Assessing the Impact of Nitrogen Fertilization, Variety Selection, Year and Their Interaction on Wheat Yield and Yield Components
by
Oussama Hnizil
1,2,*, 
Aziz Baidani
1,
Ilham Khlila
1,2,
Nasserelhaq Nsarellah
2 and
Ali Amamou
2,* 1
Laboratory of Agrifood and Health, Faculty of Sciences and Techniques, Hassan First University of Settat, Settat 26000, Morocco
2
Research Unit of Plant Breeding and Genetic Resources Conservation, Regional Center of Agricultural Research of Settat, National Institute of Agricultural Research, Settat 26000, Morocco
*
Authors to whom correspondence should be addressed.
Nitrogen 2024, 5(2), 266-287; https://doi.org/10.3390/nitrogen5020018
Submission received: 14 January 2024
/
Revised: 17 February 2024
/
Accepted: 19 February 2024
/
Published: 9 April 2024
Abstract
:This five-year study (2016–2021) in Morocco’s Mediterranean climate investigated the effect of nitrogen fertilization and genotypic selection on wheat yield and associated components. Utilizing a split-plot design, the study assessed five wheat genotypes—’Faraj’, ‘Luiza’, ‘Itri’, ‘Karim’ and ‘Nassira’—under three nitrogen application rates (120, 60 and 0 kg/ha) across thirty plots with two replicates. Interactions between nitrogen and year showed marked significance in yield (p = 0.001), biomass (p = 0.002), TKW (p = 0.003) and Spk/m2 (p = 0.001), underscoring the variability in optimal nitrogen application rates across different years. Additionally, significant interactions between variety and year were observed for biomass (p = 0.001) and G/m2 (p = 0.001), indicating variability in the performance of different varieties across years. The ‘Itri’ genotype showed the highest yield in 2017, while ‘Luiza’ was pre-eminent in 2018, with ‘Itri’ producing the most biomass. ‘Faraj’ demonstrated consistent superiority in yield and biomass during 2019 and 2020. Our integrated principal component analysis and quadratic models elucidated that an intermediate nitrogen rate of 60 kg/ha (N2) was particularly advantageous for the ‘Faraj’ and ‘Karim’ genotypes. These findings highlight the substantial impact of informed nitrogen level adjustment and genotypic selection on yield optimization.
1. Introduction
Wheat and barley, the predominant cereal crops in the Mediterranean region, are paramount for regional food security [1]. In the context of the Mediterranean region, optimized cultivation of wheat has far-reaching implications not only for food security but also for sustainable agricultural practices [2,3].
In Morocco, wheat accounts for 65% of the country’s total cereal area, covering approximately 2.9 million hectares and making it the most significant crop [4,5]. Durum wheat (Triticum turgidum var. durum Desf), an ancient wheat species, forms a substantial part of human and animal diets [6]. However, despite the crop’s economic importance, durum wheat grain yields in Morocco remain low and unpredictable in dry areas due to limitations such as low and poorly distributed rainfall [7,8,9,10].
Research in the field has highlighted challenges in wheat yield improvement, notably the inverse relationship between cereal protein content and grain yields, which complicates genotypic selection strategies for breeders [11]. This negative correlation implies that selecting higher yields often results in lower protein levels in the grains, presenting a complex balance in genotypic selection strategies [12]. Yield at harvest is a genetically controlled trait [13,14]. However, environmental factors and farming practices often lead to variations in yield [15,16,17]. Yield variability in wheat is influenced by several agronomic factors, with nitrogen management being crucial among them. Proper nitrogen fertilization can significantly enhance yield and biomass production by optimizing the plant’s nutritional status, especially in regions challenged by environmental constraints and resource limitations [10]. This approach aligns with our study’s focus on assessing the impacts of nitrogen fertilization and genotype selection on wheat productivity.
To ensure optimal yield, it is crucial to identify farming practices, which cater to this objective. One such practice is the strategic management of nitrogen fertilization, tailored to the crop’s needs at various developmental stages [18,19,20]. By understanding the relationship between nitrogen absorption and grain yield [21], farmers can better manage nitrogen inputs according to the specific requirements of the chosen variety [22].
With the increasing global demand for wheat, developing varieties with improved productivity and resource utilization is vital. Therefore, this study aims to assess the effects of nitrogen fertilization and genotype selection on wheat yield, biomass production and yield components. It seeks a comprehensive understanding of the intricate interdependencies among these variables. Through a five-year study (2016–2021), we specifically focused on comparing the performance across three categories of wheat varieties: new (‘Itri’ and ‘Luiza’), medium-aged (‘Faraj’ and ‘Nassira’) and ancient (‘Karim’). This comparison aimed to examine the influence of nitrogen application rates on each category to identify optimal strategies for maximizing wheat productivity.
2. Materials and Methods
2.1. Experimental Sites and Climate Conditions
This study was conducted over five years, from 2016 to 2021, at three experimental stations belonging to Morocco’s National Institute of Agricultural Research (INRA).
Table 1 provides detailed geographic coordinates, ecosystem types and five-year precipitation trends of these INRA experimental stations in our study, encapsulating key environmental data from the period 2016–2021.
The locations of these stations and regional variations are depicted in Figure 1, which presents dual maps illustrating the growth season duration (in days) and annual precipitation (in mm/year) across various study locations and regions in Morocco for the 2016–2017 to 2020–2021 agricultural seasons.
2.2. Experimental Design
The experiment was conducted using a split-plot design with two factors: nitrogen treatment (N) in the main plots and variety (V) in the sub-plots. Three levels of nitrogen (N) factor were used: N1 = 120 kg/ha, N2 = 60 kg/ha and N3 = 0 kg/ha. Five different varieties were used in the sub-plots. The experiment was repeated twice, resulting in a total of 30 plots. The characteristics of the varieties are summarized in Table 2.
2.3. Plot Characteristics and Agricultural Practices
Each experimental plot occupied a 2.7 m2 area, with dimensions of 2.5 m in length and 1.08 m in width. To ensure optimal growing conditions, standard agronomic practices, such as soil preparation and weeding, were employed throughout the growing season. Ammonium nitrate fertilizer with a 33.5% nitrogen content was applied in two stages to align with the crop’s growth phases: half at the tillering stage and the remaining half at the stem extension stage, ensuring targeted nutrient support. Seeds were sown at a density of 400 g/m2 around mid-November using a Wintersteiger plot seeder with an inter-row spacing of 20 cm and a sowing depth of 3.5 cm to accommodate the planting dates recommended for the three different regions under study. To manage pests and avoid the peak cycle of the Hessian fly, seeding dates were carefully selected, and manual weeding was conducted to control weed competition. Harvesting was completed by the end of June for all study years, marking the conclusion of the annual growth cycle.
2.4. Data Recording and Sampling
Data were collected from three distinct locations on six key agronomic traits, adhering to rigorous methodologies and international standards. Yield was initially measured in g/2.7 m2 and subsequently converted to kg/ha for standardization. Biomass, quantified pre-harvest, included only the above-ground plant material and was weighed in kilograms. The thousand-kernel weight (TKW) was determined using an electronic grain counter compliant with NF V03-702 [23] and ISO 520 [24] standards. The number of spikes per square meter (Spk/m2) was assessed based on designated one-square-meter sample areas within each plot.
2.5. Data Processing
Data analysis involved multiple software tools. In Excel, we computed the mean of the data collected from the three locations for later calculations. For graphical representations, such as bar plots and GT bi-plots, we used R.
Minitab 18 was specifically employed for statistical analysis. A three-way ANOVA was conducted to understand the effects of variety, nitrogen, year and their interactions on the observed measurements.
Lastly, OriginPro was utilized for principal component analysis (PCA). PCA aided in simplifying data visualization and interpretation by reducing the dimensionality of the dataset while retaining variance information.
3. Results
3.1. Genotypic Influence on Yield and Yield Components
Initial analysis of the ANOVA results for yield and biomass demonstrated significant variation among the durum wheat varieties studied. Both factors displayed a significance level of p < 0.05. This finding emphasizes that the observed differences in yield and biomass among the varieties are not due to chance alone but likely reflect distinct characteristics inherent to their genotypes.
During the five-year research span (2016–2021), a trend was observed where newer varieties tended to achieve higher yields than the oldest variety. Among the evaluated varieties, ‘Faraj’ exhibited superior productivity in yield (3430 kg/ha) and biomass production (12,196 kg/ha). Meanwhile, ‘Luiza’ and ‘Itri’ also exhibited notable yield and biomass production performance. ‘Luiza’ demonstrated a yield of 3422 kg/ha and a biomass of 11,949 kg/ha, while ‘Itri’ produced a yield of 3347 kg/ha and a biomass of 11,889 kg/ha. These results indicate the potential of both new and moderately new varieties in agricultural production. The older varieties, such as ‘Karim’ (yield: 3239 kg/ha, biomass: 11,267 kg/ha) and ‘Nassira’ (yield: 3261 kg/ha, biomass: 10,543 kg/ha), demonstrated lower but competitive productivity. The average yield and biomass values calculated across all varieties stood at 3340 kg/ha and 11,569 kg/ha. However, it is important to note that while the ANOVA results indicated significant yield differences among the varieties, the effect of variety on biomass was not statistically significant (ns).
The study also explored grain size using the metric of TKW. The ANOVA analysis for TKW revealed a highly significant difference among the varieties, denoted as p < 0.001. Regarding TKW, ‘Karim’ emerged as the leader with the greatest value of 42.46 g. ‘Itri’, however, recorded the lowest TKW of 39.21 g.
A similar trend was observed with Spk/m2 (plant number per square meter). The ANOVA analysis indicated significant differences among the varieties at p ≤ 0.01. Here, ‘Faraj’ once again asserted its superiority, recording the greatest number of spikes per square meter (332 Spk/m2). In contrast, ‘Karim’ had the lowest Spk/m2, registering 294 Spk/m2. The average plant density across all the varieties was 311 spikes per square meter.
Regarding G/m2 (grain number per square meter), the ‘Luiza’ variety proved to be the most prolific, producing the highest number of grains per square meter at 7784. ‘Nassira’, on the other hand, had the lowest grain count at 7152 grains per square meter. Across all varieties and treatments, the overall mean G/m2 was determined to be 7460 grains per square meter.
The research also evaluated the characteristics of grains per spike (G/S), showing significant differences among the varieties at p < 0.05. ‘Faraj’, ‘Luiza’ and ‘Itri’ were the top performers, each producing 27 grains per spike. Conversely, ‘Karim’ and ‘Nassira’ registered the smallest G/S values, with 26 and 24 grains per spike, respectively. These findings highlight the distinct variation in G/S across the different durum wheat varieties. The variations observed in this study can be attributed to the distinct genotypes of the varieties tested, suggesting that genetic factors play a pivotal role in determining these agronomic traits.
A visual representation in the bi-plot analysis (Figure 2) clearly presents the relationship between the wheat varieties and agronomic traits. The positioning of each variety on the bi-plot provides insights into their respective agronomic characteristics. The new varieties, ‘Luiza’ (2011) and ‘Itri’ (2017), showed a close association with grains per square meter (G/m2) and the number of grains per spike (G/S). Conversely, the ‘Nassira’ (2003) variety was associated more closely with the thousand-kernel weight (TKW). The ‘Faraj’ (2007) variety was more closely aligned with spikes per square meter (Spk/m2) than with biomass or yield. Meanwhile, the older variety ‘Karim’ (1985) found itself on the opposite side of the bi-plot, indicating differing agronomic characteristics.
3.2. Influence of Nitrogen on Biomass, Yields and Yield Components
Turning to the role of nitrogen, the ANOVA results showed a significant effect on crop yield (p < 0.05), biomass (p < 0.001) and thousand-kernel weight (TKW) (p < 0.001), as outlined in Table 3, demonstrating nitrogen’s significant influence on these metrics. The maximum yield was obtained following an application of 120 kg/ha of nitrogen, reaching 3404 kg/ha, while the minimum yield was observed with no nitrogen application, at 3234 kg/ha.
Regarding biomass, the ANOVA analysis also revealed a significant impact of nitrogen (p < 0.001). Consistent with the yield results, the greatest biomass was achieved with 120 kg of nitrogen, reaching 12,277 kg/ha. In contrast, the smallest biomass was noted for 0 kg/ha of nitrogen, standing at 10,917 kg/ha.
Additionally, the ANOVA analysis detected a significant effect of nitrogen on the TKW of the crop (p < 0.001). In alignment with this, the application of 120 kg/ha of nitrogen yielded the greatest TKW, at 41.68 g. The smallest TKW, however, was noted with 60 kg/ha of nitrogen, registering 40.56 g.
The analysis of variance (ANOVA) findings indicated a significant difference in the impact of nitrogen on the quantity of spikes per square meter (Spk/m2) with a p = 0.003. Notably, utilization of 60 units of nitrogen had the most pronounced effect on the spike count per square meter, resulting in a total of 319 Spk/m2. On the other hand, the absence of nitrogen application, marked as 0 units, yielded the lowest spike count, totaling 302 spikes per square meter.
Furthermore, the application of nitrogen showed no significant impact on the quantity of grains per square meter (p = 0.095) or the number of grains per spike (p = 0.193). The experimental results indicated that the maximum grain yield per unit area resulted from the precise application of 60 units of nitrogen, which resulted in a grain count of 7730 per square meter. Conversely, the minimum grains per square meter were observed with the application of 120 kg/ha of nitrogen, amounting to 7352 grains per square meter. The mean grains per square meter stood at 7544. Similarly, the highest number of grains per spike occurred with 0 kg/ha and 60 kg/ha of nitrogen, both delivering 27 grains per spike. The lowest number of grains per spike was obtained with 120 kg/ha of nitrogen, registering 25 grains per spike.
The selection of nitrogen doses of 120 kg/ha and 60 kg/ha was informed by a comprehensive review of preliminary trials and relevant literature, aiming to identify optimal rates, which balance environmental sustainability with yield improvements.
A closer examination of nitrogen use efficiency (NUE) reveals a more nuanced picture of nitrogen’s role in wheat production. Specifically, the NUE for the 60 kg/ha dose stood at 2.37 kg grain/kg N, surpassing the 1.42 kg grain/kg N efficiency observed for the 120 kg/ha dose. This differential efficiency highlights the diminishing returns on yield with increased nitrogen application and underscores the importance of optimizing nitrogen management to achieve sustainable wheat production.
The findings from this study illuminate the critical balance between nitrogen application and wheat yield optimization. While higher nitrogen doses can enhance yield and biomass, the efficiency of nitrogen use, as evidenced by the NUE calculations, suggests that lower application rates may offer a more sustainable pathway for maximizing wheat productivity. This balance is crucial for agronomic practices, especially in regions where environmental sustainability and resource optimization are paramount.
As illustrated in Figure 3, nitrogen application rates and variety significantly influence crop yield. Recent varieties (‘Faraj’, ‘Itri’ and ‘Luiza’) exhibit greater yields compared to older varieties (‘Karim’ and ‘Nassira’) at a given nitrogen application, which can be attributed to advancements in plant breeding. Furthermore, nitrogen application is pivotal in yield enhancement, with N1 (120 kg of nitrogen) and N2 (60 kg of nitrogen) consistently leading to superior yields over N3 (0 kg of nitrogen) across each variety. The variability in response to nitrogen levels among the varieties underscores the differences in nitrogen use efficiency and yield potential inherent to each genotype.
3.3. Insights on Interaction Effects of Agronomic Traits
Expanding the scope of the analysis, the study explored the interaction effects among variety and nitrogen, variety and year, and nitrogen and year, as detailed in Table 3.
Notably, the relationship between variety and nitrogen showed no significant differences in several agronomic traits—yield (p = 0.909), biomass (p = 0.475) and Spk/m2 (p = 0.954)—according to Table 3. These data imply that the influence of nitrogen remains stable across various varieties. This means that the outcome of nitrogen application is likely to be uniform irrespective of the wheat variety chosen, streamlining nitrogen management decisions.
When examining the interaction between variety and year, significant effects were observed for yield (p < 0.001), biomass (p < 0.001), TKW (p < 0.001), Spk/m2 (p < 0.001) and G/m2 (p < 0.001), but not for G/S (ns), indicating that the performance of different wheat varieties can vary significantly from year to year, depending on environmental conditions and other factors. Similarly, the interaction between nitrogen and year showed significant differences for yield (p < 0.001), biomass (p = 0.002), TKW (p = 0.003) and Spk/m2 (p < 0.001), with a noticeable effect on G/m2 (p < 0.05), highlighting how optimal nitrogen application rates may vary across different years, influenced by annual weather patterns and other environmental factors. Such variations are pivotal to consider in agronomic planning. The three-way interaction between variety, nitrogen and year was predominantly found to be non-significant, pointing toward a stable combined effect of these parameters on the agronomic traits.
Venturing into a deeper data analysis, the principal component analysis (PCA) (Figure 4) unravels the associations between traits and nitrogen application rates. The principal component analysis (PCA) of the data reveals two principal components, PC1 and PC2, which account for 57.71% and 42.29% of total variance, respectively. The TKW is closely associated with the greatest nitrogen application rate (N1, 120 kg/ha), indicating a positive correlation between these two variables. Similarly, the number of spikes per square meter and grains per square meter (G/m2) are closely associated with the N2 (60 kg/ha) nitrogen application rate. In contrast, yield and biomass are situated on the opposite side of the PCA plot, indicating a negative correlation with the N3 0 kg/ha application rate. Interestingly, the yield and biomass are situated between the N1 and N2 nitrogen application rates on the PCA plot. This suggests that an intermediate nitrogen application rate, between N1 and N2, could potentially result in optimal yield and biomass.
To further explore the relationship between nitrogen application rates and yield, we employed quadratic models for two distinct crop varieties, ‘Faraj’ and ‘Karim’. These models were derived from extensive field trials and were validated to ensure an accurate fit with empirical data. The equations are as follows:
Where Y represents the yield in kg/ha, and x represents the nitrogen application rates in kg/ha.
Upon optimization, for the ‘Faraj’ variety, the nitrogen application rate corresponding to maximum yield was found to be x ≈ 72.42 kg/ha. This nitrogen application rate predicts a maximum yield of approximately YFaraj ≈ 3580 kg/ha. The second derivative confirmed this as a local maximum.
Similarly, for the ‘Karim’ variety, the nitrogen dose for optimal yield was determined to be x ≈ 74.25 kg/ha, with a corresponding yield of YKarim ≈ 3294 kg/ha. Again, the second derivative confirmed this as a local maximum. Although the optimal nitrogen doses for ‘Faraj’ and ‘Karim’ are nearly identical, indicating a similar nitrogen use efficiency, ‘Faraj’ can achieve a higher yield than ‘Karim’ at this nearly equivalent nitrogen application rate, highlighting the potential for genetic factors to influence yield optimization even under similar nutrient management practices.
3.4. Comparative Analysis of Wheat Varieties under Different Nitrogen Application Rates: A Five-Year Study on Yield, Biomass and Grain Characteristics
In the inaugural year of our investigation (2017), we focused on assessing the impact of varying nitrogen application rates (N1, N2, N3) on a selection of wheat varieties (‘Faraj’, ‘Itri’, ‘Karim’, ‘Luiza’, ‘Nassira’), examining key parameters, including yield, biomass, TKW, number of spikes per square meter (Spk/m2), grains per square meter (G/m2) and the number of grains per spike (G/S). In this comprehensive study, a number of pivotal trends were identified. Across all nitrogen application rates, the ‘Itri’ variety demonstrated superior performance by achieving the greatest average yield of 3993 kg/ha, which corresponded to a notable 6.2% increase compared to the overall mean yield of 3760 kg/ha, as illustrated in Table 4. In contrast, the ‘Karim’ variety exhibited the smallest average yield of 3502 kg per ha (kg/ha), representing a reduction of approximately 6.9% compared to the mean yield across all varieties. In terms of biomass, ‘Faraj’ notably excelled at the N2 level, accumulating 14,900 kg/ha—a 11.6% increase over the general mean biomass of 13,347 kg/ha. ‘Karim’, on the other hand, recorded the smallest biomass of 11,863 kg/ha at the N2 level—a 11.1% decline from the overall mean. Interestingly, although ‘Karim’ lagged in yield and biomass, it exhibited the greatest TKW of 49.06 g at the N1 level—a 14.8% increase over the general mean TKW of 42.74 g. Conversely, ‘Luiza’ registered the lowest TKW of 39.08 g at the N2 level, which was approximately 8.6% below the mean, according to Table 4.
In the 2018 continuation of our study, we observed marked differences in wheat variety responses to nitrogen levels (N1, N2 and N3). ‘Luiza’ consistently outshone its counterparts in yield, averaging 4794 kg/ha, indicating an 8% rise from the revised mean yield of 4437 kg/ha for that year. However, at N3, ‘Luiza’ yielded slightly less than ‘Karim’, as highlighted in Table 5. Contrarily, ‘Faraj’ and ‘Nassira’ displayed suboptimal results, with yields of 4199 kg/ha and 4133 kg/ha, which were 5.4% and 6.9% below the mean, respectively. When it came to biomass, both ‘Itri’ and ‘Luiza’ were prominent, especially at the N1 level, amassing an average biomass of 14,001 kg/ha, marking a 20.4% increase over the average biomass of 11,624 kg/ha. However, notably, ‘Faraj’ yielded less at the N3 level, posting a biomass figure of 10,128 kg/ha—12.9% less than the mean. Interestingly, while ‘Faraj’ achieved the highest TKW of 48.35 g at the N3 level, surpassing the mean TKW of 45.80 g by 5.6%, ‘Itri’ fell short in this measure, with the smallest TKW of 43.46 g at the N2 level—5.1% below the mean—as reported in Table 5.
In 2019, the ‘Faraj’ variety consistently produced the largest yields across all nitrogen levels, with an average yield of 3425 kg/ha, representing a significant 16.5% increase over the overall mean yield of 2939 kg/ha, as illustrated in Table 6. In contrast, ‘Nassira’ and ‘Itri’ generally underperformed in yield, registering average yields of 2628 kg/ha and 2733 kg/ha, falling 10.6% and 7.0% below the mean, respectively. Regarding biomass, ‘Itri’ produced the highest average at the N2 level, with 13,406 kg/ha, which was a notable 28% increase over the overall mean biomass of 10,474 kg/ha. In contrast, ‘Nassira’ reported the smallest biomass of 7820 kg/ha at the N3 level, trailing the mean by 25.3%. Interestingly, despite its smaller yield, ‘Nassira’ led in TKW at the N1 level, with 39.7 g, exceeding the overall mean TKW of 37.8 g by a modest 5%. ‘Itri’, however, produced a small TKW at both the N2 and N3 levels, recording the lowest average of 34.59 g—a decline of 8.5% from the mean—as reported in Table 6.
The 2020 dataset provides critical insights into crop behavior under varying nitrogen conditions. Significantly, the ‘Faraj’ variety produced the largest yield, exceeding the overall mean yield of 1720 kg/ha. Its performance was particularly remarkable at the N2 level, where it registered a yield of 2178 kg/ha, surging ahead by 26.6% relative to the mean, as highlighted in Table 7. In stark contrast, ‘Karim’ and ‘Itri’ largely underperformed, with their yields languishing at approximately 22.0% and 14.7% below the mean, respectively. With respect to biomass accumulation, ‘Faraj’ produced the greatest biomass at the N1 and N2 levels, producing values, which were a substantial 33.5% and 27.2% higher than the average of 6305 kg/ha. ‘Karim’ was the least productive, with its biomass at the N3 level falling a concerning 25.6% below the mean. Intriguingly, while ‘Itri’ exhibited subpar yield and biomass figures, it was ‘Nassira’, which recorded the highest TKW of 42.41 g at the N3 level—an increase of 8.5% above the mean TKW of 39.08 g. Concurrently, ‘Itri’ yielded less at the N2 level, with a TKW of 36.98 g—5.4% below the mean.
In the 2021 assessment—the year completing our longitudinal examination—the ‘Itri’ variety displayed a complex but instructive performance profile. With a yield of 4319 kg/ha at the N1 nitrogen level, ‘Itri’ outperformed the overall mean yield of 3789 kg/ha by approximately 14.0%. However, it manifested a marked decline at the N3 nitrogen level, falling short of the mean by 11%. This dual behavior was also reflected in biomass accumulation; ‘Itri’ recorded values 12.2% above the mean of 17,127 kg/ha at the N1 level but plummeted to 21.3% below the mean at the N3 level. Intriguingly, ‘Karim’ led in TKW at the N1 level, registering a TKW of 42.76 g, which was 6.5% above the overall mean of 40.14 g. In contrast, ‘Itri’ marked the lowest TKW at both the N2 and N3 nitrogen levels, with values 7.4% and 8.4% below the mean, respectively, as shown in Table 8.
The ANOVA results from the five-year period highlight the significant influence of wheat variety on the yield, biomass, TKW, Spk/m2 and G/m2. The study also underscores the significant influence of nitrogen level on the yield, biomass, TKW and Spk/m2 during the 2020/2021 growing season. This suggests that nitrogen level can significantly affect these traits, especially during growing seasons with greater rainfall, such as 2020/2021 (429.8 mm).
3.5. Correlation Analysis: Yield, Yield Components and the Interaction between Nitrogen and Variety
In this scientific investigation, we explored the intricate interconnections between crop yield and a multitude of agronomic variables, encompassing biomass, TKW, spikes per square meter (Spk/m2) and grams per square meter (G/m2). The interrelationships among these variables are depicted in Figure 7, where the correlation coefficients, magnitude and orientation of each association are exhibited.
In our comprehensive five-year study, we observed a strong correlation between crop yield and biomass (r = 0.60, p < 0.001), particularly in recently released wheat varieties, such as ‘Faraj’, ‘Luiza’ and ‘Itri’, which consistently outperformed older varieties in these aspects. Interestingly, while nitrogen application significantly enhanced yield and biomass, its impact on TKW and spikes per square meter (Spk/m2) was less pronounced, suggesting a nuanced influence on different yield components. Furthermore, the uniform response of various genotypes to nitrogen fertilization indicates a stable interaction across varieties, underscoring the importance of genotype selection in tandem with tailored nitrogen management for optimal wheat production. These findings highlight the complex interplay of genetic factors and agronomic practices in determining crop productivity, offering valuable insights for future agricultural strategies.
4. Discussion
The findings of this investigation complement existing research, underscoring the pivotal role of both genotype selection and nitrogen application in influencing crop productivity. The marked superiority of recently released varieties, such as ‘Faraj’, ‘Luiza’ and ‘Itri’, in terms of yield, biomass and yield components highlights the potential benefits of adopting these improved genotypes to boost crop productivity [25,26].
It was also observed that nitrogen application significantly influences the yield, biomass and TKW—a result, which aligns with prior studies reporting enhanced wheat productivity linked with optimal nitrogen fertilization [27,28,29,30]. Nevertheless, Spk/m2, G/m2 and G/S largely demonstrated a non-response to nitrogen application; it is important to consider that the ANOVA analysis did reveal a near significant effect of nitrogen on Spk/m2 (p = 0.071). This might hint at a developing trend not yet reaching conventional significance, suggesting that factors such as environmental conditions, soil fertility and specific management practices may exert a more substantial influence on these parameters [31].
One of the critical discoveries in this study was the lack of a significant interaction between nitrogen application and variety across all the investigated parameters. This outcome implies that the response of different genotypes to nitrogen fertilization is consistent across varieties. This finding is corroborated by previous research, such as that conducted by Peake et al. [32]. Their study revealed a noteworthy correlation among the genotype, environment and agronomic management with grain yield and lodging. This indicates that specific agronomic techniques, including particular nitrogen fertilization practices, could be broadly applicable across different varieties. Our study further emphasizes the importance of understanding the interplay of these variables in order to optimize grain yield. Moreover, an extensive examination by Kartseva et al. [33] revealed that diverse approaches to mitigating osmotic stress can be ascribed to variations in genetic make-up. This emphasizes that while the response to nitrogen fertilization may be generally uniform, the reaction to stress mitigation techniques can significantly vary across different genotypes due to their unique genetic characteristics [34].
Through a meticulous analysis of the association between crop yield and biomass, a robust and affirmative correlation was unearthed, thereby fortifying the prevailing understanding that the production of biomass plays a pivotal role in determining the ultimate grain yield. This aligns with the study by Balcha and Ayana [35], which found a positive correlation between grain yield and biomass yield in wheat genotypes. The importance of biomass in realizing high yield potential in wheat is a commonly echoed sentiment in earlier studies [36,37]. The observed positive correlations between yield and Spk/m2, G/m2 and G/S indicate that enhancing plant density, grain production and the number of grains per spike could potentially lead to increased wheat yields [38,39,40,41].
An intriguing finding in this study is the negative correlation between TKW and Spk/m2, suggesting a potential trade-off between kernel weight and plant density. This observation is corroborated by previous research [42,43]. The evidence of a trade-off between kernel weight and plant density further complicates the picture. Increasing plant density could potentially lead to smaller kernels, which could limit the potential yield increase from greater plant densities [44]. Hence, the development of varieties, which can maintain kernel weight while increasing plant density, may represent a fruitful area for future research and breeding efforts. The positive correlation between Spk/m2 and G/m2 indicates that an increase in plant density could potentially stimulate greater grain production. In contrast, the negative correlation between Spk/m2 and G/S implies a potential reduction in the number of grains per spike as plant density increases.
Despite the study revealing no significant effect of the interaction between nitrogen and variety on several parameters, it offers crucial insights. The consistent reaction of various wheat varieties to nitrogen fertilization among the genotypes is consistent with prior studies, which have shown comparable responses in wheat genotypes [45,46]. This consistency suggests that the nitrogen effect is independent of variety type.
However, the significant influence of nitrogen and variety as separate factors underscores the necessity for informed selection of the appropriate variety and nitrogen fertilization strategy to achieve large crop yields. Hence, this investigation underscores the significance of comprehensively comprehending the interplay between genetic and environmental factors in relation to wheat productivity, as this is crucial for augmenting crop productivity.
Within the expansive framework of sustainable agriculture, the discoveries put forth in this study provide a persuasive argument in favor of adopting an integrated methodology for crop management. The successful cultivation of wheat requires a meticulous approach, which encompasses not only a careful choice of ideal wheat genotypes but also a profound comprehension of the intricate dynamics between various factors, including nitrogen fertilization, environmental conditions and management practices. With ever-increasing pressures on global food security due to population growth and climate change, such informed and targeted crop management strategies will be indispensable in achieving sustainable increases in crop productivity.
Consequently, the results support the notion that breeders should strive to develop new varieties with improved nitrogen efficiency. A variety possessing enhanced nitrogen utilization capabilities has the potential to yield higher crop production while concurrently minimizing the requirement for fertilization, thus serving as a means to alleviate the ecological consequences associated with excessive nitrogen fertilizer application [47]. In light of the potential consequences associated with excessive application of nitrogen in agricultural systems, the aforementioned discoveries underscore the significance of conducting scientific investigations in order to foster the establishment of sustainable and ecologically sound farming methodologies.
Another crucial aspect in this discussion is the relationship between plant density (Spk/m2), grain production (G/m2) and the number of grains per spike (G/S). The study’s findings suggest that a more thorough understanding of these relationships could offer the pathways for enhancing wheat yield. By identifying and implementing optimal plant densities, we may be able to boost grain production and, in turn, overall yield. However, caution should be exercised in pursuing this strategy, as there appears to be a negative trade-off between plant density and the number of grains per spike [48]. This observation is corroborated by previous research [49]. The evidence of a trade-off between kernel weight and plant density further complicates the picture. Increasing plant density could potentially lead to smaller kernels, which could limit the potential yield increase from greater plant densities. Hence, the development of varieties, which can maintain kernel weight while increasing plant density, may represent a fruitful area for future research and breeding efforts.
In addition to the aforementioned observations, our comprehensive five-year investigation (2017–2021) consistently demonstrated that diverse wheat varieties responded differently to varying nitrogen levels. Our study made the crucial observation that the annual climatic conditions were the main factor influencing these divergent responses. Temperature, rainfall, sunlight and other factors significantly affected the different varieties’ responses to nitrogen fertilization, suggesting a significant interaction between climatic conditions and nitrogen utilization efficiency. This underscores the need for adaptive management strategies, which account not only for the genetic attributes associated with diverse wheat varieties but also for the changing characteristics of the ecosystem in which they are grown, particularly in the newer varieties ‘Faraj’, ‘Luiza’ and ‘Itri’. Yield, biomass, TKW, the number of spikes per square meter (Spk/m2), grains per square meter (G/m2) and the number of grains per spike (G/S) varied across the different wheat varieties and nitrogen rates, supporting the findings of Yadav et al. [50], who reported significant genetic variability in these traits among different wheat varieties.
The ANOVA results over the five years indicate that both the variety and nitrogen rate significantly influence yield and other agronomic traits. This aligns with the findings of Sedri et al. [51], who reported that different physiological traits, including yield, were significantly affected by various water conditions—an environmental factor comparable to nitrogen rate in our study. The precipitation data for each year also enrich the context of the results. For instance, 2018 saw the greatest precipitation and showed significant effects of nitrogen rate on biomass and TKW, possibly due to the increased water availability enhancing nitrogen’s impact on these traits. This is supported by Shibeshi and Kassa [52], who found that environmental conditions, including water availability, can significantly influence wheat yield and other agronomic traits.
Additionally, the results suggest that the new varieties, ‘Luiza’ and ‘Itri’, have been optimized for high grain yield—a finding, which resonates with the complex trait interplay reported by Zhang et al. (2020) [53]. The ‘Faraj’ variety seems to have been bred with a focus on spike density—a trait highlighted for its importance in wheat yield by Joudi et al. (2017) [54]. The association of the ‘Nassira’ variety with high TKW suggests an emphasis on breeding for grain weight—a trait positively correlated with grain yield, as reported by Alonso et al. (2018) [55] and Racz et al. (2019) [56]. The positioning of the older variety, ‘Karim’, on the opposite side of the bi-plot suggests that it might possess different agronomic characteristics, likely due to different selection pressures during its development. These findings underscore the importance of targeted breeding strategies to optimize key agronomic traits, thereby enhancing wheat yield under various environmental conditions.
The observed positive correlation between TKW and application of the greatest nitrogen application rate (N1 120 U) is in line with the research conducted by Derkx et al. [57]. Their study on wheat demonstrated that increased nitrogen application rates resulted in increased nitrogen uptake, leading to the formation of larger—albeit fewer—grains. This phenomenon could be attributed to the presence of nitrogen, a vital element for plant physiological processes, which plays a significant role in increasing grain size and weight. The observations put forward by Wójtowicz et al. [58] provide additional evidence that the number of spikes per square meter significantly influences grain yield, especially when nitrogen doses are varied. The correlation between the density of spikes per unit area and G/m2, in conjunction with application of the N2 60 kg nitrogen application rate, suggests that a moderate level of nitrogen fertilization could potentially enhance these specific yield attributes. The findings by Guo et al. [59] support our current understanding of agronomic principles. They reported a noteworthy association between grain yield and two key factors: kernel number and thousand-kernel weight. This implies that specific clusters of wheat varieties have the potential to optimize yield traits, such as the number of grains per square meter. Moreover, the positioning of yield and biomass between the N1 and N2 nitrogen doses on the PCA plot is a significant finding. It suggests that an intermediate nitrogen dose, between N1 and N2, could potentially result in optimal yield and biomass. The findings of this study hold significant implications for the field of agronomy, particularly in relation to agricultural practices. The results indicate that a meticulous regulation of nitrogen application has the potential to enhance crop productivity to a considerable extent. This assertion is corroborated by the research conducted by Farkas et al. [60], wherein they discovered that wheat genotypes exhibiting favorable adaptability to diverse nitrogen levels exhibited the highest water use efficiency. Nevertheless, it is imperative to acknowledge that the aforementioned discoveries are predicated upon a single investigation, thereby necessitating additional scientific inquiry to validate and ascertain the precise nitrogen dosage, which optimizes both crop productivity and biomass accumulation. Future investigations should additionally take into account other variables, which have the potential to exert an influence on both crop yield and biomass production. These variables encompass, but are not limited to, soil composition, meteorological conditions and the specific genetic make-up of the cultivated crop. Huang et al. [61] suggested that good quality, high yield and high efficiency could be achieved through selection of high-quality wheat varieties and nitrogen accumulation and translocation.
In conclusion, this comprehensive study underscores the importance of both the selection of an appropriate variety and optimal nitrogen fertilization strategy for wheat cultivation. However, the results also suggest that other variables, including plant population density and grain output, exert a substantial impact on crop yield and must be considered within a holistic agricultural management approach. Furthermore, the observed trade-offs among certain variables indicate that achieving optimal yield may necessitate a careful balance between these conflicting requirements. The collaboration between breeders and agronomists is crucial to optimizing the utilization of genetic advancements in conjunction with optimal agricultural techniques, thereby enhancing wheat productivity in a sustainable manner. In light of an escalating global food demand and the urgent need for environmentally friendly agricultural methods, the importance of adopting an integrated approach cannot be overstated.
Production largely depends on the variety, year and their interaction, as indicated by Kolarić et al., Popović et al. and Stevanović et al. [62,63,64]. Profitable production based on high yields is impossible without the application of high doses of mineral or organic fertilizers, according to Stevanović et al. [64]. Fertilization is also important for grain quality. These insights reinforce the complexity of agricultural systems and the multi-faceted approach needed for yield optimization. Studies of this nature yield invaluable insights, which can guide this undertaking, presenting pragmatic approaches to augmenting efficiency and resilience in the face of constantly mounting challenges. By cultivating a deeper understanding of the intricate dynamics between genetic predispositions and environmental influences, we can enhance our preparedness to guarantee the sustenance of our food supply and advance the principles of sustainable agriculture in the forthcoming era. It is imperative to pursue further investigations in this domain, as augmenting our knowledge repository will enhance our capacity to effectively meet the escalating demands of our global population. Research endeavors such as this study serve as fundamental pillars in elucidating optimal methodologies and guiding subsequent investigations, thereby transcending the confines of wheat cultivation to encompass broader implications. The discoveries made by researchers significantly contribute to advancing our shared knowledge in the field of agronomy, with the ultimate goal of ensuring the long-term viability and improvement of global food security.
5. Conclusions
In our five-year investigation of Morocco’s Mediterranean climate, we systematically dissect the multi-faceted interactions between nitrogen fertilization, variety selection and annual variances, delineating their collective influence on wheat yield and the associated agronomic parameters. Our analyses underscore the significant yet nuanced impact of nitrogen application and varietal choice against the backdrop of year-to-year climatic fluctuations, revealing a pronounced variability in yield optimization strategies necessitated by these elements. The findings advocate for a more nuanced, adaptable approach to agronomic management, highlighting the necessity of integrating varietal selection and nitrogen application with an acute awareness of their interaction with seasonal climatic conditions. This approach is pivotal to enhancing wheat yield resilience and resource use efficiency, thus contributing to the broader objectives of sustainable agricultural practices. Our ongoing and future research will further elucidate the roles of plant density and comprehensive water management strategies, offering integrated insights for advancing agronomic efficiency and sustainability in wheat production.
Author Contributions
The following contributions were made by the authors to the study and to preparation of this manuscript: O.H. was responsible for Writing—Original Draft Preparation and Data Visualization; A.B. was responsible for Project Administration, Supervision and Resource Provision; I.K. contributed to Study Validation; N.N. was responsible for Methodology and Study Validation; and A.A. was responsible for Writing—Review and Editing, Supervision, Data Curation and Resource Provision. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
The dataset provided in this research is accessible upon inquiry from the corresponding author. The accessibility of the data is currently restricted due to technical limitations.
Acknowledgments
The authors express gratitude to their institutional affiliates for continual support, as well as colleagues for their insightful contributions to this study. Appreciation is also extended to the reviewers and the MDPI Nitrogen editorial team for their guidance on manuscript improvement.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- FAOSTAT. Available online: http://faostat.fao.org (accessed on 20 November 2022).
- Carucci, F.; Gatta, G.; Gagliardi, A.; Bregaglio, S.; Giuliani, M.M. Individuation of the best agronomic practices for organic durum wheat cultivation in the Mediterranean environment: A multivariate approach. Agric. Food Secur. 2023, 12, 12. [Google Scholar] [CrossRef]
- Chentoufi, L.; Sahri, A.; Arbaoui, M.; Belqadi, L.; Birouk, A.; Roumet, P.; Muller, M.-H. Anchoring durum wheat diversity in the reality of traditional agricultural systems: Varieties, seed management, and farmers’ perception in two Moroccan regions. J. Ethnobiol. Ethnomedicine 2014, 10, 58. [Google Scholar] [CrossRef]
- Ouaadi, N.; Ezzahar, J.; Khabba, S.; Er-Raki, S.; Chakir, A.; Ait Hssaine, B.; Le Dantec, V.; Rafi, Z.; Beaumont, A.; Kasbani, M. C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco). Earth Syst. Sci. Data 2021, 13, 3707–3731. [Google Scholar] [CrossRef]
- Astaoui, G.; Dadaiss, J.E.; Sebari, I.; Benmansour, S.; Mohamed, E. Mapping wheat dry matter and nitrogen content dynamics and estimation of wheat yield using UAV multispectral imagery machine learning and a variety-based approach: Case study of Morocco. AgriEngineering 2021, 3, 29–49. [Google Scholar] [CrossRef]
- Powell, A.J.; Kim, S.H.; Cordero, J.; Vujanovic, V. Protocooperative Effect of Sphaerodes mycoparasitica Biocontrol and Crop Genotypes on FHB Mycotoxin Reduction in Bread and Durum Wheat Grains Intended for Human and Animal Consumption. Microorganisms 2023, 11, 159. [Google Scholar] [CrossRef]
- Noor, H.; Shah, A.A.; Ding, P.; Ren, A.; Sun, M.; Gao, Z. Long-Term Nutrient Cycle in Improved Grain Yield of Dryland Winter Wheat (Triticum aestivum L.) under Hydrological Process of Plant Ecosystem Distribution in the Loess Plateau of China. Plants 2023, 12, 2369. [Google Scholar] [CrossRef]
- Kherif, O.; Haddad, B.; Bouras, F.-Z.; Seghouani, M.; Zemmouri, B.; Gamouh, R.; Hamzaoui, N.; Larbi, A.; Rebouh, N.-Y.; Latati, M. Simultaneous Assessment of Water and Nitrogen Use Efficiency in Rain-Fed Chickpea-Durum Wheat Intercropping Systems. Agriculture 2023, 13, 947. [Google Scholar] [CrossRef]
- Rossi, R.; Bitella, G.; Bochicchio, R.; Labella, R.; Angerame, F.; Urbano, M.; Amato, M. Root and Shoot Growth of a Modern and an Old Tall Durum Wheat (Triticum durum Desf.) Variety under Dual-Purpose Management. Plants 2023, 12, 588. [Google Scholar] [CrossRef]
- Maher, H.; Moussadek, R.; Ghanimi, A.; Zouidi, O.; Douaik, A.; Dakak, H.; Amenzou, N.E.; Zouahri, A. Effect of tillage and nitrogen fertilization on soil properties and yield of five durum wheat germoplasms in a dry area of Morocco. Appl. Sci. 2023, 13, 910. [Google Scholar] [CrossRef]
- Michel, S.; Löschenberger, F.; Ametz, C.; Pachler, B.; Sparry, E.; Bürstmayr, H. Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding. Theor. Appl. Genet. 2019, 132, 1745–1760. [Google Scholar] [CrossRef]
- Liu, J.; Huang, L.; Wang, C.; Liu, Y.; Yan, Z.; Wang, Z.; Xiang, L.; Zhong, X.; Gong, F.; Zheng, Y. Genome-wide association study reveals novel genomic regions associated with high grain protein content in wheat lines derived from wild emmer wheat. Front. Plant Sci. 2019, 10, 464. [Google Scholar] [CrossRef]
- Guo, Y.; Wang, G.; Guo, X.; Chi, S.; Yu, H.; Jin, K.; Huang, H.; Wang, D.; Wu, C.; Tian, J. Genetic dissection of protein and starch during wheat grain development using QTL mapping and GWAS. Front. Plant Sci. 2023, 14, 1189887. [Google Scholar] [CrossRef]
- Yu, H.; Hao, Y.; Li, M.; Dong, L.; Che, N.; Wang, L.; Song, S.; Liu, Y.; Kong, L.; Shi, S. Genetic architecture and candidate gene identification for grain size in bread wheat by GWAS. Front. Plant Sci. 2022, 13, 1072904. [Google Scholar] [CrossRef]
- Tudor, V.C.; Stoicea, P.; Chiurciu, I.-A.; Soare, E.; Iorga, A.M.; Dinu, T.A.; David, L.; Micu, M.M.; Smedescu, D.I.; Dumitru, E.A. The Use of Fertilizers and Pesticides in Wheat Production in the Main European Countries. Sustainability 2023, 15, 3038. [Google Scholar] [CrossRef]
- López-Felices, B.; Velasco-Muñoz, J.; Aznar-Sánchez, J.; Román-Sánchez, I. Factors influencing the use of rainwater for agricultural irrigation: The case of greenhouse agriculture in southeast Spain. AQUA-Water Infrastruct. Ecosyst. Soc. 2023, 72, 185–201. [Google Scholar] [CrossRef]
- Upadhyaya, T.P. Role of Agriculture in Economic Development of Assam. Cogniz. J. 2022, 2, 10–21. [Google Scholar] [CrossRef]
- Martínez-Peña, R.; Schlereth, A.; Höhne, M.; Encke, B.; Morcuende, R.; Nieto-Taladriz, M.T.; Araus, J.L.; Aparicio, N.; Vicente, R. Source-sink dynamics in field-grown durum wheat under contrasting nitrogen supplies: Key role of non-foliar organs during grain filling. Front. Plant Sci. 2022, 13, 869680. [Google Scholar] [CrossRef]
- Oliveira, P.; Patanita, M.; Guerreiro, I.; Dores, J.; Rosa, E.; Costa, M.N.; Patanita, M.I.; Boteta, L.; Ferro-Palma, J.F.; Penacho, J. Combined effects of irrigation management and nitrogen fertilization on soft wheat productive responses under Mediterranean conditions. In Proceedings of the E3S Web of Conferences, 2019 10th International Conference on Environmental Science and Development (ICESD 2019), Milan, Italy, 13–15 February 2019; pp. 1–4. [Google Scholar]
- Hirzel, J.; Matus, I.; Madariaga, R. Effect of split nitrogen applications on durum wheat cultivars in volcanic soil. Chil. J. Agric. Res. 2010, 70, 590–595. [Google Scholar] [CrossRef]
- Xiao, Z.; Gu, H.; Wu, H.; Jing, W.; Zhu, K.; Zhang, W.; Gu, J.; Liu, L.; Qian, X.; Wang, Z. Effects of planting density, levels, and forms of nitrogen application on the yield and nitrogen utilization of wheat following rice in East China. Agronomy 2022, 12, 2607. [Google Scholar] [CrossRef]
- Saudy, H.S.; Abd El–Momen, W.R.; El–Khouly, N.S. Diversified nitrogen rates influence nitrogen agronomic efficiency and seed yield response index of sesame (Sesamum indicum, L.) cultivars. Commun. Soil Sci. Plant Anal. 2018, 49, 2387–2395. [Google Scholar] [CrossRef]
- Standard NF V03-702. Available online: https://www.boutique.afnor.org/en-gb/standard/nf-v03702/cereals-determination-of-the-mass-of-1000-grains/fa015791/13877 (accessed on 5 January 2024).
- ISO 520:2010—Cereals and Pulses. Available online: https://www.iso.org/standard/52270.html (accessed on 5 January 2024).
- Osman, R.; Tahir, M.N.; Ata-Ul-Karim, S.T.; Ishaque, W.; Xu, M. Exploring the impacts of genotype-management-environment interactions on wheat productivity, water use efficiency, and nitrogen use efficiency under rainfed conditions. Plants 2021, 10, 2310. [Google Scholar] [CrossRef]
- Koua, A.P.; Baig, M.M.; Oyiga, B.C.; Léon, J.; Ballvora, A. Fungicide Application Affects Nitrogen Utilization Efficiency, Grain Yield, and Quality of Winter Wheat. Agronomy 2021, 11, 1295. [Google Scholar] [CrossRef]
- Wang, J.; Hussain, S.; Sun, X.; Zhang, P.; Javed, T.; Dessoky, E.S.; Ren, X.; Chen, X. Effects of nitrogen application rate under straw incorporation on photosynthesis, productivity and nitrogen use efficiency in winter wheat. Front. Plant Sci. 2022, 13, 862088. [Google Scholar] [CrossRef]
- Zhang, Z.; Yu, Z.; Zhang, Y.; Shi, Y. Split nitrogen fertilizer application improved grain yield in winter wheat (Triticum aestivum L.) via modulating antioxidant capacity and 13 C photosynthate mobilization under water-saving irrigation conditions. Ecol. Process. 2021, 10, 21. [Google Scholar] [CrossRef]
- Yuan, Y.; Lin, F.; Maucieri, C.; Zhang, Y. Efficient irrigation methods and optimal nitrogen dose to enhance wheat yield, inputs efficiency and economic benefits in the North China Plain. Agronomy 2022, 12, 273. [Google Scholar] [CrossRef]
- Raya-Sereno, M.D.; Ortiz-Monasterio, J.I.; Alonso-Ayuso, M.; Rodrigues Jr, F.A.; Rodríguez, A.A.; González-Perez, L.; Quemada, M. High-resolution airborne hyperspectral imagery for assessing yield, biomass, grain N concentration, and N output in spring wheat. Remote Sens. 2021, 13, 1373. [Google Scholar] [CrossRef]
- Ghafoor, I.; Habib-ur-Rahman, M.; Ali, M.; Afzal, M.; Ahmed, W.; Gaiser, T.; Ghaffar, A. Slow-release nitrogen fertilizers enhance growth, yield, NUE in wheat crop and reduce nitrogen losses under an arid environment. Environ. Sci. Pollut. Res. 2021, 28, 43528–43543. [Google Scholar] [CrossRef]
- Peake, A.S.; Bell, K.L.; Fischer, R.; Gardner, M.; Das, B.T.; Poole, N.; Mumford, M. Cultivar× management interaction to reduce lodging and improve grain yield of irrigated spring wheat: Optimising plant growth regulator use, N application timing, row spacing and sowing date. Front. Plant Sci. 2020, 11, 401. [Google Scholar] [CrossRef]
- Kartseva, T.; Dobrikova, A.; Kocheva, K.; Alexandrov, V.; Georgiev, G.; Brestič, M.; Misheva, S. Optimal nitrogen supply ameliorates the performance of wheat seedlings under osmotic stress in genotype-specific manner. Plants 2021, 10, 493. [Google Scholar] [CrossRef]
- Shahin, M.G.; Saudy, H.S.; El-Bially, M.E.; Abd El-Momen, W.R.; El-Gabry, Y.A.; Abd El-Samad, G.A.; Sayed, A.N. Physiological and agronomic responses and nutrient uptake of soybean genotypes cultivated under various sowing dates. J. Soil Sci. Plant Nutr. 2023, 23, 5145–5158. [Google Scholar] [CrossRef]
- Mecha, B.; Alamerew, S.; Assefa, A.; Dutamo, D.; Assefa, E. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.) genotypes. Adv. Plants Agric. Res. 2017, 6, 128–136. [Google Scholar]
- Gaju, O.; DeSilva, J.; Carvalho, P.; Hawkesford, M.J.; Griffiths, S.; Greenland, A.; Foulkes, M.J. Leaf photosynthesis and associations with grain yield, biomass and nitrogen-use efficiency in landraces, synthetic-derived lines and cultivars in wheat. Field Crops Res. 2016, 193, 1–15. [Google Scholar] [CrossRef]
- Al-Falahy, M.A.; Al Mahmada, D.; Dawood, K.M.; Ahmed, S.S. Response of yield and its components traits for four promising genotypes of wheat to different levels of nitrogen fertilization. Mesop. J. Agric. 2022, 50, 11–19. [Google Scholar] [CrossRef]
- Wu, X.; Chang, X.; Jing, R. Genetic insight into yield-associated traits of wheat grown in multiple rain-fed environments. PLoS ONE 2012, 7, e31249. [Google Scholar] [CrossRef]
- Yang, D.; Cai, T.; Luo, Y.; Wang, Z. Optimizing plant density and nitrogen application to manipulate tiller growth and increase grain yield and nitrogen-use efficiency in winter wheat. PeerJ 2019, 7, e6484. [Google Scholar] [CrossRef]
- Kuzay, S.; Lin, H.; Li, C.; Chen, S.; Woods, D.P.; Zhang, J.; Lan, T.; von Korff, M.; Dubcovsky, J. WAPO-A1 is the causal gene of the 7AL QTL for spikelet number per spike in wheat. PLoS Genet. 2022, 18, e1009747. [Google Scholar] [CrossRef]
- Ellen, J. Effects of plant density and nitrogen fertilization in winter wheat (Triticum aestivum L.). 1. Production pattern and grain yield. Neth. J. Agric. Sci. 1987, 35, 137–153. [Google Scholar] [CrossRef]
- Tran, B.T.; Cavagnaro, T.R.; Able, J.A.; Watts-Williams, S.J. Bioavailability of zinc and iron in durum wheat: A trade-off between grain weight and nutrition? Plants People Planet 2021, 3, 627–639. [Google Scholar] [CrossRef]
- Abdollahi Sisi, N.; Stein, N.; Himmelbach, A.; Mohammadi, S.A. High-density linkage mapping of agronomic trait QTLs in wheat under water deficit condition using genotyping by sequencing (GBS). Plants 2022, 11, 2533. [Google Scholar] [CrossRef]
- Liu, Y.; Liao, Y.; Liu, W. High nitrogen application rate and planting density reduce wheat grain yield by reducing filling rate of inferior grain in middle spikelets. Crop J. 2021, 9, 412–426. [Google Scholar] [CrossRef]
- Lucena Marinho, J.d.; Silva, S.R.; de Batista Fonseca, I.C.; de Miranda, M.Z.; Guarienti, E.M.; Zucareli, C. Technological quality of wheat grains and flour as affected by nitrogen fertilization and weather conditions. Emir. J. Food Agric. (EJFA) 2022, 34, 997–1011. [Google Scholar] [CrossRef]
- Sharma, P.K.; Gurjar, R.; Saini, R.; Meena, D.; Meena, S.; Sabal, S.K. Response of Different Physiochemical Parameters of Wheat Genotypes (Triticum aestivum L. & Triticum durum L.) in Nutrient Stress Condition. Int. J. Plant Soil Sci. 2022, 34, 1074–1083. [Google Scholar]
- Xu, A.; Li, L.; Xie, J.; Wang, X.; Coulter, J.A.; Liu, C.; Wang, L. Effect of long-term nitrogen addition on wheat yield, nitrogen use efficiency, and residual soil nitrate in a semiarid area of the loess plateau of China. Sustainability 2020, 12, 1735. [Google Scholar] [CrossRef]
- Foulkes, M.J.; Slafer, G.A.; Davies, W.J.; Berry, P.M.; Sylvester-Bradley, R.; Martre, P.; Calderini, D.F.; Griffiths, S.; Reynolds, M.P. Raising yield potential of wheat. III. Optimizing partitioning to grain while maintaining lodging resistance. J. Exp. Bot. 2011, 62, 469–486. [Google Scholar] [CrossRef]
- Herrera, J.; Calderini, D.F. Pericarp growth dynamics associate with final grain weight in wheat under contrasting plant densities and increased night temperature. Ann. Bot. 2020, 126, 1063–1076. [Google Scholar] [CrossRef]
- Yadav, R.; Gupta, S.; Gaikwad, K.B.; Bainsla, N.K.; Kumar, M.; Babu, P.; Ansari, R.; Dhar, N.; Dharmateja, P.; Prasad, R. Genetic gain in yield and associated changes in agronomic traits in wheat cultivars developed between 1900 and 2016 for irrigated ecosystems of Northwestern Plain Zone of India. Front. Plant Sci. 2021, 12, 719394. [Google Scholar] [CrossRef]
- Sedri, M.H.; Roohi, E.; Niazian, M.; Niedbała, G. Interactive effects of nitrogen and potassium fertilizers on quantitative-qualitative traits and drought tolerance indices of rainfed wheat cultivar. Agronomy 2021, 12, 30. [Google Scholar] [CrossRef]
- Shibeshi, S.; Kassa, D. Evaluation of durum wheat varieties for yield and yield related traits in highland areas of southern Ethiopia. Int. J. Agric. Res. Innov. Technol. (IJARIT) 2021, 11, 26–28. [Google Scholar] [CrossRef]
- Zheng, F.-N.; Chu, J.-P.; Zhang, X.; Fei, L.-W.; Dai, X.-L.; He, M.-R. Interactive effects of sowing pattern and planting density on grain yield and nitrogen use efficiency in large spike wheat cultivar. Acta Agron. Sin. 2020, 46, 423–431. [Google Scholar] [CrossRef]
- Joudi, M.; Ahmadi, A.; Mohammadi, V. Changes in stem and spike related traits resulting from breeding in Iranian wheat cultivars: Associations with grain yield. Czech J. Genet. Plant Breed. 2017, 53, 107–113. [Google Scholar] [CrossRef]
- Alonso, M.P.; Mirabella, N.E.; Panelo, J.S.; Cendoya, M.G.; Pontaroli, A.C. Selection for high spike fertility index increases genetic progress in grain yield and stability in bread wheat. Euphytica 2018, 214, 112. [Google Scholar] [CrossRef]
- Racz, I.; Kadar, R.; Ceclan, O.A.; Hiriscǎu, D.; Bora, F.D.; Şopterean, L.; Cǎlugǎr, R. The grain yield performance and stability characters of several spring wheat genotypes in Transylvanian plain conditions. Bull. Univ. Agric. Sci. Vet. Med. 2020, 76, 61–70. [Google Scholar] [CrossRef]
- Derkx, A.P.; Orford, S.; Griffiths, S.; Foulkes, M.J.; Hawkesford, M.J. Identification of differentially Senescing mutants of wheat and impacts on yield, biomass and nitrogen Partitioning F. J. Integr. Plant Biol. 2012, 54, 555–566. [Google Scholar] [CrossRef]
- Wójtowicz, A.; Wójtowicz, M.; Piekarczyk, J.; Kowalik, W.; Hara, P.; Oniszczuk, A.; Oniszczuk, T. The Use of Sentinel-2 Imagery for the Estimation of Winter Wheat and Triticale Yield and Number of Spikes per Square Meter. Agronomy 2020, 10, 1842. [Google Scholar]
- Guo, X.; Svane, S.F.; Füchtbauer, W.S.; Andersen, J.R.; Jensen, J.; Thorup-Kristensen, K. Genomic prediction of yield and root development in wheat under changing water availability. Plant Methods 2020, 16, 90. [Google Scholar] [CrossRef]
- Farkas, Z.; Varga-László, E.; Anda, A.; Veisz, O.; Varga, B. Effects of waterlogging, drought and their combination on yield and water-use efficiency of five Hungarian winter wheat varieties. Water 2020, 12, 1318. [Google Scholar] [CrossRef]
- Huang, X.; Wang, C.; Hou, J.; Du, C.; Liu, S.; Kang, J.; Lu, H.; Xie, Y.; Guo, T.; Ma, D. Coordination of carbon and nitrogen accumulation and translocation of winter wheat plant to improve grain yield and processing quality. Sci. Rep. 2020, 10, 10340. [Google Scholar] [CrossRef]
- Kolarić, L.; Popović, V.; Živanović, L.; Ljubičić, N.; Stevanović, P.; Šarčević Todosijević, L.; Simić, D.; Ikanović, J. Buckwheat yield traits response as influenced by row spacing, nitrogen, phosphorus, and potassium management. Agronomy 2021, 11, 2371. [Google Scholar] [CrossRef]
- Popović, V.; Ljubičić, N.; Kostić, M.; Radulović, M.; Blagojević, D.; Ugrenović, V.; Popović, D.; Ivošević, B. Genotype× environment interaction for wheat yield traits suitable for selection in different seed priming conditions. Plants 2020, 9, 1804. [Google Scholar] [CrossRef]
- Stevanović, A.; Bošković, J.; Zečević, V.; Pešić, V.; Ćosić, M.; Todosijević, L.Š.; Burić, M.; Popović, V. Variability and heritability of technological characteristics of Amaranthus leaves and seeds. Not. Bot. Horti Agrobot. 2023, 51, 13128. [Google Scholar] [CrossRef]
Figure 1.
Dual maps of growth season duration (in days) and annual precipitation (in mm/year) across different study locations and regions in Morocco, spanning the 2016–2017 to 2020–2021 agricultural seasons.
Figure 1.
Dual maps of growth season duration (in days) and annual precipitation (in mm/year) across different study locations and regions in Morocco, spanning the 2016–2017 to 2020–2021 agricultural seasons.
Figure 2.
Genotype by trait (GT) bi-plot. The trait codes are as follows: TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike.
Figure 2.
Genotype by trait (GT) bi-plot. The trait codes are as follows: TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike.
Figure 3.
Yield response to nitrogen doses across different varieties.
Figure 4.
Principal component analysis (PCA). The trait codes are as follows: TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike.
Figure 4.
Principal component analysis (PCA). The trait codes are as follows: TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike.
Figure 5.
Quadratic response of yield to varying nitrogen rates in ‘Faraj’ variety.
Figure 6.
Quadratic response of yield to varying nitrogen rates in ‘Karim’ variety.
Figure 7.
Pearson’s correlation matrix of key agronomic traits in wheat varieties under different nitrogen application rates. Note: This figure illustrates a Pearson’s correlation matrix for essential agronomic traits, namely the thousand-kernel weight (TKW), number of spikes per square meter (Spk/m2), grains per square meter (G/m2) and number of grains per spike (GS). The significance of the correlations is indicated as follows: ‘ns’ denotes not significant; * denotes significance at p ≤ 0.05; ** denotes significance at p ≤ 0.01; *** denotes significance at p ≤ 0.001.
Figure 7.
Pearson’s correlation matrix of key agronomic traits in wheat varieties under different nitrogen application rates. Note: This figure illustrates a Pearson’s correlation matrix for essential agronomic traits, namely the thousand-kernel weight (TKW), number of spikes per square meter (Spk/m2), grains per square meter (G/m2) and number of grains per spike (GS). The significance of the correlations is indicated as follows: ‘ns’ denotes not significant; * denotes significance at p ≤ 0.05; ** denotes significance at p ≤ 0.01; *** denotes significance at p ≤ 0.001.
Table 1.
Geographic coordinates, ecosystem types and five-year precipitation trends of INRA experimental stations in our study (2016–2021).
Table 1.
Geographic coordinates, ecosystem types and five-year precipitation trends of INRA experimental stations in our study (2016–2021).
| Station Name | Location (Lat, Long) | Ecosystem Type | 2016/2017 Precipitation (mm) | 2017/2018 Precipitation (mm) | 2018/2019 Precipitation (mm) | 2019/2020 Precipitation (mm) | 2020/2021 Precipitation (mm) |
|---|---|---|---|---|---|---|---|
| Sidi El Aydi (SEA) | 33.12218° N, 7.63315° W | Semi-Arid, Rainfed | 290 | 505 | 210 | 242 | 467 |
| Merchouch (MCH) | 33.61375° N, 6.71786° W | Favorable/Sub-Humid | 348 | 579 | 179 | 249 | 518 |
| Tassaout (TST) | 31.82021° N, 7.43806° W | Dry and Hot, Irrigated | 216 | 305 | 200 | 247 | 304 |
Table 2.
Variety, year of registration, drought stress response and Hessian fly reaction.
| Variety | Year of Release | Response to Stress: | Hessian Fly Resistance | Pedigree |
|---|---|---|---|---|
| Drought | ||||
| ‘Faraj’ | 2007 | Moderate resistance | R | Hybrid Nassira, Qarmal, Lahn (ICARDA) |
| ‘Itri’ | 2017 | Tolerant | S | RISSA/GAN//POHO_1/3/PLATA_3//CREX/ALLA/x Karim |
| ‘Karim’ | 1985 | Moderately tolerant | S | Bittern ‘S’ « JO’S’. AA”:S’//FG’S’ » |
| ‘Luiza’ | 2011 | Moderate resistance | S | RASCON_39/TILO_1 |
| ‘Nassira’ | 2003 | Tolerant | R | INRA Selection on CIMMYT EII, 12 TA14/BD3//Isly # CF41530-1548 |
Table 3.
Significance levels of ANOVA for genotype and nitrogen effects on durum wheat yields, yield component.
Table 3.
Significance levels of ANOVA for genotype and nitrogen effects on durum wheat yields, yield component.
| Source of Variation | Yield (kg/ha) | Biomass (kg/ha) | TKW (g) | Spk/m2 | G/m2 | G/S | |
|---|---|---|---|---|---|---|---|
| Variety | |||||||
| ‘Faraj’ (2007) | 3430 a | 12,196 a | 41.28 b | 332 a | 7484 a | 27 a | |
| ‘Itri’ (2017) | 3347 a | 11,889 ab | 39.21 c | 305 b | 7422 a | 27 a | |
| ‘Karim’ (1985) | 3239 b | 11,267 b | 42.46 a | 294 b | 7457 a | 26 a | |
| ‘Luiza’ (2011) | 3422 a | 11,949 a | 39.98 c | 302 b | 7784 a | 27 a | |
| ‘Nassira’ (2003) | 3261 b | 10,543 ab | 42.32 a | 311 ab | 7152 a | 24 a | |
| Mean | 3340 | 11,569 | 41.05 | 311 | 7460 | 26 a | |
| Nitrogen | |||||||
| N1 120 kg/ha | 3404 a | 12,266 a | 41.68 a | 305 ab | 7352 a | 25 a | |
| N2 60 kg/ha | 3376 a | 12,277 a | 40.56 b | 319 a | 7730 a | 27 a | |
| N3 0 kg/ha | 3234 b | 10,917 b | 40.92 b | 302 b | 7549 a | 27 a | |
| Mean | 3338 | 1182 | 41.05 | 308 | 7544 | 26 | |
| ANOVA | DF | Yield (kg/ha) | Biomass (kg/ha) | TKW (g) | Spk/m2 | G/m2 | G/S |
| Variety | 4 | * | ns | *** | ** | ns | * |
| Nitrogen | 2 | * | *** | *** | * | ns | ns |
| Year | 4 | *** | *** | *** | *** | *** | *** |
| Variety × Nitrogen | 8 | ns | ns | ns | ns | ns | ns |
| Variety × Year | 16 | *** | *** | *** | *** | *** | ns |
| Nitrogen × Year | 8 | *** | ** | ** | *** | * | ns |
| Variety × Nitrogen × Year | 32 | ns | ns | ns | ns | ns | ns |
TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike. ANOVA—Analysis of variance, where * = Significant at p ≤ 0.05; ** = Significant at p ≤ 0.01; *** = Significant at p ≤ 0.001; ns = Not significant. Means without a common letter also vary greatly.
Table 4.
Comparative study of wheat varieties under various nitrogen levels in 2017: yield, biomass and grain characteristics with ANOVA insights.
Table 4.
Comparative study of wheat varieties under various nitrogen levels in 2017: yield, biomass and grain characteristics with ANOVA insights.
| Year | Nitrogen Rate | Variety | Yield (kg/ha) | Biomass (kg/ha) | TKW (g) | Spk/m2 | G/m2 | G/S |
|---|---|---|---|---|---|---|---|---|
| 2017 | N1 | ‘Faraj’ | 3911 abc | 14,136 abcd | 42.07 bcdef | 475 a | 9669 bcde | 23 d |
| ‘Itri’ | 4120 a | 14,545 abc | 41.37 cdef | 435 abc | 11,729 abc | 30 ab | ||
| ‘Karim’ | 3430 bc | 12,150 cd | 49.06 a | 362 cd | 8683 de | 27 abcd | ||
| ‘Luiza’ | 4129 a | 12,401 bcd | 42.35 bcdef | 346 d | 9437 cde | 30 ab | ||
| ‘Nassira’ | 3659 abc | 12,794 abcd | 45.41 ab | 392 bcd | 8351 e | 24 cd | ||
| Mean | 3850 | 13,205 | 44.05 | 402 | 9574 | 27 | ||
| N2 | ‘Faraj’ | 3857 abc | 14,900 a | 41.48 bcdef | 464 ab | 12,104 ab | 28 abc | |
| ‘Itri’ | 3956 ab | 14,529 abc | 42.39 bcdef | 454 ab | 12,461 a | 31 a | ||
| ‘Karim’ | 3388 c | 11,863 d | 44.82 bcd | 359 cd | 8713 de | 29 ab | ||
| ‘Luiza’ | 3843 abc | 13,235 abc | 39.08 f | 400 abcd | 11,084 abcd | 30 a | ||
| ‘Nassira’ | 3545 bc | 14,777 ab | 42.84 bcdef | 462 ab | 11,156 abcd | 25 bcd | ||
| Mean | 3718 | 13,861 | 42.12 | 427 | 11,102 | 29 | ||
| N3 | ‘Faraj’ | 3564 bc | 13,274 abcd | 40.69 ef | 448 ab | 10,187 abcde | 27 abcd | |
| ‘Itri’ | 3904 abc | 12,387 bcd | 39.13 f | 402 abcd | 10,992 abcd | 31 a | ||
| ‘Karim’ | 3689 abc | 13,193 abcd | 44.98 bc | 410 abcd | 11,100 abcd | 31 a | ||
| ‘Luiza’ | 3851 abc | 13,135 abcd | 40.9 def | 432 abc | 11,084 abcd | 30 a | ||
| ‘Nassira’ | 3547 bc | 12,892 abcd | 44.49 bcde | 451 ab | 9885 abcde | 24 cd | ||
| Mean | 3711 | 12,976 | 42.04 | 429 | 10,649 | 28 | ||
| Analysis of Variance | ||||||||
| Nitrogen Rate | ns | ns | ns | ns | * | ns | ||
| Variety | ** | ns | *** | ** | * | *** | ||
| N Rate × Variety | ns | ns | ns | ns | ns | ns | ||
TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike. ANOVA—Analysis of variance, where * = Significant at p ≤ 0.05; ** = Significant at p ≤ 0.01; *** = Significant at p ≤ 0.001; ns = Not significant. Means without a common letter also vary greatly.
Table 5.
Comparative study of wheat varieties under various nitrogen levels in 2018: yield, biomass and grain characteristics with ANOVA insights.
Table 5.
Comparative study of wheat varieties under various nitrogen levels in 2018: yield, biomass and grain characteristics with ANOVA insights.
| Year | Nitrogen Rate | Variety | Yield (kg/ha) | Biomass (kg/ha) | TKW (g) | Spk/m2 | G/m2 | G/S |
|---|---|---|---|---|---|---|---|---|
| 2018 | N1 | ‘Faraj’ | 4032 cd | 10,443 cd | 43.99 bc | 265 a | 7428 b | 30 abc |
| ‘Itri’ | 4706 abc | 13,993 a | 45.14 bc | 311 a | 9723 ab | 32 abc | ||
| ‘Karim’ | 4400 abcd | 11,913 bcd | 46.9 ab | 292 a | 8683 b | 30 abc | ||
| ‘Luiza’ | 4794 ab | 14,009 a | 43.92 bc | 321 a | 9569 ab | 31 abc | ||
| ‘Nassira’ | 4122 d | 12,682 bcd | 47.42 abc | 311 a | 8308 b | 27 c | ||
| Mean | 4411 | 12,608 | 45.47 | 300 | 8743 | 30 | ||
| N2 | ‘Faraj’ | 4271 abcd | 12,846 ab | 46.01 abc | 302 a | 9593 ab | 47 a | |
| ‘Itri’ | 4124 abcd | 11,341 bcd | 43.46 c | 311 a | 9266 b | 31 abc | ||
| ‘Karim’ | 4604 abcd | 12,200 abc | 46.75 ab | 281 a | 8440 b | 29 abc | ||
| ‘Luiza’ | 4837 a | 11,397 bcd | 44.83 bc | 300 a | 9520 ab | 32 abc | ||
| ‘Nassira’ | 4073 bcd | 10,827 bcd | 45.15 bc | 284 a | 6818 b | 24 c | ||
| Mean | 4382 | 11,722 | 45.24 | 294 | 8726 | 33 | ||
| N3 | ‘Faraj’ | 4293 abcd | 10,128 d | 48.35 a | 289 a | 8238 b | 29 bc | |
| ‘Itri’ | 4576 abcd | 10,485 cd | 44.78 bc | 302 a | 8940 b | 30 abc | ||
| ‘Karim’ | 4763 abc | 10,483 cd | 46.58 abc | 308 a | 12,679 a | 44 ab | ||
| ‘Luiza’ | 4750 abc | 11,186 bcd | 46.81 ab | 300 a | 9285 b | 32 abc | ||
| ‘Nassira’ | 4204 abcd | 10,422 cd | 46.89 ab | 270 a | 7441 b | 30 abc | ||
| Mean | 4517 | 10,541 | 46.68 | 294 | 9315 | 33 | ||
| Analysis of Variance | ||||||||
| Nitrogen Rate | ns | *** | * | ns | ns | ns | ||
| Variety | ** | ns | ns | ns | * | ns | ||
| N Rate × Variety | ns | * | ns | ns | ns | ns | ||
TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike. ANOVA—Analysis of variance, where * = Significant at p ≤ 0.05; ** = Significant at p ≤ 0.01; *** = Significant at p ≤ 0.001; ns = Not significant. Means without a common letter also vary greatly.
Table 6.
Comparative study of wheat varieties under various nitrogen levels in 2019: yield, biomass and grain characteristics with ANOVA insights.
Table 6.
Comparative study of wheat varieties under various nitrogen levels in 2019: yield, biomass and grain characteristics with ANOVA insights.
| Year | Nitrogen Rate | Variety | Yield (kg/ha) | Biomass (kg/ha) | TKW (g) | Spk/m2 | G/m2 | G/S |
|---|---|---|---|---|---|---|---|---|
| 2019 | N1 | ‘Faraj’ | 3347 ab | 11,165 abc | 39.69 ab | 284 bcd | 7123 bcd | 22 abcd |
| ‘Itri’ | 2852 bc | 11,317 abc | 36.54 cd | 254 bcd | 5989 cd | 22 abcd | ||
| ‘Karim’ | 2868 bc | 9468 cd | 39.42 ab | 238 cd | 5956 cd | 22 bcd | ||
| ‘Luiza’ | 2853 bc | 10,287 bcd | 37.67 bcd | 246 bcd | 6070 cd | 22 bcd | ||
| ‘Nassira’ | 2535 ab | 8960 bcd | 39.7 a | 230 bcd | 3864 bcd | 17 bcd | ||
| Mean | 2891 | 10,239 | 38.6 | 251 | 5800 | 21 | ||
| N2 | ‘Faraj’ | 3863 a | 12,475 ab | 37.57 bcd | 367 a | 12,177 a | 28 a | |
| ‘Itri’ | 2941 bc | 13,406 a | 35.36 de | 302 abc | 7790 bcd | 22 abcd | ||
| ‘Karim’ | 3262 ab | 10,635 bc | 38.04 bc | 294 abcd | 8262 bc | 24 abc | ||
| ‘Luiza’ | 3294 ab | 12,902 ab | 37.31 bcd | 319 ab | 9558 ab | 25 ab | ||
| ‘Nassira’ | 2879 bc | 10,225 cd | 38.23 bc | 267 bcd | 6488 bcd | 20 bcd | ||
| Mean | 3248 | 11,929 | 37.3 | 311 | 8856 | 24 | ||
| N3 | ‘Faraj’ | 3064 bc | 9656 cd | 39.61 ab | 275 bcd | 7190 bcd | 23 abcd | |
| ‘Itri’ | 2407 c | 9055 cd | 33.82 e | 246 bcd | 5735 cd | 19 cd | ||
| ‘Karim’ | 2654 bc | 8742 cd | 38.29 bc | 238 cd | 6607 bcd | 23 abcd | ||
| ‘Luiza’ | 2.97 bc | 11,001 abc | 37.67 bcd | 267 bcd | 6561 bcd | 22 bcd | ||
| ‘Nassira’ | 2470 c | 7820 d | 38.15 bc | 227 d | 4679 d | 18 d | ||
| Mean | 2678 | 9255 | 37.51 | 251 | 6153 | 21 | ||
| Analysis of Variance | ||||||||
| Nitrogen Rate | ** | *** | ** | *** | *** | ns | ||
| Variety | * | * | *** | ns | * | ns | ||
| N Rate × Variety | ns | ns | ns | ns | ns | ns | ||
TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike. ANOVA—Analysis of variance, where * = Significant at p ≤ 0.05; ** = Significant at p ≤ 0.01; *** = Significant at p ≤ 0.001; ns = Not significant. Means without a common letter also vary greatly.
Table 7.
Comparative study of wheat varieties under various nitrogen levels in 2020: yield, biomass and grain characteristics with ANOVA insights.
Table 7.
Comparative study of wheat varieties under various nitrogen levels in 2020: yield, biomass and grain characteristics with ANOVA insights.
| Year | Nitrogen Rate | Variety | Yield (kg/ha) | Biomass (kg/ha) | TKW (g) | Spk/m2 | G/m2 | G/S |
|---|---|---|---|---|---|---|---|---|
| 2020 | N1 | ‘Faraj’ | 2079 ab | 8447 a | 40.82 ab | 208 a | 3777 abc | 15 bc |
| ‘Itri’ | 1436 cde | 5603 cde | 38.06 cdef | 135 bcd | 1968 d | 13 bc | ||
| ‘Karim’ | 1323 e | 4733 e | 39.73 bcde | 116 d | 1955 d | 13 c | ||
| ‘Luiza’ | 1539 cde | 6143 bcde | 37.36 ef | 143 bcd | 2792 bcd | 14 bc | ||
| ‘Nassira’ | 2130 abc | 7132 abcd | 40.2 abc | 194 abc | 2930 bcd | 15 bc | ||
| Mean | 1701 | 6411 | 39.23 | 159 | 2684 | 14 | ||
| N2 | ‘Faraj’ | 2178 a | 8029 ab | 40.29 abc | 203 a | 4069 ab | 19 a | |
| ‘Itri’ | 1504 cde | 5455 de | 36.98 f | 143 bcd | 2273 d | 15 bc | ||
| ‘Karim’ | 1447 cde | 5494 de | 39.11 bcdef | 138 bcd | 2241 d | 13 c | ||
| ‘Luiza’ | 1679 bcde | 6357 bcde | 37.08 f | 162 abcd | 2994 abcd | 15 bc | ||
| ‘Nassira’ | 1789 abc | 6747 abcd | 39.82 bcd | 178 ab | 2697 bcd | 13 c | ||
| Mean | 1719 | 6416 | 38.66 | 165 | 2857 | 15 | ||
| N3 | ‘Faraj’ | 2022 ab | 7581 abc | 40.33 abc | 208 a | 4485 a | 17 ab | |
| ‘Itri’ | 1861 abc | 6097 bcde | 37.15 f | 173 abc | 3440 abcd | 17 ab | ||
| ‘Karim’ | 1379 de | 4692 e | 39.37 bcdef | 132 cd | 2408 cd | 15 bc | ||
| ‘Luiza’ | 1685 bcde | 6015 cde | 37.42 def | 165 abc | 2903 bcd | 15 abc | ||
| ‘Nassira’ | 1754 abcd | 6059 bcde | 42.41 a | 165 abc | 2819 bcd | 14 bc | ||
| Mean | 1740 | 6089 | 39.34 | 167 | 3210 | 15 | ||
| Analysis of Variance | ||||||||
| Nitrogen Rate | ns | ns | ns | ns | ns | ns | ||
| Variety | *** | *** | *** | *** | *** | * | ||
| N Rate × Variety | ns | ns | ns | ns | ns | ns | ||
TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike. ANOVA—Analysis of variance, where * = Significant at p ≤ 0.05; *** = Significant at p ≤ 0.001; ns = Not significant. Means without a common letter also vary greatly.
Table 8.
Comparative study of wheat varieties under various nitrogen levels in 2021: yield, biomass and grain characteristics with ANOVA insights.
Table 8.
Comparative study of wheat varieties under various nitrogen levels in 2021: yield, biomass and grain characteristics with ANOVA insights.
| Year | Nitrogen Rate | Variety | Yield (kg/ha) | Biomass (kg/ha) | TKW (g) | Spk/m2 | G/m2 | G/S |
|---|---|---|---|---|---|---|---|---|
| 2021 | N1 | ‘Faraj’ | 3887 abcd | 18,989 a | 40.26 abcd | 413 abc | 15,620 ab | 30 b |
| ‘Itri’ | 4319 a | 19,225 a | 40.08 cd | 405 abcd | 14,993 ab | 35 ab | ||
| ‘Karim’ | 4204 ab | 19,080 a | 42.76 a | 421 ab | 18,236 ab | 36 ab | ||
| ‘Luiza’ | 4203 ab | 19,079 a | 39.99 cd | 424 a | 19,227 a | 39 a | ||
| ‘Nassira’ | 3544 abcd | 14,634 abc | 46.57 ab | 373 abcd | 9204 ab | 24 ab | ||
| Mean | 4031 | 18,201 | 41.93 | 408 | 15,455 | 33 | ||
| N2 | ‘Faraj’ | 3694 abcd | 15,857 abc | 38.83 de | 351 bcd | 13,030 ab | 34 ab | |
| ‘Itri’ | 4124 abc | 17,425 ab | 37.18 e | 378 abcd | 14,685 ab | 38 ab | ||
| ‘Karim’ | 3738 abcd | 18,296 ab | 40.54 abcd | 410 abcd | 16,737 ab | 32 ab | ||
| ‘Luiza’ | 3556 bcd | 17,138 abc | 38.94 de | 386 abcd | 16,419 ab | 33 ab | ||
| ‘Nassira’ | 3956 abcd | 18,566 ab | 41.88 abc | 424 a | 18,382 ab | 34 ab | ||
| Mean | 3813 | 17,456 | 39.48 | 392 | 15,852 | 34 | ||
| N3 | ‘Faraj’ | 3384 d | 15,016 bc | 39.21 de | 348 cd | 12,539 b | 36 ab | |
| ‘Itri’ | 3373 d | 13,479 c | 36.75 e | 340 d | 13,033 ab | 35 ab | ||
| ‘Karim’ | 3432 d | 16,061 abc | 40.48 abcd | 386 abcd | 16,745 ab | 35 ab | ||
| ‘Luiza’ | 3514 cd | 14,946 bc | 38.42 de | 343 cd | 13,303 ab | 36 ab | ||
| ‘Nassira’ | 3915 abcd | 19,125 a | 40.23 bcd | 408 abcd | 18,241 ab | 37 ab | ||
| Mean | 3523 | 15,725 | 39.02 | 365 | 14,772 | 36 | ||
| Analysis of Variance | ||||||||
| Nitrogen Rate | *** | ** | *** | ** | ns | ns | ||
| Variety | ns | ns | *** | ns | ns | ns | ||
| N Rate × Variety | ns | ns | ns | ns | ns | ns | ||
TKW = Thousand-kernel weight, Spk/m2 = Spikes per square meter, G/m2 = Grains per square meter, G/S = Grains per spike. ANOVA—Analysis of variance, where ** = Significant at p ≤ 0.01; *** = Significant at p ≤ 0.001; ns = Not significant. Means without a common letter also vary greatly.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Hnizil, O.; Baidani, A.; Khlila, I.; Nsarellah, N.; Amamou, A. Assessing the Impact of Nitrogen Fertilization, Variety Selection, Year and Their Interaction on Wheat Yield and Yield Components. Nitrogen 2024, 5, 266-287. https://doi.org/10.3390/nitrogen5020018
AMA Style
Hnizil O, Baidani A, Khlila I, Nsarellah N, Amamou A. Assessing the Impact of Nitrogen Fertilization, Variety Selection, Year and Their Interaction on Wheat Yield and Yield Components. Nitrogen. 2024; 5(2):266-287. https://doi.org/10.3390/nitrogen5020018
Chicago/Turabian StyleHnizil, Oussama, Aziz Baidani, Ilham Khlila, Nasserelhaq Nsarellah, and Ali Amamou. 2024. "Assessing the Impact of Nitrogen Fertilization, Variety Selection, Year and Their Interaction on Wheat Yield and Yield Components" Nitrogen 5, no. 2: 266-287. https://doi.org/10.3390/nitrogen5020018
