Integrated Management of Water, Nitrogen, and Genotype Selection for Enhanced Wheat Productivity in Moroccan Arid and Semi-Arid Regions

Abstract

Bread wheat (Triticum aestivum L.) is essential global nutrition as it provides calories and protein. This study explored the impact of irrigation, environmental factors, nitrogen fertilization, and genotype selection on yield. The experimental stations of Afourar and Sidi El Aidi in Morocco, six bread wheat varieties and varying irrigation systems, were used with varying nitrogen fertilization rates (0, 60, and 120 kg/ha for rainfed and 0, 100, and 200 kg/ha for irrigated conditions). Results showed that the variety ‘Snina’ had the highest yields and biomass, with a 58% yield increase at 120 kg/ha nitrogen under rainfed, and a 28% increase at 100 kg/ha under irrigated conditions. Irrigation significantly enhanced yield and its components. Combined with 100 kg/ha nitrogen fertilization, significant yield improvements were observed across all varieties under irrigated conditions, notably ‘Malika’ with a 32% increase and ‘Kharouba’ with a 24% increase. These varieties also show strong resilience to water stress, making them suitable for regions with variable water availability. Nitrogen fertilization efficiency is influenced by weather and site-specific variability. This study underscores the importance of integrated management strategies, including variety selection, nitrogen application, and environmental conditions, to optimize bread wheat production and ensure agricultural sustainability in the Mediterranean.

1. Introduction

Drought management is a highly relevant global problem. Bread wheat (Triticum aestivum L.) is an essential source of calories and proteins, and plays a crucial role in global nutrition. Adapted to the Mediterranean climate, characterized by dry summers and wet winters, common wheat plays a significant role in agricultural systems and food security in Morocco [1,2,3]. This crop is particularly prominent in the regions of Gharb, Saïss, and Haouz, where favorable agro-climatic conditions and advanced agricultural practices contribute to high yields [1,2,3,4].

Bread wheat (Triticum aestivum L.) cultivation is of paramount importance in the semi-arid regions of Morocco, as it covers a significant area and contributes to national food security. Approximately 1.5 million hectares are dedicated to bread wheat cultivation, representing a substantial proportion of the country’s agricultural land [5]. In these regions, irregular rainfall and frequent drought periods pose major challenges to wheat production. In response, farmers have adopted optimized agricultural practices, such as using drought-resistant wheat varieties and efficient irrigation techniques, such as drip irrigation, applying approximately 50 to 60 mm of water per irrigation cycle [6].

With water resources being limited, average annual rainfall of 200 to 400 mm is insufficient to meet crop water needs, leading to a significant decline in groundwater levels of more than 0.5 m per year in some areas [7]. Groundwater accounts for approximately 50% of the total irrigation water supply, providing significant economic benefits and playing a crucial role in supporting local livelihoods [8]. The application of fertilizers, particularly nitrogen, is also essential for stabilizing production and improving crop resilience. However, inefficient nitrogen management can lead to leaching losses, contributing to water pollution and greenhouse gas emissions [9]. Nitrogen (N) management in wheat cultivation through crop modeling and irrigation methods is crucial for optimizing water consumption and nitrogen application, thus enabling efficient resource use [10,11]. Research conducted in China has shown that optimal nitrogen fertilization rates not only increase yields, but also improves nitrogen use efficiency under various climatic conditions [12]. Furthermore, different nitrogen management practices significantly affect greenhouse gas emissions and wheat yields in conservation agriculture systems, with integrated nitrogen management reducing emissions while maintaining high yields [13]. Optimized management practices can enhance agronomic and environmental outcomes in arid regions through adequate irrigation and nitrogen fertilization [14]. Nitrogen (N), along with water supply and other nutrients such as phosphorus, is a critical factor limiting agricultural productivity. Applying nitrogen fertilizers is the most effective method increasing nitrogen accumulation in plants. The yield and quality of wheat grains strongly depend on nitrogen availability and uptake. High yields of high-quality grains can only be achieved with high nitrogen uptake rates [15,16]. Among the nutrients applied to crops, nitrogen is typically the most expensive for growers [17]. Over the past five decades, the external application of mineral nitrogen fertilizers has increased sevenfold, whereas agricultural food production has only doubled [18]. Unabsorbed nitrogen can be lost to water and the atmosphere, contributing to water pollution and greenhouse gas emissions [19,20,21,22,23,24,25,26,27,28,29]. Enhancing nitrogen use efficiency (NUE) in wheat not only reduces costs for growers but also addresses environmental issues associated with nitrogen runoff and denitrification in wheat-growing regions [21].

Genetic selection plays a crucial role in developing of more nitrogen-efficient varieties; however, achieving this objective remains a challenge. Breeding programs to date have aimed not only to improve wheat crop yields but also enhance nitrogen use efficiency (NUE) [23]. Environmental factors, such as light, temperature, and water supply, significantly influence wheat grain yield response to nitrogen fertilizer application [24]. These variables, which vary across sites and years, play crucial roles in the response of wheat genotypes to nitrogen fertilizer application [25]. Additionally, soil parameters, including water retention capacity and soil type, influence wheat grain yields [26]. The positive correlation between high nitrogen application rates and soil moisture highlights the importance of water for optimal wheat production. Variations in soil water-holding capacity, such as in sandy and clay soils, significantly affect wheat grain yield by influencing water availability to plants throughout the growing season [27].

Wheat plants have developed several adaptive mechanisms to survive drought. These mechanisms include the accumulation of osmolytes such as proline, which helps maintain the osmotic balance of plant cells. Stomatal closure is another key response that reduces water loss via transpiration. Wheat roots can also grow deeper to access groundwater reserves, and significant new variations in root architecture related to grain production have been detected in Moroccan varieties [28]. Additionally, leaves may develop thicker cuticles to reduce evaporation [29,30].

Drought negatively affects several essential physiological processes in wheat. Photosynthesis is often reduced because of stomatal closure, which limits CO₂ entry. Additionally, water stress can lead to a decrease in chlorophyll content and alter the chloroplast structure, thereby reducing photosynthetic efficiency. Oxidative stress is another major consequence of the accumulation of free radicals, which damage cell membranes, proteins, and DNA [31,32,33].

Environmental factors are crucial in selecting wheat genotypes adapted to various soil and climatic conditions while maintaining good to moderate yields and efficient nitrogen use. However, researchers must also address the challenges posed by climate change, which affects nitrogen fertilization. Heat and drought are the primary obstacles to wheat yield [34]. Additionally, extreme weather events such as floods, storms, and extreme temperatures significantly impact wheat production and pose substantial challenges for agriculture [35,36,37].

To evaluate crop variety yields, it is essential to consider genetic variability (G), environmental factors (E) such as climate (including annual variability), and geography, as well as farm management of nitrogen fertilizer (N). The combination of these factors and their interactions (G × E × N) determines sustainable and secure crop yields [38]. To date, wheat breeding programs have focused on addressing challenges related to yield potential, quality, and stress tolerance, both biotic and abiotic, through the development of predominantly higher-yielding varieties [38]. We hypothesize that increased nitrogen fertilization and irrigation practices will synergistically enhance the yield and biomass production of selected wheat varieties.

This study aims to evaluate the effect of irrigation, environmental factors, nitrogen fertilization, and genotype on wheat yield, biomass production, and yield components. It seeks to comprehensively understand the complex interactions between these factors. Over two years (2019–2021), this research was conducted under two irrigation systems: irrigated and rainfed. We focused on six Moroccan varieties of bread wheat: three new varieties (“Snina”, “Kharouba”, and “Malika”) and three older cultivars (“Achtar”, “Amal”, and “Arrehane”). The objectives of this study were: (i) to evaluate the impact of irrigation on bread wheat yield, (ii) to analyze the effect of nitrogen fertilization on productivity and assess the performance of different varieties, and (iii) to determine the influence of environmental factors and study the interactions between genotypes, nitrogen fertilization, and environmental conditions in both irrigation systems.

2. Materials and Methods

This study employed a split-plot design with three replications for each irrigation system and each year. The factors examined included environment (year and experimental site), nitrogen fertilization, varieties, and the irrigation system. The objective of this study is to comprehensively assess the effects of irrigation, environmental conditions, nitrogen fertilization, and genotype on the yield and productivity of bread wheat. This evaluation aimed to develop optimized agricultural strategies that maximize production while ensuring sustainable resource use in Mediterranean regions.

2.1. Research Sites and Climatic Conditions

This study was conducted over two years, 2019/2020 and 2021/2022, at two experimental stations affiliated with the National Institute of Agricultural Research (INRA) in Morocco. The research focused on assessing the performance of two irrigation systems: irrigated and rainfed (Figure 1).

Table 1. Geographical coordinates, ecosystem types, and precipitation trends during the agricultural season for the INRA experimental stations from 2019 to 2021. The crop year precipitation values represent the accumulated precipitation over seven months.

Station Name	Ecosystem Type	Location (Lat, Long)	2019–2020	2020–2021
Sidi El Aidi (SED)	Semi-Arid Rainfed	33.12218° N 7.63315° W	242 mm	267 mm
Afourar (AFR)	Semi-Arid Rainfed, Irrigated	32.26009° N 6.53312° W	304.5 mm	364 mm

Note: The total precipitation values for each year represent the accumulated precipitation over seven months, starting from December 2019 to June 2020 for the 2019–2020 period, and from December 2020 to June 2021 for the 2020–2021 period.

presents a comprehensive overview of the geographical data, ecosystem types, and two-year precipitation trends for the INRA experimental stations analyzed in our study. It compiles crucial environmental information for the period of the investigation. These data are essential for understanding the local conditions and climatic variations that may have affected the results of our research, especially concerning precipitation fluctuations and the types of ecosystems present at the experimental sites.

2.2. Experimental Setup

The experimental design used was a split-plot with two factors: nitrogen treatment (N) in the main plots and variety (V) in the subplots. Nitrogen factor had three levels: N1 = 0 kg/ha, N2 = 60 kg/ha, and N3 = 120 kg/ha in the rainfed experiment, and N1 = 0 kg/ha, N2 = 100 kg/ha, and N3 = 200 kg/ha in the irrigated system, while considering the different nitrogen absorption capacities under rainfed and irrigated conditions. To meet the irrigation requirements, the total amount of water required ranged from 450 to 650 mm, adjusted according to specific climatic conditions and the recorded precipitation during the growing seasons, with three supply of 90 mm for both sites over the two years. Irrigation allows for better nutrient uptake, thus justifying higher nitrogen rates. Six different varieties were tested in the subplots. Each experimental setup was replicated three times. A summary of the variety characteristics is provided in

Table 2. Detailed information on the variety used in the experiment.

Varieties	Year of Release	Main Characteristics	Zone of Adaptation
Arrehane	1996	Drought tolerant, Hessian fly resistance	Semi-arid regions
Achtar	1988	Grain quality and yield potential	Favourable and irrigated areas
Amal	1933	Yield potential and grain quality	Sub-humid regions and irrigated areas
Kharouba	2010	Drought tolerant and yield potential	Semi-arid, favourable, and irrigated areas
Malika	2016	Drought tolerant, Hessian fly resistance	Semi-arid, favourable, and irrigated regions
Snina	2017	Drought tolerant, Hessian fly resistance and grain quality	Arid and semi-arid areas

.

2.3. Plot Characteristics and Agricultural Practices

The experimental plots covered an area of 6 m², measuring 5 m in length and 1.20 m in width. To ensure optimal growing conditions, standard agronomic practices such as soil preparation and regular weeding were implemented throughout the growing season. Fertilizers based on ammonium nitrate (33.5% N) and urea (46% N) were applied in two stages. The elementary plots received 33% of the total nitrogen at the tillering stage as ammonium nitrate and 67% at the stem extension stage as urea (ratio 3:7). Before soil analysis, a uniform application of NPK (15-1515) base fertilizer was applied to all plots to ensure adequate baseline nutrition. Application rates were adjusted based on a soil analysis to ensure a targeted nutrient supply. Adhering to USA legislative limits, the EU and other relevant countries.

Seeds were sown at a density of 350 g/m² in mid-November using a Wintersteiger plot seeder. The row spacing was 20 cm and the planting depth was 3.5 cm, following the recommended planting dates for the two regions under study. Planting dates were carefully chosen to manage pests and avoid the peak of the Hessian fly cycle, with manual weeding conducted to control weed competition. Harvesting took place at the end of June each year, marking the conclusion of the annual growth cycle.

2.4. Statistical Analysis

Statistical analysis was performed using GenStat 14th Edition software. An analysis of variance (ANOVA) was conducted to investigate the effects of variety, nitrogen, environment, and their interactions on the measured variables in the two irrigation systems. For graphical representations, such as bar charts and box plots, we used the R software version 4.3.2.

Additionally, the “metan” tool was used for correlation analysis [39]. And principal component analysis was conducted using the R package version 1.0.7 “factoextra” to gain a deeper insight into how variations impact the characteristics under study [40].

3. Results

3.1. Combined Impact of Genotype, Environment and Nitrogen on the Productivity of Moroccan Bread Wheat under Different Irrigation Systems

The combined ANOVA analysis (

Table 3. ANOVA analysis of the factors affecting yield and its components in Moroccan soft wheat varieties under rainfed (RF) and irrigated (IR) conditions.

Source of Variation	Biomass kg/ha		Yield kg/ha		TKW		SPK		NG
Irrigated	RF	IR	RF	IR	RF	IR	RF	IR	RF	IR
Genotype
Achtar	4530 b	9800 b	1430 b	3660 b	29.4 ab	35.2 b	599 c	542 d	4892 b	9797 a
Amal	3700 a	8500 a	1170 a	3180 a	27.9 a	34.3 a	589 c	502 d	4974 b	7874 b
Arrehane	5150 c	10,740 c	1610 c	4100 c	30.6 bc	37.0 c	584 c	629 c	4894 b	11,322 a
Kharouba	5670 d	12,130 d	1790 d	4450 c	31.7 cd	39.8 d	578 c	698 a	4704 b	11,950 c
Malika	6500 e	13,240 e	1940 e	4930 d	33.1 d	40.4 d	410 b	591 b	4130 a	12,564 b
Snina	7570 f	15,370 f	2340 f	5490 e	34.9 e	43.7 e	333 a	794 a	4261 a	13,676 a
Nitrogen
N0	4250 a	10,670 a	1210 a	3950 a	30.2 a	39.5 a	435 a	551 a	3695 a	10,482 a
N1	5310 b	12,710 c	1500 b	4690 c	31.3 b	46.9 c	528 b	717 c	5038 b	11,999 b
N2	7000 c	11,510 b	2430 c	4230 b	32.3 c	42.3 b	583 c	610 b	5195 c	11,110 b
Site Year
AFR 20	3870 b	9970 a	1300 a	3650 a	29.4 a	40.4 d	442 a	727 d	4340 a	9098 c
AFR 21	8530 d	14,110 c	2580 c	5190 c	32.3 b	39.8 c	593 a	500 b	4987 a	13,027 b
SEA 20	3010 a	10,180 a	1010 a	3690 a	30.4 a	36.5 a	440 b	612 c	4320 b	10,177 a
SEA 21	6660 c	12,260 b	1960 b	4650 b	31.8 a	37.1 b	587 b	655 a	4924 b	12,494 a
mean	5520	11,630	1710	4290	31.3	38.4	450	626	5226	11,197
Var	***	***	***	***	**	***	***	***	***	***
ND	***	***	***	***	***	***	***	***	***	***
SY	***	***	***	***	***	***	***	***	***	***
Var × ND	***	ns	ns	**	ns	*	***	ns	***	**
ND × SY	***	***	***	***	ns	***	***	***	***	*
Var × SY	***	***	***	***	ns	***	***	***	***	***
SY × ND ×VAR	***	*	***	ns	*	***	***	***	***	ns

TKW = thousand-kernel weight, SPK = spikes per square meter, and NG = grains per square meter. RF = rainfed; IR = irrigated. Var = genotype, ND = nitrogen rate (N0 = (0 kg/ha), N1 = (60 kg/ha in rainfed and 100 Kg/ha in irrigated), N2 = (120 kg/ha in rainfed and 200 Kg/ha in irrigated)), SY = site year, AFR 20 = Afourar from 2019 to 2020, AFR 21 = Afourar from 2020 to 2021, SEA 20 = Sidi El Aidi from 2019 to 2020, SEA 20 = Sidi El Aidi from 2020 to 2021, 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.

) reveals that the performance characteristics of Moroccan bread wheat, including biomass, yield, thousand-kernel weight, number of spikes, and number of grains, are significantly influenced (p ≤ 0.001) by multiple factors. These factors include varieties, environments (site and years), nitrogen dose, and their interactions under both rainfed and irrigated conditions. This highlights the necessity of thoroughly understanding these factors to optimize production.

A significant observation concerns the interaction between genotypes and nitrogen doses. In the rainfed system, there is a highly significant difference (p ≤ 0.001) for biomass, number of grains and number of spikes, while no significant difference was detected in yield and thousand-kernel weight. In the irrigated system, significant differences were observed for yield (p < 0.01) and number of grains per square meter (p < 0.01). Additionally, a significant difference was observed for thousand-kernel weight (p < 0.05), although no significant difference was noted in yield. This suggests that the response of bread wheat varieties to nitrogen application can vary depending on water availability, highlighting the complexity of genotype-environment interactions.

In both systems, the interaction between genotypes and nitrogen doses revealed a significant difference for biomass in the irrigated system (p ≤ 0.001), but not in the rainfed system. Yield showed a significant difference in the irrigated system (p < 0.01), but not in the rainfed system. Thousand-kernel weight showed a significant difference in the irrigated system (p < 0.05), but not in the rainfed system. The number of spikes showed a significant difference in the irrigated system (p ≤ 0.001), but not in the rainfed system. Finally, the number of grains (exhibited significant differences in both systems (p ≤ 0.001 in irrigated and p < 0.01 in rainfed). This finding has practical implications for nitrogen management, emphasizing the necessity of tailored management strategies based on environmental conditions.

Similarly, the interaction between genotypes and environments showed a significant difference in biomass, yield, number of spikes, and number of grains in both systems (p ≤ 0.001). However, the thousand-kernel weight did not show a significant difference in either system. Understanding these genotype-environment interactions is crucial for selecting varieties adapted to specific agro-climatic regions, thereby maximizing yield potential and resource use efficiency.

The results of this study (Table 3) show that the interactions between genotypes, environments, and nitrogen doses significantly influence the agronomic performance of Moroccan bread wheat. In the rainfed system, significant differences were observed for biomass (p ≤ 0.001), yield (p ≤ 0.001), number of spikes (p ≤ 0.001), number of grains (p ≤ 0.001), and thousand-kernel weight (p < 0.05). In the irrigated system, only biomass and the number of grains showed significant differences (p ≤ 0.001). These results underscore the importance of adapting nitrogen management strategies based on specific environmental conditions to optimize production. Understanding these interactions is crucial for choosing varieties adapted to different agro-climatic regions, thereby maximizing yield potential and resource use efficiency.

3.2. Genotypic Effects on Yield and Yield Components

During the two-year research period from 2019/2020 and 2020/2021, a significant upward trend was observed: new varieties’ yields were generally higher than that of older ones (Table 3). Among the studied varieties, ‘Snina’ exhibited a notable biomass production, yielding 2340 kg/ha and 5490 kg/ha in rainfed and irrigated systems, respectively, and producing biomasses of 7570 kg/ha and 15,370 kg/ha, respectively. ‘Malika’ and ‘Kharouba’ also achieved impressive results. ‘Malika’ yields were 1940 kg/ha in the rainfed system and 4930 kg/ha in the irrigated system, with biomasses of 6500 kg/ha and 13,240 kg/ha, respectively. Similarly, ‘Kharouba’ produced yields of 1790 kg/ha in the rainfed system and 4450 kg/ha in the irrigated system, with biomasses of 5670 kg/ha and 12,130 kg/ha under the respective conditions. These results underscore the potential of newer and moderately recent varieties to enhance agricultural production.

Additionally, older varieties such as ‘Arrehane’, ‘Achtar’, and ‘Amal’ also demonstrated competitive performance. For instance, ‘Arrehane’ yields were 1610 kg/ha and 4100 kg/ha in rainfed and irrigated systems, respectively, with biomasses of 5150 kg/ha and 10,740 kg/ha. ‘Achtar’ produced yields of 1430 kg/ha and 3660 kg/ha, with biomasses of 4530 kg/ha and 9800 kg/ha under the same conditions. ‘Amal’ also produced respectable yields of 1170 kg/ha and 3180 kg/ha, with biomasses of 3700 kg/ha and 8500 kg/ha.

Overall, all varieties averaged a biomass of 5520 kg/ha in rainfed conditions and 11,630 kg/ha in irrigated conditions, with average yields of 1710 kg/ha in rainfed conditions and 4290 kg/ha in irrigated conditions. These results underscore the importance of new varieties in enhancing agricultural productivity while recognizing the enduring value of older varieties under specific conditions.

Moreover, the analysis of grain size through thousand-kernel weight is a crucial measure for evaluating crop quality. In this study, the variance analysis revealed a highly significant difference between varieties (p ≤ 0.001) under both irrigated and rainfed conditions. This indicates that the observed variations in TKW are influenced by differences between the varieties themselves.

Among the examined varieties, ‘Snina’ emerged as the best performer in terms of TKW, displaying the highest values in both systems, with 34.9 g in the rainfed system and 43.7 g in the irrigated system. These results suggest that ‘Snina’ produces larger grains compared to the other varieties evaluated. In contrast, ‘Amal’ had the lowest TKW among the examined varieties, measuring 27.9 g in rainfed conditions and 34.3 g in irrigated conditions. Although this may indicate lower performance in terms of grain size for ‘Amal’, the TKW analysis highlights significant differences between the studied varieties. ‘Snina’ stands out as the variety with the largest grains, while ‘Amal’ shows comparatively smaller grain size performance.

Analysis of variance also reveals significant variability among grains for the performances of different varieties in terms of the number of spikes per square meter (spikes/m²) under both natural precipitation and irrigated conditions (p ≤ 0.001). The variety ‘Achtar’ stands out by displaying the highest number of spikes per square meter under natural precipitation (599 spikes/m²), while under irrigation, the variety ‘Snina’ had the highest value (794 spikes/m²). Conversely, ‘Snina’ exhibits the lowest number of spikes under rainfed conditions, with 333 spikes/m², while under irrigation, the variety ‘Amal’ had the lowest number of spikes, with 502 spikes/m². Additionally, the average plant density across all varieties is significantly higher under irrigation (626 spikes/m²) than under natural no irrigation (450 spikes/m²).

Regarding grains per square meter, the variety ‘Amal’ showed the highest productivity under rainfed conditions, yielding the most grains per square meter at 4974 grains/m², while ‘Snina’ achieved the highest yield under irrigation with 13,676 grains/m². In contrast, the variety ‘Malika’ exhibited the lowest number of grains per square meter, recording 4130 grains/m² under rainfed conditions, while ‘Amal’ had the lowest grains/m² under irrigation with 7874 grains/m². Across all varieties and treatments, the average grainsm² was 5226 grains per square meter under rainfed conditions and 11,197 grains per square meter under irrigation.

The significant difference between rainfed and irrigated conditions suggests that water availability is a factor in determining the productivity of these varieties. The high performance of ‘Snina’ under irrigation and the poor performance of ‘Malika’ and ‘Amal’ under rainfed conditions indicate that some varieties are more dependent on additional water for optimum growth. It underscores the importance of selecting appropriate varieties, based on local water availability and irrigation practices, in order to obtain the best possible yields. These results underline the need to adopt targeted agricultural practices and allocate resources to improve crop productivity under variable environmental conditions.

3.3. Effect of Nitrogen on Yield and Yield Components

The analysis results revealed a significant impact of nitrogen on two key aspects of trial production: yield and its components. Across crops grown under both rainfed and irrigated conditions, nitrogen exhibited a significant effect on yield and its components (p ≤ 0.001) (Table 3). These findings underscore the crucial importance of nitrogen for these parameters.

Under rainfed conditions, application of 120 kg/ha of nitrogen resulted in the highest yield, reaching 2430 kg/ha, whereas no nitrogen application led to the lowest yield of only 1210 kg/ha. For the irrigated trial, the maximum yield was achieved following the application of 100 kg/ha of nitrogen, reaching 4690 kg/ha, while the minimum yield was observed without nitrogen application, at 3950 kg/ha.

Additionally, nitrogen also demonstrated a significant effect on biomass production. In the rainfed treatment, the application of 120 kg/ha of nitrogen resulted in the highest biomass, averaging 7000 kg/ha, whereas no nitrogen application led to the lowest biomass of 4250 kg/ha. In the irrigated treatment, the highest biomass was observed with 100 kg/ha of nitrogen, averaging 12,710 kg/ha, while the absence of nitrogen resulted in reduced biomass, averaging 10,670 kg/ha.

Additionally, the thousand-kernel weight showed significant differences between nitrogen rates in both systems. For crops under rainfed conditions, applying 120 kg/ha of nitrogen resulted in the highest thousand-kernel weight of 32.3 g, whereas without nitrogen, it was lowest at 30.2 g. In irrigated conditions, applying 100 kg/ha of nitrogen resulted in the highest TKW of 46.9 g, compared to the lowest TKW of 39.5 g observed without nitrogen.

Furthermore, the analysis of variance results indicated significant differences in the effect of nitrogen on the number of spikes per square meter (spikes/m²) in both cultivation systems. Under rainfed conditions, applying 120 kg/ha of nitrogen resulted in the highest number of spikes at 583 spikes/m², while no nitrogen application led to the lowest number of spikes at 435 spikes/m². In the irrigated treatment, applying 100 kg/ha of nitrogen resulted in the highest number of spikes at 717 spikes/m² compared to 551 spikes/m² without nitrogen.

Regarding grains per square meter, applying 120 kg/ha of nitrogen under rainfed conditions resulted in the highest number of grains per square meter at 5195 grains/m², whereas the absence of nitrogen led to the lowest number of grains (3695 grains/m²). In irrigated conditions, applying 100 kg/ha of nitrogen resulted in the maximum number of grains (11,999 grains/m²), with a lower number observed without nitrogen application (10,482 NG/m²). Overall, the yields of different varieties under two irrigation regimes (rainfed and irrigated) and three levels of nitrogen fertilization levels (N0, N1, AND N2) consistently increased with higher nitrogen doses, indicating a positive response to fertilization.

Under rainfed conditions, yields increased proportionally with nitrogen dose for all varieties, reaching maximum values with N2 (Figure 2). The most notable increases were observed in the Kharouba (1408 kg/ha) and Achtar (1174 kg/ha) varieties. In contrast, under irrigated conditions, although overall yields were higher, a trend of nutrient saturation was observed with high nitrogen doses, as evidenced by yield decreases between N1 and N2 for the Amal (reduction of 805 kg/ha) and Achtar (reduction of 470 kg/ha) varieties. Snina exhibited the largest yield difference between rainfed and irrigated conditions (3088.3 kg/ha), indicating a strong dependence on irrigation and a high sensitivity to water stress. Malika closely followed with a yield difference of 3011.0 kg/ha. In contrast, Achtar and Amal show less pronounced increases (2279.4 kg/ha and 2011.7 kg/ha, respectively), indicating a certain tolerance to water stress. These Figure 2a,b underscore the importance of tailored irrigation management and nitrogen application strategies to optimize yields, while considering varietal specificities and water availability.

3.4. Effect of Environment (Site × Year) on Biomass, Yield, and Components

The results from the rainfed and irrigated systems at the experimental stations Afourar (AFR) and Sidi El Aidi (SEA) between 2019 and 2021 reveal significant differences in yields and variability. The data indicated consistently higher median yields in the irrigated system compared to the rainfed system across all studied years and sites (Table 3 and Figure 3).

In the rainfed system at the AFR experimental station, median yields increased from 1500 kg/ha in 2020 to 3000 kg/ha in 2021, showing a notable improvement. Specifically, data for AFR were 1300 kg/ha in 2020 and 3650 kg/ha in 2021, indicating an increasing trend. Similarly, at the SEA experimental station, median yields in the rainfed system were 1000 kg/ha in 2020 and 2000 kg/ha in 2021. Specifically, results for SEA were 2580 kg/ha in 2020 and 5190 kg/ha in 2021, demonstrating significant variability but overall increasing yields.

For the irrigated system, median yields were more stable and higher, increasing from 3500 kg/ha in 2020 to 5000 kg/ha in 2021 at the AFR station, and from 3500 kg/ha in 2020 to 4500 kg/ha in 2021 at the SEA station. Specifically, yields for AFR were 3690 kg/ha in 2021 and 1010 kg/ha in 2020, slightly lower than the observed medians but indicating improvement. Furthermore, for SEA, yields were 1960 kg/ha in 2020 and 4650 kg/ha in 2021, showing a significant increase.

In addition to yields, several other agronomic parameters were measured and compared. Biomass was generally higher at the AFR station compared to SEA for both years, with a significant increase observed between 2020 and 2021 at both sites. For instance, at Afourar, biomass increased from 3870 kg/ha in 2020 to 8530 kg/ha in 2021, while at Sidi El Aidi, it increased from 3010 kg/ha in 2020 to 6660 kg/ha in 2021.

Moreover, the thousand-kernel weight (TKW) shows an increasing trend between 2020 and 2021 at both sites, with the highest values observed at AFR in 2021 at 141.1 g. Specifically, TKW at Afourar increased from 99.7 g in 2020 to 141.1 g in 2021, and at Sidi El Aidi, it increased from 101.8 g in 2020 to 122.6 g in 2021.

Additionally, the number of spikes per square meter (spikes/m²) remained relatively stable between years, with a slight increase observed in 2021 at both sites. The highest values were recorded at AFR in 2021 with 593 spikes/m². Specifically, at Afourar, the number of spikes increased from 442 spikes/m² in 2020 to 593 spikes/m², showing an increase from 442 spikes/m² in 2020. Similarly, at Sidi El Aidi, spikes increased from 440 spikes/m² in 2020 to 587 spikes/m² in 2021.

Finally, a general trend of increasing grains per square meter (grains/m²) was observed between 2020 and 2021. The highest values were recorded at SEA in 2021 with 4924 grains/m², while at AFR, it increased from 4340 grains/m² in 2020 to 4987 grains/m² in 2021.

3.5. Comparative Analysis of Wheat Varieties under Varied Nitrogen Application Rates: Two-Year Study in Rainfed and Irrigated Systems

In the rainfed system, which depends on precipitation, our research focused on evaluating the performance of different wheat varieties under various nitrogen application levels. This approach allowed us to understand how these varieties respond to fluctuations in rainfall and soil moisture, providing insights into their adaptability to climate change.

3.5.1. Effect of Nitrogen Fertilization and Wheat Varieties in the Rainfed System (2019–2020)

The 2019–2020 study examined the effect of nitrogen fertilization (N0, N1, AND N2) and wheat varieties (Achtar, Amal, Arrehane, Kharouba, Malika, and Snina) on several agronomic parameters: biomass, grain yield, thousand-kernel weight, number of spikes per square meter and number of grains per square meter.

First, the results (

Table 4. Effect of nitrogen application levels on the yield performance of wheat varieties (2019–2020) under rainfed conditions.

Nitrogen	Variety	Biomass (kg/ha)	Yield (kg/ha)	TKW	SPK	NG
N0	Achtar	2150 a	840 bc	27.5 abc	469 def	3023 a
	Amal	2020 a	380 a	25.8 a	276 ab	3569 a
	Arrehane	2550 a	920 bc	29.5 abcde	507 efg	3023 a
	Kharouba	2720 ab	960 bc	30.2 bcde	427 bcde	3513 a
	Malika	2900 abc	1010 bcd	30.5 bcde	222 a	2737 a
	Snina	4330 def	1240 cdef	30.9 cde	232 a	2886 a
	Mean	2778 a	891 a	29.1 a	355 a	3125 a
N1	Achtar	2290 a	840 bc	28.5 abcd	468 def	3452 a
	Amal	2030 a	710 ab	26.7 ab	318 abcd	3760 a
	Arrehane	2990 abcd	980 bc	29.7 bcde	548 efgh	3551 a
	Kharouba	3120 abcde	1110 bcd	30.9 cde	491 defg	3600 a
	Malika	4120 cdef	1120 cdef	32.6 efg	327 abcd	3483 a
	Snina	4380 ef	1360 cde	34.9 fg	288 abc	3092 a
	Mean	3155 a	1020 b	30.5 b	407 b	3490 ab
N2	Achtar	4040 bcdef	1450 defg	28.5 abcd	606 fgh	4373 a
	Amal	3070 abcde	1140 bcde	30.1 bcde	456 cdef	4263 a
	Arrehane	4070 cdef	1570 efg	30.8 cde	695 h	3904 a
	Kharouba	4870 f	1670 fg	31.6 def	650 gh	4312 a
	Malika	5090 f	1750 g	32.7 efg	409 bcde	3463 a
	Snina	5160 f	1750 g	36.5 g	375 abcde	3108 a
	Mean	4383 b	1555 c	31.7 c	532 c	3904 b
Analyse of variance
Nitrogen		***	***	***	***	**
Variety		***	***	***	***	*
Variety × nitrogen		ns	ns	ns	ns	ns

TKW = thousand-kernel weight, SPK = spikes per square meter, and NG = grains per square meter. 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.

) demonstrate that nitrogen fertilization significantly influences all parameters. Biomass increased from 2780 kg/ha without nitrogen to 4380 kg/ha with 120 kg/ha of nitrogen. Grain yield increased from 890 kg/ha without nitrogen to 1550 kg/ha with 120 kg/ha. TKW increased from 29.1 g without nitrogen to 31.7 g with 120 kg/ha. The number of spikes per m² increased from 355 to 532, and the grains/m² from 3 125 to 3 904, with all these differences being significant.

Next, the wheat varieties exhibited diverse performances. Particularly, Snina, showed high values of biomass and grain yield, reaching 5160 kg/ha and 1750 kg/ha, respectively, with 120 kg/ha of nitrogen. TKW varied from 25.80 g for Amal without nitrogen to 36.5 g for Snina with 120 kg/ha. Arrehane had the highest number of spikes with 695 spikes/m² under 120 kg/ha. The differences between varieties were significant (Table 4).

Moreover, the interaction between variety and fertilization did not show significant differences for most parameters, suggesting a similar response to fertilization among the varieties (Table 4).

In conclusion, increasing the nitrogen dose resulted in a significant increase in biomass, grain yield, TKW, and the number of spikes per square meter. Snina and Malika stood out due to their high performances. With the consistent effect of fertilization across all varieties, these results are crucial for optimizing agricultural practices and maximizing wheat productivity.

3.5.2. Effect of Nitrogen Fertilization and Wheat Varieties in the Rainfed System (2020–2021)

The 2020–2021 study continued to explore the effect of nitrogen fertilization (N0, N1, AND N2) and wheat varieties on various agronomic parameters. The results (

Table 5. Effect of nitrogen application levels on the yield performance of wheat varieties (2020–2021) under rainfed conditions.

Nitrogen	Variety	Biomass (kg/ha)	Yield (kg/ha)	TKW	SPK	NG
N0	Achtar	4580 ab	1130 a	30.2 bc	460 bcdef	4777 ab
	Amal	3940 a	1080 a	27.0 a	270 a	4495 ab
	Arrehane	5470 abc	1300 ab	29.7 bc	553 def	4502 ab
	Kharouba	5640 abc	1390 ab	30.9 cd	489 bcdef	5139 abc
	Malika	6340 abcd	1810 abcd	32.5 bc	371 abc	3981 a
	Snina	8060 cdefg	2450 defg	35.4 f	337 ab	4077 a
	Mean	5671 a	1526 a	31.0 a	413 a	3811 a
N1	Achtar	5090 abc	1420 abc	30.5 cd	521 cdef	4799 abc
	Amal	4020 ab	1190 a	28.2 ab	324 ab	4962 abc
	Arrehane	6500 abcd	1790 abcd	31.3 cd	751 gh	5039 abc
	Kharouba	7250 bcdef	2110 bcde	32.3 de	581 efg	5387 abc
	Malika	9830 efgh	2220 cdef	34.3 ef	395 abcd	4553 ab
	Snina	11,260 gh	3140 gh	35.5 f	369 abc	4231 a
	Mean	7325 b	1978 b	32.0 b	490 b	4977 b
N2	Achtar	9020 defgh	2870 efgh	31.1 cd	598 fg	5963 bc
	Amal	7120 abcde	2520 defg	29.3 bc	394 abcd	5932 bc
	Arrehane	9280 defgh	3090 fgh	32.4 de	783 h	5848 bc
	Kharouba	10,390 fgh	3500 hi	34.3 ef	614 fgh	6385 c
	Malika	10,690 gh	3570 hi	36.1 f	486 bcdef	5405 abc
	Snina	12,240 h	4240 i	36.2 f	421 abcde	5169 abc
	Mean	9790 c	3298 c	33.3 c	549 c	5767 c
Analyse of variance
Nitrogen		***	***	***	***	***
variety		***	***	***	***	***
Variety × nitrogen		ns	ns	ns	ns	ns

TKW = thousand-kernel weight, SPK = spikes per square meter, and NG = grains per square meter. ANOVA—analysis of variance, where *** = significant at p ≤ 0.001; ns = not significant. Means without a common letter also vary greatly.

) revealed a significant effect of nitrogen fertilization on all parameters. Biomass increased from 5670 kg/ha without nitrogen to 9790 kg/ha with 120 kg/ha. Grain yield increased from 1530 kg/ha without nitrogen to 3290 kg/ha with 120 kg/ha. TKW also increased from 31.0 g without nitrogen to 33.3 g with 120 kg/ha. The number of spikes per m² increased from 413 to 549, and the number of grains per square meter from 3811 to 5767, with all these differences being statistically significant. Furthermore, the varieties exhibited diverse performances. Snina maintained high levels of biomass and yield, reaching 12,240 kg/ha and 4240 kg/ha, respectively, with 120 kg/ha. TKW varied from 27.0 g for Amal without nitrogen to 36.2 g for Snina with 120 kg/ha. The differences between the varieties were significant. Additionally, the interaction between variety and fertilization was not significant for most parameters, indicating a similar response among the varieties.

In conclusion, the results for 2020–2021 indicate that an increase in nitrogen application results in a significant increase in biomass, grain yield, TKW, and the number of spikes per square meter. Snina and Kharouba stood out for their high performances. With the consistent effect of fertilization across all varieties, these results are crucial for optimizing agricultural practices and maximizing wheat productivity.

In the irrigated system, which depends on a controlled water supply, our research shifted focus to evaluate the performance of various Moroccan bread wheat under different nitrogen application levels. This approach helped us understand how these varieties respond to fluctuations in water availability and soil moisture, providing valuable insights into their ability into their adaptation to changing climatic conditions.

3.5.3. Effect of Nitrogen Fertilization on Wheat Varieties’ Yield, and Grain Characteristics in an Irrigated System (2019–2020)

The analysis of data for the 2019–2020 agricultural year in an irrigated system revealed the effects of different nitrogen doses (N0, N1, AND N2) on six wheat varieties in terms of yield and their components (

Table 6. Effect of nitrogen application levels on the yield performance of wheat varieties (2019–2020) under irrigated conditions.

Nitrogen	Variety	Biomass (kg/ha)	Yield (kg/ha)	TKW	SPK	NG
N0	Achtar	8140 bcd	2830 abc	32.8 ab	510 abc	8439 abcd
	Amal	6140 a	2320 a	31.2 a	687 defg	11,796 efg
	Arrehane	8360 bcde	3020 bcd	35.5 abcd	510 abc	8093 abc
	Kharouba	9720 defg	3720 ef	37.9 bcdef	365 a	5984 a
	Malika	10,230 efg	4090 efgh	38.9 cdef	763 fghi	9511 bcde
	Snina	12,860 ij	4210 fgh	41.6 efgh	723 efghi	9231 bcde
	Mean	9241 a	3365 a	36.3 a	593 a	8842 a
N1	Achtar	9590 cdef	3470 cde	37.2 bcde	705 defgh	11,277 efg
	Amal	7710 abc	2640 ab	36.1 abcd	722 efghi	12,565 g
	Arrehane	10,540 fgh	3730 ef	37.6 bcde	642 cdefg	10,180 cdefg
	Kharouba	11,550 ghij	4490 gh	40.2 cdefg	591 bcde	7661 abc
	Malika	12,380 hij	4580 h	43.2 fgh	853 bc	10,924 defg
	Snina	13,340 j	4570 gh	44.8 gh	868 i	10,214 cdefg
	Mean	10,851 c	3913 c	39.8 b	730 c	10,470 b
N2	Achtar	8550 bcde	3440 cde	35.2 abc	615 cdef	9979 cdef
	Amal	6980 ab	2620 ab	31.2 a	715 defghi	12,478 fg
	Arrehane	10,080 efg	3670 def	39.2 cdef	560 bcd	9368 bcde
	Kharouba	10,770 fgh	3920 efg	40.7 defgh	444 ab	6953 ab
	Malika	11,100 fghi	4280 fgh	42.3 efgh	794 ghi	10,051 cdefg
	Snina	13,290 j	4420 gh	45.7 h	784 ghi	10,038 cdefg
	Mean	10,128 b	3725 b	39.0 b	652 b	9811 b
Analyse of variance
Nitrogen		***	***	***	**	***
variety		***	***	***	***	***
Variety × nitrogen		ns	ns	ns	ns	ns

TKW = thousand-kernel weight, SPK = spikes per square meter, and NG = grains per square meter, 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.

). Biomass showed a significant increase with higher nitrogen doses. For N0, the average biomass was 9250 kg/ha, which increased to 10,860 kg/ha under N1 and 10,130 kg/ha under N2. The Snina variety had the highest biomass values under all doses, reaching a peak of 13,340 kg/ha under N1. This increase in biomass is attributed to improved nitrogen availability, which promotes vegetative growth.

Grain yield also increased with nitrogen doses. The average yields were 3670 kg/ha for N0, 3910 kg/ha for N1, and 3720 kg/ha for N2. The varieties Malika and Snina showed particularly high yields, with Snina reaching 4570 kg/ha under N1 and 4420 kg/ha under N2. These results indicate that irrigation, combined with adequate nitrogen fertilization, optimizes grain production.

Thousand-kernel weight (TKW) varied among varieties and nitrogen doses. Average values were 36.3 g for N0, 39.8 g for N1, and 39.0 g for N2. The Snina variety exhibited the highest TKW across all nitrogen doses, reaching 45.7 g under N2. This suggests that higher nitrogen doses can improve grain quality by increasing their weight. The number of spikes per square meter showed significant variations among different varieties and nitrogen doses. Under N0, the average was 593 spikes/m², increasing to 730 spikes/m² under N1, and slightly decreasing to 652 spikes/m² under N2. Varieties Amal and Malika showed the highest number of spikes, particularly under N1, suggesting a positive response to irrigation and nitrogen fertilization.

The number of grains per square meter also varied with nitrogen doses, with averages of 8842 grains/m² for N0, 10,470 grains/m² for N1, and 9811 grains/m² for N2. The Amal variety exhibited notably high grain numbers across all nitrogen doses, reaching 12,565 grains/m² under N1, highlighting its productivity under ideal irrigation and fertilization conditions.

Additionally, the analysis of variance revealed that the effects of nitrogen doses and varieties on the measured parameters (biomass, yield, TKW, spikes/m², and grains/m²) were significant (p < 0.001), except for the variety × nitrogen interaction, which was not significant (ns). This indicates that while varieties and nitrogen doses individually have a significant impact on performance, their interactions did not show significant variations.

3.5.4. Impact of Nitrogen Fertilization on Wheat Varieties’ Yield, and Grain Characteristics in an Irrigated System (2020–2021)

The analysis of data for 2020–2021 in an irrigated system shows the impact of nitrogen doses (N0, N1, AND N2) on six wheat varieties (Achtar, Amal, Arrehane, Kharouba, Malika, and Snina) regarding biomass, yield, thousand-kernel weight, number of spikes per square meter (spikes/m²), and number of grains per square meter (grains/m²). Increasing nitrogen significantly improves biomass, yield, and TKW. The average biomass increased from 12,290 kg/ha (N0) to 14,430 kg/ha (N1), with N2 showing a statistically significant difference at 128.3 kg/ha, despite no significant difference between N1 and N3. Grain yield increased from 4460 kg/ha (N0) to 5440 kg/ha (N1) and 4850 kg/ha (N2). Snina and Malika showed the best performances, with Snina reaching the highest values. The TKW varied from 37.3 g (N0) to 39.8 g (N1) and 38.2 g (N2), with Snina display the highest values. The number of spikes also increased with nitrogen, reaching 819 spikes/m² (N1). The number of grains per square meter increased to 13.418 grains/m² (N1), with Snina showing the best performance. The analysis of variance showed significant effects of nitrogen dose and variety, but not their interaction (

Table 7. Effect of nitrogen application levels on the yield performance of wheat varieties (2020–2021) under irrigated conditions.

Nitrogen	Variety	Biomass (kg/ha)	Yield (kg/ha)	TKW	SPK	NG
N0	Achtar	10,110 ab	3710 abc	34.3 a	697 abc	11,141 abcd
	Amal	9180 a	3130 a	34.3 a	714 abc	12,514 bcdef
	Arrehane	10,540 ab	3910 abcd	35.7 ab	665 abc	10,736 abc
	Kharouba	12,520 bcde	4450 bcdef	38.9 abcd	510 a	8316 a
	Malika	14,390 defg	5220 fgh	38.0 abcd	694 abc	13,828 cdef
	Snina	17,010 ghi	6270 hi	42.4 cde	774 bcd	14,589 f
	Mean	12,291 a	4578 a	37.3 a	676 a	11,854 a
N1	Achtar	12,060 abcde	4620 cdef	36.0 ab	831 cd	14,727 f
	Amal	11,700 abcd	4840 def	37.6 abc	807 cd	14,428 ef
	Arrehane	13,890 cdefg	5530 fghi	37.8 abc	793 cd	12,013 bcdef
	Kharouba	14,870 efgh	5150 efg	39.3 abcd	685 a	9500 ab
	Malika	15,880 fgh	5940 ghi	40.0 bcd	935 d	14,731 f
	Snina	18,180 i	6540 i	46.2 e	860 cd	15,109 f
	Mean	14,440 b	5437 c	39.8 c	819 c	13,418 c
N2	Achtar	10,400 ab	4160 abcde	34.6 ab	840 cd	14,491 f
	Amal	9410 ab	3540 ab	35.6 ab	770 bcd	13,163 cdef
	Arrehane	11,070 abc	4750 cdef	36.8 ab	688 abc	11,297 abcde
	Kharouba	13,690 cdef	4600 bcdef	39.1 abcd	580 ab	8738 a
	Malika	14,830 efgh	5520 fghi	39.9 bcd	852 cd	13,883 def
	Snina	17,560 hi	6540 i	43.3 de	807 cd	14,505 f
	Mean	12,826 a	4851 b	38.2 b	756 b	12,679 b
Analyse of variance
Nitrogen		***	***	***	***	***
variety		***	***	***	***	***
Variety × nitrogen		ns	ns	ns	ns	ns

TKW = thousand-kernel weight, SPK = spikes per square meter, and NG = grains per square meter. ANOVA—analysis of variance, where *** = significant at p ≤ 0.001; ns = not significant. Means without a common letter also vary greatly.

).

3.6. Correlation Analysis: Yield, Yield Components

The Pearson correlation matrices for irrigated and rainfed systems (Figure 4) reveal interesting differences and similarities in the factors influencing yield.

In both systems, biomass exhibits a very strong positive correlation (R = 0.88; p ≤ 0.001) with yield, underscoring its importance as a key indicator of increased yields regardless of irrigation conditions. This robust relationship can be attributed to biomass effectively reflecting the overall health and productivity of crops.

Additionally, thousand-kernel weight shows a moderate positive correlation with yield in both systems (R = 0.55 in irrigated and R = 0.51 in non-irrigated) with (p ≤ 0.001). This highlights that heavier grains are associated with better yields, emphasizing the critical role of grain quality in achieving high yields. However, this correlation is slightly weaker in the non-irrigated system, possibly due to water stress conditions impacting grain development and maturation.

The number of spikes per square meter and the number of grains per square meter (NG) behave differently depending on the system. In the irrigated system, spikes shows a low positive correlation with yield (R = 0.33; p ≤ 0.001), suggesting that a higher number of spikes can positively influence overall yield. However, this correlation is negligible in the non-irrigated system (R = 0.18), indicating that under water stress conditions, the number of spikes per unit area has minimal impact on yield.

Similarly, NG demonstrates a weak but significant correlation with yield in the irrigated system (R = 0.33; p ≤ 0.001) and a moderate correlation in the non-irrigated system (R = 0.40; p ≤ 0.001). This implies that in the absence of irrigation, the number of grains per spike becomes a more crucial determinant of yield. Under water stress, a plant’s ability to increase the number of grains per spike can compensate for the reduction in total spike count, thereby sustaining satisfactory yields.

In conclusion, while biomass remains the primary determinant of yield in both systems, the relative importance of thousand-kernel weight and the number of grains per spike varies with irrigation levels. Thousand-kernel weight retains significance in both systems, albeit with slightly diminished influence under water stress. Conversely, the number of grains per spike assumes greater importance in non-irrigated conditions, underscoring the need to tailor crop management strategies according to irrigation conditions to maximize yields.

3.7. Principal Component Analysis (PCA) of Agronomic Characteristics of Wheat under Rainfed and Irrigated Conditions

Principal component analysis conducted on the agronomic characteristics of wheat under rainfed conditions provides a comprehensive overview of the variance explained by each component, the correlations between variables, and the influence of different environments, varieties, and nitrogen doses (Figure 4).

The scree plot (Figure 5A) illustrates the variance explained by each principal component. The first principal component (Dim1) accounts for 59.6% of the total variance, followed by the second principal component (Dim2) with 22.4%. Together, these components capture 82% of the total variance, indicating their substantial explanatory power. The third, fourth, and fifth components contribute less to the variance, with 13.6%, 3%, and 1.4%, respectively.

In the correlation circle (Figure 5B), variables such as TKW (thousand-kernel weight), biomass, and yield exhibit strong correlations with Dim1, while NG (number of grains) and SPK (spikes) are more closely linked to Dim2. The length and direction of the vectors indicate the magnitude and direction of each variable’s contribution to the principal components.

The biplot (Figure 5C) illustrates how different environments (AFR 2020, AFR 2021, SEA 2020, SEA 2021) are distributed in the space of the first two principal components. AFR 2020 and AFR 2021 are closely grouped, indicating similar environmental conditions or responses, whereas SEA 2020 and SEA 2021 show greater dispersion, suggesting variability across these environments.

In the biplot (Figure 5D), different wheat varieties (Achtar, Amal, Arrehane, Kharouba, Malika, and Snina) are represented based on the first two principal components. Varieties such as Kharouba and Snina show significant differentiation along Dim1, while others are more clustered. The vectors indicate how variables like TKW, biomass, yield, NG, and SPK contribute to distinguishing between the varieties.

The biplot (Figure 5E) displays the distribution of nitrogen doses (N0, N1, AND N2) along the first two principal components. The N2 dose exhibits a distinct separation along Dim1, indicating a strong response of wheat varieties to higher nitrogen doses. Variables such as yield and biomass are prominently associated with Dim1, emphasizing their sensitivity to nitrogen levels.

These analyses provide a comprehensive understanding of how agronomic characteristics, environments, wheat varieties, and nitrogen levels interact and contribute to the variability observed in the dataset.

A scree plot (Figure 6A) illustrates the proportion of total variance explained by each principal component. The first principal component accounts for 57.3% of the total variance, followed by the second component at 28.1%. Together, these first two principal components capture 85.4% of the total data variance. This distribution indicates that the first two dimensions effectively summarize most of the information in the data, justifying their use in subsequent PCA analyses.

Figure 6B displays a correlation circle highlighting the contribution of different variables (TKW, biomass, yield, SPK, NG) to the first two principal components. Vectors representing TKW, biomass, and yield align in the same direction, indicating a strong positive correlation among these variables. The vectors for SPK and NG show positive and negative correlations, respectively, with the other variables. The cos2 plot confirms that all variables are well represented by the first two dimensions, underscoring their significance in the analysis.

Figure 6C presents a PCA biplot illustrating the distribution of samples based on environments (AFR 2020, AFR 2021, SEA 2020, and SEA 2021). The distinct environmental groups show minimal overlap, suggesting significant environmental impacts on the measured variables. SEA environments appear to exert a stronger influence on TKW, biomass, and yield, while AFR environments affect NG and SPK more prominently. This indicates that specific environmental conditions at each experimental station affect the measured parameters differently.

In Figure 6D, a PCA biplot represents various wheat varieties (Achtar, Amal, Arrehane, Kharouba, Malika, and Snina). The dispersion of varieties in the first two dimensions indicates considerable variability among them. Varieties like Achtar, Amal, Kharouba, and Snina show significant influence on TKW, biomass, and yield, whereas NG and SPK are influenced more by varieties such as Arrehane. This dispersion reflects the genetic and phenotypic diversity of the varieties in their response to the measured variables.

Figure 6E presents a PCA biplot for different nitrogen doses (N0, N1, AND N2). The overlapping groups corresponding to nitrogen doses suggest similar effects of different doses on the measured variables. Increasing nitrogen doses appears to promote biomass and yield production while potentially impacting other variables differently.

This comprehensive PCA analysis provides insights into how different variables, environments, wheat varieties, and nitrogen doses interact and contribute to the observed data variability.

4. Discussion

4.1. Influence of Irrigation on Yield and Its Components

Our study complements existing research by examining the influences of nitrogen fertilization levels, wheat varieties, and environmental factors (annual conditions and cultivation practices) on yield and its components.

Irrigation significantly improved the yield and biomass in our study. Modern bread wheat varieties, such as ‘Snina’ and ‘Malika’, exhibit substantial increases in yield under irrigation. These findings align with those of Mekonnen et al. [41] who reported significant yield improvements in crops under irrigation in semi-arid regions. The application of supplemental irrigation during critical growth stages, such as stem elongation and anthesis, has proven particularly beneficial, enhancing both yield and water productivity [42]. Li et al. [43] demonstrated that optimized irrigation practices enhance water use efficiency and drought resistance in wheat. Similarly, Zhang et al. [44] found that irrigation during key growth stages increases the number of spikes and thousand-kernel weight, contributing to higher overall yields. Kumar et al. [45] confirmed that regular irrigation mitigates the adverse effects of climate variability on wheat yield, especially in arid and semi-arid regions. Luo et al. [46] emphasized the importance of irrigation management in maximizing yield and maintaining production stability under water stress conditions. Chen et al. [47] also reported that the combination of irrigation and appropriate fertilization significantly improves plant growth and grain quality. The integration of cropping systems and irrigation methods can significantly influence water consumption, nitrogen fate, and crop yield. Specifically, in the North China Plain, different irrigation methods and cropping systems have shown varying effects on water use efficiency and nitrogen dynamics, underscoring the importance of tailored management practices for different regions and environmental conditions [10].

4.2. Genotype Selection and Nitrogen Application

Apart from irrigation, genotype selection and nitrogen application are pivotal factors in enhancing crop productivity. Recent studies indicate that modern wheat varieties demonstrate superior nitrogen utilization efficiency and higher yields compared to older varieties [48].

The results demonstrate that modern varieties, such as ‘Snina’, ‘Malika’, and ‘Kharouba’, exhibited higher nitrogen use efficiency and yields compared to older varieties like ‘Achtar’ and ‘Amal’. These conclusions are consistent with those of Xu et al. [23], who showed that modern wheat varieties possess improved mechanisms for nitrogen uptake and utilization, resulting in higher yields. The application of nitrogen had a significant impact on biomass, thousand-kernel weight, number of grains per square meter, and number of spikes per square meter, corroborating Hawkesford [49] on the importance of nitrogen for photosynthesis and protein synthesis.

Additionally, Li et al. [50] found that nitrogen fertilization enhanced physiological processes in wheat, leading to improved growth and yield. Zhao et al. [51] indicated that nitrogen application significantly increases the number of grains and spikes, which are critical yield components. Fischer et al. [52] demonstrated a positive correlation between the nitrogen use efficiency and grain yield in wheat.

Optimized nitrogen management practices are essential for maximizing wheat yield under varying environmental conditions [53]. The role of nitrogen in enhancing wheat resilience to abiotic stresses was highlighted by Zhang et al. [54]. The importance of genetic selection of nitrogen-efficient varieties to achieve sustainable wheat production [45].

4.3. Influence of the Environment on Yield and Its Components

Our study highlights the significant impact of the environment on yield and biomass. In the second year (2021), yields and their components exceeded those of the first year (2020), attributed to more favorable climatic conditions. Previous research underscores the critical role of climatic variables such as temperature, precipitation, and sunshine duration in crop growth and biomass production [55]. Additionally, significant variations were observed between sites, with Afourar (AFR) generally yielding higher than Sidi El Aidi (SEA) in 2021, likely due to microclimatic differences and soil fertility [56].

4.4. Interaction between Nitrogen Application Rate, Genotypes and Environment

The interaction between nitrogen application levels and genotypes revealed varied responses among genotypes to the applied nitrogen levels (Var × ND interaction). Genotypes such as ‘Snina’ and ‘Malika’ demonstrated higher responsiveness to increased nitrogen levels, significantly boosting biomass and yield under both irrigated and rainfed conditions. These findings are in line with those of Sylvester-Bradley and Kindred [57], who found that nitrogen use efficiency (NUE) varies significantly among genotypes and environmental conditions.

Further evidence supporting these results was provided by Foulkes et al. [58], who demonstrated that genotype-specific responses to nitrogen application can significantly influence yield and biomass production, emphasizing the importance of selecting appropriate genotypes for specific nitrogen management strategies. Gao et al. [59] also reported that environmental factors, such as soil moisture and temperature, interact with nitrogen application rates to affect wheat growth and productivity, corroborating our observations. Moreover, Muurinen et al. [60] highlighted the critical role of genotype-environment interactions in determining NUE, suggesting that breeding programs should focus on developing genotypes optimized for specific environmental conditions and nitrogen application regimes. Similarly, Gaju et al. [61] found that genotypes with higher NUE tended to perform better under diverse environmental conditions, reinforcing the need for targeted genotype selection. Hawkesford et al. [62], underscored the importance of integrating nitrogen management with genetic selection to enhance wheat yield and sustainability. Their research indicated that optimizing nitrogen use according to genotype-specific requirements can lead to significant improvements in yield and environmental sustainability.

The effectiveness of nitrogen application also varies with annual conditions and site-specific factors (ND × SY interaction), influenced by climatic variations and local soil characteristics. Higher yields and biomass in 2021 compared to 2020 are attributed to favorable climatic conditions, including adequate precipitation and optimal temperatures [63]. Additionally, AFR and SEA sites exhibit distinct performance differences, with AFR generally outperforming SEA, particularly in 2021. These variations underscore the need for adaptive fertilization strategies based on annual climate conditions and specific site attributes to optimize nitrogen use efficiency [21].

However, the performance of applied nitrogen is significantly enhanced under irrigation compared to rainfed conditions. Adequate water availability under irrigation ensures efficient nitrogen absorption, promoting higher biomass and yields compared to rainfed conditions. Nonetheless, applied nitrogen still demonstrates significant productivity improvements even under rainfed conditions, albeit with lower but observable yields and biomass compared to irrigated conditions [63].

The SY × Var interaction underscores how genotype-specific yield variations are influenced by site-specific conditions, annual variations, and irrigation status. Under irrigation, this interaction is more predictable and manageable, with maximum yields achieved when specific genotypes are cultivated in well-adapted sites with optimized nitrogen management each year [57]. For example, genotypes with high nitrogen use efficiency like Snina and Malika exhibit superior yields when managed under optimal nitrogen conditions. Irrigation stabilizes yields despite annual climate variations by ensuring consistent water availability and optimizing nitrogen uptake efficiency by plants [64,65].

Conversely, rainfed conditions introduce more variability in crop performance due to reliance on precipitation. Understanding and managing yield variations require careful consideration of interactions among sites, years, and genotypes. Drought-tolerant varieties such as Snina and Malika perform better under rainfed conditions when nitrogen applications are optimized for peak precipitation periods. Moderate nitrogen application can mitigate plant stress during dry years, highlighting the importance of flexible management strategies that adapt to variable climatic conditions [66,67].

The SY × Var interaction emphasizes the need for management strategies tailored to local conditions. Genotypes with moderate nitrogen use efficiency require precise management to achieve their full potential. In sites with fertile soils and stable precipitation, these genotypes can perform optimally with moderate nitrogen application. However, in less fertile soils or regions with irregular precipitation, intensive nitrogen management and the selection of stress-tolerant varieties are essential to avoid yield losses [21].

The SY × ND × Var interaction is crucial for understanding and optimizing crop performance across environments, particularly under irrigated and rainfed conditions observed at Afourar (AFR) and Sidi El Aidi (SEA) experimental stations. Under irrigation, optimizing nitrogen application according to specific sites and genotypes maximizes yields. Studies indicate that certain genotypes, such as Snina and Malika, respond favorably to higher nitrogen doses in well-irrigated conditions, promoting optimal growth and efficient nitrogen uptake [66]. For instance, at Afourar, where irrigation is well regulated, adjusting nitrogen doses stabilizes yields despite annual climate variations, enhancing nitrogen uptake and nutrient use efficiency by plants [64].

In rainfed conditions like those at Sidi El Aidi, nitrogen management and genotype selection become critical due to precipitation variability. Drought-tolerant varieties like Snina and Malika perform better when nitrogen doses are tailored to peak precipitation periods. During dry years, reducing nitrogen application can prevent excessive plant stress and optimize yield [45,46]. Flexible management practices that adjust annually are necessary to optimize nitrogen use efficiency and mitigate water stress risks due to annual precipitation variability [19,68].

The SY × ND × Var interaction underscores the importance of adapting management strategies to local conditions. In fertile sites with stable precipitation, genotypes like Snina, which possess moderate nitrogen use efficiency, can achieve optimal performance with specific nitrogen applications. However, in less fertile soils or regions with irregular precipitation, intensive nitrogen management and precise adjustments are essential to prevent yield losses [21,68].

4.5. Physiological Effects of Drought on Wheat

Drought significantly impacts several physiological processes in wheat, including photosynthesis, pigment content, and stomatal regulation. Under drought conditions, stomatal closure limits CO₂ uptake, thereby reducing photosynthetic efficiency [31]. Additionally, chlorophyll degradation under water stress diminishes the plant’s ability to perform photosynthesis, increasing susceptibility to oxidative damage [69]. Chlorophyll loss, coupled with the accumulation of reactive oxygen species (ROS), results in cellular damage that adversely affects growth and productivity [70]. Drought-tolerant varieties exhibit more effective antioxidant mechanisms, thereby maintaining cellular integrity under water stress.

Moreover, drought affects the synthesis of photosynthetic pigments such as carotenoids, which play a crucial role in protecting against photooxidative damage [71]. Deeper and denser root systems in some varieties enhance water and nutrient uptake [28]. While the accumulation of compatible solutes like proline helps plants maintain cellular turgor under stress conditions [72]. Hormonal responses, particularly those involving abscisic acid (ABA), are vital in mediating stomatal closure and activating stress defense mechanisms [73]. Our observations, indicating reduced yield and biomass in varieties such as ‘Malika’ and ‘Amal’ under rainfed conditions, align with these physiological responses.

4.6. Correlation Analysis and Principal Component Analysis (PCA)

Pearson correlation matrices provide valuable insights into linear relationships between various agronomic variables under both irrigated and rainfed conditions. The high correlation between yield and biomass (R = 0.88) in both systems indicates that biomass is a critical determinant of yield, regardless of irrigation conditions. This finding aligns with previous research emphasizing biomass production as a key indicator of crop performance under favorable and water-stressed conditions [65,66,67,68,69,70,71,72,73,74].

There is a significant positive correlation between thousand-kernel weight (TKW) and yield in both systems (R = 0.51 under rainfed conditions and R = 0.55 under irrigated conditions). This relationship suggests that heavier grains contribute to higher yield, consistent with observations by [75], indicating that grain weight is a key factor in cereal productivity under both water stress and irrigation.

The correlation between the number of grains and yield varies between the two systems. The positive and significant correlation (R = 0.40) between the number of grains per square meter (NG) and yield under rainfed conditions observed in our study was comparable to the findings of Zhao et al. [76] and Raza et al. [33]. These studies also reported that an increase in the number of grains per spike could compensate for the reduction in the total number of spikes under water stress conditions. Fischer [77] demonstrated that variations in the number of grains per spike are strongly influenced by growth conditions, making this metric a good indicator of crop performance in water-limited environments. In contrast, under irrigated conditions, the correlation is slightly weaker (R = 0.33), suggesting that irrigation can modify the dynamics between the number of grains and yield, likely by enabling compensation between the number and weight of grains [78].

The correlation between spikes per square meter (SPK) and yield is significant under irrigation (R = 0.33) but not significant under non-irrigated conditions (R = 0.18). This indicates that the number of spikes plays a more important role in determining yield when water is not a limiting factor. These results suggest that under irrigation, the number of spikes can be a more reliable indicator of productivity [65].

Pearson correlation matrices reveal complex relationships between different agronomic variables under irrigated and rainfed conditions. Biomass emerges as a determining factor of yield in both conditions, while grain weight and the number of grains show variable relationships depending on water availability. These observations highlight the importance of adaptive crop management based on irrigation conditions to optimize yield.

Principal component analysis (PCA) shows that the first two principal components capture the majority of the variance in the data, with 82% under non-irrigated and 85.4% under irrigated conditions. This distribution suggests that the first two dimensions are sufficient to summarize most of the information in the data, justifying their use in subsequent PCA analyses [79,80].

The variety biplots illustrate the distribution of soft wheat varieties based on the first two principal components. Under rainfed conditions, the varieties “Kharouba” and “Snina” are particularly prominent on Dim1. Conversely, under irrigated conditions, the varieties “Achtar”, “Amal”, “Kharouba”, and “Snina” exhibit a strong influence on TKW, biomass, and yield. The vectors of the variables indicate their respective contributions: “yield” and “biomass” strongly affect Dim1, while “NG” and “SPK” have a greater impact on Dim2. This indicates significant genetic variability among the varieties, affecting their response to cultivation conditions and treatments [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63].

The environment biplots illustrate the distribution of environments (AFR 2020, AFR 2021, SEA 2020, and SEA 2021). Under non-irrigated conditions, the environmental groups are distinct with minimal overlap. Under irrigated conditions, SEA environments strongly influence TKW, biomass, and yield, while AFR environments influence NG and SPK more. This highlights the impact of environmental conditions on variety performance [81,82].

The nitrogen dose biplots illustrate the distribution of nitrogen doses (N0, N1, AND N2). Under non-irrigated conditions, the groups corresponding to nitrogen doses exhibit similar effects on the measured variables, with some distinction for the N2 dose on Dim1. Under irrigated conditions, traits like TKW, biomass, and yield display a positive correlation with higher nitrogen doses, while NG is negatively correlated with these doses. This indicates that increasing nitrogen doses enhances biomass and yield production but may have adverse effects on other variables [21,64].

PCA revealed that the first two principal components explain most of the variance in the data, which is sufficient to capture the essential information. Different environments, varieties, and nitrogen doses distinctly influence the measured variables. Thousand-kernel weight (TKW), biomass, and yield variables are strongly correlated and influenced by specific conditions such as environment, variety, and nitrogen dose. These results highlight the importance of considering these factors when managing irrigated and rainfed systems to optimize yields and biomass production.

5. Conclusions

This paper provides a comprehensive study of the importance of integrated management strategies to optimize soft wheat production in arid and semi-arid Mediterranean regions. Our results show that varietal selection, nitrogen application, and irrigation management play crucial roles in improving yields and resilience to water stress. Among the recommended varieties, ‘Snina’ stands out for its high yield and efficient use of nitrogen under both rainfed and irrigated conditions, with significant yield increases when irrigation is available. Kharouba’ shows good drought tolerance and significant yield improvements under rainfed conditions with nitrogen application, making it suitable for both semi-arid and rainfed regions. Malika excelled in irrigated areas, showing substantial yield improvements with additional water resources. ‘Achtar’ and ‘Amal’ show some tolerance to water stress, with less pronounced yield increases under irrigation, making them suitable for areas with limited water resources. When nitrogen was applied under rainfed conditions, yields increased in proportion to the nitrogen dose, reaching maximum values with N2 (120 kg/ha), notably for ‘Kharouba’ and ‘Achtar.’ Under irrigated conditions, although overall yields are higher, high nitrogen doses can lead to nutrient saturation and reduced yields, particularly for ‘Amal’ and ‘Achtar.’ Therefore, it is advisable to apply 100 kg/ha nitrogen to all varieties. Irrigation management should focus on providing supplementary irrigation during critical growth stages, such as stem elongation and anthesis, to maximize the yield. This is particularly important for ‘Snina’ and ‘Malika’, which require significant irrigation for optimum performance, while ‘Achtar’ and ‘Amal’, being more tolerant to water stress, require less intensive irrigation. These integrated strategies, which combine varietal selection, nitrogen application, and irrigation management, are essential for improving wheat productivity and sustainability in drought-prone Mediterranean regions.