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Article

Influence of Nitrogen Fertilizer Rate on Yield, Grain Quality and Nitrogen Use Efficiency of Durum Wheat (Triticum durum Desf) under Algerian Semiarid Conditions

by
Nadia Boulelouah
1,*,
Mohamed R. Berbache
2,
Hanane Bedjaoui
3,
Nora Selama
1 and
Nazih Y. Rebouh
4,*
1
Department of Agricultural Sciences, University of Batna 1, Batna 05000, Algeria
2
Laboratory of Ecosystem Diversity and Agricultural Production System Dynamics in Arid Zones (DESDPAZA), University of Biskra, Biskra 07000, Algeria
3
Laboratory of Promotion of Innovation of Agriculture in Arid Regions, Department of Agronomy(LPIAAR), University of Biskra, Biskra 07000, Algeria
4
Department of Environmental Management, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
*
Authors to whom correspondence should be addressed.
Agriculture 2022, 12(11), 1937; https://doi.org/10.3390/agriculture12111937
Submission received: 19 September 2022 / Revised: 6 November 2022 / Accepted: 15 November 2022 / Published: 17 November 2022
(This article belongs to the Special Issue Effects of Fertilizer and Irrigation on Crop Production)
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Abstract

:
Nitrogen fertilizer application is conditioned closely by the amount of rainfall and its distribution. The current study aims at studying the effect of nitrogen (N) application rate on grain yield (GY), grain protein content (GPC), and nitrogen use efficiency (NUE) of durum wheat under Algerian semiarid conditions. Field trials were conducted during two contrasting and successive growing seasons (a dry year = 2016–2017 and a wet year = 2017–2018) on a local variety named Bousselam. A randomized complete block design (RCBD) was used with four replicates. Seven gradual treatments of fertilizer rate were studied: T1 = 0 unity of nitrogen (UN), T2 = 100 UN, T3 = 110 UN, T4 = 120 UN, T5 = 130 UN, T6 = 140 UN, and T7 = 150 UN. Results showed a significant difference between the dry and wet years. Only the GPC was higher in the dry year compared to the wet year having a difference of 2.94%. However, all other studied parameters were higher in the wet year, which resulted in a yield difference of 4.38 t ha−1. In addition, a significant effect of N rate on GY, thousand grain weight (TGW), and GPC was observed. A considerable difference between 120 UN and 150 UN was not noted in both years of study. Furthermore, the agronomic efficiency (AE) increased significantly with rainfall amount achieving a difference of 16.2 kg·kgN−1 between years. Finally, the results showed that using a high N amount led to a decrease in AE. Based on GY, apparent recovery efficiency (ARE), agronomic efficiency (AE), and marginal rate return (MRR) recorded in both years, the nitrogen application rate of 120 UN is recommended to be applied to wheat crops in Algerian semiarid conditions.

1. Introduction

Cereal production, especially wheat, is increasing worldwide due to the burgeoning population, which is projected to increase to 9.1 billion in 2050 [1]. Wheat is a staple crop for achieving the food security of the world’s population [2].
Food consumption in the Maghreb (North-West Africa) countries, including Libya, Tunisia, Algeria, Morocco, Mauritania, etc., is essentially based on cereals. Wheat, therefore, occupies an important place in grain production, with a significant proportion in the agricultural system of these countries. Algerians consumed 11,100 million metric tons (MMT) of wheat during 2021/22, and the predicted consumption for 2022/23 is about 11,150 MMT [3]. The improvement of wheat production becomes necessary to meet daily needs and to achieve food self-sufficiency in these regions.
However, many constraints harm wheat production, such as limited and irregular rainfall, since a majority of the cereals-producing areas in Algeria are in arid and semiarid zones [4]. Furthermore, durum wheat produces less efficiently under low-yielding conditions. Indeed, in the presence of environmental changes, the number of grains per unit of area (number of grains·m−2) of wheat becomes the component responsible for yield sensitivity [5]. More so, cultivation techniques, including tillage, fertilization systems, and plant protection strategies, are poorly mastered by farmers [6,7].
One of the main reasons for limited yield and grain quality is the lack of knowledge in nitrogen (N) fertilization practice.
The differences in yield were also related to differences in water and nitrogen use efficiencies. Raun et al. [8] reported that usually, 33% of applied N fertilizer is retained or present in the soil while the rest is lost by leaching and volatilization, which is directly related to the degree of fertilizer use efficiency. In another study [9], the authors demonstrated that nitrogen use efficiency (NUE) could achieve 87% for wheat if nitrogen (N) application takes into account soil characteristics and climatic conditions. Indeed, NUE is closely related to the amounts and distribution of rainfall. Furthermore, the synchronization of nitrogen inputs and the optimal timing of its application play a key role in increasing NUE and minimizing nitrogen loss [10]. In addition, increasing the GY and enhancing the NUE can be managed by adding supplement units of N under low amounts and irregular distribution of rainfall with respect to the environment [11,12,13].
There are various nitrogen use efficiency indicators [14,15,16], such as agronomic efficiency (AE), which depends on soil characteristics and environmental factors and decreases with increasing nitrogen application [14,17]. For a total delivered rate of 100 kg (N), the average AE in Africa was estimated at 13.6 kg dry matter (DM)·kgN−1. However, the world average of AE was estimated at 19.6 kgDM·kgN−1 [15]. The apparent recovery efficiency (ARE) is also used as a nitrogen use efficiency indicator. Its target value is between 50% and 90%. The lower values under this interval cause soil and water pollution by N. Higher values deplete the soil stock of nitrogen [18]. In Africa, the average ARE value is close to 63% for cereals, with a world average of 55%, for wheat. Different work represents that the range of ARE varies between 22% and 68% to genotype and N rate with a value of 57% [15,17]. The NHI is close to the physiological efficiency (NUEp). The good or bad efficiency of nitrogen distribution in the plant at maturity is expressed by a nitrogen harvest index (NHI). It can vary from 47 to 80% [19] and can be improved by decreasing the nitrogen concentration of the stems [20]. It seems that increasing stem size increases NHI in low nitrogen amounts, and the phenomenon is reversed in nitrogen-unlimited conditions [21].
In Mediterranean conditions, the N application rate depends mainly on rainfall in the vegetative period [22,23]. Ren et al. [24] demonstrated that rainfall in the early stages favored high yields with 210 UN in wet years, but the application of 150 UN resulted in the highest grain yields in dry years. The variation in N application rate was extensively studied in Mediterranean conditions [25,26,27,28]. In the Algerian regions, there has been anarchy in the application of nitrogen over the years [29] due to the lack of knowledge in the management of nitrogen fertilization practices [7,30]. Furthermore, the optimal application of nitrogen with fewer environmental problems in these areas is unknown. Indeed, in a similar region, Ayadi et al. [31] demonstrated that 150 UN increased GY but reduced NUE. In this context, to achieve economic benefits and environmental conservation from N leaching, we hypothesize that decreasing N rates under 150 UN by a regular step would conserve grain yield without reducing NUE. Based on this assumption, the aim of this study is (i) to examine how the addition of gradually increasing amounts of N affects the GY, the GPC, and the NUE and verify if these parameters enhance GY under Algerian semiarid conditions, (ii) to investigate the relationship between rainfall amounts and NUE, (iii) to determine the principal components presenting a direct effect on GY and the main indicators of NUE, and (iv) to suggest a sustainable approach using an environment-friendly N amount with a minimum of N losses and economic profitability.

2. Material and Methods

2.1. Site Description and Soil Characteristics

Field trials were conducted under semiarid conditions, at Ain-Mouss-Setif, among farmers’ lands (36°08′ N, 5°20′ E, and altitude of 962 m) during two successive growing seasons, 2016–2017 and 2017–2018.
The soil is clayey-sand (sand 36.53%, silt 21.37%, clay 41.1%), deep, and more homogeneous. The pH value (in H2O) is 8.21, the organic C is 1.56%, and the electrical conductivity is 0.23 mS/cm. The major NPK elements are deficient and within the limit for K. The N = 949 mg·kg−1, the P2O5 value is 7.56 mg·kg−1, and the K value is 40 mg·kg−1.

2.2. Plant Material

The durum wheat variety used in this study is called Bousselam. It is a pedigree selection obtained by The Algerian Field Crop Technical Institute (ITGC)-Setif-Algeria. Farmers highly appreciate this variety for its productivity, grain size, and resistance to cold and drought. Its thousand grain yield (TGW) varies from 40 to 60 g. The seed rate is 350 seeds·m−2 (136 kg·ha−1) with 17 cm of row spacing.

2.3. Climatic Data

Recorded weather data indicates that the average mean annual rainfall is about 450 mm, in Ain-Mouss-Sétif. The two consecutive growing seasons (2016–2017 and 2017–2018) were very contrasting. The area received total annual precipitation of 195.84 mm in 2017 (dry year) and 468.38 mm in 2018 (wet year). The average annual minimum and maximum temperatures were 3.1 °C and 28.3 °C in 2017 and 4.4 °C and 27.5 °C, respectively, in 2018. A quantity of 40 mm of rainfall was recorded in June for both years, coinciding with the grain filling. Three months earlier, during the wheat growing season, the amount of rain had exceeded 20 mm in 2017 and reached 230 mm by 2018 (Figure 1).

2.4. Experimental Design and Treatments

The experimental design adopted was a randomized complete block design (RCBD) with four replicates. Each block or replication was divided into seven sub-plots with each one representing a treatment of nitrogen fertilizer application rate, which was investigated and had a dimension of 30 m2 (5 m × 6 m). The total area of the experimental plot was 1139 m2 by adding the inter-blocks (1 m) and inter-plots (0.50 m). The number of sub-plots was 28 (07 treatments × 04 blocks). The seven treatments of nitrogen fertilizer rate were T1 = 0 UN, T2 = 100 UN, T3 = 110 UN, T4 = 120 UN, T5 = 130 UN, T6 = 140 UN, and T7 = 150 UN. The applied amount of nitrogen began with 100 UN, which is the common rate applied by Algerian farmers, as reported by Abdelguerfi and Zeghida [30]. Then, this amount was gradually increased by 10 UN up to 150 UN, where Ayadi et al. [32] indicated that, in the same conditions, GY decreased if the amount increased beyond this rate (150 UN).
In both years, each nitrogen fertilizer rate was divided into equal application rates at the tillering (GS25) and stem elongation (GS32) stages. The fertilizer urea containing 46% of N was used as a source of nitrogen. A vegetable crop was used as a precursor for both years.

2.5. Parameters Measurements

The yield and components parameters’ data were taken from the 1 m2 area in the middle row of the harvestable area of the experimental plots. At the maturity stage, straw and grain were harvested and dried at 80 °C for 72 h until constant weight, then weighed and finally milled. The GY (t·ha−1) and TGW (g) were measured at 0% moisture. Additionally, the number of spikes per unit of area (spikes·m−2), and the number of grains per unit of area (number of grains·m−2), were measured at the harvest.

2.6. Grain Protein Content and Nitrogen Use Efficiency

The dried samples were milled, and the grain and straw N content was determined using the Kjeldahl method (ISO 20483, 2006). The GPC (%) of each treatment was calculated as per the formula below (1) [33].
Protein (%) = [(N × 100)/(100 − W)] × K
where N is the nitrogen content in the grain [%]; W is the moisture content of the grain or its processed products [%]; K is the conversion coefficient of nitrogen content to protein, equal to 5.7 for wheat.
The nitrogen use efficiency component shown in Table 1 was estimated using the following formulas described by [14].

2.7. Economical Parameters

To evaluate the profitability of different treatments of N application rate, a simple economic analysis was carried out using two parameters, as indicated in Table 2, according to Ronga et al. [34]. The price was the price paid for 1 tonne (t) (309 USD·t−1), and the cost was the cost of N fertilizer applied (446.31 USD·t−1 for Urea 46%).

2.8. Statistical Analysis

The collected data were subjected to analysis of variance (ANOVA) using the general linear model procedure of the R computer Software version v3.5.1 to extrapolate the difference among treatments. Furthermore, the student’s t-test was used to determine the difference between years using the Rcmdr package. Treatments showing differences were studied by Newman & Keul’s protected least significant difference (LSD) test for mean separation at a 5% level of significance. The correlation matrix was studied using the package ‘corrplot’. Moreover, the package ‘Lavaan’ and ‘semPlot’ were used to determine the path coefficient test. It divides the correlation coefficient analysis into direct and indirect effects of different sources. A simple correlation coefficient analysis can only show the degree of a high or low positive or negative relationship between an independent variable and a dependent variable. The degree of correlation cannot indicate the direct or indirect effect of the independent variable on the dependent variable. It is possible that this relationship is to be attributed not only to a single independent variable but also to the effect of a second variable or a set of independent variables. The use of path coefficient analysis (P) can contribute to the deepening of this correlation analysis [35,36,37]. The path coefficient analysis was done to differentiate the real association between grain yield and its components and other agronomic criteria. Some investigations using this path coefficient analysis identified the main traits influencing GY in winter and durum wheat under different environmental conditions [38,39].

3. Results

3.1. Effect of N Fertilizer Rate on GY, Its Components, and GPC

Table 3 represents the result of the effect of the fertilizer nitrogen rate on grain yield (GY), the components of yield, such as the number of spikes·m−2, the number of grains·m−2 and TGW, and the GPC. The effect of years was highly significant for all parameters (p < 0.001). During the wet year (2018), the Bousselam variety expressed its potential. The difference in GY between the two contrasting years was 4.38 t·ha−1, and the GPC was 2.94%.
Taking into account the N fertilizer rate, GY appears to be significantly increasing from 3.49 to 6.91 t·ha−1 for T1 = 0 UN to T7 = 150 UN, while the GPC value ranged between 9.8% to 12.4% during the wet year. An amount of more than 2.7 t·ha−1 was achieved by adding 50 UN to the lowest fertilizer N rate studied (T1 = 100 UN).
The current study revealed that GPC was significantly influenced by N application. It increased by 5.1% from T1 = 0 UN to T4 = 120 UN in the dry year (2017) but with a significant and slight increase in the wet year (2018), by 2.6% from T2 = 100 UN to T7 = 150 UN. GPC reached 17.5% with treatment T4 = 120 UN, which was the highest value observed in both years (Table 3).
The number of grains·m−2 varied from 9783 (T1 = 0 UN) to 18,624 grains·m−2 (T7 = 150 UN) and was significantly influenced (p < 0.001) by N fertilizer rates in the wet year (2018), oppositely to the dry year (2017), where lower values were observed without a significant difference between N fertilizer rates (Table 3).
No significant difference was found in the TGW in the wet year (2018), and the highest value was achieved (52.2 g) with treatment T6 (140 UN). During the dry year (2017), the variety Bousselam recorded a grain weight of less than 39 g.
The correlations between the GY and its components: number of spikes·m−2, number of grains·m−2, TGW, and GPC are indicated in Figure 2. They are carried out on the entire N application rate, and the two studied years. The correlations are positive and very significant (p < 0.001) between GY and its components (number of spikes·m−2, number of grain·m−2) in the wet year (2018). This correlation is significant (p < 0.05) with only TGW in the dry year (2017). The quality (GPC) was correlated only with the number of spikes·m−2, in the wet year (2018).

3.2. Effect of N Fertilizer Rate on NUE and Its Components

The results showed that the NUE components varied significantly among the two years (2017 and 2018) trials. Concerning the fertilizer N rate, only AE and ARE were highly significant (Table 4). The AE varied from 3.4 to 6.63 kg/kg N in the dry year (2017) and from 10.51 to 30.57 kg/kg N in the wet year (2018). The highest value of AE was obtained with treatment T4 (120 UN) in the wet year. The ARE increased from 4.13 to 29.78 % and from 38.27 to 81.08% for 2017 and 2018, respectively.
The NHI values varied between 76.19 % to 90.05%, but this parameter was not influenced significantly by the N fertilizer rates during the two years (Table 4).
According to Figure 3, the GY was positively and highly significantly correlated (p < 0.01, p < 0.001) with AE, ARE, and NHI in the wet year (2018). In the dry year (2017), it was correlated positively and significantly (p < 0.05) with AE and ARE. In the dry year (2017), the GPC was correlated negatively and highly significantly (p < 0.001) with agro-physiological efficiency (APE) and positively and significantly (p < 0.01) with ARE. With regard to these results, it appears that AE and ARE, and APE are the most important NUE components for GY and GPC.
Concerning NUE components and their correlation with GPC, it was negatively correlated only to APE with a coefficient of determination (R2 = 0.47) in the dry year (2017) (R2 = 0.16) in the wet year (2018), as shown in Figure 4. This points out that the N uptake and its remobilization to grain negatively influence grain protein.
All the correlation results were subjected to a path coefficient analysis to detect each variable’s direct or indirect effect on grain yield in the two growing seasons, depending on the nitrogen fertilizer application. In the first step (Table 5), a path coefficient analysis was conducted on GY and its components. The variables are the number of grains·m−2, TGW, N.S, and GPC, and in the second step (Table 6), the variables are the NUE and its components: NHI, ARE, GPC, APE, and AE.
Among the components of GY, the results of the path coefficient analysis showed that only the number of grains·m−2 had a positive and high direct effect (p = 1.03) on GY in the wet year (2018). The direct effect on GY was masked by the indirect effects through the number of spikes·m−2 (N.S) (p = 0.95). This indirect effect increased the correlation between GY and the number of grains·m−2 (N.G).
According to Table 5, in the wet year (2018), the GPC and number of spikes·m−2 (N.S) recorded a negative and insignificant direct effect with p = −0.03 and p = −0.00, respectively, on GY. The TGW had a positive and insignificant direct effect (p = 0.16). In the dry year (2017), The N.S and TGW recorded direct and positive effects on GY with a value of p = 0.35 and p = 0.43, respectively. The positive correlation between GY and TGW (r = 0.57 *) (Figure 2) comes from the positive and significant indirect effect on the number of grains·m−2 (p = 0.60), while the direct one was almost insignificant (p = 0.06).
During the wet year (2018), the GY was directly positively influenced by two variables: NHI (p = 0.56) and ARE (p = 0.78). This means that yield has increased with the N fertilizer application and its remobilization to grain. The variables GPC and AE have a low and negative effect on GY (p = −0.21 and p = −0.18, respectively). A direct effect of the variable APE was positive but insignificant (p = 0.07) (Table 6).
In the dry year (2017), the grain yield was influenced directly by two variables in a positive way: AE (p = 0.38) and ARE (p = 0.34). The GPC and the NHI have a low and negative effect on GY (p = −0.18 and p = −0.21, respectively). Like the wet year (2018), the direct effect of the variable APE was positive but insignificant (p = 0.09).

3.3. Economic Efficiency of Tested N Rates

The data presented in the Table 7 revealed that N fertilization did not influence the marginal rate return (MRR) during both years of experimentation, while N doses significantly influenced the marginal net return during the wet year only. The average values of MNR and MRR were higher in the wet year (2017–2018) (1584.87 USD/ha and 13.71) compared to the dry year. For this economic aspect, the variable MNR showed a significant variation in the wet year between the different nitrogen applications.
The graphic representation of the interaction between GY, AE, MRR, and apparent N loss (Figure 5), indicates that 120 UN·ha−1 is the right N application rate in both years. It can balance the economic aspect defined by considerable GY reaching 6 t·ha−1 and 1.2 t·ha−1 in wet and dry years with an interesting, profitable marginal rate return, MRR, by increased GY per kg N fertilizer AE. Furthermore, a reduction in apparent N loss to avoid the potential environmental threat was because of the excessive N rate.
The results are uneven over years but they put us at the same N application rate (120 kg). Based on Figure 5, the two considered years are different. However, depending on the GY recorded 6 t·ha−1, the AE > 30%, and a low apparent N loss, it can be considered that N rate = 120 UN is the optimal N rate when adding the economic aspect where the margin had reached fifteen times more than their fertilizer costs.

4. Discussion

In both growing seasons, the effect of the years was highly significant for GY, its components (Nspike/m2, Ngrains·m−2, and TGW), and the GPC. Under rainfed conditions, the increase in N fertilizer rate increased GY significantly, as reported in several studies [32,40]. The maximum grain yield was noted in the wet year (WY2018) with the maximum nitrogen rate (150 UN).
The significant difference in GY (4 t·ha−1) between the two trial years (DY2017 and WY 2018) can be explained in light of the good distribution of rainfall over the wheat vegetative development on the GY. It occurs during the vegetative growth period, where the crop receives a total of 230 and 15 mm rainfall in the wet and dry years, respectively. The favorable soil moisture regime might have contributed to better N uptake and N translocation to the sinks [41], thereby increasing the yield attributing characters, which finally contributed to higher grain yield in the wet year. Moreover, the effect of N inputs was well expressed by the assimilation of N during the pre-anthesis like that was observed in the wet year (2018) and reported in similar results [42,43,44].
The GY was correlated to the number of grains·m−2, which was the main component in the development of yield in the wet year (2018). Indeed, the path coefficient analysis during the wet year (2018) demonstrated that the number of grains·m−2 has a direct effect on GY that match with other works on wheat [45,46] and can be explained by the reducing of N amount at tillering and increasing at recovery growing stages [47], contrary to the dry year (2017). In this year, the N inputs favored the TGW over the number of grains which is indicated by a positive and indirect effect between the number of grains·m−2 and TGW (Table 5). This positive correlation between the number of grains·m−2 and TGW supports the results achieved by Gweyi-Onyango et al. [48] on rice crops. However, the study of Giambalvo et al. [42] on durum wheat showed a significant decreasing effect in the number of grains·m−2 and TGW.
The TGW is a key component of the final yield. The year effect was very significant, but the N input had a less significant effect on grain weight in the dry year (2017), and the Bousselam variety did not express its potential (TGW < 35 g) (Table 3). This could be explained by the climate scene, where the cumulative rainfall of the three months (March, April, and May) was less than 15mm. The average temperature could not increase above 20 °C until June (Figure 1). In addition, these weather variations were not in favor of the dynamics of accumulation of dry matter in grain (source-sink relations) at the beginning of grain filling and even before this stage, where biomass production is carried out [31,49].
Derbal et al. [22] also stated this reasoning; they concluded that rainfall conditions in semiarid areas provide the best opportunity to produce high-quality durum wheat. Indeed, in the wet year (2018), results have shown that the Bousselam variety expressed its potential on TGW (>50 g), and the effect of N application was not significant.
The presence of an inverse relationship between grain yield and GPC has been deduced in several studies [50,51], while some studies pointed out a simultaneous increase in GY and GPC with adequate N fertilization during the vegetative stage followed by one late topdressing in irrigated wheat grains [52,53]. In the current study, the grain protein content increased with increasing N application over the two years (Table 3). The results are in agreement with previous studies, which showed that the protein content increased linearly with the increase in N application [54,55]. Furthermore, at a low nitrogen concentration in the plant, there is an insufficient translocation of assimilates from the organs to the grain, while in a situation of excess nitrogen in the soil, the plants often stop nitrogen uptake due to the nitrogen concentration of amino acids in the plant organs [56].
In the dry year (2017), the Bousselem variety recorded a high GPC (17%) at 120 UN only, while in the wet season, despite the high yield [6.9 t/ha], the GPC was only 12% even with N dose of 150 UN. The higher GPC in the dry year could be explained by the presence of nitrogen assimilates absorbed through the amount of rainfall (41.91 mm) recorded in June, which corresponds to the grain-filling stage. Abdellaoui et al. [57] reported that a variation in GPC was affected by post-anthesis nutrients and weather conditions and confirmed by Giunta et al. [58], who found that N uptake in post-anthesis allows getting both high-grain protein and yields. Brown and Petrie [59] also reported that the late intake of N caused an increase in protein content.
The different NUE indicators were significantly higher in the wet year (2018), with 468 mm of rainfall than in the dry year (2017), with only 195 mm. Several works go towards this positive and significant improvement of NUE with moisture. Gauer et al., 1962 [54] have demonstrated that moisture conditions make possible the improvement of NUE and thus increased GY, especially in rainfall areas where supplement irrigation was fundamental to ameliorate the crop yield and NUE of some crops [14]. Souissi et al. [10] reported that irrigation has a positive effect on NUE. But during the pre-anthesis phase, adequate soil moisture can increase NUEs, and the water deficit during this phase can limit N movement and may ensure a reduction in N uptake and NUE [41]. In addition, the two major physiological processes underlying grain N supply for wheat are late post-anthesis uptake and translocation of N stored in vegetative parts before flowering [20,60], which influence the improvement of nitrogen use efficiency. More so, it is pertinent to point out that in both years of the current study, the indicators AE and ARE were higher, with 120 kg of N application rate.
The positive correlations between GY and NUE (AE and ARE) (Figure 3) are consistent with those demonstrated by Fageria et al. [61]. They reported that a significant positive association between GY and NUE can improve grain yield. Nevertheless, through the path analysis, it appears that the positive correlation between GY and AE (r = 0.67 ***) is instead due to the positive and indirect effect of ARE (p = 0.86*0.78), which means that it is important to take into account the mineralization of N in the soil. Indeed, González-Montaner et al., 1997 [45] showed that the increase of the AE is dependent on mineral N in soil and environmental factors and confirmed the importance of improving ARE in breeding programs to improve NUE.
López-Bellido et al. [62] recommended the application of N fertilizer primarily as a top dressing in durum wheat between tillering and stem elongation stages to improve crop NUE and reduce losses through the leachate process and runoff losses. In this study, N was applied at tillering and stem elongation stages, contributing to the increase of NUE.
For NHI, the values were steady for the two years separately, with a slight difference between them (Table 4). It was not influenced significantly by the fertilizer N rates and was higher than that obtained under the same conditions by Benchelali et al. [23]. Fageria [61] reported that the higher NHI positively correlates with GY in the wet year. Furthermore, the study of the two parameters NHI and NUEp can explain the mechanism of nitrogen use of the different applied rates in the two study years. It could be assumed that the presence of water favored the remobilization of pre-anthesis assimilated nitrogen towards the grains, thus increasing the values of NHI and allowing protein synthesis [19,20].
The second objective of this investigation was to determine an eco-friendly, economical and agronomical N application rate for highly refined research on the N use efficiency of local varieties.
According to Figure 5, where agronomic (NUE, GY, GPC), economic (MRR), and environmental (apparent loss of N) parameters were combined, the 120 UN rate showed better results. In a wet year (2018), the application of N at 120 UN recorded both a higher grain yield (6.27 t·ha−1) and grain quality 11.4% GPC, gave the highest marginal return (15.64) and was found to be more eco-friendly with high apparent recovery efficiency (ARE = 81.08%). Moreover, in the dry year (2017), the application of 120 UN recorded the highest GY (1.2 t·ha−1), similar to the treatment T7 = 150 UN, and reached the highest GPC (17.5%) and ARE = 29.78%.
The field trials should be extended to other local and modern varieties for their quality potential (GPC), especially to test N applications with different splitting schedules of N. This is to practice 4R N management (right source, right rate, right time, and right place) [63].
Results were uneven through the years, but they put us at the same N application rate (120 kg). In the semiarid regions, reducing the amount of nitrogen application for economic benefit (MNR) and environmental conservation (apparent N loss) is interesting.
The results obtained in this work should help to improve the accuracy of the nitrogen fertilizer advice and additional N management, both in terms of the level to be applied and weather variation. This work also shows that the current evolution of fertilizer application practices ensures economic sustainability and the quality of cereal production while respecting the environment.

5. Conclusions

To adapt nitrogen fertilization to the needs of the crop according to the objectives of grain yield, quality, and reduction of the risk of nitrogen loss. It is necessary to determine the total amount of nitrogen fertilization in the presence of climatic variability. This study confirmed that decreasing N rates under 150 UN had maintained the grain yield without reducing NUE. Indeed, a consistent and significant difference between the two study years (dry and wet) was recorded as the higher value in GPC on the dry year compared with as registered in the wet year. Further, a significant difference was obtained for GY and AE in reverse with GPC.
Based on the results of the grain yield, apparent nitrogen loss, agronomic efficiency, and MRR, the present investigation demonstrated that nitrogen application at the rate of 120 UN could be recommended as a nitrogen rate for Algerian farmers in semiarid regions. In dry years, this nitrogen application with complementary irrigation at the time of nitrogen application could ensure a better NUE in order to engage in sustainable agriculture. This study is an initiative for the development of a nitrogen fertilization grid in semiarid regions of Algeria, aiming at reasoning the long-term fertilization practices in several wheat-growing areas.

Author Contributions

Conception and design of the research proposal, N.B.; Investigation., H.B.; Revising, Interpretation of the data, N.B. and M.R.B.; writing the manuscript, N.B. and M.R.B.; Data collection, N.S., M.R.B. and N.B.; Statistical analysis, M.R.B. and N.B.; Revising, writing—review and editing, N.Y.R.; funding acquisition, N.Y.R. All authors have read and agreed to the published version of the manuscript.

Funding

This paper has been supported by the RUDN University Strategic Academic Leadership Program (Rebouh N.Y). SARL SMID du TELL–Sétif (Algeria) also supported this work as a part of its wheat development program (RéQuaBlé) with farmers Bouras M.S and Mahnane S.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Agriculture 12 01937 g001 550
Figure 1. Monthly distribution of rainfall and minimum and maximum temperatures of the two growing seasons (2017 and 2018).
Figure 1. Monthly distribution of rainfall and minimum and maximum temperatures of the two growing seasons (2017 and 2018).
Agriculture 12 01937 g001
Agriculture 12 01937 g002 550
Figure 2. Correlation matrix of the grain yield (GY) and their components: Number of spikes·m−2 (N·spikes·m−2), number of grain·m−2 (Ngrain·m−2), TGW, and the GPC, for the two years (left: wet year, right: dry year).
Figure 2. Correlation matrix of the grain yield (GY) and their components: Number of spikes·m−2 (N·spikes·m−2), number of grain·m−2 (Ngrain·m−2), TGW, and the GPC, for the two years (left: wet year, right: dry year).
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Agriculture 12 01937 g003 550
Figure 3. Correlation matrix of the nitrogen use efficiency components: agronomic efficiency (AE), agro-physiological efficiency (APE), apparent recovery efficiency (ARE), nitrogen harvest index (NHI), grain protein content (GPC), and grain yield (GY), for the two years, (left: wet year, right: dry year).
Figure 3. Correlation matrix of the nitrogen use efficiency components: agronomic efficiency (AE), agro-physiological efficiency (APE), apparent recovery efficiency (ARE), nitrogen harvest index (NHI), grain protein content (GPC), and grain yield (GY), for the two years, (left: wet year, right: dry year).
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Agriculture 12 01937 g004 550
Figure 4. Relation between grain protein content (GPC %) and nitrogen agro-physiological efficiency (APE) in a dry year (2017); left [R2 = 0.47 **, p-value < 0.01], and in a wet year (2018) right [R2 = 0.16 ns]. (ns: no significant, **: p < 0.01).
Figure 4. Relation between grain protein content (GPC %) and nitrogen agro-physiological efficiency (APE) in a dry year (2017); left [R2 = 0.47 **, p-value < 0.01], and in a wet year (2018) right [R2 = 0.16 ns]. (ns: no significant, **: p < 0.01).
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Agriculture 12 01937 g005 550
Figure 5. Nitrogen rate effects on grain yield, agronomic efficiency, apparent N loss, and marginal nitrogen return in the wet year (a) and the dry year (b).
Figure 5. Nitrogen rate effects on grain yield, agronomic efficiency, apparent N loss, and marginal nitrogen return in the wet year (a) and the dry year (b).
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Table
Table 1. Nitrogen use efficiency component.
Table 1. Nitrogen use efficiency component.
TraitDescriptionFormulaUnits
AEAgronomic Efficiency(Gf − Gu)/Nakg/kgN
APEAgro-Physiological EfficiencyGf − Gu/Nf − Nukg/kgN
AREApparent Recovery Efficiency(Nf − Nu/Na) × 100%
NHINitrogen Harvest Index(Nuptake in grain/Nt) × 100%
Gf: grain yield in the fertilized plot, Gu: grain yield in the unfertilized plot, Na: quantity of nitrogen applied, Nf: total N uptake in the fertilized plot, Nu: total N uptake of the unfertilized plot, Nt: a total of the N uptake per plant was calculated as the sum of N accumulated in the grain and N uptake in the straw.
Table
Table 2. Economic parameters of treatments (rate of nitrogen application).
Table 2. Economic parameters of treatments (rate of nitrogen application).
TraitDescriptionFormulaUnit
MNRMarginal Net Return(GY × price) − (Na × cost)US$/t
MRRMarginal Rate ReturnMNR/(Na × cost)
GY: grain yield, Na: quantity of applied nitrogen.
Table
Table 3. Results of the ANOVA for the effect of N rate on GY and its components, GPC, and the t-test for the effect of year.
Table 3. Results of the ANOVA for the effect of N rate on GY and its components, GPC, and the t-test for the effect of year.
N° Spikes·m−2N° Grains·m−2TGW (g)GY (t·ha−1)GPC (%)
Effect years2016/2017225.523396.7436.331.0914.47
2017/2018468.0415126.3450.965.4711.53
t-testp<0.001<0.001<0.001<0.001<0.001
Effect nitrogen rate (2016/17)T1 = 0 UN197 ± 34.47 a2665.1 ± 673.92 a33.7 ± 0.75 a0.64 ± 0.12 a12.4 ± 0.81 a
T2 = 100 UN261.5 ± 26.19 a2845.4 ± 375.29 a35.1 ± 0.88 ab0.91 ± 0.02 ab13.4 ± 0.63 ab
T3 = 110 UN205.5 ± 30.39 a3582.8 ± 225 a38.6 ± 0.66 b1.16 ± 0.20 ab14.5 ± 0.90 ac
T4 = 120 UN235.7 ± 23.47 a3585.5 ± 486.34 a35.6 ± 0.99 ab1.2 ± 0.19 ab17.5 ± 0.5 c
T5 = 130 UN237.7 ± 12.51 a3511.8 ± 504.54 a37.1 ± 0.72 ab1.26 ± 0.12 b12.5 ± 0.57 a
T6 = 140 UN238.5 ± 5.75 a3714.8 ± 476.22 a37.4 ± 1.62 ab1.18 ± 0.09 ab15.8 ± 0.82 bc
T7 = 150 UN220.7 ± 10.13 a4082.4 ± 735.72 a36.3 ± 0.28 ab1.24 ± 0.02 ab14.9 ± 0.29 ac
Test Fpn.sn.s<0.02<0.03<0.001
Correlation.C n.sn.s0.50 *0.63 ***0.41 *
Effect nitrogen rate (2017/18)T1 = 0 UN313.2 ± 14.47 a9782.8 ± 262 a51.6 ± 0.43 a3.49 ± 0.09 a11.1 ± 0.29 ab
T2 = 100 UN373 ± 35 ab11,666 ± 1285 ab51 ± 1.25 a4.2 ± 0.49 ab 9.8 ± 0.61 a
T3 = 110 UN395.2 ± 21 ab13,493 ± 1016 ac51.2 ± 0.6 a4.95 ± 0.46 ac11.8 ± 0.66 ab
T4 = 120 UN533.7 ± 76.9 bc18,229.3 ± 2708 c48 ± 1.36 a6.27 ± 0.94 bc11.4 ± 0.28 ab
T5 = 130 UN484.5 ± 25.6 bc16,112.1 ± 1007 ac50.8 ± 1.4 a5.8 ± 0.49 ac11.8 ± 0.40 ab
T6 = 140 UN569.2 ± 28 c17,977.7 ± 865 bc52.2 ± 0.97 a6.71 ± 0.26 c12.1 ± 0.64 b
T7 = 150 UN607.2 ± 14.6 c18,623.6 ± 1482 c51.7 ± 0.48 a6.91 ± 0.59 c12.4 ± 0.36 b
Test Fp<0.001<0.001n.s<0.001<0.02
Correlation.C 0.71 ***0.68 ***n.s0.68 ***n.s
For each year, values within each row followed by the same letter are not significantly different at the 0.05 probability level (n.s: no significant, *: p < 0.05, ***: p < 0.001).
Table
Table 4. Results of the ANOVA for the effect of fertilizer N rate on the nitrogen use efficiency components: agronomic efficiency (AE), agro-physiological efficiency (APE), apparent recovery efficiency (ARE), nitrogen harvest index (NHI), and the t-test for the effect of year.
Table 4. Results of the ANOVA for the effect of fertilizer N rate on the nitrogen use efficiency components: agronomic efficiency (AE), agro-physiological efficiency (APE), apparent recovery efficiency (ARE), nitrogen harvest index (NHI), and the t-test for the effect of year.
AE (kg/kgN)APE (kg/kgN)ARE (%)NHI (%)
Effect years2016/20175.6034.1816.0183.29
2017/201821.6226.2762.7979.48
t-testp<0.001<0.001<0.001<0.05
Effect nitrogen rate (2016/17)T1 = 0 UN___81.53 ± 5.19 a
T2 = 100 UN3.4 ± 0.79 a30.91 ± 1.37 a4.13 ± 0.9 a88.29 ± 1.25 a
T3 = 110 UN7.51 ± 0.46 c28.59 ± 4.82 a4.2 ± 1.1 a90.05 ± 1.18 a
T4 = 120 UN6.34 ± 0.68 bc20.81 ± 0.22 a29.78 ± 3.6 c79.89 ± 3.06 a
T5 = 130 UN6.63 ± 0.38 bc31.53 ± 3.75 a17.63 ± 1.1 b83.05 ± 3.81 a
T6 = 140 UN4.64 ± 0.85 ab22.42 ± 2.84 a20.09 ± 2.2 b84.96 ± 3.35 a
T7 = 150 UN4.54 ± 0.52 ab23.79 ± 1.77 a16.59 ± 2.25 b77.23 ± 1.62 a
Test Fp<0.01n.s<0.001n.s
Correlation.C 0.69 ***n.sn.sn.s
Effect nitrogen rate (2017/18)T1 = 0 UN___83.14 ± 0.73 a
T2 = 100 UN10.51 ± 0.87 a31.46 ± 0.28 a38.27 ± 3.33 a76.19 ± 3 a
T3 = 110 UN22.07 ± 0.22 b32.47 ± 1.28 a59.44 ± 4.1 ac77.62 ± 1.49 a
T4 = 120 UN30.57 ± 3.24 b38.79 ± 1.34 a81.08 ± 14.01 b79.40 ± 1.7 a
T5 = 130 UN20.68 ± 3.5 ab35.63 ± 1.56 a59.17 ± 5.6 ab79.40 ± 0.85 a
T6 = 140 UN23.03 ± 1.92 b34.67 ± 2.47 a66.51 ± 3.16 b81.45 ± 1.02 a
T7 = 150 UN22.84 ± 3.99 b31.56 ± 2.46 a72.24 ± 11.35 b79.15 ± 0.91 a
Test Fp<0.001n.s<0.001n.s
Correlation.C 0.77 ***n.s0.85 ***n.s
For each year, values within each row followed by the same letter are not significantly different at the 0.05 probability level (n.s: no significant, *** p < 0.001).
Table
Table 5. Path coefficient of the direct and indirect effects of the number of spikes·m−2 (NS), number of grains·m−2 (NG), thousand grain weight (TGW), and grain protein content (GPC) in the dry year and the wet year.
Table 5. Path coefficient of the direct and indirect effects of the number of spikes·m−2 (NS), number of grains·m−2 (NG), thousand grain weight (TGW), and grain protein content (GPC) in the dry year and the wet year.
Dry year (2016/2017) NGTGWNSGPCCorrelation with GY
NG0.0600.2580.1300.0460.494
TGW0.0360.4300.0740.0310.570
NS0.0220.0900.3500.0030.465
GPC0.0200.0950.0070.1400.261
Wet year (2017/2018) NGTGWNSGPCCorrelation with GY
NG1.030−0.026−0.0290.0000.976
TGW−0.1650.1600.0040.000−0.001
NS0.979−0.022−0.0300.0000.926
GPC0.3500.011−0.0120.0000.349
Table
Table 6. Path coefficient analysis of the direct and indirect effects of AE, APE, ARE, NHI and GPC in the dry year and wet year.
Table 6. Path coefficient analysis of the direct and indirect effects of AE, APE, ARE, NHI and GPC in the dry year and wet year.
Dry year (2016/2017) AEAPEARENHIGPCCorrelation with GY
AE0.3800.0290.078−0.0170.0000.470
APE0.1220.0900.129−0.0800.1170.378
ARE0.087−0.0340.3400.116−0.0860.422
NHI0.0300.034−0.187−0.2100.032−0.300
GPC0.000−0.0590.1630.038−0.180−0.038
Wet year (2017/2018) AEAPEARENHIGPCCorrelation with GY
AE−0.1800.0410.6710.196−0.0570.671
APE−0.1040.0700.2180.1180.0710.373
ARE−0.1550.0200.7800.129−0.0740.700
NHI−0.0630.0150.1790.560−0.1300.561
GPC−0.049−0.0240.2730.347−0.2100.338
Table
Table 7. Results of the ANOVA for the effect of fertilizer N rate on MNR and MRR and the t-test for the effect of year.
Table 7. Results of the ANOVA for the effect of fertilizer N rate on MNR and MRR and the t-test for the effect of year.
MNR (USD/ha)MRR
Effect years2016/2017229.791.98
2017/20181584.8713.71
t-testp<0.001<0.001
Effect nitrogen rate (2016/17)0 UN198.23 ± 39.64 a_
110 UN183.54 ± 8.2 a1.89 ± 0.08 a
100 UN252.33 ± 62.82 a2.36 ± 0.58 a
120 UN256.53 ± 61.71 a2.20 ± 0.53 a
130 UN261.74 ± 38.6 a2.07 ± 0.31 a
140 UN228.16 ± 30.46 a1.68 ± 0.22 a
150 UN238.24 ± 7.87 a1.64 ± 0.05 a
Test Fpn.sn.s
Correlation.C n.sn.s
Effect nitrogen rate (2017/18)0 UN1077.29 ± 30.86 a_
100 UN1175.74 ± 154.18 ab12.12 ± 1.59 a
110 UN1424.15 ± 142.91 ac13.34 ± 1.33 a
120 UN1821.49 ± 290.76 ac15.64 ± 2.50 a
130 UN1667.23 ± 153.41 ac13.22 ± 1.22 a
140 UN1937.9 ± 81.14 bc14.27 ± 0.60 a
150 UN1990.36 ± 184.41 c13.68 ± 1.27 a
Test Fp<0.003n.s
Correlation.C 0.66 ***n.s
For each year, values within each row followed by the same letter are not significantly different at the 0.05 probability level; 1 USD = 145.63 Da. (n.s: no significant, ***: p < 0.001).
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Boulelouah, N.; Berbache, M.R.; Bedjaoui, H.; Selama, N.; Rebouh, N.Y. Influence of Nitrogen Fertilizer Rate on Yield, Grain Quality and Nitrogen Use Efficiency of Durum Wheat (Triticum durum Desf) under Algerian Semiarid Conditions. Agriculture 2022, 12, 1937. https://doi.org/10.3390/agriculture12111937

AMA Style

Boulelouah N, Berbache MR, Bedjaoui H, Selama N, Rebouh NY. Influence of Nitrogen Fertilizer Rate on Yield, Grain Quality and Nitrogen Use Efficiency of Durum Wheat (Triticum durum Desf) under Algerian Semiarid Conditions. Agriculture. 2022; 12(11):1937. https://doi.org/10.3390/agriculture12111937

Chicago/Turabian Style

Boulelouah, Nadia, Mohamed R. Berbache, Hanane Bedjaoui, Nora Selama, and Nazih Y. Rebouh. 2022. "Influence of Nitrogen Fertilizer Rate on Yield, Grain Quality and Nitrogen Use Efficiency of Durum Wheat (Triticum durum Desf) under Algerian Semiarid Conditions" Agriculture 12, no. 11: 1937. https://doi.org/10.3390/agriculture12111937

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