Peanut Growth and Yield Responses Are Influenced by Plant Density, Microbial Consortium Inoculation, and Amino Acid Application

Abstract

Integrating optimal plant density, microbial bioinoculants, and foliar amino acid application represents a key strategy to enhance sustainable peanut production. Therefore, the objective of this research was to investigate the combined impact of plant density (P), microbial consortium (M) bioinoculants, and foliar amino acid application (A) on the morpho-physiological and agroproductive responses of peanut production. Under field conditions, the experiment was arranged in a split–split plot with four replicates. Two plant densities of 41,667 and 83,334 plants/ha were the main plots, soil inoculation with M at 0 mL m⁻², 100 mL m⁻², and 200 mL m⁻² were the subplots, and the foliar application of VIUSID^® agro at 0 mL L⁻¹, 0.60 mL L⁻¹, and 1.20 mL L⁻¹ were the sub-subplots. Results indicated that peanut plant cultivated at a density of 83,334 plants/ha, inoculated with 100 mL m⁻² of microbial consortium, and supplemented 0.60 mL L⁻¹ of amino acid significantly enhanced the growth and physiological responses and increased peanut yield in a sustainable manner. Therefore, the findings of this study suggest that this integrated approach improved resource utilization, promoted balanced vegetative and reproductive development, and strengthened stress resilience, ultimately leading to higher productivity under sustainable management practices.

1. Introduction

Global agricultural production frequently discovers environmentally friendly methods that enhance crop growth and productivity [1]. Healthy food production is achieved by considering the increase in the global population, the effects of climate change, and the gradual reliance on fertilizers derived from chemical industries, which have led to contamination and degradation of essential natural resources necessary for food production [2]. To tackle these challenges, prioritization of sustainable agricultural practices is essential, focusing on organic methods and regenerative techniques that enhance soil health and biodiversity [3]. A resilient food system that benefits both people and the planet can be established by investing in innovative technologies and supporting local farming communities [4].

Peanut is the sixth-most economical and oleaginous crop on a global scale [5]. This crop is significant due to its nutritional profile, which includes 26% protein, 48% oil, and 3% fiber, along with high levels of calcium, thiamine, and niacin, and also is considered as having the potential to serve as an economical nutritional supplement to address malnutrition [6]. Moreover, it is significant for human and animals consumption because of its vegetable oil and protein content [7]. Furthermore, Peanuts are also widely utilized as a biofertilizer, in crop rotation, and in crop associations to enhance soil chemical and physical properties and microbial diversity [8,9,10,11].

Human-managed peanut production techniques can generate problems over time owing to monoculture, seed viability loss, and decreasing yields [12]. To avoid these consequences, a variety of alternatives must be used, including proper planting density management, the application of biostimulants and biofertilizers, proper agricultural practices (crop rotation and association), and pest and disease control, among others [13]. In this context, correct plant density management is one of the most important techniques for crop production, including peanuts [14].

It is critical to use appropriate planting densities to increase peanut yield because they optimize the use of light energy by the leaves, promote nutrient absorption, increase the plant’s photosynthetic rate, and increase dry matter accumulation, all of which boost production [15]. Plant density is managed using a variety of factors, including soil fertility, moisture (both soil and air), germination rate, and the agronomic properties of the varieties or cultivars [16,17]. The spatial pattern of plants is another agronomic factor that can influence crop growth and yield, as well as crop competitiveness against invasive plant species [18]. However, it has been widely reported that a uniform planting pattern improves spatial uniformity and the leaf area index, reduces reciprocal shading, and accelerates leaf closure, resulting in greater radiation capture by the leaves and increased crop growth and yield [19]. Several recent studies found that plant density altered the growth response patterns and production of peanut crops [9,14,15,17,20].

Biostimulants have also been widely utilized to increase agricultural yield and ecological management [21]. Biostimulants are microorganisms or substances (hormones, amino acids, minerals, nutrients, beneficial elements, and vitamins) that, when applied to plants, seeds, or the rhizosphere, can stimulate plant natural processes to improve nutrient absorption and efficiency, abiotic stress tolerance, and, in general, promote crop growth, development, and quality, regardless of nutrient content [22]. The use of microbial consortia as biostimulants in peanut production is a promising area of research because it can harness the synergy of various microorganisms to improve peanut plant growth and health [15]. In this way, microbial consortia can improve the efficiency of nutrient mobilization, making nutrients like phosphorus, potassium, and micronutrients more accessible to plants [23]. They can also improve biological nitrogen fixation (BNF), increase resistance to biotic and abiotic stresses, act as biological pathogen control, improve soil structure, and stimulate the production of phytohormones like auxins, gibberellins, and cytokinins, which regulate plant growth and development [24]. Several recent studies have detailed the benefits of microbial consortia in crop development and productivity, including oilseed plants like soybean [25,26], sunflower [27], and peanuts [15,28].

There are several documented findings in the literature of trials that analyze the foliar application of amino acids as promoters of plant growth [29]. Commercial VIUSID^® agro is a bioproduct that possesses these qualities after undergoing a biocatalytic molecular activation process that increases its biological activity and the biochemical reactivity of all of its molecules [30]. This commercial biostimulant is made up of a combination of amino acids, including aspartic acid, arginine, glycine, and tryptophan, which function as plant growth promoters by influencing and regulating numerous physiological and biochemical processes within plant cells [31]. Several recent field experiments have shown that foliar spraying of VIUSID^® agro improves growth and production in a variety of plant species, including sesame [32], chard [33], sunflower [27], and tomatoes [34].

Many recent studies have focused on determining the individual effects of plant density, inoculation with microbial consortia, and applying biostimulants. However, few studies address the combined effects of these strategies. For example, a recent study found that inoculation with a microbial consortium and foliar application of VIUSID^® agro increased sunflower growth and productivity under drought conditions [27]. As a result, recent crop management advances could benefit from advantageous combinations of plant density, microbial consortium, and growth promoters to improve long-term food production. Consequently, this research aimed to explore the impact of combining plant density, microbial consortium inoculation, and foliar application of amino acid on peanut growth and productive responses.

2. Materials and Methods

2.1. Growing Conditions

The research was carried out in the field areas of the collective farm areas “Emilio R. Capestani,” Cabaiguán, Sancti Spíritus, Cuba, located at 22°08′34.6″ N 79°29′04.6″ W. The Tobacco Experimental Station provided daily averages of temperature, relative humidity, and precipitation throughout the experiment.

During the experiment the Meteorological Station of Cabaiguán, Cuba, monitored daily temperatures (20–24 °C), relative humidity (75–98%), rainfall (455.60 mm), and day/night photoperiod (13/11 h). The soil in the experimental field was Sialitic Brown, with a pH = 5.9, organic matter (1.15 g kg⁻¹), organic carbon (16.88 g kg⁻¹), total nitrogen (1.68 g kg⁻¹), available phosphorous (3.33 mg kg⁻¹), and available potassium (95.39 mg kg⁻¹).

2.2. Vegetal Material

The cream-colored bush peanut variety Crema VC-504 was utilized; it is listed in the Official List of Commercial Varieties, falls under the certified category, and is untreated. Seeds were chosen based on their uniformity, color, and lack of spots. The seeds were sown manually at 0.80 m between lines and 0.15 and 0.30 m between plants, and two seeds in each space were used to achieve population densities of 83,334 and 41,667 plants/ha, respectively. Further, ten days after emerging, the plant abundance was manually eliminated, leaving one plant per space.

2.3. Experimental Design

Under field conditions, the treatments were arranged in split–split plots in a randomized block design with four replications. The main plots were two population densities (P₁ = 41,667 and P₂ = 83,334 plants/ha), while the subplots consisted of soil inoculation with a microbial consortium at 0 mL m⁻² (M₀), 100 mL m⁻² (M₁, 5.4 × 10⁶ colony forming units (CFU) of Bacillus subtilis, 3.6 × 10⁶ CFU of Lactobacillus bulgaricum, 22.3 × 10⁶ CFU of Saccharomyces cereviciae), and 200 mL m⁻² (M₂, 10.8 × 10⁶ CFU of Bacillus subtilis, 7.2 × 10⁶ CFU of Lactobacillus bulgaricum, 44.6 × 10⁶ CFU of Saccharomyces cereviciae), and the sub-subplots were composed of the foliar application of amino acids at 0 (A₀), 0.60 mL L⁻¹ (A₁), and 1.2 mL L⁻¹ (A₂). The experimental plots had seven furrows, 72 m² for the main plot, 36 m² for the subplots, and 12 m² for the sub-subplots. The effective area for sampling and observations was 3.20 m².

2.4. Microbial Consortium and VIUSID^® Agro Composition

The microbial consortium consists of Bacillus subtilis nato B/23-45-10 (5.4 × 10⁴ colony forming units (CFU) mL⁻¹), Lactobacillus bulgaricum B/103-4-1 (3.6 × 10⁴ CFU mL⁻¹), and Saccharomyces cereviciae L-25-7-12 (22.3 × 10⁵ CFU mL⁻¹), with a quality certificate distributed by the Instituto Cubano de Investigaciones de Derivados de la Caña de Azúcar (ICIDCA), Cuba, with the R-ID-B-Prot-01-01 code. On the other hand, the commercial VIUSID^® Agro declared composition is as follows: aspartic acid (1.6% m/m), arginine (2.5% m/m), glycine (2.4% m/m), and tryptophan (0.5% m/m), with a pH of 6.80 and a net mass of 1.14 kg [30].

2.5. Microbial Consortium and VIUSID^® Agro Treatments

Following seed sowing, a pressure-retained backpack sprayer (Matabi, 16 L, Goizper Group, Antzuola, Guipuzcoa, Spain) was used to apply the microbial consortium concentrations to the soil, and both seeds and microbial consortium were promptly covered, as was recently recommended by Hurtado et al. [35]. In contrast, amino acid concentrations were sprayed on the leaves every seven days, according to the recommendation recently suggested by Calero Hurtado et al. [34], using a backpack sprayer with retained pressure (Matabi, 16 L, Goizper Group, Spain) until the phenological state R3, as was defined previously by Munger et al. [36].

2.6. Sampling Procedures and Determination of Growth and Productive Parameters

Thirty plants from each plot (120 plants per treatment) were randomly selected and marked from the usable area of each plot to measure the following growth variables at 30 and 45 days after emergence (DAE), such as plant height (PH, cm), which was measured using a graduated ruler from the base of the stem to the last internode of the main stem. Relative plant growth (RG) was determined for all plants by measuring peanut height from the shoot tip to the stem collar on the 30th and 60th days after seeding and was calculated using Equation (1).

$$\mathrm{R}\mathrm{G}={\displaystyle \frac{\mathrm{P}\mathrm{H}60-\mathrm{P}\mathrm{H}30}{\mathrm{P}\mathrm{H}30}}$$

(1)

Number of stems per plant (NTP) was determined by direct counting. Total chlorophyll content (TCC, SPAD values) was measured on the first fully developed leaf (leaf + 1) from top to bottom of the plant’s upper third using a portable chlorophyll meter (model TYS-B, Lab Equipment—Hinotek, Shanghai, China) between 10:00 and 11:00 a.m.

On the other hand, at harvest time (R9, 102 DAE), the following productive parameters were assessed in the same 120 plants per treatment that were evaluated for growth parameters mentioned above. The number of matured pods per plant (NMP) was determined by counting all matured fruits with seeds on the evaluated plants. We also count the matured pod with one seed (MP₁); matured pod with two seeds (MP₂); matured pod with three seeds (MP₃); and matured pod with four seeds (MP₄). The number of seeds per plant (NGP) was performed by direct count and was divided by the seed quantity by fruit. The number of seeds per plant (NSP) was determined by direct count. The number of seeds per pod (NGP) was determined by counting all seeds and dividing by all NMP. The mass of the seeds per plant (MSP, g) was determined by weighing all seeds (14%) of each head on a micro scale (model BS 124S and precision ± 0.01 g). The seed yield (SY, t ha⁻¹) of peanut plants was determined using the MSP and the number of plants/ha.

2.7. Dates Analysis

The obtained data were tested for normality and variance homogeneity using the Shapiro–Wilk and Bartlett tests, respectively. A multivariate ANOVA was used to analyze the data. When the Fisher (F) test was significant (p < 0.05), the Scott-Knott test (p < 0.05) was used for mean comparison. All statistical analyses and comparisons were performed using R software version 4.4 [37].

3. Results

3.1. Impacts of Combining Plant Density, Bacterial Consortium Inoculation, and Foliar Application of VIUSID Agro on Peanut Plant Height and Relative Growth

Plant density, microbial consortium inoculation, and amino acid application significantly affected plant height (PH) and relative growth (RG), with significant (p < 0.01) interaction at all factor levels (

Table 1. Effect of different combined treatments on the plant height (PH30 and PH60) and relative growth of peanut plants (RG).

Treatments	PH30	PH60	RG
P1M0A0	10.65 ± 0.15 j	22.35 ± 0.12 n	1.10 ± 0.01 h
P1M0A1	12.80 ± 0.10 f	28.90 ± 0.43 j	1.26 ± 0.02 e
P1M0A2	13.85 ± 0.35 e	32.55 ± 0.22 g	1.35 ± 0.02 c
P1M1A0	11.60 ± 0.20 h	25.70 ± 0.37 l	1.22 ± 0.04 f
P1M1A1	14.55 ± 0.35 d	33.55 ± 0.38 f	1.32 ± 0.01 d
P1M1A2	16.15 ± 0.25 c	37.45 ± 0.08 d	1.31 ± 0.03 d
P1M2A0	11.10 ± 0.20 i	24.85 ± 0.10 m	1.24 ± 0.03 e
P1M2A1	13.75 ± 0.05 e	32.85 ± 0.09 g	1.39 ± 0.04 b
P1M2A2	14.80 ± 0.30 d	31.55 ± 0.72 h	1.13 ± 0.01 g
P2M0A0	12.15 ± 0.35 g	26.80 ± 0.83 k	1.21 ± 0.01 f
P2M0A1	14.10 ± 0.40 e	32.45 ± 0.08 g	1.30 ± 0.01 d
P2M0A2	16.20 ± 0.30 c	35.50 ± 0.13 e	1.19 ± 0.01 f
P2M1A0	14.20 ± 0.40 e	30.55 ± 0.62 i	1.15 ± 0.02 g
P2M1A1	16.35 ± 0.45 c	38.20 ± 0.27 c	1.34 ± 0.01 c
P2M1A2	17.80 ± 0.10 a	42.70 ± 0.13 a	1.36 ± 0.01 c
P2M2A0	14.65 ± 0.15 d	29.55 ± 0.78 j	1.02 ± 0.01 i
P2M2A1	15.75 ± 0.15 c	41.10 ± 0.17 b	1.61 ± 0.01 a
P2M2A2	17.05 ± 0.15 b	38.15 ± 0.52 c	1.24 ± 0.01 e
F value
P	**	**	*
M	**	**	*
A	**	**	*
P × M	**	**	**
P × A	**	**	**
M × A	*	**	**
P × M × A	*	**	**

Data are presented as mean values (n = 3) ± standard deviation (SD). Values followed by different lowercase letters in the same column indicate significant difference, according to the Scott-Knott test (p < 0.05); P: plant density level; M: microbial consortium concentrations; A: amino acid concentrations. * or ** indicates that the F values are significant at p < 0.05 or p < 0.01, respectively. P × M: P—M interaction; P × A: P—A interaction; M × A: M—A interaction; P × M × A: P—M—A interaction.

). The PH under P₂ was significantly higher than that under P₁ by 9% at PH30 and 20% at PH60 across M and A treatments. In the case of P1, M1 increased the PH30 by 20% and 23% and the PH60 by 11% and 15%, respectively, compared to the M₀ treatment (Table 1). With regard to P₂, PH30 and PH60 under M₁ were significantly higher than those under M₀ (by 26% and 13%) and M₂ (by 21% and 10%), respectively; however, the M₁ treatment exhibited a higher PH30 than the M0 treatment (Table 1). Concerning P₁ and P₂, A₂ showed higher PH30 than that under A₀ (by 14% and 38%) and under A₁ (by 10% and 34%), and the increases in PH60 were 14% and 35% higher than the A1 and A0 treatments. Additionally, A₁ resulted in significantly higher PH30 and PH60 than the A0 treatment (Table 1). In the case of the M₀, M₁, and M₂ treatments, A₂ significantly increased PH30 by 53% and 21%, by 43% and 21%, and by 62% and 19% and PH60 by 35% and 18%, by 29% and 16%, and by 57% and 24% compared to the A₀ and A₁ treatments, respectively. Nevertheless, A₁ supplementation promotes higher PH30 and PH60 than the A₀ treatment (Table 1). In the cases of PH30 and PH60, P₂M₁A₂ treatment was significantly higher than P₁M₀A₀ (by 67%) and P₂M₀A₀ (by 47%). In addition, compared to P₁M₀A₀, the rest of the combined treatments significantly increased PH30 and PH60, including the P₂M₀A₀ treatment (Table 1).

3.2. Impacts of Combining Plant Density, Bacterial Consortium Inoculation, and Foliar Application of VIUSID Agro on Peanut Total Chlorophyll and Number of Stems per Plant

The TCC values showed interactive effects among P, M, and A factors at TCC30 (p < 0.01) and TCC60 (p < 0.01) (

Table 2. Effect of different combined treatments on the total chlorophyll content (SPAD values) and number of stems per plant (NTP) of peanut plants.

Treatments	TCC30	TCC60	NTP60
P1M0A0	41.89 ± 0.97 f	41.88 ± 0.20 f	9.35 ± 0.17 e
P1M0A1	46.36 ± 0.48 b	43.27± 0.22 e	10.19 ± 0.14 d
P1M0A2	47.37 ± 0.52 a	44.50 ± 0.59 d	10.93 ± 0.20 c
P1M1A0	43.54 ± 0.32 d	43.66 ± 0.40 e	11.51 ± 0.32 b
P1M1A1	45.74 ± 0.18 b	46.83 ± 0.33 b	12.35 ± 0.52 a
P1M1A2	47.04 ± 0.62 a	48.66 ± 0.12 a	10.39 ± 0.11 d
P1M2A0	40.13 ± 0.23 h	42.97 ± 0.84 e	10.25 ± 0.13 d
P1M2A1	44.68 ± 0.10 c	45.48 ± 1.42 c	9.33 ± 0.03 e
P1M2A2	46.27 ± 0.71 b	47.68 ± 0.92 b	8.97 ± 0.06 f
P2M0A0	38.89 ± 0.03 i	40.11 ± 0.82 g	8.71 ± 0.09 f
P2M0A1	40.82 ± 0.27 g	43.00 ± 0.32 e	7.76 ± 0.10 h
P2M0A2	42.11 ± 0.29 f	45.92 ± 0.13 c	7.34 ± 0.10 i
P2M1A0	41.01 ± 0.09 g	39.02 ± 0.38 h	8.35 ± 0.06 g
P2M1A1	43.42 ± 0.41 d	44.93 ± 0.23 d	7.78 ± 0.14 h
P2M1A2	45.71 ± 0.86 b	42.93 ± 0.60 e	7.12 ± 0.06 i
P2M2A0	37.28 ± 0.37 j	38.98 ± 0.31 h	8.14 ± 0.06 g
P2M2A1	41.22 ± 0.36 g	46.23 ± 0.47 c	6.84 ± 0.01 j
P2M2A2	42.92 ± 0.04 e	42.23 ± 0.19 f	6.47 ± 0.14 k
F value
P	**	**	*
M	**	**	**
A	**	**	**
P × M	**	**	**
P × A	**	**	**
M × A	**	**	**
P × M × A	**	**	**

Data are presented as mean values (n = 3) ± standard deviation (SD). Values followed by different lowercase letters in the same column indicate significance, according to the Scott-Knott test (p < 0.05); P: plant density level; M: microbial consortium concentrations; A: amino acid concentrations. * and ** indicates that the F values are significant at 0.05 and 0.01 probability levels, respectively. P × M: P—M interaction; P × A: P—A interaction; M × A: M—A interaction; P × M × A: P—M—A interaction.

). Concerning TCC30 and TCC60, P₁ was higher than that under P₂ (Table 2). In the case of both P₁ and P₂ densities, the M₁ treatment significantly increased TCC30 and TCC60 compared to the M₀ and M₂ treatments, while the M₂ treatment was significantly higher than the M₀ treatment (Table 2). Similarly, under both P₁ and P₂ densities, the A₁ and A₂ treatments significantly increased the TCC30 and TCC60 compared to the A₀ treatment. Furthermore, in the case of M₀, M₁, and M₂ treatments, applying A₂ significantly increased TCC30 and TCC60 compared to the A₀ and A₁ treatments. Whereas, at the same time, supplying A₁ showed higher TCC30 and TCC60 than the M₀ treatment (p < 0.01) (Table 2). On the other hand, compared to P₁M₀A₀ and P₂M₀A₀, P₁M₀A₂ resulted in 13% and 22% increases in peanut TCC30. Similarly, the TCC60 was highest in the P₁M₁A₂ treatment by 16% and 21% compared to the P₁M₀A₀ and P₂M₀A₀ treatments (Table 2).

The number of stems per plant (NTP) showed significant interactive effects among P, M, and A factors at NTP60 (p < 0.01) (Table 2). Regarding the M₀, M₁, and M₂ treatments, the NTP60 under P1 density was significantly higher than that under P₂ by 22% and 24%, respectively (Table 2). In the case of P₁ and P₂ densities, the A₁ and A₂ treatments decreased NTP60 by 18% and 12%, respectively, compared to the A₀ treatment. Further, under the P₂ treatment, the A₁ and A₂ treatments decreased NTP60 by 17% and 11%, respectively, when compared to the A₀ treatment (Table 2). In relation to the M₀, M₁, and M₂ treatments, applying A₁ and A₂ treatments decreased NTP30 (by 33%, 10%, and 20%) and NTP60 (by 9%, 12%, and 14%) compared to the A₀ treatment (Table 2). Additionally, the combined P₁M₁A₁ treatment resulted in 32% and 42% increases in NTP60 compared to the P₁M₀A₀ and P₂M₀A₀ treatments. Conversely, the TCC60 under P₂M₂A₂ was significantly lower than that under the other combined treatments (Table 2).

3.3. Impacts of Combining Plant Density, Bacterial Consortium Inoculation, and Foliar Application of VIUSID Agro on Peanut Productive Parameters

For MP1, MP2, MP3, MP4, and NMP, a significant (p < 0.01) interaction was observed among the P, M, and A factors (

Table 3. Effect of different combined treatments on the matured pod with different amounts of seeds and the number of matured pods per plant of peanut plants (NMP).

Treatments	MP1	MP2	MP3	MP4	NMP
P1M0A0	7.15 ± 0.21 a	4.51 ± 0.11 k	2.36 ± 0.10 n	0.65 ± 0.06 k	13.16 ± 0.43 m
P1M0A1	2.73 ± 0.04 g	6.84 ± 0.12 f	12.72 ± 0.16 a	4.63 ± 0.18 f	26.91 ± 0.27 c
P1M0A2	4.77 ± 0.02 d	9.44 ± 0.14 a	6.71 ± 0.05 i	4.81 ± 0.06 f	25.73 ± 0.20 d
P1M1A0	3.30 ± 0.06 f	8.64 ± 0.02 b	5.68 ± 0.08 j	2.82 ± 0.06 i	20.44 ± 0.42 i
P1M1A1	2.84 ± 0.16 g	6.56 ± 0.15 f	12.42 ± 0.07 b	7.62 ± 0.08 b	29.42 ± 0.34 a
P1M1A2	2.50 ± 0.04 h	7.65 ± 0.11 d	10.13 ± 0.12 e	9.40 ± 0.15 a	29.67 ± 0.10 a
P1M2A0	6.09 ± 0.15 b	3.86 ± 0.12 m	2.73 ± 0.07 n	3.92 ± 0.10 h	16.60 ± 0.18 l
P1M2A1	2.29 ± 0.01 i	5.76 ± 0.10 i	10.67 ± 0.12 d	6.41 ± 0.09 c	25.13 ± 0.13 e
P1M2A2	3.88 ± 0.16 e	5.88 ± 0.02 i	7.49 ± 0.15 g	5.12 ± 0.02 e	22.37 ± 0.29 h
P2M0A0	5.29 ± 0.02 c	6.18 ± 0.03 h	3.32 ± 0.08 m	2.28 ± 0.07 j	17.06 ± 0.05 l
P2M0A1	2.44 ± 0.20 h	6.77 ± 0.09 f	10.41 ± 0.16 d	9.33 ± 0.23 a	28.94 ± 0.07 c
P2M0A2	4.60 ± 0.17 d	7.41 ± 0.21 e	7.05 ± 0.09 h	4.67 ± 0.18 f	23.72 ± 0.10 g
P2M1A0	3.73 ± 0.10 e	6.53 ± 0.18 f	4.15 ± 0.14 l	3.31 ± 0.08 h	17.71 ± 0.04 k
P2M1A1	2.72 ± 0.07 g	6.55 ± 0.04 f	11.70 ± 0.21 c	7.80 ± 0.02 b	28.75 ± 0.10 b
P2M1A2	2.32 ± 0.02 i	4.12 ± 0.11 l	12.19 ± 0.08 b	5.81 ± 0.11 d	24.44 ± 0.04 f
P2M2A0	4.71 ± 0.07 d	5.44 ± 0.16 j	4.63 ± 0.11 k	2.68 ± 0.07 i	17.45 ± 0.16 k
P2M2A1	2.50 ± 0.07 h	6.47 ± 0.14 f	9.31 ± 0.22 f	5.52 ± 0.10 d	23.78 ± 0.53 g
P2M2A2	2.08 ± 0.03 j	7.99 ± 0.12 c	5.91 ± 0.13 j	3.80 ± 0.05 g	19.77 ± 0.50 j
F value
P	**	*	**	*	**
M	**	**	**	**	**
A	**	**	**	**	**
P × M	**	**	**	**	**
P × A	**	**	**	**	**
M × A	**	**	**	**	**
P × M × A	**	**	**	**	**

Data are presented as mean values (n = 3) ± standard deviation (SD). Values followed by different lowercase letters in the same column indicate significance, according to Scott-Knott test (p < 0.05); P: plant density level; M: microbial consortium concentrations; A: amino acid concentrations. MP1: matured pod with one seed; MP2: matured pod with two seeds; MP3: matured pod with three seeds; MP4: matured pod with four seeds; * or **: indicates that the F values are significant at p < 0.05 or p < 0.01, respectively. P × M: P—M interaction; P × A: P—A interaction; M × A: M—A interaction; P × M × A: P—M—A interaction.

). In the case of MP1, P₁ density was higher than the P₂ density across all M and A concentrations (Table 3). For both P₁ and P₂ densities, MP1 increased significantly under M₀ compared to M₁ and M₂, but at the same time, M₂ was higher than the M₁ treatment (Table 3). Compared to A₀, A₁ and A₂ exhibited lower MP1, and A₁ was lower than A₂ under both P₁ and P₂ densities, respectively. Furthermore, MP1 under A₀ was significantly higher than under A₁ and A₂; however, A₁ was lower than the A₂ treatment across M₀, M₁, and M₂ concentrations (Table 3). In the case of MP1, P₁M₀A₀ was significantly higher than the other combined treatments (Table 3).

The MP2 production under P₂ was slightly increased compared to that under P₁ through all M and A concentrations (Table 3). For P₁, M₁ raised the MP2 by 15% and 48% compared to the M₀ and M₂ treatments, respectively (p < 0.01). However, M₂ lowered the MP2 by 23% compared to the M₀ treatment. It was found that M₂ and M₀ had similar effects on MP2 and were 23% higher than the M₁ treatment under P2 density (Table 3). Concerning the P₁, A₂ increased MP2 by 44% and 20% compared to the A₀ and A₁ treatments, respectively, but A₁ produced significantly more MP2 than A₀. Conversely, the MP2 had significant effects under the A₂ and A₁ treatments under P₂, but it was 10% higher than it was under the A₀ treatment (Table 3). Under M₀ and M₂, A₂ produced higher MP2 than the A₁ and A₀ treatments, but A₂ was significantly superior in relation to the A₂ treatment. In the case of M₁, A₀ showed higher MP2 production in comparison with A₁ and A₂. In contrast, under A₀, M₁ produced significantly more MP2 than M₂ and M₀. In the case of the A₁ and A₂ treatments, M₀ increased MP2 compared to the M₂ and M₁ treatments (Table 3). On the other hand, P₁M₀A₂ produced more MP2 than P₁M₀A₀ and P₂M₀A₀ and also was significantly higher than the other combined treatments (Table 3).

Under P₁, the MP3 production was significantly higher than that under P₂ in all M and A concentrations (Table 3). In the cases of P₁ and under P₂ densities, M₁ increased MP3 compared to M₀ (by 35% and 42%) and M₂ (by 30% and 35%), although M₂ showed higher MP3 than M₀. Similarly, A₁ significantly increased MP3 compared to M₀ and M₂; however, at the same time, M₂ showed a significant difference in comparison with M₂ (Table 3). Additionally, for the M₀, M₁, and M₂ concentrations, applying A₁ significantly increased MP3 compared to A₀ and A₂. However, simultaneously, A₂ exhibited higher MP3 than A₀. In contrast, under A₀, A₁, and A₂ concentrations, M₁ was significantly higher than M₀ and M₂, but at the same time, M₂ was significantly lower than M₀ (Table 3). In relation to MP3 production, the P₁M₀A₁ was significantly higher than the other combined treatments (Table 3).

A higher MP4 was observed under P₂ density than that under P₁ density across all M and A levels (Table 3). In the case of P₁, the MP4 production under M₁ was significantly higher than that under M₀ and M₂; nonetheless, M₂ significantly increased MP4 in relation to the M₂ treatment. Moreover, under P₂, supplying M₁ and M₂ showed similar effects on MP4 production and was significantly higher than that of the A₀ treatment (Table 3). For the P₁ density, the MP4 under A₂ was significantly higher than that under A₀ and A₁, and simultaneously, A₁ was higher than A₀. Nevertheless, in the case of P₂ density, A₁ significantly increased MP4 compared to A₀ and A₂, but at the same time, A₂ produced more MP4 than A₀ (Table 3). Additionally, under M₀, M₁, and M₂ treatments, the MP4 under A₁ was significantly higher than that under A₀ and A₂, but simultaneously, A₁ showed a significant difference relative to the A₀ treatment. Moreover, for the A₀ treatment, supplying M₁ and M₂ treatments revealed equal effects on MP4 and were higher compared to the M₀ treatment. Furthermore, under A₁ and A₂ treatments, M₁ produced more MP4 than the M₀ and M₂ treatments, while M₂ exhibited lower MP4 than the M₀ treatment (Table 3). On the other hand, the P₁M₁A₂ and P₂M₀A₁ treatments exhibited the highest MP4 production and showed a significant difference in comparison with the rest of the combined treatments (Table 3).

The NMP production was slightly higher in the P₁ than the P₂ density across M and A treatments (Table 3). For the P₁ and P₂ densities, M₁ increased NMP by 47% and 52% at M₀ and by 25% and 29% at M₂; however, the M₂ produced significantly more NMP than the M₀ treatment (Table 3). Under P₁ and P₂ densities, A₂ increased NMP by 48% and 30% and by 43% and 30%, respectively, compared to the A₀ and A₁ treatments; however, supplying A₁ treatment resulted in higher NMP than A₀ treatment (Table 3). In the cases of M₀, M₁, and M₂ treatments, the NMP under A₂ was significantly higher than that under A₀ (by 51%, 60%, and 59%) and A₁ (by 15%, 32%, and 14%), respectively; nonetheless, A₁ treatment produced significantly more NMP than A₀ treatment. Additionally, concerning the A₀, A₁, and A₂ treatments, NMP under M₁ was significantly higher than that under M₀ (by 16%, 24%, and 23%) and M₂ (by 10%, 11%, and 10%); however, M₂ treatment produced significantly more NMP than M₀ treatment (Table 3). Furthermore, the NMP was highest in the P₁M₁A₁ and P₁M₁A₂ treatments and was significantly higher than the other combined treatments (Table 3).

A significant interaction (p < 0.01) among P, M, and A factors was observed on NSP, NGP, MSP, and SY (

Table 4. Effect of different combined treatments on the number of seeds per plant (NSP), number of seeds per pod (NGP), mass of the seeds per plant (MSP), and seed yield (SY) of peanut plants.

Treatments	NSP	NGP	MSP	SY
P1M0A0	23.32 ± 0.78 n	1.77 ± 0.008 m	11.67 ± 0.34 m	0.49 ± 0.020 m
P1M0A1	73.07 ± 0.56 d	2.72 ± 0.012 e	23.86 ± 0.22 c	0.99 ± 0.015 h
P1M0A2	63.02 ± 0.30 g	2.45 ± 0.003 i	22.81 ± 0.02 d	0.95 ± 0.006 i
P1M1A0	48.90 ± 0.98 j	2.39 ± 0.003 j	18.12 ± 0.34 i	0.75 ± 0.10 k
P1M1A1	83.65 ± 0.74 b	2.84 ± 0.008 c	26.08 ± 0.28 a	1.09 ± 0.016 g
P1M1A2	85.76 ± 0.53 a	2.89 ± 0.013 b	26.31 ± 0.08 a	1.10 ± 0.001 g
P1M2A0	37.68 ± 0.15 m	2.27 ± 0.0023 k	14.72 ± 0.14 l	0.61 ± 0.010 l
P1M2A1	71.45 ± 0.67 e	2.84 ± 0.013 c	22.28 ± 0.11 e	0.93 ± 0.006 i
P1M2A2	58.57 ± 0.64 h	2.62 ± 0.003 f	19.83 ± 0.23 h	0.83 ± 0.015 j
P2M0A0	36.69 ± 0.28 m	2.15 ± 0.013 l	13.76 ± 0.04 l	1.15 ± 0.006 g
P2M0A1	70.37 ± 0.31 e	2.88 ± 0.003 b	19.71 ± 0.04 f	1.64 ± 0.005 b
P2M0A2	59.23 ± 0.52 h	2.50 ± 0.003 h	19.13 ± 0.09 g	1.59 ± 0.005 c
P2M1A0	42.46 ± 0.41 k	2.40 ± 0.028 j	14.28 ± 0.03 k	1.19 ± 0.001 e
P2M1A1	82.05 ± 0.55 c	2.85 ± 0.013 c	23.19 ± 0.08 b	1.93 ± 0.006 a
P2M1A2	84.50 ± 0.25 a	2.92 ± 0.008 a	23.35 ± 0.06 b	1.95 ± 0.006 a
P2M2A0	40.17 ± 0.18 l	2.30 ± 0.008 k	14.08 ± 0.13 k	1.17 ± 0.015 e
P2M2A1	65.40 ± 1.24 e	2.75 ± 0.008 d	19.18 ± 0.41 g	1.60 ± 0.038 c
P2M2A2	50.95 ± 2.14 i	2.58 ± 0.048 g	15.94 ± 0.40 j	1.33 ± 0.035 d
F value
P	**	*	**	**
M	**	**	**	**
A	**	**	**	**
P × M	**	**	**	**
P × A	**	**	**	**
M × A	**	**	**	**
P × M × A	**	**	**	**

Data are presented as mean values (n = 3) ± standard deviation (SD). Values followed by different lowercase letters in the same column indicate significance, according to the Scott-Knott test (p < 0.05); P: plant density level; M: microbial consortium concentrations; A: amino acid concentrations. * or **: indicates that the F values are significant at p < 0.05 or p < 0.01, respectively. P × M: P—M interaction; P × A: P—A interaction; M × A: M—A interaction; P × M × A: P—M—A interaction.

). Concerning the concentrations of M and A, P₂ tended to produce significantly more NSP than the P1 density (Table 4). In comparison to the M₀0 and M₂ treatments, the M₁ treatment raised NSP for both P₁ and P₂ densities by 31% and 15%, respectively, whereas the M₂ treatment demonstrated greater NSP than the M₀ treatment (Table 4). In the case of both P₁ and P₂ densities, A₁ and A₂ revealed equal effects on NSP and were higher by 95% and 112% and by 96% and 114% than the A₀ treatment, respectively (Table 4). Additionally, under the M₀, M₁, and M₂ treatments, the NSP under A₂ was significantly higher than that under M₀ (by 109%, 115%, and 103%) and M₁ (by 17%, 15%, and 14%), respectively. Moreover, the A₁ was significantly more NSP than A₀ (by 80%, 87%, and 78%). Similarly, for the A₀, A₁, and A₂ treatments, the NSP under M₁ was significantly higher than that under M₀ (by 24%, 29%, and 27%) and M₂ (by 11%, 17%, and 18%), respectively. Moreover, M₂ exhibited higher NSP (by 11%, 10%, and 8%) than the M₀ treatment (Table 4). Furthermore, the P₁M₁A₂ and P₂M₁A₂ treatments revealed the highest NSP and showed a significant difference (p < 0.01) compared to the other combined treatments (Table 4).

In comparison to P₁ density, P₂ increased NGP under all M and A concentrations (Table 4). Concerning the P₁ and P₂ densities, the NGP under M₁ was significantly boosted under M_{2 (}by 11% and 12%) and M₀ (by 25% and 23%), respectively (Table 4). Additionally, under the P₁, A₂ increased NGP by 19% and 25% and by 6% and 7% over the A₀ and A₁ treatments; however, this last treatment produced significantly more NGP than the A₀ treatment (Table 4). Moreover, for the M₀, M₁, and M₂ concentrations, the NGP under A₂ was significantly higher than under A₀ (by 17%, 28%, and 26%) and under A₁ (by 5%, 7%, and 6%), respectively; nevertheless, A₁ increased NGP by 13%, 21%, and 22% when compared to the A₀ treatment (Table 4). In contrast, in the cases of A₀, A₁, and A₂ concentrations, M₁ strongly increased NGP under M₀ (by 12%, 20%, and 22%) and M₂ (by 7%, 6%, and 8%), respectively; although, at the same time, M₂ inoculation increased NGP by 6%, 13%, and 14% compared to M₀, respectively (Table 4). Furthermore, the P₂M₁A₂ treatment produced the highest NGP and showed a significant difference from the other combined treatments (Table 4).

In the case of the different microbial consortium and amino acid concentrations, P₁ density increased MSP in comparison with the P₂ density (Table 4). For both P₁ and P₂, the MSP under M1 was significantly increased under M0 (by 30% and 31%) and M2 (by 13% and 11%), respectively; however, M₂ resulted in a 16% increase over M₀ (Table 4). In the cases of P₁ and P₂ densities, the MSP under A₂ treatment was significantly higher than under A₀ (by 98% and 83%) and A₁ (by 59% and 50%), respectively, but at the same time, A₁ produced higher MSP than A₀ (Table 4). Therefore, under the M₀, M₁, and M₂ treatments, applying A₂ considerably raised MSP against the A₀ (by 70%, 84%, and 94%) and against A₁ (by 16%, 14%, and 13%), respectively, whereas applying A1 resulted in MSP that was 48%, 62%, and 72% higher than the A₀ treatment (Table 4). Additionally, under A₀, A₁, and A₂ treatments, the MSP under M₁ inoculation was significantly higher than that under M₀ (by 38%, 51%, and 49%) and M₂ (by 18%, 11%, and 12%), respectively, whereas M₂ showed greater MSP by 17%, 36%, and 33%, respectively, compared to the M₀ treatment (Table 4). Furthermore, the P₁M₁A₁ and P₁M₁A₂ treatments demonstrated the highest MSP and a significant difference when compared to the other combined treatments (Table 4).

Under both M and A concentrations, P₂ increased SY compared to the P₁ density (Table 4). The SY under M₁ was significantly higher than that under M₀ (31% and 81%) and M₂ (by 13% and 7%); however, M₂ yielded 16% and 69% more than the M₀ treatment (Table 4). Similarly, at P₁ and P₂ densities, the SY was increased significantly in the A₂ treatment by 54% and 118% under A₀ and by 6% and 5% under A₁, respectively, whereas applying A₁ treatments increased the SY by 54% and 107% compared to the A₀ treatment (Table 4). Additionally, applying A₂ increased SY by 93%, 82%, and 103% and by 10%, 8%, and 7% at M₀, M₁, and M₂ concentrations, respectively, in comparison to the A₀ and A₁ treatments, while providing A₁ increased SY by 75%, 69%, and 90% in comparison to the A₀ treatment (Table 4). Under A₀, A₁, and A₂ treatments, the SY was significantly increased in the M1 treatment by 55%, 49%, and 46% under the M₀ treatment and by 28%, 14%, and 15% under M2, respectively, whereas supplying A₂ increased SY by 21%, 31%, and 37% more than the A₀ treatment (Table 4). However, in the combined P₂M₁A₁ and P₂M₁A₂ treatment, the SY was higher than that observed in response to the other combined treatments (Table 4).

4. Discussion

In contemporary agriculture, combining foliar amino acid treatments, microbial bioinoculants, and appropriate plant density is a comprehensive approach that stimulates plant physiological processes, increases resource use efficiency, and promotes sustained yield enhancement. A different way to improve plant vigor is to combine plant density with microbial bioinoculants (P₂M₁) and/or with exogenous application of amino acids (P₂A₂). This approach can improve growth rates and resilience against environmental stressors. Recent studies have shown that combining plant density with a microbial consortium improves peanut growth [15]. Similarly, combining plant density with foliar amino acid application promotes sesame and soybean growth [32,38]. The integration of these strategies creates a synergistic effect: the microbial bioinoculants enhance N fixation/uptake, P solubilization, and water status, and they secrete growth regulators (IAA, cytokinins, and GAs) or ACC-deaminase, resulting in plant height increment [39], while amino acids serves as a significant source of nitrogen and a precursor for polyamines, cand reduce the metabolic cost of synthesizing essential compounds, and allow more rapid response to high density conditions [29,34].

In agricultural studies, microbial consortia inoculation and exogenous amino acid application are widely used to promote plant growth [28,30]. This finding was consistent with recent research on cowpea [40] and sunflower plants [25]. A possible explanation for these facts is that the MC inoculation would have stimulated the phytohormone production (auxins, cytokinins, and gibberellins) and also could have enhanced nutrient availability, which are vital for cell elongation and division in stems, directly contributing to greater plant height [41]. Moreover, the foliar application can serve as precursors for proteins, enzymes, and other vital compounds, which would have allowed the plant to allocate more energy toward vegetative growth, particularly stem elongation [29]. Therefore, this result indicates that the combined effect of improved soil microbial activity and direct foliar nutrient support creates an optimal environment for vigorous peanut plants.

As discussed above, a taller peanut plant was observed under P₂M₁A₂ treatment. This is probably the first report that represents an important strategy of plants to improve peanut plant growth. A possible explanation for these findings can be by pairing biochemical capacity (A) with improved nutrient/water delivery and hormonal cues (M). Also, this higher pH can be explained by complementary physiological mechanisms. For example, increased plant density reduces the red: far-red light ratio, activating phytochrome-mediated shade-avoidance pathways that enhance gibberellin biosynthesis and auxin signaling, thereby promoting internode elongation [42]. However, elongation responses to density are highly influenced by the plant’s nutritional and hormonal status. In this sense, the microbial consortium contributes by improving root nutrient acquisition, solubilizing phosphorus, increasing nitrogen uptake, and producing phytohormones like indole-3-acetic acid and gibberellin-like metabolites that support stem elongation [43]. In parallel, foliar-applied amino acids act as readily available nitrogen and carbon sources, accelerating protein synthesis, stimulating enzymatic activity (e.g., nitrate reductase), and interacting with hormonal pathways to maintain leaf metabolic activity during fast shoot growth [44]. Thus, plant growth stimulation is the result of an integrated mechanism in which shade-induced hormonal cues signal elongation, while microbial inoculants and amino acids provide the nutrients, metabolic precursors, and additional hormonal support required to sustain such growth without premature resource depletion.

An essential aspect of agricultural studies involves measuring total chlorophyll as SPAD values to determine the chlorophyll content in a plant’s leaves [45]. In our study, at a density of 83,334 plants/ha, the resource competition is higher compared to 41,667 plants/ha. However, an optimal microbial consortium concentration can mitigate this resource competition [28]. Moreover, increased plant density in peanuts combined with an optimal MC rate likely enhances chlorophyll content through a synergistic effect, probably on nutrient uptake and plant stress mitigation [19]. For example, this microbial consortium contains plant growth-promoting microorganisms (PGPMs) like Bacillus spp., which helps the plant’s ability to access essential nutrients like nitrogen (N) and phosphorus (P), which are critical for chlorophyll synthesis [46]. Likewise, these PGPMs have the ability to fix atmospheric N, solubilize inorganic P, and generate phytohormones that encourage root growth, thereby increasing the surface area and chlorophyll content of the plant [47]. Hence, higher plant density, when supported by microbial activity, creates a more competitive environment while still allowing for adequate resource partitioning, resulting in an increase in chlorophyll content in peanut leaves. Previous observations indicated similar effects from the combination of high plant density and soil microbial consortium inoculation in peanut plants [32]. Furthermore, the combination of high plant density and optimum microbial inoculation might boost chlorophyll content in peanut plants, promoting sustainable crop output.

Lower density (P₁) and amino acid application also significantly increased the SPAD values of peanut leaves, which was in agreement with the findings of previous research on peanut crops [15,45]. Firstly, optimal plant density ensures efficient light interception and resource utilization, reducing intraspecific competition while maximizing photosynthetic efficiency [48]. This study showed that at 83,334 plants/ha, competition for resources increases, which might have a negative influence on chlorophyll concentration due to shade and lower light availability [49]. However, the application of amino acids at 1.20 mL L⁻¹ acts as a significant mitigating factor [50]. For example, at higher densities, foliar-applied amino acids at 1.20 mL L⁻¹ likely can enhance N metabolism and serve as precursors for chlorophyll synthesis and improve the activity of key enzymes such as glutamine synthetase and nitrate reductase [29]. Studies have shown that exogenous amino acid application enhances photosynthetic pigment stability by improving membrane integrity and reducing chlorophyllase activity under high-density planting conditions [32,51]. Furthermore, the synergistic effect of optimal plant density and amino acid supply promotes better stomatal conductance and Rubisco activity, leading to higher chlorophyll retention [52].

As mentioned earlier, when combined, an optimized plant density, a microbial consortium inoculant, and foliar amino acids can enhance chlorophyll (SPAD) levels through complementary mechanisms related to canopy light–N status, nutrient acquisition, and chlorophyll biogenesis. For example, adjusting density customizes the canopy microclimate through mechanisms such as self-shading and leaf nitrogen dilution, enabling leaves to retain higher greenness at intermediate or optimal stand densities. This is in contrast to sparse or overcrowded conditions, where SPAD values can decrease non-linearly with increasing density [48,53]. Moreover, PGPMs can improve root functioning, which influences nutrient capture (N fixation/assimilation and P and Fe mobilization) and supply phytohormones and stress-mitigating metabolites, which together enhance leaf N status and photosynthetic apparatus—often observed as significant SPAD gains [46]. Simultaneously, exogenous amino acids then act as readily assimilable N/C units and specific biostimulants: several serve as precursors or regulators for chlorophyll synthesis and PSII electron transfer, while also bolstering antioxidant systems; these effects translate into measurable increases in chlorophyll/SPAD across crops [54]. Recent crop studies also confirm that targeted foliar amino acids elevate chlorophyll pools and photosynthetic performance, reinforcing their role as a fast, foliar route to sustain pigment content alongside microbial and density management [27,55]. These results suggest that plant density, microbial consortium inoculant, and foliar amino acids may be used as useful alternatives in modern agriculture because they play an important role in ensuring sustainable peanut growth and fostering beneficial plant and soil properties.

A key agronomic characteristic of peanut plants is the quantity of stems produced, which has a direct impact on photosynthetic capacity, canopy architecture, and pod yield [15]. For example, at lower P₁ density, peanut plants experience reduced competition, which allows greater interception of light and allocation of resources to branching and stem development, often resulting in a higher number of stems per plant [15,28]. Conversely, at P₂, the increased proximity of plants intensifies shading and root competition, which tends to reduce branching and individual stem production but increases stem number per unit area due to the higher plant population [39]. Inoculating peanut plants with microbial consortia (100 mL m⁻²) can boost stem production at both P₁ and P₂ densities by improving nutrient acquisition, hormonal regulation, and stress mitigation [56,57]. Likewise, foliar applications of amino acid (A₁) can increase NTP in both plant densities by acting as signaling molecules, precursors for protein synthesis, or energy-sparing additives [27,34]. Thus, in our study, the higher NTP observed at the P₁M₁A₁ treatment is probably because, together, these practices can create a more favorable physiological environment that maximizes vegetative vigor, allowing peanut plants to increase stem number under both lower and optimal densities by reducing resource limitations and stimulating endogenous growth-regulating mechanisms.

Plant density had a significant impact on matured pod production. In this study the P₁ density promotes higher MP1 and MP2 but significantly produces less MP3, MP4, and NMP compared to the P₂ density. These facts can be interpreted as a density-dependent tradeoff, whereby optimal density supports a higher number of seeds per pod through effective resource partitioning, while lower density promotes pod initiation but compromises pod filling [58]. Moreover, under both P₁ and P₂ densities, the inoculation with M₁ or foliar application of A₂ produced more MP3, MP4, and NMP. These positive effects can be attributed to synergistic effects on peanut reproductive development and resource allocation [59]. Furthermore, P₂M₁A₂ and P₂M₁A₁ treatments yielded the highest NMP values, highlighting how reduced competition allows for more efficient resource allocation toward reproductive development [60]. These combined alternatives can increase carbon and nitrogen partitioning to reproductive sinks, lowering the incidence of poorly filled pods with one or two seeds and favoring the formation of pods with more seeds [59,61]. These findings suggest that combining plant density, microbial inoculation, and amino acid supplementation maximizes resource utilization, resulting in improved peanut seed quality and quantity.

In this study, P₂ density improves the NSP, NGP, NMP, and SY in comparison with the P₂ density. These results can be explained by the fact that an optimal planting density can improve peanut productivity traits such as NGP, NSP, MSP, and SY by balancing intraspecific competition and efficient resource use [61,62]. For example, an optimal density maximizes light interception and improves canopy structure, favoring better assimilate partitioning towards reproductive organs, increasing pod and seed number per plant [60]. Conversely, at lower densities, even while individual plants have better access to light, nutrients, and water, the total canopy fails to intercept solar radiation effectively, resulting in poorer photosynthetic efficiency at the field scale [14].

On the other hand, soil inoculation with M₁ combined with foliar application of A₁ or A₂ can synergistically enhance peanut yield components and productivity under both P₁ and P₂ density because these practices can improve plant nutrient acquisition, stress tolerance, and metabolic efficiency [15,63]. This synergy effect between M₁ and A₁/A₂ also aligns with recent findings observed in sunflower plants under water-deficit conditions [27]. Our results demonstrated a synergy effect facilitated by both vegetative vigor (boosting PH and NTP) and reproductive efficiency (improving NMP, NSP, NGP, MSP, and SY), especially at P₂. This dual approach decreases density-induced stress at P₂ populations while compensating for decreased interplant competition at P₁, resulting in stable and increased peanut yield. Additionally, a notable improvement of NMP, NSP, NGP, MSP, and SY was observed in the P₂M₁A₁ and P₂M₁A₂ treatments. The simultaneous optimization of planting density, soil inoculation with microbial consortia, and foliar application of amino acids can significantly enhance the yield components and overall productivity of peanut plants due to their synergistic effects on resource use efficiency, physiological performance, and soil–plant interactions [15,64]. Moreover, when applied together, these strategies create a favorable agroecosystem where nutrient availability, metabolic efficiency, and canopy structure are harmonized, ultimately resulting in improved pod filling, higher seed weight, and increased peanut yield potential [15,28,32]. Furthermore, this integrated strategy aligns with broader trends in sustainable peanut production that emphasize reducing reliance on chemical fertilizers, improving microbial health, and maximizing yield through sustainable agronomy strategies.

5. Conclusions

In summary, the synergistic use of microbial consortium inoculants and foliar-applied amino acids, particularly under high plant density (83,334 plants/ha), substantially boosted peanut growth and yield components—PH, NSP, NGP, MSP, and overall seed yield. The findings of this study showed that combining P₂M₁A₁ or P₂M₁A₂ significantly enhanced peanut growth and yield, resulting in a viable strategy for promoting sustainable peanut production. Collectively, the findings of this highlight that the combined manipulation of plant density, microbial inoculation, and amino acid foliar application offers a powerful agronomic strategy to optimize peanut reproductive efficiency and yield, which could be offered as programs for the sustainable development of modern agriculture.

As far as policy is concerned, these findings underscore the significance of encouraging sustainable intensification methods that lessen dependency on artificial inputs and enhance resource efficiency. Promoting the use of biostimulants and bioinoculants through extension initiatives, subsidies, and agricultural policies could hasten the shift to environmentally friendly and climate-smart production methods. In order to assess these practices’ long-term impacts on crop resilience and soil health, future research should concentrate on scaling them under various agroecological conditions and incorporating them into comprehensive management frameworks. In order to improve farmer profitability, guarantee food security, and support global sustainability objectives in agriculture, such initiatives will be essential.