Mixture design of microalgal consortia reveals crop-specific biostimulant formulations for bean and rice

Mixture design of microalgal consortia reveals crop-specific biostimulant formulations for bean and rice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mixture design of microalgal consortia reveals crop-specific biostimulant formulations for bean and rice Madelem Giraldo, Carlos Lopera, Natalia Herrera This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9238333/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Microalgae have emerged as promising biostimulants for sustainable agriculture; however, the rational design of microalgal consortia tailored to specific crops remains poorly explored. In this study, a mixture design approach was applied to develop optimized microalgal formulations based on Chlorella vulgaris , Scenedesmus sp ., and Arthrospira platensis , using bean ( Phaseolus vulgaris L., ecotype ‘Sangre Toro’) and rainfed rice ( Oryza sativa L., cv. ‘Fedearroz 2020’) as model crops. A simplex lattice design combined with response surface methodology and desirability analysis enabled the identification of optimal species combinations for each crop. The cubic model showed the best fit (P < 0.05), with high predictive capacity and non-significant lack of fit. Optimal formulations differed markedly between crops, revealing species-specific responses: In beans, the optimal formulation consisted of 31.6% C . vulgaris and 68.4% Scenedesmus sp., whereas in rainfed rice the best mixture included 62.3% A. platensis and 37.7% C . vulgaris . These findings demonstrate that microalgal formulations should be designed as crop-specific systems rather than universal formulations. This study provides a quantitative framework for the rational development of tailored microalgal bioinputs, contributing to more efficient and sustainable agricultural practices. Chlorella vulgaris Scenedesmus sp. Arthrospira platensis biostimulant microalgal consortia crop-specific Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Key points • Microalgal biostimulants show crop-specific responses • Different optimal formulations were identified for bean and rice • Statistical modeling improves biostimulant design efficiency • Microalgae enhance plant growth and biomass production 1. INTRODUCTION The rapid increase in the world's population has been accompanied by rising food demand, requiring more efficient crop management to meet it. To achieve higher yields, farmers have adopted high application rates of inorganic fertilizers as their primary fertilization option (Prashar et al. 2025 ). However, this excessive use has negatively impacted soil, water, and air quality, and compromised the sustainability of agricultural systems (Gonçalves et al. 2023 ; Chabili et al. 2024 ). Likewise, poor agricultural practices and the lack of rational management of soil and its biota have reduced the potential to improve food production, creating a wide gap between demand and supply. The search for sustainable agricultural development, in which new agricultural production strategies can mitigate negative effects and be more environmentally friendly, has become increasingly relevant. Recent reviews highlight that new-generation fertilizers and biological approaches, including biofertilizers and biostimulants, are key to sustainable agriculture and to reducing the use of chemical inputs (Arora et al. 2024 ). Several studies have shown that microalgae promote plant growth by producing bioactive substances, including phytohormones, amino acids, vitamins, and polysaccharides (Gonçalves et al. 2023 ; Chabili et al. 2024 ). These microorganisms contain macro and micronutrients and produce metabolites that improve nutrient availability, plant physiological processes, and soil health (Osorio-Reyes et al. 2023 ). Some indirect effects of microalgae components associated with improved plant development include increased nutrient utilization, improved seed germination rates, and mitigation of abiotic stress, such as drought and salinity (Renganathan et al. 2024 ). In addition, microalgae can positively influence soil structure and edaphic microbiome diversity, promoting overall soil fertility and resilience, in line with circular bioeconomy strategies in agriculture (Renganathan et al. 2024 ; Hoque et al. 2025 ). The inclusion of microalgae in crop management plans could make the use of synthetic chemicals more efficient, reduce negative environmental impacts, and lower production costs. Despite the increasing use of microalgae as biofertilizers and biostimulants, most studies have focused on single strains or empirically defined consortia, with limited consideration of species interactions and their crop-dependent effects. Microalgae have been widely recognized as effective tools to improve soil fertility, nutrient availability, and plant growth through the production of bioactive compounds and the stimulation of soil microbial activity (Gonçalves et al. 2023 ; Chabili et al. 2024 ). However, their application in microalgal consortia remains limited, and their potential to identify crop-specific optimal formulations has not been fully explored. It is proposed that microalgal formulations should not be conceived as universal inputs, but rather as crop-specific systems whose performance depends on consortium composition. Accordingly, this study applies a mixture design strategy to develop and optimize microalgal consortia tailored to two agronomically relevant crops, providing a predictive framework for the rational design of microalgae based bioinputs. This study evaluated a formulation composed of a mixture of the microalgae Chlorella vulgaris , Scenedesmus sp., and Arthrospira platensis , with the aim of maximizing key biometric variables using the bean ( Phaseolus vulgaris ) ecotype "Sangre toro" and the rainfed rice variety "Fedearroz 2020" as indicator species representing legumes and grasses plants. The biostimulant effect of this formulation was also evaluated. A mixture design approach was implemented to identify optimal species combinations and to explore crop-specific responses. The overall experimental and analytical framework of the study, including microalgae cultivation, formulation design, application to crops, and statistical optimization of responses, is summarized in Fig. 1 . 2. MATERIALS AND METHODS Microalgae and cultivation The biomass of the microalgae Chlorella vulgaris , Scenedesmus sp., and Arthrospira platensis was supplied in liquid suspension by the Natural Products Laboratory of the University of Antioquia. Chlorella vulgaris and Scenedesmus sp. were cultivated in BBM medium (pH 7.5) and Arthrospira platensis in Zarrouk medium (pH 9.5). The cultures were initially carried out in 500 mL Erlenmeyer flasks fitted with a sterilizable rubber stopper with two outlets: one to maintain constant aeration at 1.0 L/min and the other to release pressure from the culture. This system was kept on open shelves at 25°C, under a 12-hour light/12-hour darkness photoperiod. Each shelf received white light with an intensity of 100 µmol m⁻² s⁻¹. Cultures were renewed every 12 days by adding 10 mL of inoculum to 400 mL of fresh medium (Herrera and Echeverri 2021 ). Subsequently, the culture was scaled up to 60 L in closed cylindrical bioreactors, maintained in an open environment with sunlight exposure, and aerated at approximately 80 to 100 L/min using an OLF 750 oil-free air compressor. The cultures in the bioreactors lasted approximately 30 to 45 days, using the specific culture medium for each microalga. Ambient temperatures ranged from 27°C to 32°C. Microalgae harvest A . platensis was harvested after 30 days by filtration with a 100 mm mesh. In the case of Chlorella vulgaris and Scenedesmus sp. , biomass separation was achieved by sedimentation, eliminating the need for aeration after 45 days, allowing the biomass to accumulate at the bottom of the bioreactor. Finally, cell concentration was estimated by optical density (OD 630). Optical density measurements were correlated with dry biomass using calibration curves established for each microalgal species. 300 mL of culture was taken, transferred to 96-well plates, and measured at 630 nm with a Multiskan Spectrum spectrophotometer (Thermo Scientific). Experimental design The experiment was conducted in greenhouse facilities at Ecosphaira (Envigado, Antioquia, Colombia), located at 1,750 m above sea level in the rural area of the El Salado neighborhood. To determine the optimal proportion of liquid microalgae biomass, STATGRAPHICS Centurion XIX statistical software was used to implement a Simplex-Lattice mixture design augmented with three components (independent variables): C . vulgaris (A), Scenedesmus spp . (B), and A . platensis (C). There were no restrictions on the inclusion levels of the mixture components, which were expressed as fractions of the mixture (A + B + C = 1). A total of 14 mixtures (formulations) were generated (Table 1 ). To measure the biometric variables (response variables), two indicator plant species were used: the bean ( Phaseolus vulgaris ) ecotype "Sangre toro" and rainfed rice variety Fedearroz 2020 ( Oryza sativa ). Each mixture was evaluated using independent experimental units, with a total of 28 replicates per crop species distributed across the 14 formulations, resulting in 56 experimental units in total. Table 1 Experimental design generated using a simplex–lattice mixture design. RUN BLOCK Components Chlorella vulgaris Scenedesmus sp. Arthrospira platensis 1 1 0.666667 0.000000 0.333333 2 1 0.666667 0.166667 0.166667 3 1 0.000000 0.333333 0.666667 4 1 0.000000 1.000000 0.000000 5 1 0.0000 0.666667 0.333333 6 1 0.333333 0.000000 0.666667 7 1 0.333333 0.333333 0.333333 8 1 0.000000 0.000000 1.000000 9 1 1.000000 0.000000 0.000000 10 1 0.166667 0.166667 0.666667 11 1 0.166667 0.666667 0.166667 12 1 0.333333 0.666667 0.000000 13 1 0.333333 0.333333 0.333333 14 1 0.666667 0.333333 0.000000 Substrate and plant material The substrate used for this test to grow indicator plants was obtained from a local commercial supplier and consisted of a 70%–30% (w/w) mixture of AFM-1 Germination Mix and medium-coarse vermiculite, providing a defined supply of macro- and micronutrients, including nitrogen (as NO₃⁻ and NH₄⁺), phosphorus, potassium, calcium, magnesium, and trace elements such as Zn and Mn, along with a mineral matrix rich in silicates (SiO₂, Al₂O₃, MgO) that supports structural stability and nutrient retention. Seeds of each plant species were purchased from Asofril and Fedearroz. The seeds were pre-germinated in a humid chamber in an incubator (Memmert, Germany) at 30°C for 4 days. Subsequently, two seeds per pot were sown in 20-ounce pots, and 10 days later, seedlings were thinned to one per pot by selecting the best-developed plant. The plants were grown under controlled conditions in a greenhouse (relative humidity: 82–87%; temperature: 22–25°C), and the substrate moisture was maintained between 50% and 60% of its maximum moisture retention capacity. They were fed weekly with Hoagland's nutrient solution (Hoagland and Arnon). Preparation and application of mixtures The liquid suspension biomass of each microalga was used to formulate the different mixtures (Table 1 ). Each pot received two applications of 5 mL of the corresponding mixture. This contained: A. platensis : 1 × 10⁵ cells/mL, Scenedesmus sp .: 3.3 × 10⁸ cells/mL, and Chlorella vulgaris : 8.3 × 10⁸ cells/mL. The first application was made 10 days after sowing, and the second 15 days after the first application. Measurement of biometric variables "Sangre toro" bean When the plants reached the pre-flowering stage, 40 days after sowing, at the time when the flower bud was observed, the following biometric variables were determined: Plant height: measured as the distance between the base of the stem and the last meristem, diameter of the third true leaf: a measurement was taken longitudinally of the terminal leaflet of the compound leaf with a millimeter ruler, Stem diameter: measured with a caliper (MP Tools) in the middle third. Total biomass (TBB): The aerial and root parts of each plant were harvested, transferred to paper bags, and dehydrated at 72°C in a dehydrator (Innova) until a constant weight was obtained. To harvest the aerial part, the stem was cut flush with the substrate. To harvest the root, the substrate was removed from the pot and washed several times with distilled water to remove excess substrate. The sum of the aerial biomass and root biomass was recorded as total dry biomass. Fedearroz 2020 upland rice variety When the plants reached the final primordium stage, 60 days after sowing, the following biometric variables were determined: total biomass (TBT), measured as for bean plants, transferred to paper bags, and dehydrated at 72°C until a constant weight was obtained. The sum of the above-ground biomass and root biomass was recorded as total dry biomass. Validation of the mixture To validate the optimal microalgae mixture and its biostimulant effects, a second experiment was conducted in a greenhouse using a completely randomized design with a single-factor arrangement of treatments. The factor evaluated was the microalgae dose with two levels or treatments: i) (+) microalgae, the plants received 5mL of the optimal microalgae mixture; ii) control, the plants did not receive microalgae. The treatments were applied to the same indicator plants used for the mixture design, and the same procedures were followed. Ten replicates were used for each plant species. Before performing the analysis of variance (ANOVA) to test the hypothesis that microalgae exert biostimulant effects on "Sangre toro" bean and rice plants, the assumptions of normality of residuals and homogeneity of variances were evaluated. Normality and homoscedasticity were assessed using the Shapiro Wilk W test and Levene’s test, respectively. Data analysis Parameters such as sum of squares, mean square, degrees of freedom, F values, p values, and coefficients of determination (R2) were obtained from an analysis of variance (ANOVA) generated by the statistical software STATGRAPHICS Centurion XIX. The statistical significance of the models was determined at the 5% significance level (α = 0.05). To determine the statistical significance of the model, the F-ratio (R/r) was used at the 95% significance level. The adjusted (R2-adj) and predicted (R2-pred) coefficients of determination were used to compare the different models and select an appropriate model for each response. Response surface graphs generated from the selected models were used to study the interactive effects of the components (microalgae) on the responses. The objective of this statistical approach was to identify the microalgae mixture that maximized the biometric variables studied. The optimal mixture was validated, and biostimulant effects were investigated using a one-way analysis of variance in STATGRAPHICS Centurion XIX. 2. RESULTS The evaluated microalgal mixture, composed of Scenedesmus sp., A . platensis , and C . vulgaris , was observed using inverted phase-contrast microscopy (Fig. 2 ). A. platensis exhibited a larger cell size compared to the other species. "Sangre toro" bean Model adjustment The P values (P < 0.05), as well as those for R2 and adjusted R2, indicate that the cubic model is the most appropriate for all variables evaluated. R2 indicates that the adjusted cubic model explains 89.1%, 90.7%, 87.4%, and 75.2% of the variability observed in height, TDB, leaf diameter, and stem diameter of bean plants, respectively (Table S1 ). There is a statistically significant relationship between height and the components of microalgae ( C. vulgaris, Scenedesmus sp., and A. platensis ). This same relationship holds for the other biometric variables, and it is concluded, with 95% confidence, that the selected models are suitable for predicting responses (Table S2). The equations of the fitted cubic models for each variable are presented below: Height = 21,0348*A + 21,9996*B + 23,8939*C + 80,7764*A*B + 18,7662*A*C + 1,99614*B*C − 179,816*A*B*C − 73,2456*A*B*(A-B) + 14,2545*A*C*(A-C) − 26,9122*B*C*(B-C) Total dry biomass (TDB) = 2,58419*A + 2,10807*B + 2,2791*C + 6,8726*A*B − 0,0826338*A*C + 0,863115*B*C − 8,46163*A*B*C − 12,701*A*B*(A-B) + 0,288499*A*C*(A- C) − 2,83246*B*C*(B-C) Leaf diameter = 4,04995A + 5,61771*B + 3,8961C + 5,87292*A*B + 5,47076*A*C + 6,49605*B*C − 29,3196*A*B*C + 8,54348*A*B*(A-B) − 1,60295*A*C*(A-C) − 16,8289*B*C*(B-C) Stem diameter = 0,303654*A + 0,290751*B + 0,309927*C + 0,283101*A*B + 0,0148424*A*C − 0,0406976*B*C − 0,509315*A*B*C − 0,399111*A*B*(A-B) + 0,01597*A*C*(A-C) − 0,158668*B*C*(B-C) Where, A = Chlorella vulgaris B = Scenedesmus sp. C = Arthrospira platensis Response surface evaluation - optimization The combination of microalgae that maximizes each variable, except leaf diameter, includes Scenedesmus sp. in a higher proportion and excludes Arthrospira platensis (Table 2 ). For leaf diameter, the combination that maximizes its value contains Arthrospira platensis in a higher proportion and does not include Chlorella vulgaris. This behavior is shown graphically in Fig. 3 , which illustrates the predicted response as a function of the microalgal composition ( C . vulgaris , Scenedesmus sp., and A . platensis .) The height of the surface represents the value of the variable. Table 2 A combination of components that maximizes the response variables Responses Optimum value Combination of components (%) Chlorella vulgaris Scenedesmus sp. Arthrospira platensis Height 45.0578 0.338713 0.661287 9.77515E-11 TDB 4.76111 0.298251 0.701749 2.02883E-10 Leaf diameter 7.19041 2.49336E-10 0.298951 0.701049 Stem diameter 0.387765 0.315072 0.684928 1.45122E-9 Multiple response optimization Multiple response optimization allowed the identification of optimal factor combinations based on a desirability function ranging from 0 to 1, where values approaching 1 indicate optimal system performance, while values close to 0 denote that one or more responses fall outside acceptable limits. Using the desirability function, it was found that the mixture containing 31.6% C . vulgaris , 68.4% Scenedesmus sp., and 0% A . platensis was optimal for stimulating the biometric variables evaluated. With this mixture, a desirability of 0.89 was achieved, which is very close to 1, indicating that the configuration yields favorable results across all responses (Fig. 4 ). The height, TDB, leaf diameter, and stem diameter of bean plants treated with the optimal mixture were predicted to be 45.03 cm, 4.75 g, 6.74 cm, and 0.38 cm, respectively. Validation of the optimal microalgae mixture and evaluation of its biostimulant effect For all variables, both tests yielded P-values ≥ 0.05, indicating that residuals were normally distributed and variances were homogeneous. For plant height, a Box Cox transformation was applied to satisfy these assumptions. The experimental data for height, TDB, leaf diameter, and stem diameter of plants treated with the optimal mixture were 46.417 cm/plant, 4.80 g/plant, 6.807 cm, and 0.398 cm, respectively. The proximity of the experimental data to the predicted values indicates that the mixture design algorithm accurately estimated the results. The results of the analysis of variance (ANOVA) indicate a significant biostimulant effect (P < 0.05) of microalgae on all biometric variables evaluated. When microalgae were applied, increases of 14.8%, 13.1%, 12.5%, and 11.8% were achieved for height, TDB, leaf diameter, and stem diameter, respectively (Fig. 5 ). The biostimulant effect of microalgae was visible in plant height, above-ground biomass, and root biomass (Fig. 6 ). Rice Model fit All models, except the special cubic model, were statistically significant (Table 3 ), with P-values < 0.05 at a 95% confidence level. The cubic model was selected as the most appropriate for total dry biomass (TDB) because, in addition to being statistically significant, it represents the most comprehensive model and exhibited the highest R² and adjusted R² values. The cubic model explains 82.3% of the variability observed in TDB. There is a significant relationship between TDB and the components or microalgae ( C . vulgaris , Scenedesmus sp., and A . platensis ). It is concluded, with 95% confidence, that the selected model is adequate for predicting TDB. Table 3 Effects of the full model estimated for total dry biomass (TDB) Source Sum of Squares Df Mean Square F-Ratio P-Value SE R-Squared (%) Adj. R-Squared (%) Mean 73,1272 1 73,1272 Blocks 0,00188929 1 0,00188929 0,74 0,3962 Linear 0,0320554 2 0,0160277 11,33 0,0003 0,0376105 50,00 43,75 Quadratic 0,0148422 3 0,00494739 5,44 0,0063 0,0301638 71,86 63,82 Special Cubic 0,000274455 1 0,000274455 0,29 0,5952 0,0306859 72,26 62,55 Cubic 0,00677863 3 0,00225954 3,19 0,0310 0,0266281 82,25 71,80 Error 0,0120539 17 0,000709054 Total 73,1951 28 The significance of the fitted cubic model was assessed using analysis of variance (ANOVA) (Table S3). The P-value obtained for total dry biomass (TDB) (P = 0.0089) was lower than the significance level (α = 0.05), indicating a significant relationship between TDB and the mixture components ( C . vulgaris , Scenedesmus sp., and A . platensis ). The lack-of-fit test was performed to evaluate the adequacy of the selected model. As the lack-of-fit P-value exceeded the significance threshold (α = 0.05), the cubic model was considered adequate to describe and predict TDB at a 95% confidence level. The fitted cubic model equation for total dry biomass (TDB) is presented below: Total dry biomass (TDB) = 1,57502*A + 1,54706*B + 1,64797*C − 0,0670144*A*B + 0,197615*A*C + 0,225018*B*C + 0,299066*A*B*C − 0,383184*A*B*(A-B) + 0,0595245 *A*C*(A- C) + 0,263834*B*C*(B-C) Where: A = Chlorella vulgaris B = Scenedesmus sp. C = Arthrospira platensis Response surface - optimization The microalgae mixture that maximizes TDB (1.66 g/plant) includes A . platensis in the highest proportion (62.3%), followed by C . vulgaris (37.7%) and does not include Scenedesmus sp. (Fig. 7 ). Validation of the optimal microalgae mixture and evaluation of its biostimulant effect The experimental data for TDB of plants treated with the optimal mixture yielded an average of 1.502 g/plant. The proximity of the experimental data to the predicted data indicates that the mixture design algorithm accurately estimated the results. For all variables, both tests yielded P-values ≥ 0.05, indicating that residuals were normally distributed and variances were homogeneous. The biostimulant effect of microalgae on rainfed rice plants was tested with an ANOVA. The P value of < 0.05 indicates that microalgae have a significant biostimulant effect on TDB. When microalgae were applied to plants, TDB increased by 12.2% (Fig. 8 ). Changes in above-ground and root biomass due to the application of microalgae were visibly detected (Fig. 9 ). DISCUSSION A central contribution of this study is the demonstration that microalgal formulations should not be approached as universal inputs, but rather as crop-specific systems requiring tailored optimization. Although previous studies have demonstrated that microalgae can enhance plant growth, nutrient uptake, and soil fertility through multiple mechanisms, including the production of phytohormones and the stimulation of soil microbial communities (Parmar et al. 2023 ), these approaches have largely been empirical and have not accounted for interactions among species or crop-dependent responses. The contrasting optimal formulations observed for bean and rice clearly indicate that the effectiveness of microalgal consortia is strongly dependent on plant species, supporting a paradigm shift from generic biofertilizers toward rationally designed, crop-targeted bioinputs. The results of this study provide novel evidence supporting the use of quantified microalgal mixtures as biostimulants, highlighting that plant responses are species-specific and depend on the composition of the microalgal consortium. Microalgae are considered to exert primarily biostimulant effects, with potential indirect nutritional contributions through enhanced nutrient availability. Microalgae may exert direct effects on soil microbial communities, which are key regulators of nutrient cycling and bioavailability (Sun et al. 2026 ). These interactions are particularly relevant within the rhizosphere, the narrow soil zone adjacent to the root surface, where intense biochemical exchanges between plants and microorganisms modulate nutrient dynamics and microbial mediated processes such as mineralization and nutrient mobilization. In this context, microalgal applications can influence both the structure and function of the rhizosphere microbiome, thereby indirectly affecting plant growth and nutrient uptake (Jose et al. 2024 ). Unlike most research, this study identifies specific combinations of C . vulgaris , Scenedesmus sp., and A . platensis that maximize biometric variables in a legume ( Phaseolus vulgaris L. ) and a cereal ( Oryza sativa ). The effect of microalgae is not limited to a direct influence on plant physiology. Microbial communities associated with the species-specific rhizosphere can utilize microalgal biomass and exudates as carbon sources, thereby altering microbial structure and activity. The stimulatory and beneficial effects of microalgae on soil microorganisms have been widely reported, often described as synergistic interactions. Through these interactions, microalgae may enhance microbial mediated processes such as nutrient mineralization and mobilization. Ultimately, it is the soil microbiota that governs nutrient dynamics and availability to plants, thereby indirectly modulating plant growth and performance (Ng et al. 2024 ) . The microalgae mixtures exerted biostimulant effects, with different responses among species. This difference, depending on the crop, is consistent with the literature, highlighting that the efficacy of microalgal bioproducts depends both on the plant species and the composition of the microalgal mixture, which may be associated with variations in the physiological sensitivity of each species to the metabolites released by microalgae. (Ronga et al. 2019 ). Previous studies have documented that Chlorella and Scenedesmus produce and release phytohormones, such as auxins and cytokinins, as well as other bioactive compounds that stimulate germination, cell elongation, and root development, which may explain the increase in biometric variables in beans (Fig. 5 ) observed in this study (Parmar et al. 2023 ). Similarly, studies with Scenedesmus grown in enriched media showed increases in above ground and below ground biomass in crops such as pak choi, supporting the ability of this genus of microalgae to act as a biofertilizer (Álvarez-González et al. 2025 ). A. platensis is characterized for its elevated nitrogen content and the production of bioactive compounds such as polysaccharides and pigments, which may enhance plant metabolism and stress tolerance. Furthermore, its role in the optimal formulation identified for rice (Figs. 7 , 8 , and 9 ) is consistent with previous studies reporting that cyanobacteria can release key micronutrients (Herrera and Echeverri 2021 ) and bioactive compounds that promote plant nutrition and efficiency (El-Shazoly et al. 2025 ). It has been observed that the application of microalgae can increase the availability of elements such as iron and zinc in the soil, which is associated with greater photosynthetic efficiency and tissue development (Sun et al. 2026 ). This is relevant for crops such as rice, where balanced nutrition in the early stages is crucial for final yield (Shankar et al. 2021 ). The benefits observed in this study are also consistent with evidence that microalgae can improve soil structure, increase organic matter and promote positive interactions with soil microbiota. Such interactions can improve nutrient availability to plants and reduce dependence on synthetic fertilizers, thereby supporting sustainable agricultural practices (Gurau et al. 2025 ). Plants were grown under nutrient-sufficient conditions using Hoagland’s solution, indicating that the observed effects are primarily attributable to biostimulant activity rather than nutrient supply; however, microalgae are also known to contribute to plant nutrition through the release of nutrients and organic matter, suggesting a potential biofertilizing role under nutrient-limited conditions. Some authors highlight that applications of microalgal biostimulants have demonstrated positive effects on a wide range of crops. For example, extracts and live cultures of Chlorella sorokiniana significantly improved nutrition and root development in wheat, with these effects being attributed to both nutrient release and phytohormone production (Chovancek et al. 2025 ). In addition, other reviews have noted that bioactive compounds in microalgae, such as amino acids and polysaccharides, can mitigate the effects of abiotic stress and improve nutrient use efficiency, a key aspect for agricultural systems under environmental stress (Brito-Lopez et al. 2025 ). Despite these positive effects, which are consistent with the literature, the diversity of responses observed between beans and rice indicates that there is no single microalgal formulation that works optimally for all crops. This is consistent with reviews showing that different genera and species of microalgae can have variable effects due to differences in their metabolomic profiles and the ways they interact with plants and soil (Farid et al. 2019 ; Herrera et al. 2025 ). From an agronomic perspective, the integration of microalgae into management plans can contribute to sustainable agriculture by reducing the use of chemical fertilizers, improving soil fertility, and promoting biogeochemical processes favorable to plant nutrition, coinciding with recent circular economy proposals that seek to harness the potential of these microorganisms in nutrient recovery and the improvement of sustainable cropping systems (Hu et al. 2025 ). Although the biostimulant effects are well documented under controlled conditions, field applications still require further research to validate their effects under real agronomic conditions and across various cropping systems (Herrera et al. 2025 ). This is particularly relevant for promoting the technological adoption of microalgal formulations and ensuring their effectiveness in a variety of climates, soils, and management practices. These findings confirm that there is no universal microalgal formulation and reinforce the need to design specific bio-inputs according to crop physiology, integrating mechanisms associated with nutrient supply, organic carbon, and soil microbiota stimulation, in line with current approaches to sustainable agriculture. CONCLUSIONS This study demonstrates that plant responses to microalgal formulations are species-dependent. In the case of the bean ( Phaseolus vulgaris ) ecotype "Sangre toro" the mixture that optimized biometric variables consisted of 31.6% C . vulgaris and 68.4% Scenedesmus sp., without the inclusion of A . platensis . In contrast, for rainfed rice, the optimal formulation corresponded to a mixture composed of 62.3% A . platensis and 37.7% C . vulgaris , without the participation of Scenedesmus sp., which shows different physiological requirements between the two plant species. The microalgal mixtures evaluated had a positive biostimulant effect on bean and rice plants, as reflected in improvements in the biometric variables analyzed. This effect can be attributed to a combination of multiple mechanisms, including the supply of essential inorganic nutrients, the incorporation of organic carbon and bioactive metabolites, and the stimulation of soil microbiota, which favors nutrient availability and plant physiological processes. The results suggest that microalgae are a viable alternative for integration into the agronomic management plans of different crops, with the potential to reduce the use of inorganic fertilizers and promote more sustainable plant growth. However, the variability observed across plant species underscores the need to validate these formulations in other crops and under different conditions to broaden their applicability and optimize their agricultural use. Declarations Acknowledgments The authors gratefully acknowledge the Universidad de Antioquia and Minciencias for their support of this research. Authors contributions Carlos Lopera: Methodology. Madelen Giraldo: Methodology, Investigation, Formal analysis and Conceptualization. Natalia Herrera: Methodology, Investigation, Formal analysis and Conceptualization. All authors read and approved the manuscript. Funding This work was supported by Minciencias (Colombia) (COD 1115-914-91810, Ct 111-2022). Data availability The data sets that were generated and analyzed in the current study are available from the corresponding author upon reasonable request. Conflict of interest The authors declare no competing interests. References Álvarez-González, A., Serrano, L., Gorchs, G., and Uggetti, E. (2025) Exploring the biostimulant potential of Scenedesmus sp. grown in wastewater: impacts on plant growth and photosynthetic activity of lettuce. Chemosphere , 382. Arora, P. K., Tripathi, S., Omar, R. A., Chauhan, P., Sinhal, V. K., Singh, A., Srivastava, A., Garg, S. K., et al. (2024) Next-generation fertilizers: the impact of bionanofertilizers on sustainable agriculture. Microb. Cell Fact. , 23. Brito-Lopez, C., Van Der Wielen, N., Barbosa, M., and Karlova, R. (2025) Plant growth-promoting microbes and microalgae-based biostimulants: Sustainable strategy for agriculture and abiotic stress resilience. Philosophical Transactions of the Royal Society B: Biological Sciences , 380. Chabili, A., Minaoui, F., Hakkoum, Z., Douma, M., Meddich, A., and Loudiki, M. (2024) A Comprehensive Review of Microalgae and Cyanobacteria-Based Biostimulants for Agriculture Uses. Plants , 13. Chovancek, E., Poque, S., Bayram, E., Borhan, E., Jokel, M., Rantanen, I. M., Haznedaroglu, B. Z., Himanen, K., et al. (2025) Stepwise processing of Chlorella sorokiniana confers plant biostimulant that reduces mineral fertilizer requirements. Bioresour. Technol. , 418. El-Shazoly, R. M., Yousef, S., Hifney, A. F., and Abdel-Wahab, D. A. (2025) Effectiveness of Algae as a Low-Cost Alternative Input to Stimulate Sesamum Indicum L. Growth and Productivity for Sustainable Purposes. J. Soil Sci. Plant Nutr. , 25, 8006–8025. Farid, R., Mutale-joan, C., Redouane, B., Mernissi Najib, E., Abderahime, A., Laila, S., and El Arroussi, H. (2019) Effect of Microalgae Polysaccharides on Biochemical and Metabolomics Pathways Related to Plant Defense in Solanum lycopersicum. Appl. Biochem. Biotechnol. , 188, 225–240. Gonçalves, J., Freitas, J., Fernandes, I., and Silva, P. (2023) Microalgae as Biofertilizers: A Sustainable Way to Improve Soil Fertility and Plant Growth. Sustainability (Switzerland) , 15. Gurau, S., Imran, M., and Ray, R. L. (2025) Algae: A cutting-edge solution for enhancing soil health and accelerating carbon sequestration – A review. Environ. Technol. Innov. , 37. Herrera, N. and Echeverri, F. (2021) Evidence of quorum sensing in cyanobacteria by homoserine lactones: The origin of blooms. Water (Switzerland) , 13. Herrera, N., Puentes-Escobar, T. C., Álvarez-Pulido, J. A., Sanchez, R., Restrepo, J., and Echeverri, L. F. (2025) Effect of microalgae and a cyanobacteria mixture on germinative activity, growth, and cannabinoid production in Cannabis sativa plants. J. Appl. Phycol. , 37, 4207–4217. Hoagland, D. R. and Arnon, D. I. The Water-Culture Method for Growing Plants without Soil. Hoque, M. M., Iannelli, V., Padula, F., Radice, R. P., Saha, B. K., Martelli, G., Scopa, A., and Drosos, M. (2025) Microalgae: Green Engines for Achieving Carbon Sequestration, Circular Economy, and Environmental Sustainability—A Review Based on Last Ten Years of Research. Bioengineering , 12. Hu, L., Zhou, Y., Gong, Y., Luo, Y., Liu, X., and Jiang, Z. (2025) Microalgal proteins for a circular bioeconomy: Nutritional, material, and chemical valorization. Bioresour. Technol. , 436. Jose, S., Renuka, N., Ratha, S. K., Kumari, S., and Bux, F. (2024) Bioprospecting of microalgae from agricultural fields and developing consortia for sustainable agriculture. Algal Res. , 78. Ng, Z. Y., Ajeng, A. A., Cheah, W. Y., Ng, E. P., Abdullah, R., and Ling, T. C. (2024) Towards circular economy: Potential of microalgae – bacterial-based biofertilizer on plants. J. Environ. Manage. , 349. Osorio-Reyes, J. G., Valenzuela-Amaro, H. M., Pizaña-Aranda, J. J. P., Ramírez-Gamboa, D., Meléndez-Sánchez, E. R., López-Arellanes, M. E., Castañeda-Antonio, M. D., Coronado-Apodaca, K. G., et al. (2023) Microalgae-Based Biotechnology as Alternative Biofertilizers for Soil Enhancement and Carbon Footprint Reduction: Advantages and Implications. Mar. Drugs , 21. Parmar, P., Kumar, R., Neha, Y., and Srivatsan, V. (2023) Microalgae as next generation plant growth additives: Functions, applications, challenges and circular bioeconomy based solutions. Front. Plant Sci. , 14. Prashar, D., Singh, A., Dhama, V., Singh, P. K., Kumar, M., Kumar, S., Pandey, D., and Verma, G. (2025) Effect of Organic and Inorganic Fertilizers on Crop Yield and Soil Fertility: A Comprehensive Review. Journal of Experimental Agriculture International , 47, 16–22. Renganathan, P., Gaysina, L. A., Holguín-Peña, R. J., Sainz-Hernández, J. C., Ortega-García, J., and Rueda-Puente, E. O. (2024) Phycoremediated Microalgae and Cyanobacteria Biomass as Biofertilizer for Sustainable Agriculture: A Holistic Biorefinery Approach to Promote Circular Bioeconomy. Biomass (Switzerland) , 4, 1047–1077. Ronga, D., Biazzi, E., Parati, K., Carminati, D., Carminati, E., and Tava, A. (2019) Microalgal biostimulants and biofertilisers in crop productions. Agronomy , 9. Shankar, T., Malik, G. C., Banerjee, M., Dutta, S., Maitra, S., Praharaj, S., Sairam, M., Kumar, D. S., et al. (2021) Productivity and nutrient balance of an intensive rice–rice cropping system are influenced by different nutrient management in the red and lateritic belt of West Bengal, India. Plants , 10. Sun, Z., Liu, X., Ugya, A. Y., Liu, H., Sun, L., and Luo, G. (2026) Microalgae and cyanobacteria as a tool for agricultural sustainability: a review of biofertilizer and biostimulant potential. Front. Plant Sci. , 16. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9238333","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617432313,"identity":"3fbc75e6-fe9a-4838-8353-134b5dc82307","order_by":0,"name":"Madelem Giraldo","email":"","orcid":"","institution":"Cadajfer LATAM SAS (Ecosphaira®)","correspondingAuthor":false,"prefix":"","firstName":"Madelem","middleName":"","lastName":"Giraldo","suffix":""},{"id":617432318,"identity":"b2266d4f-4f8c-4821-a644-a00aa82f1a53","order_by":1,"name":"Carlos Lopera","email":"","orcid":"","institution":"Cadajfer LATAM SAS (Ecosphaira®)","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Lopera","suffix":""},{"id":617432319,"identity":"35a9e6ce-cc01-4953-9a7b-7b6e6cb30ae5","order_by":2,"name":"Natalia Herrera","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie2Qv2rDMBCHzxw4i+jspekrXDH0D4E8i4TAXhLasZBCPGVK6Nq8hR+gg4IhWgxetbVg8OzSxUMoVSClLThqxw76Fv10uu+EBODx/FdeCIAOeQjA9ys6+u0Z/6bEf1TgSxHZb8rloNq0/HZsw6ppX5/Gaa7lpoW7kcgGhepTrpcSI07SBh2vHxs5zctGRlCmImMJ71NISbAKApkEkSmc5mZCUbAoRBYx6lWqGjtOc6DnBnGn5imZm7cueHcoRob2lsKGEBFUwclMwijIXEp9ccVJMyoTDJZKn6/Lxla2abw49pZK1KbdzYaktwidmp2daGkr96PThyM/9gn7ud2PD139Ho/H43HyATFgYU3PJ5DwAAAAAElFTkSuQmCC","orcid":"","institution":"University of Antioquia","correspondingAuthor":true,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Herrera","suffix":""}],"badges":[],"createdAt":"2026-03-26 22:38:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9238333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9238333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106248268,"identity":"a6f933ae-ecc2-4d1d-9e61-46ee142f00fd","added_by":"auto","created_at":"2026-04-06 16:30:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5130509,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework of microalgal biostimulant development using mixture design and crop-specific evaluation in bean and rice.\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/fdbd2b2eeccc48444871c373.png"},{"id":106248271,"identity":"d5d3da3c-ab25-4da2-8d5e-1c4279eed1bb","added_by":"auto","created_at":"2026-04-06 16:30:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6708180,"visible":true,"origin":"","legend":"\u003cp\u003eInverted microscope photographs (40X) of the microalgae applied to the crops. A. \u003cem\u003eScenedesmus \u003c/em\u003esp\u003cem\u003e; B. A. platensis\u003c/em\u003e; C. \u003cem\u003eChlorella vulgaris, \u003c/em\u003eand D. Mixture with the three species of microalgae.\u003c/p\u003e","description":"","filename":"FIG2..png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/b605ebe7e6205317f46e6966.png"},{"id":106403175,"identity":"c98ae230-3c2f-49b9-9ffe-549217788085","added_by":"auto","created_at":"2026-04-08 09:13:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2761116,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface plots illustrating the effects of microalgal mixture composition (\u003cem\u003eChlorella vulgaris\u003c/em\u003e, \u003cem\u003eScenedesmus \u003c/em\u003esp., and \u003cem\u003eArthrospira platensis\u003c/em\u003e) on the evaluated variables in “Sangre Toro” bean plants. A: plant height; B: total dry biomass (TDB); C: leaf diameter; D: stem diameter.\u003c/p\u003e","description":"","filename":"FIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/c79f69f2a32dda5f56c2b7dd.png"},{"id":106402721,"identity":"7423ed47-da11-4062-8f3d-cc3e08c95864","added_by":"auto","created_at":"2026-04-08 09:12:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":668032,"visible":true,"origin":"","legend":"\u003cp\u003eDesirability response surface showing the optimal microalgal mixture and predicted responses for the evaluated variables.\u003c/p\u003e","description":"","filename":"FIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/48341a7dc8b9d57593d03c0a.png"},{"id":106403506,"identity":"234394aa-59fa-46a4-a439-5d9b2807b8e9","added_by":"auto","created_at":"2026-04-08 09:14:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5235453,"visible":true,"origin":"","legend":"\u003cp\u003eBiostimulant effect of microalgae on biometric variables in “Sangre Toro” bean plants. A: height; B: TDB; C: leaf diameter; D: stem diameter. The vertical bars indicate the standard error of the mean (n=10). Averages with different letters differ significantly according to ANOVA (P ≤ 0.05).\u003c/p\u003e","description":"","filename":"FIG5.png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/e1827835e515a0b2a8847ba4.png"},{"id":106248273,"identity":"d8425196-0f3b-425b-bfe6-23ab5e6e707a","added_by":"auto","created_at":"2026-04-06 16:30:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2018574,"visible":true,"origin":"","legend":"\u003cp\u003eVisible differences in biometric variables due to the application of microalgae. A: height and above-ground biomass; B: root biomass.\u003c/p\u003e","description":"","filename":"FIG6.png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/7c5c4598e1530a6ee37d595b.png"},{"id":106248276,"identity":"25232975-f15e-43eb-84ae-649d12256c4d","added_by":"auto","created_at":"2026-04-06 16:30:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":864237,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface for TDB indicating the optimal mixture composed of \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis \u003c/em\u003eand \u003cem\u003eC\u003c/em\u003e.\u003cem\u003evulgaris\u003c/em\u003e, excluding \u003cem\u003eScenedesmus \u003c/em\u003esp.\u003c/p\u003e","description":"","filename":"FIG7.png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/17c9901c295331036dd0c323.png"},{"id":106403526,"identity":"367005a9-240f-49ac-902d-c58115bcc5a4","added_by":"auto","created_at":"2026-04-08 09:14:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1869931,"visible":true,"origin":"","legend":"\u003cp\u003eBiostimulant effect of microalgae on TDB. The vertical bars indicate the standard error of the mean (n=10). Means with different letters indicate significant differences according to ANOVA (P ≤ 0.05).\u003c/p\u003e","description":"","filename":"FIG8.png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/fca35320e67aa723b6f78258.png"},{"id":106248275,"identity":"062ef5e7-f222-4c0d-bebc-11bd908c3353","added_by":"auto","created_at":"2026-04-06 16:30:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3321844,"visible":true,"origin":"","legend":"\u003cp\u003eVisible differences in biometric variables due to the application of microalgae. A: above-ground biomass; B: root biomass.\u003c/p\u003e","description":"","filename":"FIG9.png","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/b2c64a37f82782232bbfdf4d.png"},{"id":108492853,"identity":"29638353-4a3f-4abe-ac58-45a172b4fb97","added_by":"auto","created_at":"2026-05-05 09:58:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25849450,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/4c2449c8-7f6e-4399-b44c-c05a63678049.pdf"},{"id":106248269,"identity":"82281faa-5250-4937-a6e3-4bd2c3f1a89b","added_by":"auto","created_at":"2026-04-06 16:30:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24308,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9238333/v1/12f0358154048aa166389aa6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mixture design of microalgal consortia reveals crop-specific biostimulant formulations for bean and rice","fulltext":[{"header":"Key points","content":"\u003cp\u003e\u0026bull; Microalgal biostimulants show crop-specific responses \u003c/p\u003e\n\n\u003cp\u003e\u0026bull; Different optimal formulations were identified for bean and rice\u003c/p\u003e\n\n\u003cp\u003e\u0026bull; Statistical modeling improves biostimulant design efficiency\u003c/p\u003e\n\n\u003cp\u003e\u0026bull; Microalgae enhance plant growth and biomass production\u003c/p\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe rapid increase in the world's population has been accompanied by rising food demand, requiring more efficient crop management to meet it. To achieve higher yields, farmers have adopted high application rates of inorganic fertilizers as their primary fertilization option (Prashar et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, this excessive use has negatively impacted soil, water, and air quality, and compromised the sustainability of agricultural systems (Gon\u0026ccedil;alves et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chabili et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Likewise, poor agricultural practices and the lack of rational management of soil and its biota have reduced the potential to improve food production, creating a wide gap between demand and supply.\u003c/p\u003e \u003cp\u003eThe search for sustainable agricultural development, in which new agricultural production strategies can mitigate negative effects and be more environmentally friendly, has become increasingly relevant. Recent reviews highlight that new-generation fertilizers and biological approaches, including biofertilizers and biostimulants, are key to sustainable agriculture and to reducing the use of chemical inputs (Arora et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Several studies have shown that microalgae promote plant growth by producing bioactive substances, including phytohormones, amino acids, vitamins, and polysaccharides (Gon\u0026ccedil;alves et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chabili et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These microorganisms contain macro and micronutrients and produce metabolites that improve nutrient availability, plant physiological processes, and soil health (Osorio-Reyes et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome indirect effects of microalgae components associated with improved plant development include increased nutrient utilization, improved seed germination rates, and mitigation of abiotic stress, such as drought and salinity (Renganathan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, microalgae can positively influence soil structure and edaphic microbiome diversity, promoting overall soil fertility and resilience, in line with circular bioeconomy strategies in agriculture (Renganathan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hoque et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The inclusion of microalgae in crop management plans could make the use of synthetic chemicals more efficient, reduce negative environmental impacts, and lower production costs.\u003c/p\u003e \u003cp\u003eDespite the increasing use of microalgae as biofertilizers and biostimulants, most studies have focused on single strains or empirically defined consortia, with limited consideration of species interactions and their crop-dependent effects. Microalgae have been widely recognized as effective tools to improve soil fertility, nutrient availability, and plant growth through the production of bioactive compounds and the stimulation of soil microbial activity (Gon\u0026ccedil;alves et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chabili et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, their application in microalgal consortia remains limited, and their potential to identify crop-specific optimal formulations has not been fully explored. It is proposed that microalgal formulations should not be conceived as universal inputs, but rather as crop-specific systems whose performance depends on consortium composition. Accordingly, this study applies a mixture design strategy to develop and optimize microalgal consortia tailored to two agronomically relevant crops, providing a predictive framework for the rational design of microalgae based bioinputs.\u003c/p\u003e \u003cp\u003eThis study evaluated a formulation composed of a mixture of the microalgae \u003cem\u003eChlorella vulgaris\u003c/em\u003e, \u003cem\u003eScenedesmus\u003c/em\u003e sp., and \u003cem\u003eArthrospira platensis\u003c/em\u003e, with the aim of maximizing key biometric variables using the bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e) ecotype \"Sangre toro\" and the rainfed rice variety \"Fedearroz 2020\" as indicator species representing legumes and grasses plants. The biostimulant effect of this formulation was also evaluated. A mixture design approach was implemented to identify optimal species combinations and to explore crop-specific responses. The overall experimental and analytical framework of the study, including microalgae cultivation, formulation design, application to crops, and statistical optimization of responses, is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e \u003cb\u003eMicroalgae and cultivation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe biomass of the microalgae \u003cem\u003eChlorella vulgaris\u003c/em\u003e, \u003cem\u003eScenedesmus\u003c/em\u003e sp., and \u003cem\u003eArthrospira platensis\u003c/em\u003e was supplied in liquid suspension by the Natural Products Laboratory of the University of Antioquia. \u003cem\u003eChlorella vulgaris\u003c/em\u003e and \u003cem\u003eScenedesmus sp.\u003c/em\u003e were cultivated in BBM medium (pH 7.5) and \u003cem\u003eArthrospira platensis\u003c/em\u003e in Zarrouk medium (pH 9.5). The cultures were initially carried out in 500 mL Erlenmeyer flasks fitted with a sterilizable rubber stopper with two outlets: one to maintain constant aeration at 1.0 L/min and the other to release pressure from the culture. This system was kept on open shelves at 25\u0026deg;C, under a 12-hour light/12-hour darkness photoperiod. Each shelf received white light with an intensity of 100 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1;. Cultures were renewed every 12 days by adding 10 mL of inoculum to 400 mL of fresh medium (Herrera and Echeverri \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Subsequently, the culture was scaled up to 60 L in closed cylindrical bioreactors, maintained in an open environment with sunlight exposure, and aerated at approximately 80 to 100 L/min using an OLF 750 oil-free air compressor. The cultures in the bioreactors lasted approximately 30 to 45 days, using the specific culture medium for each microalga. Ambient temperatures ranged from 27\u0026deg;C to 32\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicroalgae harvest\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e was harvested after 30 days by filtration with a 100 mm mesh. In the case of \u003cem\u003eChlorella vulgaris\u003c/em\u003e and \u003cem\u003eScenedesmus sp.\u003c/em\u003e, biomass separation was achieved by sedimentation, eliminating the need for aeration after 45 days, allowing the biomass to accumulate at the bottom of the bioreactor. Finally, cell concentration was estimated by optical density (OD 630). Optical density measurements were correlated with dry biomass using calibration curves established for each microalgal species. 300 mL of culture was taken, transferred to 96-well plates, and measured at 630 nm with a Multiskan Spectrum spectrophotometer (Thermo Scientific).\u003c/p\u003e \u003cp\u003e \u003cb\u003eExperimental design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe experiment was conducted in greenhouse facilities at Ecosphaira (Envigado, Antioquia, Colombia), located at 1,750 m above sea level in the rural area of the El Salado neighborhood.\u003c/p\u003e \u003cp\u003eTo determine the optimal proportion of liquid microalgae biomass, STATGRAPHICS Centurion XIX statistical software was used to implement a Simplex-Lattice mixture design augmented with three components (independent variables): \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e (A), \u003cem\u003eScenedesmus spp\u003c/em\u003e. (B), and \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e (C). There were no restrictions on the inclusion levels of the mixture components, which were expressed as fractions of the mixture (A\u0026thinsp;+\u0026thinsp;B + C\u0026thinsp;=\u0026thinsp;1). A total of 14 mixtures (formulations) were generated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To measure the biometric variables (response variables), two indicator plant species were used: the bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e) ecotype \"Sangre toro\" and rainfed rice variety Fedearroz 2020 (\u003cem\u003eOryza sativa\u003c/em\u003e). Each mixture was evaluated using independent experimental units, with a total of 28 replicates per crop species distributed across the 14 formulations, resulting in 56 experimental units in total.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental design generated using a simplex\u0026ndash;lattice mixture design.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eRUN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eBLOCK\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eComponents\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChlorella vulgaris\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eScenedesmus\u003c/b\u003e \u003cb\u003esp.\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eArthrospira platensis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSubstrate and plant material\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe substrate used for this test to grow indicator plants was obtained from a local commercial supplier and consisted of a 70%\u0026ndash;30% (w/w) mixture of AFM-1 Germination Mix and medium-coarse vermiculite, providing a defined supply of macro- and micronutrients, including nitrogen (as NO₃⁻ and NH₄⁺), phosphorus, potassium, calcium, magnesium, and trace elements such as Zn and Mn, along with a mineral matrix rich in silicates (SiO₂, Al₂O₃, MgO) that supports structural stability and nutrient retention.\u003c/p\u003e \u003cp\u003eSeeds of each plant species were purchased from Asofril and Fedearroz. The seeds were pre-germinated in a humid chamber in an incubator (Memmert, Germany) at 30\u0026deg;C for 4 days. Subsequently, two seeds per pot were sown in 20-ounce pots, and 10 days later, seedlings were thinned to one per pot by selecting the best-developed plant. The plants were grown under controlled conditions in a greenhouse (relative humidity: 82\u0026ndash;87%; temperature: 22\u0026ndash;25\u0026deg;C), and the substrate moisture was maintained between 50% and 60% of its maximum moisture retention capacity. They were fed weekly with Hoagland's nutrient solution (Hoagland and Arnon).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePreparation and application of mixtures\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe liquid suspension biomass of each microalga was used to formulate the different mixtures (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each pot received two applications of 5 mL of the corresponding mixture. This contained: \u003cem\u003eA. platensis\u003c/em\u003e: 1 \u0026times;\u003csup\u003e10⁵\u003c/sup\u003ecells/mL, \u003cem\u003eScenedesmus sp\u003c/em\u003e.: 3.3 \u0026times;\u003csup\u003e10⁸\u003c/sup\u003ecells/mL, and \u003cem\u003eChlorella vulgaris\u003c/em\u003e: 8.3 \u0026times;\u003csup\u003e10⁸\u003c/sup\u003ecells/mL. The first application was made 10 days after sowing, and the second 15 days after the first application.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMeasurement of biometric variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e\"Sangre toro\" bean\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhen the plants reached the pre-flowering stage, 40 days after sowing, at the time when the flower bud was observed, the following biometric variables were determined: Plant height: measured as the distance between the base of the stem and the last meristem, diameter of the third true leaf: a measurement was taken longitudinally of the terminal leaflet of the compound leaf with a millimeter ruler, Stem diameter: measured with a caliper (MP Tools) in the middle third. Total biomass (TBB): The aerial and root parts of each plant were harvested, transferred to paper bags, and dehydrated at 72\u0026deg;C in a dehydrator (Innova) until a constant weight was obtained. To harvest the aerial part, the stem was cut flush with the substrate. To harvest the root, the substrate was removed from the pot and washed several times with distilled water to remove excess substrate. The sum of the aerial biomass and root biomass was recorded as total dry biomass.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFedearroz 2020 upland rice variety\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhen the plants reached the final primordium stage, 60 days after sowing, the following biometric variables were determined: total biomass (TBT), measured as for bean plants, transferred to paper bags, and dehydrated at 72\u0026deg;C until a constant weight was obtained. The sum of the above-ground biomass and root biomass was recorded as total dry biomass.\u003c/p\u003e \u003cp\u003e \u003cb\u003eValidation of the mixture\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo validate the optimal microalgae mixture and its biostimulant effects, a second experiment was conducted in a greenhouse using a completely randomized design with a single-factor arrangement of treatments. The factor evaluated was the microalgae dose with two levels or treatments: i) (+) microalgae, the plants received 5mL of the optimal microalgae mixture; ii) control, the plants did not receive microalgae. The treatments were applied to the same indicator plants used for the mixture design, and the same procedures were followed. Ten replicates were used for each plant species. Before performing the analysis of variance (ANOVA) to test the hypothesis that microalgae exert biostimulant effects on \"Sangre toro\" bean and rice plants, the assumptions of normality of residuals and homogeneity of variances were evaluated. Normality and homoscedasticity were assessed using the Shapiro Wilk W test and Levene\u0026rsquo;s test, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eParameters such as sum of squares, mean square, degrees of freedom, F values, p values, and coefficients of determination (R2) were obtained from an analysis of variance (ANOVA) generated by the statistical software STATGRAPHICS Centurion XIX. The statistical significance of the models was determined at the 5% significance level (α\u0026thinsp;=\u0026thinsp;0.05). To determine the statistical significance of the model, the F-ratio (R/r) was used at the 95% significance level. The adjusted (R2-adj) and predicted (R2-pred) coefficients of determination were used to compare the different models and select an appropriate model for each response. Response surface graphs generated from the selected models were used to study the interactive effects of the components (microalgae) on the responses. The objective of this statistical approach was to identify the microalgae mixture that maximized the biometric variables studied. The optimal mixture was validated, and biostimulant effects were investigated using a one-way analysis of variance in STATGRAPHICS Centurion XIX.\u003c/p\u003e"},{"header":"2. RESULTS","content":"\u003cp\u003eThe evaluated microalgal mixture, composed of \u003cem\u003eScenedesmus\u003c/em\u003e sp., \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e, and \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e, was observed using inverted phase-contrast microscopy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eA. platensis\u003c/em\u003e exhibited a larger cell size compared to the other species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e\"Sangre toro\" bean\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModel adjustment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe P values (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as those for R2 and adjusted R2, indicate that the cubic model is the most appropriate for all variables evaluated. R2 indicates that the adjusted cubic model explains 89.1%, 90.7%, 87.4%, and 75.2% of the variability observed in height, TDB, leaf diameter, and stem diameter of bean plants, respectively (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a statistically significant relationship between height and the components of microalgae (\u003cem\u003eC. vulgaris, Scenedesmus\u003c/em\u003e sp., \u003cem\u003eand A. platensis\u003c/em\u003e). This same relationship holds for the other biometric variables, and it is concluded, with 95% confidence, that the selected models are suitable for predicting responses (Table S2).\u003c/p\u003e \u003cp\u003eThe equations of the fitted cubic models for each variable are presented below:\u003c/p\u003e \u003cp\u003eHeight\u0026thinsp;=\u0026thinsp;21,0348*A\u0026thinsp;+\u0026thinsp;21,9996*B\u0026thinsp;+\u0026thinsp;23,8939*C\u0026thinsp;+\u0026thinsp;80,7764*A*B\u0026thinsp;+\u0026thinsp;18,7662*A*C\u0026thinsp;+\u0026thinsp;1,99614*B*C\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;179,816*A*B*C\u0026thinsp;\u0026minus;\u0026thinsp;73,2456*A*B*(A-B)\u0026thinsp;+\u0026thinsp;14,2545*A*C*(A-C)\u0026thinsp;\u0026minus;\u0026thinsp;26,9122*B*C*(B-C)\u003c/p\u003e \u003cp\u003eTotal dry biomass (TDB)\u0026thinsp;=\u0026thinsp;2,58419*A\u0026thinsp;+\u0026thinsp;2,10807*B\u0026thinsp;+\u0026thinsp;2,2791*C\u0026thinsp;+\u0026thinsp;6,8726*A*B\u0026thinsp;\u0026minus;\u0026thinsp;0,0826338*A*C\u003c/p\u003e \u003cp\u003e+\u0026thinsp;0,863115*B*C\u0026thinsp;\u0026minus;\u0026thinsp;8,46163*A*B*C\u0026thinsp;\u0026minus;\u0026thinsp;12,701*A*B*(A-B)\u0026thinsp;+\u0026thinsp;0,288499*A*C*(A- C)\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;2,83246*B*C*(B-C)\u003c/p\u003e \u003cp\u003eLeaf diameter\u0026thinsp;=\u0026thinsp;4,04995A\u0026thinsp;+\u0026thinsp;5,61771*B\u0026thinsp;+\u0026thinsp;3,8961C\u0026thinsp;+\u0026thinsp;5,87292*A*B\u0026thinsp;+\u0026thinsp;5,47076*A*C\u0026thinsp;+\u0026thinsp;6,49605*B*C\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;29,3196*A*B*C\u0026thinsp;+\u0026thinsp;8,54348*A*B*(A-B)\u0026thinsp;\u0026minus;\u0026thinsp;1,60295*A*C*(A-C)\u0026thinsp;\u0026minus;\u0026thinsp;16,8289*B*C*(B-C)\u003c/p\u003e \u003cp\u003eStem diameter\u0026thinsp;=\u0026thinsp;0,303654*A\u0026thinsp;+\u0026thinsp;0,290751*B\u0026thinsp;+\u0026thinsp;0,309927*C\u0026thinsp;+\u0026thinsp;0,283101*A*B\u0026thinsp;+\u0026thinsp;0,0148424*A*C\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0,0406976*B*C\u0026thinsp;\u0026minus;\u0026thinsp;0,509315*A*B*C\u0026thinsp;\u0026minus;\u0026thinsp;0,399111*A*B*(A-B)\u0026thinsp;+\u0026thinsp;0,01597*A*C*(A-C)\u003c/p\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0,158668*B*C*(B-C)\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;\u003cem\u003eChlorella vulgaris\u003c/em\u003e\u003c/p\u003e \u003cp\u003eB\u0026thinsp;=\u0026thinsp;\u003cem\u003eScenedesmus\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003eC\u0026thinsp;=\u0026thinsp;\u003cem\u003eArthrospira platensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eResponse surface evaluation - optimization\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe combination of microalgae that maximizes each variable, except leaf diameter, includes \u003cem\u003eScenedesmus\u003c/em\u003e sp. in a higher proportion and excludes \u003cem\u003eArthrospira platensis\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For leaf diameter, the combination that maximizes its value contains \u003cem\u003eArthrospira platensis\u003c/em\u003e in a higher proportion and does not include \u003cem\u003eChlorella vulgaris.\u003c/em\u003e This behavior is shown graphically in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003c/p\u003e \u003cp\u003ewhich illustrates the predicted response as a function of the microalgal composition (\u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e, \u003cem\u003eScenedesmus\u003c/em\u003e sp., \u003cem\u003eand A\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e.) The height of the surface represents the value of the variable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA combination of components that maximizes the response variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eResponses\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOptimum value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCombination of components (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChlorella vulgaris\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eScenedesmus\u003c/b\u003e \u003cb\u003esp.\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eArthrospira platensis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.0578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.338713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.77515E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.76111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.298251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.02883E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.19041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49336E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.298951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.701049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.387765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.315072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.684928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45122E-9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMultiple response optimization\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMultiple response optimization allowed the identification of optimal factor combinations based on a desirability function ranging from 0 to 1, where values approaching 1 indicate optimal system performance, while values close to 0 denote that one or more responses fall outside acceptable limits.\u003c/p\u003e \u003cp\u003eUsing the desirability function, it was found that the mixture containing 31.6% \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e, 68.4% \u003cem\u003eScenedesmus\u003c/em\u003e sp., and 0% \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e was optimal for stimulating the biometric variables evaluated. With this mixture, a desirability of 0.89 was achieved, which is very close to 1, indicating that the configuration yields favorable results across all responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The height, TDB, leaf diameter, and stem diameter of bean plants treated with the optimal mixture were predicted to be 45.03 cm, 4.75 g, 6.74 cm, and 0.38 cm, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eValidation of the optimal microalgae mixture and evaluation of its biostimulant effect\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor all variables, both tests yielded P-values\u0026thinsp;\u0026ge;\u0026thinsp;0.05, indicating that residuals were normally distributed and variances were homogeneous. For plant height, a Box Cox transformation was applied to satisfy these assumptions. The experimental data for height, TDB, leaf diameter, and stem diameter of plants treated with the optimal mixture were 46.417 cm/plant, 4.80 g/plant, 6.807 cm, and 0.398 cm, respectively. The proximity of the experimental data to the predicted values indicates that the mixture design algorithm accurately estimated the results.\u003c/p\u003e \u003cp\u003eThe results of the analysis of variance (ANOVA) indicate a significant biostimulant effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of microalgae on all biometric variables evaluated. When microalgae were applied, increases of 14.8%, 13.1%, 12.5%, and 11.8% were achieved for height, TDB, leaf diameter, and stem diameter, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe biostimulant effect of microalgae was visible in plant height, above-ground biomass, and root biomass (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRice\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModel fit\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll models, except the special cubic model, were statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at a 95% confidence level. The cubic model was selected as the most appropriate for total dry biomass (TDB) because, in addition to being statistically significant, it represents the most comprehensive model and exhibited the highest R\u0026sup2; and adjusted R\u0026sup2; values.\u003c/p\u003e \u003cp\u003eThe cubic model explains 82.3% of the variability observed in TDB. There is a significant relationship between TDB and the components or microalgae (\u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e, \u003cem\u003eScenedesmus\u003c/em\u003e sp., and \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e). It is concluded, with 95% confidence, that the selected model is adequate for predicting TDB.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of the full model estimated for total dry biomass (TDB)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSource\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSum of Squares\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMean Square\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF-Ratio\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-Value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eR-Squared (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eAdj. R-Squared (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73,1272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73,1272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,00188929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,00188929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,3962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,0320554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,0160277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0376105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e43,75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuadratic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,0148422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,00494739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0301638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e71,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e63,82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial Cubic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,000274455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,000274455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,5952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0306859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e62,55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCubic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,00677863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,00225954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,0310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0266281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e71,80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,0120539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,000709054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73,1951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe significance of the fitted cubic model was assessed using analysis of variance (ANOVA) (Table S3). The P-value obtained for total dry biomass (TDB) (P\u0026thinsp;=\u0026thinsp;0.0089) was lower than the significance level (α\u0026thinsp;=\u0026thinsp;0.05), indicating a significant relationship between TDB and the mixture components (\u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e, \u003cem\u003eScenedesmus\u003c/em\u003e sp., and \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e). The lack-of-fit test was performed to evaluate the adequacy of the selected model. As the lack-of-fit P-value exceeded the significance threshold (α\u0026thinsp;=\u0026thinsp;0.05), the cubic model was considered adequate to describe and predict TDB at a 95% confidence level.\u003c/p\u003e \u003cp\u003eThe fitted cubic model equation for total dry biomass (TDB) is presented below:\u003c/p\u003e \u003cp\u003eTotal dry biomass (TDB)\u0026thinsp;=\u0026thinsp;1,57502*A\u0026thinsp;+\u0026thinsp;1,54706*B\u0026thinsp;+\u0026thinsp;1,64797*C\u0026thinsp;\u0026minus;\u0026thinsp;0,0670144*A*B\u0026thinsp;+\u0026thinsp;0,197615*A*C\u0026thinsp;+\u0026thinsp;0,225018*B*C\u0026thinsp;+\u0026thinsp;0,299066*A*B*C\u0026thinsp;\u0026minus;\u0026thinsp;0,383184*A*B*(A-B)\u0026thinsp;+\u0026thinsp;0,0595245 *A*C*(A- C)\u0026thinsp;+\u0026thinsp;0,263834*B*C*(B-C)\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;\u003cem\u003eChlorella vulgaris\u003c/em\u003e\u003c/p\u003e \u003cp\u003eB\u0026thinsp;=\u0026thinsp;\u003cem\u003eScenedesmus\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003eC\u0026thinsp;=\u0026thinsp;\u003cem\u003eArthrospira platensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eResponse surface - optimization\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe microalgae mixture that maximizes TDB (1.66 g/plant) includes \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e in the highest proportion (62.3%), followed by \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e (37.7%) and does not include \u003cem\u003eScenedesmus\u003c/em\u003e sp. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eValidation of the optimal microalgae mixture and evaluation of its biostimulant effect\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe experimental data for TDB of plants treated with the optimal mixture yielded an average of 1.502 g/plant. The proximity of the experimental data to the predicted data indicates that the mixture design algorithm accurately estimated the results.\u003c/p\u003e \u003cp\u003eFor all variables, both tests yielded P-values\u0026thinsp;\u0026ge;\u0026thinsp;0.05, indicating that residuals were normally distributed and variances were homogeneous.\u003c/p\u003e \u003cp\u003eThe biostimulant effect of microalgae on rainfed rice plants was tested with an ANOVA. The P value of \u0026lt;\u0026thinsp;0.05 indicates that microalgae have a significant biostimulant effect on TDB. When microalgae were applied to plants, TDB increased by 12.2% (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eChanges in above-ground and root biomass due to the application of microalgae were visibly detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eA central contribution of this study is the demonstration that microalgal formulations should not be approached as universal inputs, but rather as crop-specific systems requiring tailored optimization. Although previous studies have demonstrated that microalgae can enhance plant growth, nutrient uptake, and soil fertility through multiple mechanisms, including the production of phytohormones and the stimulation of soil microbial communities (Parmar et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), these approaches have largely been empirical and have not accounted for interactions among species or crop-dependent responses. The contrasting optimal formulations observed for bean and rice clearly indicate that the effectiveness of microalgal consortia is strongly dependent on plant species, supporting a paradigm shift from generic biofertilizers toward rationally designed, crop-targeted bioinputs. The results of this study provide novel evidence supporting the use of quantified microalgal mixtures as biostimulants, highlighting that plant responses are species-specific and depend on the composition of the microalgal consortium. Microalgae are considered to exert primarily biostimulant effects, with potential indirect nutritional contributions through enhanced nutrient availability.\u003c/p\u003e \u003cp\u003eMicroalgae may exert direct effects on soil microbial communities, which are key regulators of nutrient cycling and bioavailability (Sun et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). These interactions are particularly relevant within the rhizosphere, the narrow soil zone adjacent to the root surface, where intense biochemical exchanges between plants and microorganisms modulate nutrient dynamics and microbial mediated processes such as mineralization and nutrient mobilization. In this context, microalgal applications can influence both the structure and function of the rhizosphere microbiome, thereby indirectly affecting plant growth and nutrient uptake (Jose et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Unlike most research, this study identifies specific combinations of \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e, \u003cem\u003eScenedesmus\u003c/em\u003e sp., \u003cem\u003eand A\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e that maximize biometric variables in a legume (\u003cem\u003ePhaseolus vulgaris L.\u003c/em\u003e) and a cereal (\u003cem\u003eOryza sativa\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eThe effect of microalgae is not limited to a direct influence on plant physiology. Microbial communities associated with the species-specific rhizosphere can utilize microalgal biomass and exudates as carbon sources, thereby altering microbial structure and activity. The stimulatory and beneficial effects of microalgae on soil microorganisms have been widely reported, often described as synergistic interactions. Through these interactions, microalgae may enhance microbial mediated processes such as nutrient mineralization and mobilization. Ultimately, it is the soil microbiota that governs nutrient dynamics and availability to plants, thereby indirectly modulating plant growth and performance (Ng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eThe microalgae mixtures exerted biostimulant effects, with different responses among species. This difference, depending on the crop, is consistent with the literature, highlighting that the efficacy of microalgal bioproducts depends both on the plant species and the composition of the microalgal mixture, which may be associated with variations in the physiological sensitivity of each species to the metabolites released by microalgae. (Ronga et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Previous studies have documented that \u003cem\u003eChlorella\u003c/em\u003e and \u003cem\u003eScenedesmus\u003c/em\u003e produce and release phytohormones, such as auxins and cytokinins, as well as other bioactive compounds that stimulate germination, cell elongation, and root development, which may explain the increase in biometric variables in beans (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) observed in this study (Parmar et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, studies with \u003cem\u003eScenedesmus\u003c/em\u003e grown in enriched media showed increases in above ground and below ground biomass in crops such as pak choi, supporting the ability of this genus of microalgae to act as a biofertilizer (\u0026Aacute;lvarez-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). \u003cem\u003eA. platensis\u003c/em\u003e is characterized for its elevated nitrogen content and the production of bioactive compounds such as polysaccharides and pigments, which may enhance plant metabolism and stress tolerance. Furthermore, its role in the optimal formulation identified for rice (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) is consistent with previous studies reporting that cyanobacteria can release key micronutrients (Herrera and Echeverri \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and bioactive compounds that promote plant nutrition and efficiency (El-Shazoly et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt has been observed that the application of microalgae can increase the availability of elements such as iron and zinc in the soil, which is associated with greater photosynthetic efficiency and tissue development (Sun et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This is relevant for crops such as rice, where balanced nutrition in the early stages is crucial for final yield (Shankar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The benefits observed in this study are also consistent with evidence that microalgae can improve soil structure, increase organic matter and promote positive interactions with soil microbiota. Such interactions can improve nutrient availability to plants and reduce dependence on synthetic fertilizers, thereby supporting sustainable agricultural practices (Gurau et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlants were grown under nutrient-sufficient conditions using Hoagland\u0026rsquo;s solution, indicating that the observed effects are primarily attributable to biostimulant activity rather than nutrient supply; however, microalgae are also known to contribute to plant nutrition through the release of nutrients and organic matter, suggesting a potential biofertilizing role under nutrient-limited conditions.\u003c/p\u003e \u003cp\u003eSome authors highlight that applications of microalgal biostimulants have demonstrated positive effects on a wide range of crops. For example, extracts and live cultures of \u003cem\u003eChlorella sorokiniana\u003c/em\u003e significantly improved nutrition and root development in wheat, with these effects being attributed to both nutrient release and phytohormone production (Chovancek et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, other reviews have noted that bioactive compounds in microalgae, such as amino acids and polysaccharides, can mitigate the effects of abiotic stress and improve nutrient use efficiency, a key aspect for agricultural systems under environmental stress (Brito-Lopez et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite these positive effects, which are consistent with the literature, the diversity of responses observed between beans and rice indicates that there is no single microalgal formulation that works optimally for all crops. This is consistent with reviews showing that different genera and species of microalgae can have variable effects due to differences in their metabolomic profiles and the ways they interact with plants and soil (Farid et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Herrera et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom an agronomic perspective, the integration of microalgae into management plans can contribute to sustainable agriculture by reducing the use of chemical fertilizers, improving soil fertility, and promoting biogeochemical processes favorable to plant nutrition, coinciding with recent circular economy proposals that seek to harness the potential of these microorganisms in nutrient recovery and the improvement of sustainable cropping systems (Hu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although the biostimulant effects are well documented under controlled conditions, field applications still require further research to validate their effects under real agronomic conditions and across various cropping systems (Herrera et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This is particularly relevant for promoting the technological adoption of microalgal formulations and ensuring their effectiveness in a variety of climates, soils, and management practices.\u003c/p\u003e \u003cp\u003eThese findings confirm that there is no universal microalgal formulation and reinforce the need to design specific bio-inputs according to crop physiology, integrating mechanisms associated with nutrient supply, organic carbon, and soil microbiota stimulation, in line with current approaches to sustainable agriculture.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study demonstrates that plant responses to microalgal formulations are species-dependent. In the case of the bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e) ecotype \"Sangre toro\" the mixture that optimized biometric variables consisted of 31.6% \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e and 68.4% \u003cem\u003eScenedesmus\u003c/em\u003e sp., without the inclusion of \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e. In contrast, for rainfed rice, the optimal formulation corresponded to a mixture composed of 62.3% \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eplatensis\u003c/em\u003e and 37.7% \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e, without the participation of \u003cem\u003eScenedesmus\u003c/em\u003e sp., which shows different physiological requirements between the two plant species.\u003c/p\u003e \u003cp\u003eThe microalgal mixtures evaluated had a positive biostimulant effect on bean and rice plants, as reflected in improvements in the biometric variables analyzed. This effect can be attributed to a combination of multiple mechanisms, including the supply of essential inorganic nutrients, the incorporation of organic carbon and bioactive metabolites, and the stimulation of soil microbiota, which favors nutrient availability and plant physiological processes. The results suggest that microalgae are a viable alternative for integration into the agronomic management plans of different crops, with the potential to reduce the use of inorganic fertilizers and promote more sustainable plant growth. However, the variability observed across plant species underscores the need to validate these formulations in other crops and under different conditions to broaden their applicability and optimize their agricultural use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Universidad de Antioquia and Minciencias for their support of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarlos Lopera: Methodology. Madelen Giraldo: Methodology, Investigation, Formal analysis and Conceptualization. Natalia Herrera: Methodology, Investigation, Formal analysis and Conceptualization. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Minciencias (Colombia) (COD 1115-914-91810, Ct 111-2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets that were generated and analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e\u0026Aacute;lvarez-Gonz\u0026aacute;lez, A., Serrano, L., Gorchs, G., and Uggetti, E. (2025) Exploring the biostimulant potential of Scenedesmus sp. grown in wastewater: impacts on plant growth and photosynthetic activity of lettuce. \u003cem\u003eChemosphere\u003c/em\u003e, 382.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArora, P. K., Tripathi, S., Omar, R. A., Chauhan, P., Sinhal, V. K., Singh, A., Srivastava, A., Garg, S. K., \u003cem\u003eet al.\u003c/em\u003e (2024) Next-generation fertilizers: the impact of bionanofertilizers on sustainable agriculture. \u003cem\u003eMicrob. Cell Fact.\u003c/em\u003e, 23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrito-Lopez, C., Van Der Wielen, N., Barbosa, M., and Karlova, R. (2025) Plant growth-promoting microbes and microalgae-based biostimulants: Sustainable strategy for agriculture and abiotic stress resilience. \u003cem\u003ePhilosophical Transactions of the Royal Society B: Biological Sciences\u003c/em\u003e, 380.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChabili, A., Minaoui, F., Hakkoum, Z., Douma, M., Meddich, A., and Loudiki, M. (2024) A Comprehensive Review of Microalgae and Cyanobacteria-Based Biostimulants for Agriculture Uses. \u003cem\u003ePlants\u003c/em\u003e, 13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChovancek, E., Poque, S., Bayram, E., Borhan, E., Jokel, M., Rantanen, I. M., Haznedaroglu, B. Z., Himanen, K., \u003cem\u003eet al.\u003c/em\u003e (2025) Stepwise processing of Chlorella sorokiniana confers plant biostimulant that reduces mineral fertilizer requirements. \u003cem\u003eBioresour. Technol.\u003c/em\u003e, 418.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Shazoly, R. M., Yousef, S., Hifney, A. F., and Abdel-Wahab, D. A. (2025) Effectiveness of Algae as a Low-Cost Alternative Input to Stimulate Sesamum Indicum L. Growth and Productivity for Sustainable Purposes. \u003cem\u003eJ. Soil Sci. Plant Nutr.\u003c/em\u003e, 25, 8006\u0026ndash;8025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarid, R., Mutale-joan, C., Redouane, B., Mernissi Najib, E., Abderahime, A., Laila, S., and El Arroussi, H. (2019) Effect of Microalgae Polysaccharides on Biochemical and Metabolomics Pathways Related to Plant Defense in Solanum lycopersicum. \u003cem\u003eAppl. Biochem. Biotechnol.\u003c/em\u003e, 188, 225\u0026ndash;240.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGon\u0026ccedil;alves, J., Freitas, J., Fernandes, I., and Silva, P. (2023) Microalgae as Biofertilizers: A Sustainable Way to Improve Soil Fertility and Plant Growth. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e, 15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurau, S., Imran, M., and Ray, R. L. (2025) Algae: A cutting-edge solution for enhancing soil health and accelerating carbon sequestration \u0026ndash; A review. \u003cem\u003eEnviron. Technol. Innov.\u003c/em\u003e, 37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrera, N. and Echeverri, F. (2021) Evidence of quorum sensing in cyanobacteria by homoserine lactones: The origin of blooms. \u003cem\u003eWater (Switzerland)\u003c/em\u003e, 13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrera, N., Puentes-Escobar, T. C., \u0026Aacute;lvarez-Pulido, J. A., Sanchez, R., Restrepo, J., and Echeverri, L. F. (2025) Effect of microalgae and a cyanobacteria mixture on germinative activity, growth, and cannabinoid production in Cannabis sativa plants. \u003cem\u003eJ. Appl. Phycol.\u003c/em\u003e, 37, 4207\u0026ndash;4217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoagland, D. R. and Arnon, D. I. The Water-Culture Method for Growing Plants without Soil.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoque, M. M., Iannelli, V., Padula, F., Radice, R. P., Saha, B. K., Martelli, G., Scopa, A., and Drosos, M. (2025) Microalgae: Green Engines for Achieving Carbon Sequestration, Circular Economy, and Environmental Sustainability\u0026mdash;A Review Based on Last Ten Years of Research. \u003cem\u003eBioengineering\u003c/em\u003e, 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, L., Zhou, Y., Gong, Y., Luo, Y., Liu, X., and Jiang, Z. (2025) Microalgal proteins for a circular bioeconomy: Nutritional, material, and chemical valorization. \u003cem\u003eBioresour. Technol.\u003c/em\u003e, 436.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJose, S., Renuka, N., Ratha, S. K., Kumari, S., and Bux, F. (2024) Bioprospecting of microalgae from agricultural fields and developing consortia for sustainable agriculture. \u003cem\u003eAlgal Res.\u003c/em\u003e, 78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg, Z. Y., Ajeng, A. A., Cheah, W. Y., Ng, E. P., Abdullah, R., and Ling, T. C. (2024) Towards circular economy: Potential of microalgae \u0026ndash; bacterial-based biofertilizer on plants. \u003cem\u003eJ. Environ. Manage.\u003c/em\u003e, 349.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsorio-Reyes, J. G., Valenzuela-Amaro, H. M., Piza\u0026ntilde;a-Aranda, J. J. P., Ram\u0026iacute;rez-Gamboa, D., Mel\u0026eacute;ndez-S\u0026aacute;nchez, E. R., L\u0026oacute;pez-Arellanes, M. E., Casta\u0026ntilde;eda-Antonio, M. D., Coronado-Apodaca, K. G., \u003cem\u003eet al.\u003c/em\u003e (2023) Microalgae-Based Biotechnology as Alternative Biofertilizers for Soil Enhancement and Carbon Footprint Reduction: Advantages and Implications. \u003cem\u003eMar. Drugs\u003c/em\u003e, 21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParmar, P., Kumar, R., Neha, Y., and Srivatsan, V. (2023) Microalgae as next generation plant growth additives: Functions, applications, challenges and circular bioeconomy based solutions. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e, 14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrashar, D., Singh, A., Dhama, V., Singh, P. K., Kumar, M., Kumar, S., Pandey, D., and Verma, G. (2025) Effect of Organic and Inorganic Fertilizers on Crop Yield and Soil Fertility: A Comprehensive Review. \u003cem\u003eJournal of Experimental Agriculture International\u003c/em\u003e, 47, 16\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenganathan, P., Gaysina, L. A., Holgu\u0026iacute;n-Pe\u0026ntilde;a, R. J., Sainz-Hern\u0026aacute;ndez, J. C., Ortega-Garc\u0026iacute;a, J., and Rueda-Puente, E. O. (2024) Phycoremediated Microalgae and Cyanobacteria Biomass as Biofertilizer for Sustainable Agriculture: A Holistic Biorefinery Approach to Promote Circular Bioeconomy. \u003cem\u003eBiomass (Switzerland)\u003c/em\u003e, 4, 1047\u0026ndash;1077.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRonga, D., Biazzi, E., Parati, K., Carminati, D., Carminati, E., and Tava, A. (2019) Microalgal biostimulants and biofertilisers in crop productions. \u003cem\u003eAgronomy\u003c/em\u003e, 9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShankar, T., Malik, G. C., Banerjee, M., Dutta, S., Maitra, S., Praharaj, S., Sairam, M., Kumar, D. S., \u003cem\u003eet al.\u003c/em\u003e (2021) Productivity and nutrient balance of an intensive rice\u0026ndash;rice cropping system are influenced by different nutrient management in the red and lateritic belt of West Bengal, India. \u003cem\u003ePlants\u003c/em\u003e, 10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, Z., Liu, X., Ugya, A. Y., Liu, H., Sun, L., and Luo, G. (2026) Microalgae and cyanobacteria as a tool for agricultural sustainability: a review of biofertilizer and biostimulant potential. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e, 16.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chlorella vulgaris, Scenedesmus sp., Arthrospira platensis, biostimulant, microalgal consortia, crop-specific","lastPublishedDoi":"10.21203/rs.3.rs-9238333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9238333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroalgae have emerged as promising biostimulants for sustainable agriculture; however, the rational design of microalgal consortia tailored to specific crops remains poorly explored. In this study, a mixture design approach was applied to develop optimized microalgal formulations based on \u003cem\u003eChlorella vulgaris\u003c/em\u003e, \u003cem\u003eScenedesmus sp\u003c/em\u003e., and \u003cem\u003eArthrospira platensis\u003c/em\u003e, using bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L., ecotype \u0026lsquo;Sangre Toro\u0026rsquo;) and rainfed rice (\u003cem\u003eOryza sativa\u003c/em\u003e L., cv. \u0026lsquo;Fedearroz 2020\u0026rsquo;) as model crops. A simplex lattice design combined with response surface methodology and desirability analysis enabled the identification of optimal species combinations for each crop. The cubic model showed the best fit (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with high predictive capacity and non-significant lack of fit. Optimal formulations differed markedly between crops, revealing species-specific responses: In beans, the optimal formulation consisted of 31.6% \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e and 68.4% \u003cem\u003eScenedesmus\u003c/em\u003e sp., whereas in rainfed rice the best mixture included 62.3% A. platensis and 37.7% \u003cem\u003eC\u003c/em\u003e. \u003cem\u003evulgaris\u003c/em\u003e. These findings demonstrate that microalgal formulations should be designed as crop-specific systems rather than universal formulations. This study provides a quantitative framework for the rational development of tailored microalgal bioinputs, contributing to more efficient and sustainable agricultural practices.\u003c/p\u003e","manuscriptTitle":"Mixture design of microalgal consortia reveals crop-specific biostimulant formulations for bean and rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 16:30:40","doi":"10.21203/rs.3.rs-9238333/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe3dae68-532e-4b7a-a3a9-530964a3e86d","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-03T10:46:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T05:13:57+00:00","index":20,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T10:55:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 16:30:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9238333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9238333","identity":"rs-9238333","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}