Use of Amino Acids and Slow-Release Urea-Based Biostimulants to Enhance Yield and Grain Quality in Durum Wheat Under No-Tillage Conditions in Semi-Arid Region

Abstract

Optimizing resources to produce higher quality food is key to promoting more resilient agroecosystems. Although the use of biostimulants in agriculture has been gaining importance in recent years, their success depends on edaphoclimatic conditions and on the specific plant species. For this reason, the main aim of this study was to evaluate the effect of biostimulants (amino acids obtained from the enzymatic hydrolysis of plant extracts) on durum wheat yield variables and grain quality (protein content). Five treatments (control treatment—T1, biostimulants—T2, slow-release urea—T3, biostimulants plus slow-release urea—T4, Mg and micronutrients—T5) were tested in a field experiment conducted over 3 seasons in the south of Spain; all were dosed at 120 kg N ha⁻¹. The number of spikes increased significantly with biostimulant treatments in the first season (up to 33%, T2 and T4), while the highest significant grain yields were obtained with biostimulants applied individually in the first season (29.5%-T2) and biostimulants in combination with slow-release urea the second season (27.3%-T4), related to T1. Grain protein concentration was influenced by the treatment only in the second season, the driest during the study, when it was increased with biostimulants up to 4.2% with T2 in comparison with T1. Total protein production increased (28.1%T2) in the first season, (8.1–21.9% for T2–T4) in the second season and (6.5% T4) in the third season, when biostimulants were applied alone or in combination with slow-release urea, respectively. In general, plants treated with Mg and micronutrients produced a lower number of spikes, less yield, and reduced total protein compared to those doses with biostimulants. The application of amino acids as biostimulants was demonstrated to enhance durum wheat yield and total protein production and could be a potential tool for promoting nitrogen use efficiency in semi-arid areas.

1. Introduction

Agriculture faces continuous issues, but the greatest challenge is supplying sufficient quantities and quality of food to the ever-growing human population while reducing its negative impact on the environment. The scarcity of resources along with regional and international conflicts make this mission even more complicated [1]. In addition, agriculture depends on chemically synthesized inputs, such as fertilizers and plant protection products, to achieve high yields. The misuse of these inputs, i.e., applying higher rates than required and at the wrong times, leads to an excess that remains in the soil [2] and is potentially washed out and lost via leaching. This alters the functionality of soil and its ability to provide ecosystem services, causing soil pollution and/or acidification, altering nutrient and organic matter cycling, and the eutrophication of water bodies, among other issues, resulting in decreased productivity and becoming a threat to our agroecosystems and food production systems [3]. In addition, poor agricultural practices, for example, higher fertilizer doses than required for crops or excessive tillage, negatively influence soil microbial communities and soil biodiversity [2].

Nitrogen (N) is the most limiting nutrient for agriculture worldwide and, for that reason, N fertilizers are essential for crop growth and protein synthesis in plants [4]. Low soil N availability for crops including cereals curbs plant growth, yield and grain quality [5]. For this reason, N fertilizers are commonly applied in agriculture. However, the continued application of N fertilizers at a high rate is not considered sustainable in the long term because of their high production and environmental costs (in terms of the energy needed and carbon dioxide, CO₂, produced), and the related impacts [6]. In this regard, greenhouse (nitrous oxide, N₂O) and toxic (ammonia or NH₃; nitric oxide, NO; and nitrogen dioxide, N₂O) gases are produced after dosing the soil with N fertilizers [7,8,9]. Farmers should therefore adjust their fertilization strategies to crop requirements, edaphoclimatic conditions and potential production in order to minimize excess N application [10] and enhance N use efficiency (NUE), or the amount of N that is absorbed by the crop out of the total N applied.

A plausible alternative for increasing NUE in crops and achieving the goal of producing high yield without reducing the quality of the product is the use of biostimulants [11]. The use of biostimulants could also help reduce the dependence of agriculture on synthetic inputs. Additionally, biostimulant production implies waste valorization or circular economy. Biostimulants are defined as substances or micro-organisms that promote nutrient availability and plant assimilation by stimulating physiological processes [12]. They can be formulated from a wide range of sources, such as plant or animal residues, and agro-industrial waste; for example, the enzymatic hydrolysis of plant extracts or hydrolysate extracted from chicken feathers that has proved useful for improving maize yields and quality parameters [12]. Moreover, certain microorganisms are often considered to be biostimulants. For example, Trichoderma helps fight abiotic stresses and even improves N assimilation, yield and certain grain quality parameters in durum wheat [13,14], lettuce and rocket [15]; it also improves water and nutrient uptake capacity and stimulates the radicular system of vegetable plants [6].

In line with this, biostimulants fuel the capacity of plants to transport, store and recycle N from the soil [11,16,17]. Additional research found that the efficiency of N fertilizers increases yield up to 23% in lettuce plants compared with those that do not receive biostimulants (extracts from the enzymatic hydrolysis of Fabaceae tissues, including peas, beans, soybeans and alfalfa) [18]. The total amount of substances needed to promote specific physiological processes in plants is rather small, as shown by [18] who used a biostimulant containing 1% total nitrogen, 6% amino acids, 10% organic carbon, sulfurated compounds, and 8 mg kg⁻¹ of triacontanol, present in Fabaceae and which acts as a plant growth regulator. Additionally, ref. [19] described an improvement in leaf chlorophyll and K, Ca and Mg content in rocket leaves when biostimulants based on legume-derived protein and tropical plant extracts were applied. The success of biostimulant application depends on the physiological process that is being encouraged. In this sense, critical stages for production and grain quality in cereals are tillering and grain filling. In the latter, protein is accumulated, and grain yield and quality [11] could be affected if insufficient N is available for the plant [20]. Furthermore, biostimulants are commonly used in combination with N fertilizers to improve grain production and quality [4,11,21] under different fertility and environmental conditions.

The main aim of this study was to evaluate the effect of biostimulants (containing a mixture of amino acids obtained from the enzymatic hydrolysis of plant extracts), either alone or in combination with slow-release urea, on yield and grain quality in a semi-arid region under Mediterranean climate (southern Spain) when compared to a control treatment and another involving Mg and micronutrients (Mn, Zn and Cu), commonly used for biofortification [22]. In our study, we selected durum wheat as a representative crop from the south of Spain (Andalusia) as well as other mediterranean countries, like Italy. Considering the increasing frequency of drought events due to climate change, the ability of biostimulants to enhance plant performance under water scarcity represents a promising strategy especially in arid and semi-arid environments [6]. A field experiment with a randomized block design was established and evaluated over 3 seasons. Plant production (spike numbers, straw and grain yield) and grain quality (protein content) were evaluated at grain maturity each season. We hypothesized that the biostimulants applied either alone or in combination with slow-release urea would promote grain yield and grain protein content when rainfall is not a limiting factor, compared to the other treatments with no biostimulants. We also hypothesized that the biostimulants would have a greater positive effect on the analyzed wheat variables than the treatment containing Mg and micronutrients, as physiological processes related to N acquisition and protein production would not be stimulated in the absence of the biostimulants.

2. Material and Methods

2.1. Localization, Experimental Design and Treatments

The study was established in a field located near the city of Córdoba (southern Spain) in the Rabanales Experimental Farm (University of Córdoba, 37°55′25.58″ N, 4°43′26.41″ W, 116 m asl). This field has a slope of 10%, varying between a sandier area with boulders (mainly quartzite; Quaternary terrace) in the upper part and a lower area with a higher clay content that is more fertile (vertic-type properties).

The field experiment was carried out over three seasons (2015, 2016 and 2017). Durum wheat (Triticum durum) cv. Euroduro was planted by direct sowing (180 kg ha⁻¹) in each of the three seasons. This sowing method, in addition to non-tillage, was introduced into this area of the farm more than 10 years prior to the beginning of the experiment. A randomized block design with 4 blocks (replications) and 5 treatments was established. The different plots (experimental units) were 15 m wide and 200 m long. In each block, the 5 treatments (Table 1) were randomly applied in 2014–2015 and the same plots and experimental design were used in the subsequent two seasons (2015–2016 and 2016–2017); in other words, the treatments were applied each season. The treatments implemented in this study received the same total amount of basal and top dressing fertilizer, 123.4 kg N ha⁻¹ (of which 4.4 kg N ha⁻¹ was provided together with the seed in the form of microgranules, 39 kg N ha⁻¹ in a first dressing and 80 kg N ha⁻¹ in the second dressing; all N applied as urea). However, T2 to T5 included an extra application of N (<3 kg N ha⁻¹). T2 to T4 were sprayed when the wheat had 4–6 true leaves at a rate of between 2 (T2) and 10 (T3) L ha⁻¹, mixed with the post-emergence herbicide (see Table S1 for more information), applied during the first week of December in the first season and in the last week of November in the two subsequent seasons. More details are shown in Table 1. Therefore, the first treatment was a control (T1) that received only the background N fertilizer applied to the whole experimental field. The second treatment (T2) was based on biostimulants, a mixture of amino acids (glutamic acid-50%, aspartic acid, alanine, glycine, proline, serine, valine) obtained from the enzymatic hydrolysis of plant extracts to enhance grain yield. The third treatment (T3) was a slow-release urea fertilizer (containing ureic and urea-formaldehyde N) to enhance the quality of the grain; the fourth treatment (T4) was a mixture of the two previous treatments (T2 and T3); and finally, the last treatment (T5) was a mix of urea with magnesium, manganese, zinc and copper, commonly used for crop biofortification in this region. The treatments applied in the trial were formulated by different laboratories and marketed under registered trademarks and formulations, meaning that the biostimulants and other compounds used were not developed by the research team. The application rates followed the manufacturer’s recommended guidelines. The only treatment modified by the research team was T4, which was treatment T2 enriched with 80% of treatment T3 (slow-release urea), with the aim of improving crop production and quality with an optimized fertilizer combination.

2.2. Climate Conditions

The meteorological data was obtained from the weather station located at Córdoba Airport. The area has a Mediterranean climate with hot dry summers; the absolute maximum temperature reaches more than 45 °C in July and August and less than 10 mm rainfall is expected in these two months and winters have moderate daily mean temperatures, normally >8 °C (see Figure 1A, for 2001–2018). In the study period, a similar quantity of rainfall fell in both the winter (71.9 mm) and spring (63.1 mm), with March being the wettest month (ranging from 3.9 mm in 2012 to 266.4 mm in 2013; see Figure 1A. The daily mean temperature rises during the summer months (between 22.8 °C and 30.3 °C) and falls quickly in October and November (to between 9.9 °C and 20.8 °C). Figure 1B–D show the monthly rainfall and mean temperatures for each season evaluated in the study. In 2015–2016, rainfall was 514.5 mm, the driest of the three seasons studied in the months corresponding to the season from September to August. The 2016–2017 season was better than the previous one, with total precipitation of 549.4 mm; this was exceptionally high in November (142.2 mm). Finally, in 2017–2018, rainfall was the most abundant in the study period, with 596.2 mm, which was accentuated in the month of March (262.4 mm).

2.3. Soil Sampling and Analysis

Representative soil samples from each block and the topsoil (0–0.25 m depth) were collected at the beginning of the experiment using an Edelman auger (6 cm diameter). The soil samples were left to dry for one week at room temperature (25 °C) and sieved to 2 mm in the laboratory before being subjected to different analyses. After dispersing the soil particles with sodium hexametaphosphate, the soil granulometry was analyzed using the Robinson pipette method, resulting in 26 ± 4% sand, 21 ± 2% silt and 53 ± 2% clay. The organic carbon content [23] was 13 ± 4 g kg⁻¹. The carbonate content, according to [24], was 120 ± 30 g kg⁻¹, and total C and N were determined via combustion in a Leco 928 series elemental macroanalyzer (LECO Corporation, St. Joseph, MI, USA) manufactures in United States, with an organic C:N ratio of 8.5 ± 0.5 being obtained. Soil pH, determined potentiometrically in a 1:2.5 soil/water suspension (pH_1:2.5 soil/water; pH meter GLP 21 Crison Instruments SA) was 8.2 ± 0.5, while the electrical conductivity of the soil, determined in a 1:5 soil/water suspension (EC_1:5) using a conductivity meter (micro CM 2200, Crison Instruments SA, Alella, Spain), was 115 ± 10 µS cm⁻¹. The available P in the soil (1.5 g) was extracted using 30 mL of 0.5 M NaHCO₃ (buffered at pH 8.5) after shaking for 30 min according to Olsen et al. (1954), and then measured with the Molybdate blue method [25]; low-medium values (5.5 ± 0.5 mg P kg⁻¹) were obtained.

2.4. Plant Sampling and Analysis

Plant samples were collected using 0.5 m × 0.5 m metal frames, which were randomly arranged within the different blocks and treatments in the field experiment. Four samples/metal frames were collected per experimental plot at grain maturity (the second or third week of June; see Table S1). The samples were dried at 65 °C for 48 h. They were then weighed (to calculate plant biomass), threshed, and the grain weighed again (grain yield). Straw weight was calculated after separating the grains from the wheat plants. The weight and number of spikes was determined after separating the stalk from the spike, counting, and subsequently weighing them, and additionally, the protein content was measured in one grain sample in each season and repetition. However, only one sample per treatment (composite of 4 samples previously collected per repetition) was used in this case.

The Harvest Index (HI) was calculated by dividing the weight of the grain obtained by the total dry weight of the straw. The Kjeldahl method was used to determine the N concentration of the composite sample. For this purpose, the organic N in the grain was converted into inorganic N (NH₄⁺); it was then distilled to obtain and analyze the NH₃ to estimate the protein content in the grain.

2.5. Statistical Analyses

The data obtained was subjected to normality and homoscedasticity (Levene’s test) tests and, subsequently, a repeated measures analysis of variance (ANOVA; 3 seasons, 2015, 2016 and 2017) with one factor (5 treatments) was run. In addition, as there were significant interactions between time and treatment for multiple variables (Table S2), a one-way ANOVA (5 treatments) was performed for each season separately (Table S3). Then, a comparison between means was performed when the differences were significant (p-value ≤ 0.05), by applying the Tukey post hoc test.

3. Results

Despite the time × treatment interaction except for the HI (Table S2), in general, the greatest biomass, spike number, straw production, grain yield, and grain protein concentration were observed during the first season analyzed, followed by the third season, with significantly lower values in the second season (Table S2). To explain the interactions found, the figures generated by the statistical analysis performed (one-way ANOVA for each variable) are analysed below.

3.1. Spike Number

Figure 2 shows that there were only significant differences in the 2015–2016 season (Figure 2A) in terms of the number of spikes as a function of the treatment (p-value ≤ 0.0001). For that season (Figure 2A), T1 (347 ± 33 spikes ha⁻¹) produced the lowest number of spikes, significantly fewer than those produced by T5 (388 ± 48 spikes ha⁻¹), T3 (446 ± 28 spikes ha⁻¹), T2 (457 ± 38 spikes ha⁻¹) and T4 (465 ± 37 spikes ha⁻¹).

3.2. Straw and Grain Yield

Figure 3A–C show that there were significant differences in straw production in 2015–2016 (p-value ≤ 0.001) and 2017–2018 (p-value = 0.076) but not in 2016–2017 (p-value = 0.754). In the first season (Figure 3A), T2, T3, T4 and T5 produced the most straw (between 6427 ± 967 kg ha⁻¹ for T5 and 6840 ± 602 kg ha⁻¹ for T3), significantly higher than that produced with T1 or the control (5530 ± 771 kg ha⁻¹). However, in 2017–2018, T2 and T5 were the only treatments that produced significant increases in straw production compared with T1 (T1, 3185 ± 867 kg ha⁻¹; T2, 4133 ± 1352 kg ha⁻¹; T5, 3976 ± 874 kg ha⁻¹). The lowest values for straw production were obtained in 2016–2017 (between T5, 2614 ± 762 kg ha⁻¹, and T4, 2816 ± 857 kg ha⁻¹).

In terms of yield, significant differences were found in 2015–2016 (Figure 3D; p-value ≤ 0.001) and 2016–2017 (Figure 3E; p-value ≤ 0.001). In the first two seasons, T1 produced the lowest durum wheat yields (5431 ± 689 kg ha⁻¹, in 2015–2016, and 2704 ± 667 kg ha⁻¹, in 2016–2017). In 2015–2016, plants treated with T2 (7029 ± 689 kg ha⁻¹) obtained the highest yields, followed by T3 (6710 ± 559 kg ha⁻¹) and T4 (6754 ± 616 kg ha⁻¹), and then T5 (6232 ± 713 kg ha⁻¹), while in 2016–2017, T4 produced the highest yields (3443 ± 612 kg ha⁻¹), followed by T3 (3249 ± 584 kg ha⁻¹), then T2 (2825 ± 573 kg ha⁻¹), and, finally, T5 (2810 ± 908 kg ha⁻¹). Although no significant differences were obtained in the 2017–2018 season (p-value = 0.119; Figure 3F), T2, T3, T4 and T5 produced between 316 (T4) and 810 (T3) kg ha⁻¹ more grain than the T1 or control plants (mean values).

3.3. Harvest Index

Figure 4 shows that the HI was only significantly influenced by the treatment in the 2017–2018 season (p-value = 0.0065). In that season, the highest HI was produced by T4 (1.38 ± 0.17), followed by T3 (1.36 ± 0.21) and T1 (1.29 ± 0.14), and then by T5 (1.23 ± 0.10) and T2 (1.19 ± 0.19, Figure 4C).

3.4. Protein

Grain protein concentration (Figure 5A–C) ranged from 12.4% in 2015–2016 to 14.6% in 2017–2018 (mean values). In this case, the results were nearly significant in the second season evaluated (p-value = 0.0778); the highest concentration of grain protein was obtained for T2 (13.86 ± 0.13%), followed by T4 (13.39 ± 0.24%), T1 (13.30 ± 0.37%), T3 (13.29 ± 0.68%), and then, T5 (12.99 ± 0.06%, Figure 5B). It should be highlighted that T2 and T5 produced the lowest variability in grain protein concentration according to the violin and box and whisker plots. However, total protein production (kg ha⁻¹) was significantly influenced by the treatment in each season (Figure 5D–F). The highest values were observed in 2015–2016 (Figure 5D, between 450 and 1050 kg ha⁻¹), followed by the values calculated for 2017–2018 (Figure 5F, between 250 and 1000 kg ha⁻¹), and the lowest values were obtained in 2016–2017 (Figure 5E, between 250 and 650 kg ha⁻¹); these values were significant for all treatments in 2015–2016, T4 only in 2016–2017, and T3 only in 2017–2018, compared to the control (T1).

4. Discussion

According to our results, foliar applications of amino acid-based biostimulants (T2 and T4) are viable options for increasing durum wheat productivity (number of ears, yield) and/or grain quality) in semi-arid regions under similar conditions to the ones tested here. Foliar fertilization is often used to supply biostimulants and essential micronutrients to crops, increasing the efficiency of their use and improving yields and even crop quality [26,27,28]. Several studies have shown that applying amino acids influences the physiological activities of plants. This could be explained by the fact that amino acids are water-soluble and trigger an osmoregulatory effect, stimulating cell growth at specific phenological stages, resulting in increased crop growth and yield [29]. The results obtained in this study are in line with previous work that has already shown that the use of biostimulants containing amino acids applied to leaves leads to improved wheat yield variables [17]; however, in our study biostimulants were applied during the first phenological stages. Similarly, the use of biostimulants based on fulvic acids or bacteria in combination with arbuscular mycorrhizal fungi improved thousand-kernel weight in wheat grown on sandy loam and clay loam soils [30,31]. Moreover the use of biostimulants is associated with the promotion of genes that are expressed to enhance N transport and assimilation [11], directing the crop to improve N uptake, thereby boosting crop development and crop yield, and directly influencing grain quality [32,33].

However, the efficacy of the different treatments evaluated in this study depended on the quantity of rainfall and its distribution in each season. Thus, although all the treatments (T2 to T5) increased the number of ears, straw production and yield during the wettest season (2015–2016), in the other two seasons evaluated, the effect was a function of treatment. The critical period in terms of water requirements corresponds to the formation of the ear (Z50-Z59), as it marks the beginning of the most delicate phase of the cycle, which is closely related to the flowering phase (Z60-Z69) and grain filling (Z70-Z89). This period mainly occurs during the months of February to May, which highlights the importance of monitoring rainfall during these but especially previous months, with the availability of water, soil and rainfall, from January to March, being key for proper crop development in this area. In this regard, the cumulative rainfall from January to March was 247.8 mm in the 2015–2016 season, 211.8 mm in the 2016–2017 season and 420.8 mm in the 2017–2018 season. These results are in line with the highest number of spikes and yield obtained in the 2015–2016 season related to the other two seasons, which also affected the effects of the different treatments evaluated in this study. It is important to note that, although water availability is essential to ensure yield, excessive rainfall during the developmental stages can be as detrimental as its deficit, as it promotes waterlogging conditions that limit grain filling. This may partly explain why, despite the high rainfall recorded in the 2017–2018 season, no significant differences were observed compared to the 2015–2016 season.

This could be partially explained by the fact that the rainfall distribution was more suitable for wheat flowering and heading in the first season. In the second season (2016–2017), only the application of biostimulants in combination with slow-release urea (T4) significantly increased grain yield over the control (T1), while an increased but non-significant effect on straw yield for the third season (2017–2018) was also observed for the application of both biostimulants on their own (T2) and Mg and micronutrients (T5). However, it should be noted that the highest harvest index was obtained for the crops treated with biostimulants in combination with slow-release urea (T4) in the last two seasons of the experiments, where the rainfall distribution was not the most suitable for wheat development. According to these results, T4 is likely to have alleviated potential drought stress, which is of interest for biostimulant development in semi-arid regions [34]. In agreement with this, ref. [35] reported that amino acid application reduces the effects of drought on wheat by positively modifying the relative water content, photosynthetic pigments, total soluble sugars, total carbohydrates, total free amino acids, enzymes activities and minerals content in plant (NPK% and uptakes). These results partly support the first hypothesis of this study, especially with regard to grain yield, because a significant increase in yield was anticipated for the treatments incorporating the biostimulants (T2 and/or T4) and, the positive effects were not exclusively limited to the wettest season.

In our study, grain quality (protein concentration) was significantly modified by the treatment in the second season, which was the driest. In that season, biostimulant application (T2) produced the highest grain protein content while the Mg and micronutrient treatment (T5) produced the lowest. This implies that even when the conditions are not optimum for wheat development, biostimulants positively affect grain quality. However, compared to the control (T1) the total protein produced per unit area increased with all the treatments in the first season, but only with the application of biostimulants in combination with slow-release urea (T4) in second season. This information confirmed the second part of the first hypothesis of this study, related to grain quality, in which we expected biostimulants to have a greater effect on grain quality than the Mg and micronutrient treatment because the latter does not stimulate physiological processes related to N acquisition and protein production. Previous studies have shown that applying amino acids improves N assimilation in a wide range of crops, including wheat, lettuce and rocket [15]; and yield and grain quality in maize [12]. Since N is essential for protein production and grain quality in the case of cereal, there is no doubt that improving N uptake through physiological processes will result in efficient protein production in cereals. It is worth highlighting that the treatment including Mg and micronutrients(T5) produced the lowest grain protein content in the second season and the lowest total protein per unit area in the first season (except in comparison with T1) and second season (similar to T1). In contrast, other studies involving similar conditions and soils found that micronutrient fertilization enhanced grain quality in cereals [36,37,38]; however, that work was predominantly focused on micronutrient concentrations in grain. For that reason, these results confirm our second hypothesis: the biostimulant treatment had a more positive effect on most of the wheat variables analyzed than the Mg and micronutrient treatment, since physiological processes related to N acquisition and protein production are not triggered in the absence of biostimulants.

Previous research shows an improvement in N absorption when the fertiliser is applied in the area of maximum root activity, increasing the efficiency of N assimilation and crop yield. In addition, it reduces stress from excessive ammonium concentration in soil and losses due to volatilization [22]. The use of biostimulants in semi-arid areas has been shown to promote CO₂ assimilation, stomatal behavior and transpiration, confirming that biostimulants not only improve productivity but also induce physiological adaptations to adverse climatic conditions [39]. An improvement in chlorophyll content and antioxidant enzyme activity was observed, indicating a better physiological response to stress conditions [40]. There are biostimulants whose action improves plant tolerance to water and salt stress by modulating hormonal pathways. These physiological responses strengthen the ability of plants to maintain growth under adverse conditions [6].

Agriculture is moving towards climate neutrality. For this reason, knowledge must be generated to design the most appropriate strategies under different edaphoclimatic conditions for the various crops in order to produce enough quality food to feed the human population while minimizing the impact on the environment. The results of this study are in line with this goal and help us find solutions for curbing the application of chemically synthesized fertilizers in agriculture as we obtained higher values in yield-related variables and grain quality when biostimulants were applied either alone or in combination with a small dose of slow-release urea. Additionally, the effects of the biostimulant treatments on crop development and grain quality were highly dependent on the variability in rainfall during our study period (three seasons); this provides useful information for decision-making under these conditions. However, we should also consider the limitations of this study. Wheat was grown on the same plots for 3 consecutive seasons, while farmers in this area normally cultivate cereal for one or two consecutive seasons and then grow a leguminous (faba bean or chickpea) or oleaginous (sunflower or rapeseed) crop. Independently of that, the results seem consistent and the effect of biostimulants on durum wheat plants was evidently more strongly influenced by annual rainfall and its distribution over the growing season. The next steps are to develop field experiments in similar conditions (semiarid areas) where the effects of these or similar biostimulants are evaluated: (i) in the same experimental field but using the typical crop rotations of the area; (ii) including strategies in which total N applied to the crops is reduced by around 10–20% to confirm whether these biostimulants could produce a similar grain yield and quality; and (iii) to assess the effects on the soil in which the crops are grown (main physical, chemical and biological properties, including nutrient availability, soil microbial activity and structure).

5. Conclusions

The scarce rainfall and its variable distribution observed over the three study seasons, typical of Mediterranean areas, were key when comparing the effects of the different strategies or treatments. The results obtained here highlight the importance of evaluating the use of biostimulants or the application of micronutrients under specific conditions, as not all treatments produced the same crop response under different conditions (i.e., rainfall in this case). In our study, the use of biostimulants individually (amino acids obtained from the enzymatic hydrolysis of plant extracts) in treatment 2, and in combination with slow-release urea in treatment 4, was an appropriate treatment for improving durum wheat production variables and grain quality, especially in treatment 4 in the driest season. This information should be considered when designing more sustainable strategies for agricultural systems in line with national and European policies (Green Deal and Farm to Fork Strategy) as nature-based solutions following the principles of circular economy. Future work on the use of these biostimulants in wheat crops in combination with different nitrogen rates is required to evaluate whether nitrogen fertilization could be reduced and how soil properties (nutrient availability in the short term and also organic carbon content in the long term) are affected.

Author Contributions

Conceptualization, A.M.-M., A.R.S.-R. and F.M.-G.; methodology, A.M.-M. and F.M.-G.; software, A.M.-M.; validation, A.M.-M., A.R.S.-R. and E.J.G.-S.; formal analysis, A.M.-M.; investigation, A.M.-M. and A.R.S.-R.; resources, A.M.-M., A.R.S.-R., E.J.G.-S. and F.M.-G.; data curation, A.M.-M.; writing—original draft preparation, A.M.-M., A.R.S.-R. and F.M.-G.; writing—review and editing, A.M.-M., A.R.S.-R. and F.M.-G.; visualization, A.M.-M., A.R.S.-R., E.J.G.-S. and F.M.-G.; supervision, A.M.-M., A.R.S.-R., E.J.G.-S. and F.M.-G.; project administration, E.J.G.-S. and F.M.-G.; funding acquisition, A.R.S.-R., E.J.G.-S. and F.M.-G. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data will be available upon responsible request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Footnotes

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Figures and Table

Figure 1 Average monthly temperature and cumulative monthly rainfall from 2001 to 2018 (A), and first ((B), 2015–2016), second ((C), 2016–2017) and third ((D), 2017–2018) seasons of the field experiment.

Figure 2 Violin and box and whisker plots for spike number (number of spikes ha⁻¹) as a function of the different seasons ((A) 2015–2016; (B) 2016–2017; (C): 2017–2018) and foliar treatments applied (n = 16). Treatments: T1, control treatment; T2, biostimulants; T3, slow-release urea; T4, biostimulant treatment and slow-release urea; T5, Mg and micronutrients. The letters show significant differences between the treatment measures for each season according to the Tukey post hoc test (p-value ≤ 0.05). The lack of letters indicates that there were no significant differences between the treatments.

Figure 3 Violin and box and whisker plots for straw and grain production (kg of straw and grain ha⁻¹) as a function of the different seasons (A,D): 2015–2016; (B,E) 2016–2017; (C,F) 2017–2018) and foliar treatments applied (n = 16). Treatments: T1, control treatment; T2, biostimulants; T3, slow-release urea; T4, biostimulant treatment and slow-release urea; T5, Mg and micronutrients. The letters show significant differences between the treatment measures for each season according to the Tukey post hoc test (p-value ≤ 0.05). The lack of letters indicates that there were no significant differences between the treatments.

Figure 4 Violin and box and whisker plots for the Harvest Index (grain dry weight/plant dry weight) as a function of the different seasons (A) 2015–2016; (B) 2016–2017; (C) 2017–2018) and foliar treatments applied (n = 16). Treatments: T1, control treatment; T2, biostimulants; T3, slow-release urea; T4, biostimulant treatment and slow-release urea; T5, Mg and micronutrients. The letters show significant differences between the treatment measures for each season according to the Tukey post hoc test (p-value ≤ 0.05). The lack of letters indicates that there were no significant differences between the treatments.

Figure 5 Violin and box and whisker plots for protein concentration in grain and total protein (% and kg ha⁻¹, respectively) as a function of the different seasons (A,D) 2015–2016; (B,E) 2016–2017; (C,F) 2017–2018) and foliar treatments applied (n = 16). Treatments: T1, control treatment; T2, biostimulants; T3, slow-release urea; T4, biostimulant treatment and slow-release urea; T5, Mg and micronutrients. The letters show significant differences between the treatment measures for each season according to the Tukey post hoc test (p-value ≤ 0.05). The lack of letters indicates that there were no significant differences between the treatments.

Table 1

Treatments assessed in the field experiment. All treatments received the same total amount of basal and top-dressing fertiliser, 123.4 kg N ha⁻¹, but T2, T3, T4 and T5 received an extra nitrogen dose (<3 kg·ha⁻¹).

Treatment	Name	Origin	Rate (L ha−1)	Composition (Weight %)	Composition (kg ha−1)
T1	Control (C)	-	-	-	-
T2	Biostimulants	Enzymatic hydrolysis of plant extracts	2	2.7% organic N (amino acids), 4.8% inorganic N	0.15 kg N ha−1
T3	Slow-release urea	Synthetic	10	28.5% N (11.5% ureic N and 17%-urea formaldehyde)	2.85 kg N ha−1
T4	Biostimulants plus slow-release urea	Same as T2 and T3	10 (T2 and 80% of T3)	See T2 and T3	2.43 kg N ha−1
T5	Mg andmicronutrients	Synthetic	3	3.9% Ureic N, 9.1% Mg, 9.1% Mn, 4.9% Zn, 3% Cu	0.12 kg N ha−1, 0.27 kg Mg ha−1, 0.27 kg Mn ha−1, 0.15 kg Zn ha−1, 0.09 kg Cu ha−1

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15092150/s1, Table S1: Crop management in the three assessed seasons.; Table S2: Repeated measures ANOVA (time and treatment; mean ± standard error). Treatments: T1, control treatment; T2, biostimulants; T3, slow-release urea; T4, biostimulants treatment and slow-release urea; T5, Mg and micronutrients. Different letters show significant differences between treatment measures for each season according to the Tukey post-hoc test (p ≤ 0.05). The lack of letters indicates that there were not significant differences between treatments. Table S3: One-way ANOVA (treatment; mean ± standard error) for each season, independently. Treatments: T1, control treatment; T2, biostimulants; T3, slow-release urea; T4, biostimulants treatment and slow-release urea; T5, Mg and micronutrients. Different letters show significant differences between treatment measures for each season according to the Tukey post-hoc test (p ≤ 0.05). The lack of letters indicates that there were not significant differences between treatments.

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