits strategy into computational breeding models.
Computational breeding has adopted an interdisciplinary approach to plant breeding. By introducing and integrating algorithms and predictive models of the equilibrium-based strategy into plant breeding, we aim to achieve the maximum biological yield and quality of the new varieties [27]. Additionally, computational breeding and predictive analysis will significantly impact germplasm selection to be more accurate by eliminating varieties with no potential. Thus, breeding programs gain the potential to enhance predictability and improve overall efficiency by applying and monitoring yield, protein, and starch equilibrium patterns [28]. Since environmental factors can influence the selection of new varieties, by using the equilibrium model, breeding programs can improve the germplasm pool, as it consistently enhances the genetic resources in relation to dynamic environmental conditions.
In this study, we aim to (i) identify the varieties best adapted to environmental conditions over a five-year period based on yield performance across 25 varieties, (ii) determine the highest-yielding varieties with superior grain quality, (iii) assess the correlations between quality traits and yield, and (iv) explore the potential of utilizing the yield, protein, and starch equilibrium of indigenous varieties in wheat breeding programs, which could serve as a foundation for using computational breeding methods.

2. Materials and Methods

2.1. Germplasm

In this experiment, 25 winter wheat genotypes developed in Romania were used at both the SCDA Lovrin and Fundulea research institutes. These varieties were selected based on their prevalence in agricultural practice in the western region of Romania. The selection included Romanian varieties known for their productivity and adaptability to diverse cultivation environments.
Out of the 25 winter wheat varieties, 16 were newly developed at SCDA Lovrin (Lovrin01–Lovrin16), while 5 varieties—Alex; Ciorian; Lv 90; Dacic; and Crișana—were previously registered by SCDA Lovrin. Additionally, Glosa, Boema, Litera, and Otilia, developed by the research institute in Fundulea, were included to demonstrate the efficiency of the equilibrium-based strategy. The experiment was conducted over a five-year period from 2019 to 2023 in the Lovrin fields area in the western region of Romania.

2.2. Field Experimental Design and Technology Used

The experiment was conducted at the Lovrin Agricultural Research Station. The field testing was carried out over a five-year period to evaluate crop yield under specific agronomic conditions. Each experimental plot covered an area of 10 m2, with three replications for each treatment and a plant density of 550 plants/m2. In autumn, a complex NPK (15-15-15) fertilizer was applied at a rate of 200 kg/ha, corresponding to 30 kg/ha of N, 30 kg/ha of P2O5, and 30 kg/ha of K2O.
Furthermore, in spring, 200 kg/ha of ammonium nitrate was used to meet the crop’s nitrogen requirements. Additionally, during the vegetative stage, the herbicide Axial was applied to control weeds. The 25 wheat varieties were arranged in three replicates per cultivar in a completely randomized block design in the fields of the Lovrin Research Institute under non-irrigated conditions.

2.3. Method for Determining Wheat Starch and Protein Content

To assess the starch and protein content of wheat, a Near-Infrared (NIR) spectroscopic analysis was conducted using the Inframatic NIR 9200 grain analyzer (Perten Instruments, Hägersten, Sweden). The wheat samples were collected directly from the field or storage batches without requiring prior milling. The biological material was placed in standardized, clean containers to prevent contamination. Samples were collected over a period of five years from each of the three repetitions in the experimental fields, ensuring comprehensive and reliable data.
The analysis was performed by placing wheat samples from each of the 25 analyzed varieties into the measurement compartment of the Inframatic NIR 9200 device (Perten Instruments, Hägersten, Sweden, where the absorption spectrum was recorded in the near-infrared range. The recorded absorption spectrum was then compared against pre-established calibration curves developed from control samples, ensuring accurate estimations of starch and protein content. These calibration models are based on reference chemical analyses and are essential for precise grain composition evaluation.

2.4. Temperature and Precipitation Measurement Methods

To ensure accurate meteorological data collection, records were used from the meteorological station in Lovrin. This station provided daily recordings, ensuring precise monitoring of climatic conditions that could influence wheat growth and development. All data obtained from the years 2019 to 2023 were considered and processed for statistical analysis.

2.5. Statistical Analysis

In this study, the statistical methods used to analyze the correlations between wheat yield, starch, and protein. Analysis of variance (ANOVA), correlation analysis, and regression were used. ANOVA was applied to assess the influence of climatic variables (precipitation and temperature), study years, and the interaction between these factors on yield. The ANOVA test was also conducted to determine the variability of protein and starch content.
K-means clustering and PCA (Principal Component Analysis) were performed to assess the influence of yield and principal quality components of wheat for the 25 analyzed varieties.
The software used for the analyses was R Studio 4.4.2, which was also used for graphical analysis and calculating the correlation coefficient. The Hmisc v5.1-1 package was used to compute correlation coefficients, the ComplexHeatmap package to visualize the correlation matrix, and the Circlize package was used to define custom color gradients for the heatmap visualization.
Furthermore, for K-means, ggplot2 was used for generating layered visualizations, and ggrepel was used for non-overlapping text label placement in scatter plots.
All statistical analyses, including correlation significance, were performed at a significance level of p < 0.05.

3. Results

The experimental study was conducted over a period of five years, analyzing 25 varieties. The analyses of yield, starch, and protein served as the starting point for identifying varieties lacking the potential to achieve maximum biological equilibrium. In the first stage, the degree of correlation among the 25 varieties was analyzed over the five-year period. This was followed by a cluster analysis to identify new varieties with maximum biological balance compared to Glosa.

3.1. Correlation Between Yield, Stretch, and Protein

Results are illustrated in Figure 1, revealing a high variability in starch content and yield over the five years analyzed.
In 2019, a low negative correlation of −0.35 was observed between yield and protein, while a similarly low positive correlation of 0.36 was noted between yield and starch. (Figure 1a).
In 2020, the negative correlation between yield and protein became stronger and statistically significant (−0.74, p < 0.001), while a strong positive and significant correlation was observed between yield and starch (0.78, p < 0.001) (Figure 1b).
In 2021, the correlation between yield and protein was very low (0.10, not significant), and the correlation between yield and starch was slightly negative (−0.13, not significant). Nonetheless, the negative correlation between protein and starch remained strong and significant (−0.93, p < 0.001) (Figure 1c).
In 2022, a moderate negative correlation between yield and protein was recorded (−0.41, p < 0.05), while the correlation between yield and starch remained positive (0.34, not significant) (Figure 1d).
In 2023, the negative correlation between yield and protein increased further in strength and significance (−0.56, p < 0.01), while the correlation between yield and starch remained strong and statistically significant (0.65, p < 0.001).
Considering all the years combined, the overall correlations confirmed the trends refer to a moderate negative correlation between yield and protein (−0.50, p < 0.05), a positive correlation between yield and starch (0.51, p < 0.01), and a highly significant inverse correlation between protein and starch (−0.93, p < 0.001) (Figure 1f and Table 1).
One of the closest correlation equilibrium indices and the highest among the experimental years analyzed was observed in 2020, revealing the strongest trade-off between protein, starch, and yield (Table 1). The consistency in correlation patterns remained similar across all years, with the most relevant one in 2020 and 2023.

3.2. Analysis of the Influence of Precipitation and Temperature on Wheat Yield

The ANOVA results indicate that precipitation has a significant influence on yield (p < 0.05). In contrast, temperature does not have a significant effect (p > 0.05), as it remained similar throughout the experimental period (Table 2).
The results also suggest that there is a significant difference in yield among the 25 analyzed varieties (p < 0.05), reflecting the different ways in which these varieties adapt to varying climatic conditions (Table 3).
The analysis of variance regarding yield influence over a five-year period demonstrates a significant difference depending on the experimental study year. Nevertheless, genetic factors have a substantial role in the yield capacity of the wheat varieties, with a high degree of significance both within and across years (p < 0.001).
According to environmental conditions, precipitation played a significant role in correlation variation, indicating that the accumulation of protein and starch is highly influenced by precipitation. On the other hand, temperature variation did not significantly influence the outcomes of the experiment (Table 2 and Figure 2).
A difference in equilibrium correlation coefficients was present in 2021. Unlike other years, the results in 2021 show a near-zero correlation between protein/yield and a low negative starch/yield correlation (−0.13) (Table 1 and Figure 1).

3.3. Coefficients of Variation Analysis of Wheat Varieties of the Period 2019–2023

Analysis of the coefficient of variation per cultivar demonstrates the stability of the cultivars over the analyzed years. The average coefficient of variation for yield was 11.43%. The cultivars with lower variability (genetically more stable) were Alex, Ciprian, Glosa, Lv90, Otilia, Litera, Lovrin02, Lovrin03, Lovrin04, Lovrin05, Lovrin06, Lovrin10, Lovrin12, and Lovrin16. On the other hand, the cultivars with lower adaptability to the environmental conditions in Romania, in terms of yield, were Boema, Dacic, Crișana, Lovrin01, Lovrin07, Lovrin08, Lovrin09, Lovrin11, Lovrin13, Lovrin14, and Lovrin15. Cultivars that exhibited high yield variability showed a fluctuating efficiency, which may be associated with a reduced adaptability to variations in precipitation. However, cultivars with low variability demonstrated a good capacity to compensate for stress factors in terms of yield, starch content, and protein content (Table 4).
Based on the obtained results, the wheat varieties that consistently maintained a coefficient of variation below 10% for yield, starch, and protein are Alex, Ciprian, Glosa, Otilia, Litera, Lovrin03, Lovrin04, and Lovrin16.
The most unstable variety, exhibiting a coefficient of variation above 10% for yield, starch, and protein, is Lovrin07, making it more sensitive to environmental conditions and less predictable in agronomic performance.
Conversely, the most stable variety, with the lowest combined variability for yield, starch, and protein, is Glosa. This variety exhibits the most balanced physiological profile, with minimal fluctuations and a predictable yield, making it an optimal choice. Therefore, Glosa not only proves to be one of the most stable varieties in the studied regions but also serves as a key reference point in breeding programs.

3.4. Clustering Analyses Among Varieties Regarding Yield, Protein, and Starch

To better distinguish the wheat varieties based on their equilibrium correlation coefficients (protein, starch, and yield), a K-means clustering analysis was performed. The purpose of this analysis was to determine whether certain wheat varieties maintain a stable equilibrium between biological yield potential and grain quality characteristics (Figure 3).
According to our results across 5 testing years, Glosa consistently positioned itself in the central zones of the clusters throughout the five-year analysis, indicating that it maintains a stable equilibrium between yield, protein, and starch. Thus, the hypothesis would be that, to preserve this equilibrium, the selection of new varieties could be carried out based on the balance between protein, starch, and yield.
By using Glosa as a reference in relation to the three evaluated elements in this study, we successfully identified varieties that either match or exceed the biological yield capacity of the reference variety, as well as eliminated low-potential varieties.
Cluster analysis used allows for grouping and, potentially, eliminating low-potential varieties. Based on the analyses, varieties such as Dacic and Lovrin11 were identified as having the lowest yield over the five-year evaluation period, registering both the lowest yield and protein content. Conversely, the cluster with the most productive varieties includes Lovrin04, Lovrin06, and Lovrin07. These varieties demonstrated high yields; however, their protein content was relatively low. According to the results obtained, varieties that combine balanced yield with optimal protein content are the most desirable.
The varieties with optimal levels of yield and protein content were as follows: Glosa, Lovrin08, Lovrin02, and Boema (Cluster 4). Additionally, varieties with higher protein content but lower yield were observed in Cluster 6: Lovrin09, Lovrin13, Lovrin15, Lovrin16, Ciprian, and Crisana (Figure 3).
The central cluster zones identified through K-means analysis were predominant over the five-year evaluation. Thus, varieties maintaining a stable protein/starch/yield ratio exhibited consistency for the equilibrium. Indicating that the maximal biological potential is in the region of varieties such as Glosa, Boema, Lovrin02, and Lovrin08.
Additionally, as we can see in Figure 4, similar patterns were also observed for starch and yield content across 5 testing years, where Glosa, Boema (Cluster 4), Lovrin02, and Lovrin08 (Cluster 2) were close to each other.
Furthermore, the relationship between protein and starch positions Lovrin02 and Lovrin08 (Cluster 7) in a cluster with higher starch than Glosa and Boema (Cluster 4) (Figure 5). However, higher starch in Lovrin02 and Lovrin08 than in Glosa and Boema did not influence the yield/protein content relationship, making them similarly efficient.
The selection method based on the optimal equilibrium between yield and protein could have significantly reduced the elimination process’s workload by identifying and discarding low-potential varieties at an earlier stage (first two years of testing). This approach would have facilitated a more efficient selection process, preventing the advancement of varieties with unbalanced traits.
Implementing this selection strategy at the initial stages, resources could be redirected toward varieties that maintain a stable physiological balance, ensuring higher efficiency in breeding programs and improving the overall selection process of newly developed wheat genotypes.
According to our results, varieties that deviate significantly from the reference (Glosa) have not demonstrated substantial genetic gains. Most new high-performance varieties fall within similar parameter ranges, suggesting that the region of maximum biological equilibrium between protein content, yield, and starch yield is relatively stable.
This finding reinforces the importance of targeted breeding strategies aimed at maintaining this equilibrium. Rather than focusing solely on maximizing one trait, wheat improvement programs should prioritize varieties that naturally align with the optimal balance zone, ensuring both agronomic performance and grain quality.

3.5. PCA of Wheat Varieties Regarding Yield, Starch, and Protein

The analysis of variance regarding the five-year average highlights a change and an influence of environmental conditions depending on the variety in terms of protein and gluten content (p < 0.01). On the other hand, starch did not reveal a significant difference among varieties analyzed (p > 0.05) (Table 5).
Figure 6A,B reveal the percentages of variance explained by each principal component. The first principal component (PC1) explains 77.3% of the total variance, while the second component (PC2) explains 20.4%. Together, the two components account for 97.7% of the variance, making them the primary contributors.
The remaining component contributes marginally (2.3%) and is therefore less relevant. Analyzing Figure 7A, we observe that Protein and Starch have a strong contribution to Dim-1, suggesting a significant contribution. Starch and Protein are the main contributors (39%), followed by Yield, which shows a lower contribution (around 22%).
On the other hand, Figure 7B suggests a strong contribution of Yield on Dim-2 (77%), while Protein and Starch have minimal contributions (11%), indicating that this component mainly captures variability associated with Yield differences among samples.

3.6. Clustering of Wheat Varieties Regarding Yield, Starch, and Protein

According to our results, there is a consistent disruption and pattern in the Protein/Yield, Protein/Starch, and Starch/Yield equilibrium for the wheat varieties analyzed.
Figure 8 illustrates that, based on the analysis of yield, starch, and protein, the wheat varieties Lovrin08, Lovrin10, and Lovrin12 form a sub-cluster within the main cluster that includes the reference variety Glosa. However, when analyzing the Protein/Yield equilibrium, the varieties that exhibited optimal levels of both yield and protein content were Glosa, Lovrin08, Lovrin02, and Boema (Figure 3), all of which are included in Cluster 4. Indeed, the results underscore the importance of analyzing each indicator separately to accurately identify the best potential varieties. On the other hand, the necessity of examining all equilibrium ratios is needed to gain a comprehensive understanding of the relationships among yield, protein, and starch in wheat varieties.

4. Discussion

The analyses for yield, protein, and starch equilibrium of wheat varieties in the study have demonstrated that there is an independent relationship among these three elements. A direct proportional relationship was observed between starch and yield, while an inverse proportional relationship was found between protein and yield, as well as between protein and starch.
The independent relationship and the patterns among 5 years of experiments of the varieties have enabled the development of a selection strategy for wheat varieties with the maximum biological potential.
Studies reveal an influence of nitrogen allocation on protein synthesis and grain yield. Influenced genetically, high-yielding varieties tend to allocate available nitrogen preferentially towards biomass accumulation rather than protein synthesis, resulting in reduced grain protein content [29,30]. Conversely, varieties with higher protein content exhibited lower yield, likely due to the competitive metabolic pathways involved in nitrogen assimilation and carbohydrate accumulation [31,32]. This trend was most evident in 2020 and 2023, when the highest trade-offs between yield and protein content were recorded.
Starch is one of the main components of wheat, having several industrial applications [33]. The role of starch as a primary reserve carbohydrate further reinforces the yield-starch correlation. Studies have shown that the reduced conversion of sucrose to starch in the final grain filling stage is the main cause of lower grain weight and, consequently, reduced yield [34,35,36]. The varieties with higher starch content exhibited increased yield, likely due to enhanced carbohydrate metabolism and efficient translocation of photoassimilates to the grain. This suggests that high-yielding varieties prioritize starch synthesis as an adaptive mechanism for optimizing energy storage and grain weight under varying environmental conditions [37].
The study conducted in the experimental area of Lovrin over a five-year period highlights the influence of climatic factors on wheat yield, demonstrating the importance of precipitation and annual variability in determining wheat yield.
Precipitation increasingly impacts both the yield and quality of wheat. The ANOVA results underscore the significant impact of precipitation on wheat yield, while temperature did not exhibit a statistically significant effect [38]. Years with higher precipitation levels tended to favor starch accumulation and overall grain mass, whereas drier conditions were associated with increased protein content, possibly due to nitrogen concentration effects in limited biomass [39].
The variability in equilibrium correlations was most pronounced in 2021, where a near-zero correlation between yield and protein was observed. The role of precipitation in modulating protein and starch synthesis highlights the necessity of considering climatic adaptability in breeding programs.
Studies have shown that greater temperature variability is a major factor in reducing global wheat yield [40,41,42]. Wheat varieties are particularly affected by climatic conditions during the stages of spike development and grain filling [43,44]. Precipitation during the growth and development periods of wheat has a significant impact on both yield and the quality of the crop, particularly regarding protein content [45]. On the other hand, extreme rainfall during the final maturation stages can impact wheat quality, reducing its suitability and reducing protein content [46,47].
Temperatures in the experiment were noticed to have no significant impact on yield or quality during the experimental period due to the same patterns of temperature in the period of 2019–2023. However, the results of other researchers, obtained under different temperature conditions, have shown that temperatures can negatively impact yield and quality due to increased water stress [48,49,50]. According to the studies by Salazar-Gutierrez and Johnson (2013) [51], temperatures below the normal threshold significantly affect the growth rate of wheat plants, while excessively high temperatures render growth inefficient. They concluded that optimal temperatures for germination and early growth fall within the range of 12 to 25 °C. Studies of Prasad, P.V.V. (2008) [52] highlight that high nighttime temperatures reduce photosynthesis and biomass accumulation in wheat, leading to decreased yield. These effects become particularly noticeable when temperatures exceed 30 °C.
Our study identified the varieties Glosa, Lovrin08, Lovrin02, and Boema as the most stable varieties in terms of maintaining an optimal balance between yield, protein, and starch content. These varieties demonstrated minimal fluctuations across the five-year period, making them valuable genetic resources for breeding programs aimed at improving both yield potential and grain quality. On the other hand, varieties with poor biological potential, such as Dacic, Lovrin13, Lovrin09, and Lovrin15, were possible to be detected through the analyses and proposed for discard.
Furthermore, cluster analysis further reinforced and revealed the physiological trade-offs between yield and quality traits. The varieties were classified into distinct clusters, allowing for the identification of those with the best balance between high yield and protein content.
Although certain varieties maintain a constant partner of performance of the yield, protein, and starch equilibrium, certain limitations should be considered. The study did not account for the impact of soil nutrient availability beyond nitrogen, which could further influence protein and starch accumulation. According to numerous authors, there are several important factors to consider regarding the yield, protein, and starch equilibrium in wheat varieties. The increase in yield depends on AGN (the total amount of nitrogen in the plant) and NHI (the percentage of nitrogen transferred to the grains). Additionally, under low nitrogen supply, yield is influenced by the plant’s ability to efficiently uptake and allocate nitrogen [32,53]. Therefore, breeding programs must take this aspect into account, as the relationship between nitrogen and wheat variety is essential for sustainable recommendations and selection. Furthermore, significant progress has been made in plant breeding using molecular markers [54,55,56]. Recent studies have identified genomic regions associated with genes involved in the synthesis of proteins and starch [57]. The findings open the study to direct applications in computational breeding by providing a framework for the quality index—Yield; Protein; and Starch Equilibrium—for predictive modeling of wheat performance under varying environmental conditions by monitoring variety selection or genetically engineered varieties; which strongly maintain equilibrium across different environmental conditions. A similar perspective was proposed in another study as well [58]. Quantitative genetics provides a theoretical framework, and it can be used to relate information on the inheritance and heritability of traits. Using Yield, Protein, and Starch Equilibrium method, predictions of selection response in a breeding program can be made [59,60].
In perspective, studies could include more in-depth analyses of the correlations between various factors and AGN (the total amount of nitrogen in the plant) and NHI (the percentage of nitrogen transferred to the grains) in relation to the Yield, Protein, and Starch Equilibrium method.

5. Conclusions

According to our results, yield, protein, and starch equilibrium provided information on trait inheritance and heritability, as similar equilibrium patterns were observed across the 25 analyzed varieties over a five-year testing period. The analysis of this equilibrium in indigenous wheat varieties revealed independent relationships among these factors, with a direct correlation between starch and yield and an inverse correlation between protein and yield, as well as between protein and starch.
The equilibrium breeding model identified Glosa, Lovrin08, Lovrin02, and Boema as the most stable varieties in terms of yield, protein content, and starch, emphasizing their potential for breeding programs and revealing the way to achieve maximum biological potential.
To reduce the limitations of the equilibrium breeding model, breeding programs should prioritize the use of indigenous varieties in the current area with strong adaptability to the cultivation area. Furthermore, in perspective, studies could explore the correlation between AGN (the total amount of nitrogen in the plant) and NHI (the percentage of nitrogen transferred to the grains) to provide deeper insights into trait inheritance.

Author Contributions

Data curation: G.G., E.O., I.S. (Ioan Sarac) and I.P.; Formal analysis: M.V.B., E.O., P.R. and G.G.; Funding acquisition: N.M.H.; Investigation: A.L.A. and G.G.; Methodology: G.G., I.S. (Ioan Sarac), M.V.B. and P.R.; Project administration: N.M.H.; Resources: N.M.H., G.G., E.O. and A.H.; Valida-tion: G.G., I.S. (Ionel Samfira), E.O., C.B., A.L.A., C.P., I.S., (Ioan Sarac), N.M.H. and I.P.; Visuali-zation: G.G., I.S. (Ionel Samfira), E.O., M.V.B. and C.P.; Writing—original draft: G.G. and E.O.; Writing—review and editing: E.O. and C.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research work was supported by Research Contract 5755/05.07.2024, “Implementation of Statistical Interpretation Methods for Scientific Research Results from SCDA Lovrin Laboratories”, and by the project “Increasing the Impact of Excellence in Research on Innovation and Technology Transfer Capacity at USV Timisoara”, code 6PFE.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The authors would like to thank the funding institutions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 15 01280 g001
Figure 1. Correlation coefficients analysis of the wheat varieties evaluated for starch, yield, protein, and gluten across the testing years: (a) 2019, (b) 2020, (c) 2021, (d) 2022, (e) 2023, and (f) 2019–2023. Note: In the correlation matrix, statistical significance is denoted by asterisks: * for p < 0.05, ** for p < 0.01, and *** for p < 0.001. Cells marked as NA indicate non-applicable values, corresponding to self-correlations, and were therefore excluded from the analysis.
Figure 1. Correlation coefficients analysis of the wheat varieties evaluated for starch, yield, protein, and gluten across the testing years: (a) 2019, (b) 2020, (c) 2021, (d) 2022, (e) 2023, and (f) 2019–2023. Note: In the correlation matrix, statistical significance is denoted by asterisks: * for p < 0.05, ** for p < 0.01, and *** for p < 0.001. Cells marked as NA indicate non-applicable values, corresponding to self-correlations, and were therefore excluded from the analysis.
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Figure 2. Correlation heatmap precipitation factors across years of testing.
Figure 2. Correlation heatmap precipitation factors across years of testing.
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Figure 3. Cluster analyses of the wheat varieties evaluated, yield, and protein content across 5 testing years.
Figure 3. Cluster analyses of the wheat varieties evaluated, yield, and protein content across 5 testing years.
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Figure 4. Cluster analyses of the wheat varieties evaluated, starch and yield content across 5 testing years.
Figure 4. Cluster analyses of the wheat varieties evaluated, starch and yield content across 5 testing years.
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Figure 5. Cluster analyses of the wheat varieties evaluated, starch and protein content across 5 testing years.
Figure 5. Cluster analyses of the wheat varieties evaluated, starch and protein content across 5 testing years.
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Figure 6. (A) Scree plot of P.C.A.; (B) biplot of P.C.A.
Figure 6. (A) Scree plot of P.C.A.; (B) biplot of P.C.A.
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Figure 7. (A) Contribution to PCA Dimension 1; (B) Contribution to PCA Dimension 2.
Figure 7. (A) Contribution to PCA Dimension 1; (B) Contribution to PCA Dimension 2.
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Figure 8. Clustering of wheat varieties regarding yield, starch, and protein.
Figure 8. Clustering of wheat varieties regarding yield, starch, and protein.
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Table 1. Equilibrium correlation coefficients by year: protein, starch, and yield.
Table 1. Equilibrium correlation coefficients by year: protein, starch, and yield.
YearProtein/YieldStarch/YieldProtein/Starch
2019−0.350.36−0.99 ***
2020−0.74 ***0.78 ***−0.96 ***
20210.10−0.13 −0.93 ***
2022−0.41 *0.34−0.81 ***
2023−0.56 **0.65 ***−0.89 ***
Overall −0.50 *0.51 **−0.93 ***
Significance levels: *** p < 0.001, ** p < 0.01, and * p < 0.05.
Table 2. ANOVA analysis of the influence of precipitation, temperature, and years on yield, including interactions between factors.
Table 2. ANOVA analysis of the influence of precipitation, temperature, and years on yield, including interactions between factors.
Source of VarianceSum of SquaresDegrees of FreedomF-Valuep-Value
Precipitation53,169,095438.88716p < 0.001
Temperature5.57 × 10 −2244.07 × 10−28Ns
Yield7.87 × 10 84575.652p < 0.001
Precipitation x Temperature1.04 × 10 916190.7714p < 0.001
Precipitation x Yield70,482,8911612.88756p < 0.001
Temperature x Yield1.35 × 10 81624.63772p < 0.001
Precipitation x Temperature x Yield2.67 × 10 964122.0561p < 0.001
Residual1.07 × 10 8312
Table 3. ANOVA test for the effect of variety, year, and their interaction on yield.
Table 3. ANOVA test for the effect of variety, year, and their interaction on yield.
Source of VarianceSum of SquaresDegrees of FreedomF-Valuep-Value
Year2.07 × 10 94408.3426p < 0.001
Variety1.46 × 10 8244.7911p < 0.001
Variety x Year2.24 × 10 8961.8421p < 0.001
Residual3.17 × 10 8250
Table 4. Coefficients of Variation for Starch, Yield, and Protein in Different Wheat Varieties of the period 2019–2023.
Table 4. Coefficients of Variation for Starch, Yield, and Protein in Different Wheat Varieties of the period 2019–2023.
SoiCV_YieldCV_StarchCV_Protein
Alex7.70%4.03%7.19%
Ciprian8.01%5.00%8.04%
Glosa7.64%5.14%5.42%
Boema17.86%2.93%4.10%
Lv906.62%2.91%12.40%
Otilia9.19%4.57%6.78%
Litera 8.81%3.48%6.10%
Dacic13.51%3.13%9.80%
Crișana15.05%9.40%7.73%
Lovrin0112.00%3.98%10.09%
Lovrin0210.57%3.53%6.87%
Lovrin038.93%3.29%7.94%
Lovrin047.16%5.99%8.00%
Lovrin0510.63%5.01%11.80%
Lovrin069.89%4.27%13.14%
Lovrin0712.44%11.07%17.17%
Lovrin0812.50%2.76%8.51%
Lovrin0915.06%8.14%10.63%
Lovrin1010.63%7.95%11.22%
Lovrin1111.57%3.32%11.02%
Lovrin127.46%2.33%11.49%
Lovrin1315.92%4.59%3.73%
Lovrin1414.06%3.53%9.79%
Lovrin1523.05%2.99%3.98%
Lovrin169.48%4.95%4.60%
Media11.43%4.73%8.70%
Table 5. Analysis of Variance of Protein, Starch, and Yield Content.
Table 5. Analysis of Variance of Protein, Starch, and Yield Content.
Source of VarianceDf Sum of Squares F-Statisticp-ValueSignificance Level
Protein2489.422.35p = 0.0017p < 0.01
Starch24204.570.68p = 0.8598Not Significant
Yield249634.442.75p = 0.0002p < 0.01
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Gorinoiu, G.; Petolescu, C.; Agapie, A.L.; Buzna, C.; Rain, P.; Horablaga, N.M.; Horablaga, A.; Samfira, I.; Boldea, M.V.; Petrescu, I.; et al. Yield, Protein, and Starch Equilibrium of Indigenous Varieties: An Open Door for Computational Breeding in Enhancing Selection Strategies. Agronomy 2025, 15, 1280. https://doi.org/10.3390/agronomy15061280

AMA Style

Gorinoiu G, Petolescu C, Agapie AL, Buzna C, Rain P, Horablaga NM, Horablaga A, Samfira I, Boldea MV, Petrescu I, et al. Yield, Protein, and Starch Equilibrium of Indigenous Varieties: An Open Door for Computational Breeding in Enhancing Selection Strategies. Agronomy. 2025; 15(6):1280. https://doi.org/10.3390/agronomy15061280

Chicago/Turabian Style

Gorinoiu, Gabriela, Cerasela Petolescu, Alina Laura Agapie, Ciprian Buzna, Petru Rain, Nicolae Marinel Horablaga, Adina Horablaga, Ionel Samfira, Marius Valentin Boldea, Irina Petrescu, and et al. 2025. "Yield, Protein, and Starch Equilibrium of Indigenous Varieties: An Open Door for Computational Breeding in Enhancing Selection Strategies" Agronomy 15, no. 6: 1280. https://doi.org/10.3390/agronomy15061280

APA Style

Gorinoiu, G., Petolescu, C., Agapie, A. L., Buzna, C., Rain, P., Horablaga, N. M., Horablaga, A., Samfira, I., Boldea, M. V., Petrescu, I., Sarac, I., & Onisan, E. (2025). Yield, Protein, and Starch Equilibrium of Indigenous Varieties: An Open Door for Computational Breeding in Enhancing Selection Strategies. Agronomy, 15(6), 1280. https://doi.org/10.3390/agronomy15061280

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