Model-Based Experimental Design to Optimize Moroccan Sweet Pepper Cultivation in Nursery: A Comprehensive Study of Phytophthora capsici Management Using Experimental Factorial Design

Abstract

This research addresses the challenge of managing Phytophthora capsici in nurseries, emphasizing an integrated approach for optimal pepper health employing model-based experimental design. The study employs a factorial design and randomized block experiment to manipulate factors such as pathogen inoculum, Trichoderma treatment, and indoor environmental conditions. The factorial design provides insights into the intricate interactions that influence Phytophthora capsici dynamics. By identifying pathogen inoculum thresholds varying the amount [0; 500,000; 1000,000 (CFU/ml)], optimizing Trichoderma concentrations giving the range of [0; 1000; 10,000,000 (CFU/ml)], and assessing the impact of environmental conditions, we can enhance our understanding of biocontrol efficacy. The results offer valuable insights for the development of effective and tailored disease management strategies. The study’s implications extend to efficient resource utilization identifying optimal environmental conditions; T° > 20°C, RH% < 80%, and biocontrol treatment optimal concentration of 10,000,000 CFU/ml for the development of targeted disease management strategies. This research highlights the importance of experimental factorial design in understanding the complexity of Phytophthora communities applying a more flexible structured approach; blocking factors in 3x2 and 2x2x2x2, to improve the accuracy of treatment effect estimation, the results have significant implications for future research in Phytophthora root rot diseases control and monitoring.

1. Introduction

1.1 Pepper production, and disease management impact

Moroccan sweet peppers, along with other solanaceous vegetable crops, hold a pivotal position as primary target products in horticulture due to their significant dominance in volume to meet global market demands [1]. However, high-yield production potential is challenged by unsustainable management practices and diseases including root rot disease caused by Phytophthora capsici, which threatens overall productivity. Phytophthora capsici is a significant threat to a variety of horticultural plants, including the Moroccan sweet pepper cultivation. The impact of Phytophthora capsici on pepper is influenced by various factors such as inoculum prevalence during and after infection, and environmental conditions. This monocyclic disease is initiated by triggering oospore germination under optimal conditions, such as heavy rains and high soil moisture. The zoospore cycle is critical for infection at different stages of pepper growth, causing seed rotting and damping-off during pre-emergence and contributing to symptoms such as root rot and plant death at later stages [2, 3].

Successful adaptation of root rot disease control in off-season-pepper-nurseries cultivation is particularly focused on environmental conditions control that is monitoring high humidity, and temperature and optimizing soil drainage. Implementing transplanting technology management and monitoring environmental systems in nurseries improves the health of pepper seedlings. Emphasizing the importance of such practices is crucial, not least in addressing the demands of the competitive pepper market. In the integrated management of root rot disease, implementing preventive measures can be more effective creating an inhospitable environment for the inoculum source of Phytophthora. One valuable strategy to enhance disease management is the incorporation of beneficial biological control agents (BCAs) in seedling and during transplantation. In nursery settings, it is equally important to take a preventive approach by monitoring and regulating environmental factors [4, 5].

1.2 Importance of model-based experimental design in Phytophthora management in nurseries

To prevent disease development, the identification of potential stressors in the nursery is paramount. To systematically study the effects of multiple factors, a well-structured design of experimental units is essential. Within this experimental design, specific combinations are assigned to screen for Phytophthora capsici and to select Trichoderma isolates with potential biocontrol effect [6, 7].

In the context of biocontrol agents, it is important to consider real-world applicability and empirical approaches and field experiments are crucial in this regard [4, 8]. Still, the integration of model-based with pre-defined criteria into the experimental approach, including the combination of multiple factors as well as their interactions, provides a powerful strategy for understanding and selecting effective biocontrol agents. This integration of correlated structures for Trichoderma treatment and other environmental factors enables accurate estimation of plot-to-plot variability [8, 9, 10].

Phytophthora capsici poses formidable challenges due to its complex biology and diverse life cycle. The importance of experimental factorial design in disease control strategies is crucial. Here’s an exploration of why this approach is critical [6, 9, 10, 11, 12, 13].

1. Experimental factorial design allows for the systematic manipulation of multiple factors simultaneously, providing a nuanced view of biocontrol dynamics.

2. Factorial experiments facilitate the quantification of pathogen inoculum thresholds, which is critical for identifying critical thresholds associated with reduced Phytophthora incidence.

3. It enables researchers to optimize biocontrol measures by assessing the effectiveness of different Trichoderma concentrations.

4. This allows researchers to study how environmental conditions like temperature and humidity affect the efficacy of biocontrol agents to support the integration of new monitoring tools into biocontrol trials and contribute to the evaluation of their efficacy.

5. It allows researchers to test multiple variables simultaneously, optimizing the use of resources.

6. It also contributes to the development of customized disease management strategies that are adapted to specific conditions.

In summary, factorial design is a key tool for reducing the incidence of Phytophthora capsici. Its ability to dissect complex interactions, account for variability and optimize biocontrol measures positions it as a cornerstone in the development of effective and tailored strategies for managing this challenging pathogen. Indeed, the application of factorial design in horticulture recognizes practical constraints and optimizes resource use in root rot disease management.

To investigate the effectiveness of different Phytophthora management strategies, a randomized block design was implemented in a controlled nursery environment. Also, we investigate the complex dynamics of Phytophthora inoculum in nurseries using a factorial design. The study aims to elucidate interactions between multiple factors, including pathogen inoculum levels, Trichoderma concentrations, and environmental conditions, to optimize root rot disease management [8, 9, 10, 14].

2. Material and method

2.1 Obtaining culture of Phytophthora

A culture of Phytophthora capsici was obtained by isolating Phytophthora mycelium from pepper roots exhibiting blight disease at the IAV CHA greenhouse in Ait-melloul, Morocco. To achieve this, pepper roots displaying clear symptoms of rot were identified and collected. Surface baiting was performed on the selected roots using two different agar media: corn meal agar (CMA) and a modified CMA selective medium (CMAm), following the method proposed by Tsao and Ocana in 1969. The roots were disinfected by immersing root fragments, ranging from 5 to 10 mm, in a 10% solution of sodium hypochlorite. After disinfection, the fragments were rinsed for 1 minute in distilled deionized water to remove any residual disinfectant. Finally, the disinfected root fragments were dried on sterile filter paper to remove excess moisture. The dried root fragments were placed on the surface of corn meal agar medium (CMA) and incubated in the dark at a constant temperature of 21°C for 5 to 7 days [15].

2.2 Preparation of Phytophthora capsici zoospores inoculum

To prepare the inoculum of Phytophthora capsici, consisting of sporangia and zoospores, the method outlined by Chen and Zentmyer [16] was meticulously followed. The mycelium culture was grown in a V8 agar medium and then carefully cut into 5 to 7 cm pieces under aseptic conditions to serve as the source for inoculum preparation.

To ensure cleanliness, all cut pieces underwent two aseptic washes using sterilized deionized water. The washed mycelium pieces were then placed in sterile glass petri dishes and flooded with 10 ml of sterilized deionized water. The washed mycelium pieces were then placed in sterile glass petri dishes and flooded with 10 ml of sterilized deionized water. Non-sterile soil was added to the water in the petri dishes. The petri dishes were prepared and then incubated at 22°C for 3 days [16].

2.3 Assessment of zoospores release

To evaluate sporangia production after 3 days of incubation, sporangia were observed microscopically. Zoospore release was induced by subjecting the glass petri dishes containing mycelium pieces to a thermal shock. The dishes were placed at 4°C for 30 to 60 minutes, followed by a return to the incubator at 22°C for 15 to 20 minutes. The method of thermal shock, described by Chen and Zentmyer (1970), Dhingra and Sinclair (1985), Guo and KO (1993), and Nielsen et al. (2006), facilitated the release of zoospores [16, 17, 18, 19].

2.4 Obtaining Trichoderma subculture

The standard procedure for soil sampling was followed to collect soil samples near Argania roots in the Argania forest, Agadir province, Morocco [20]. The dilutions (ranging from 10⁻² to 10⁻⁵) were spread on a potato dextrose agar (PDA) medium supplemented with ascorbic acid and incubated at room temperature for 1 to 2 weeks. The collected samples were then suspended and diluted. Colonies resembling Trichoderma spp. were isolated from the plated dilutions and transferred to fresh PDA plates. For subculture preparation, 10 μl of sterile saline spore solution was added to each colony. Each spore suspension was then inoculated onto the surface of a fresh 3% malt extract agar (MEA) plate. The subculture rounds were incubated for 2 to 4 days at 28°C. The purified subcultures were grown and maintained on MEA or PDA medium. They were identified at the genus level based on their morphological and cultural characteristics, as described in previous work [21].

2.5 Pepper root-dipping inoculation

For each cultivar, the roots were dipped in a spore suspension of Trichoderma species. This study aims to examine the preparation of Phytophthora inoculum, inoculation procedures, and disease assessment, with and without Trichoderma spp. treatments. Both Trichoderma and Phytophthora were in suspension and diluted to a fixed concentration in sterile deionized water before inoculation. The root-dipping method was selected as the inoculation technique for this research to facilitate the rapid and direct percolation of Trichoderma spore suspensions into the root tissue of host plants. During transplanting, pepper seedling roots were immersed in fungal suspensions. Initially, the roots were dipped in a suspension containing 10⁸ conidia/ml of various Trichoderma spp. for 15 to 20 minutes. The roots were immersed in a suspension containing 5.10⁵ conidia/ml of Phytophthora zoospores for 3 to 5 minutes. This process was repeated as described in Ref. [22].

2.6 Disease incidence evaluation

Disease incidence was evaluated by quantifying the percentage of plant units displaying typical symptoms associated with each corresponding pathogen. The disease incidence percentage (DI%) was calculated by dividing the number of plant units with typical symptoms by the total number of plant units and multiplying by 100 (see Eq. (1)).

$$\mathit{DI}\%=\frac{\mathit{\text{Number of infected plant unit}}\mathrm{s}}{\mathit{\text{number of plant}}\mathrm{s}}\ast 100$$E1

DI% was evaluated in both above-ground and root units. Symptoms such as yellowing and necrosis in leaves, wilt and/or stunt in shoots, and rots and discoloration in stems were identified and measured. Furthermore, symptoms and signs retrieved from roots, crowns, stems, and leaves were documented for each pathogen. The study examined signs of pathogen components, such as mycelium and propagules, under microscopic observation [23, 24].

2.7 Randomized complete block

2.7.1 Experimental design

Pepper cultivars were grown for 4 weeks in 77 peat trays. Upon transplantation into 3 L pots, seedlings were successively inoculated with the pathogen and the biocontrol. The pots were filled with sterile substrate at a 3:1 w/w peat-to-sand ratio and fertilized using a composition of NPK and oligo-elements (20-20-20 hydrosoluble NPK plus magnesium, iron, and manganese) at a concentration of 250 mg/l.

The experimental design consisted of four randomized complete blocks, each with four replicates in every experimental unit [25]. Each experimental unit was composed of four pots (Figure 1).

Figure 1.

The experimental design consisted of four randomized complete blocks representing the first initial nine runs, for a total of 16 runs the same design is replicated four times.

2.8 Full factorial experimental design

The study employed a full factorial experimental design to investigate the relationships between Phytophthora incidence, biocontrol measures, and environmental monitoring in nursery settings. The methodology comprises two experiments, each focusing on specific factors that influence disease dynamics [8, 10, 14].

2.8.1 Experiment 1

In this first model-based factorial experiment, controlled variables were deliberately selected for both the pathogen and biocontrol agent. The aim was to investigate the direct correlation between overall DI% and the concentration of the pathogen inoculum, as well as the application of Trichoderma treatment. Each factor has three established levels.

The levels of pathogen inoculum concentration were set at 0, 500,000 and 1000,000 colony-forming units per milliliter (CFU/ml), and the levels of Trichoderma treatment were set at 0, 1000 and 10,000,000 (CFU/ml). This design enables a confident evaluation of the impact of different inoculum concentrations and Trichoderma treatments on the incidence of Phytophthora.

2.8.2 Experiment 2

The second experiment examines the complex interactions among four critical input factors: pathogen inoculum (Pi), biocontrol treatment (Tt), temperature (T°), and relative humidity (RH%) monitoring. A full factorial design is used to assess two levels for each factor, resulting in a comprehensive matrix of experimental conditions (see Figure 2). The pathogen inoculum concentration levels are 0 and 1000,000 (CFU/ml), while the Trichoderma treatment levels are 0 and 10,000,000 (CFU/ml). The temperature levels tested were 20 and 40°C, while the relative humidity levels were 30 and 80%.

Figure 2.

Blocking factorial in a 3x3 arrangement (a), with four repetitions across four blocks. And (b) blocking factorial in 2x2x2x2 configuration for the combination of all four factors (pathogen inoculum (Pi), biocontrol treatment (Tt), temperature (T°), and relative humidity (RH%) with only one replicate.

To ensure robustness, the first design was evaluated in a 3x3 arrangement, with four repetitions across four blocks. The second design employed a more intricate 2x2x2x2 configuration, featuring a single repetition. This design facilitated a thorough examination of both interaction and main effects (refer to Figure 2(a) and (b) in the result).

3. Results

3.1 Phytophthora capsici culture

Phytophthora capsici was confidently isolated and identified using a selective CMA medium. The white mycelium exhibited an alternate diffusible structure on CMA selective media, and microscopic observation revealed non-septate mycelium and characteristic lemon/olive-shaped sporangia structures. Sub-culturing the mycelium on PDA improved the visibility of the alternate structure, making it easier to obtain a pure culture of the pathogen. A culture-based approach successfully identified the fungi at the genus level, highlighting the significance of culture characteristics in identifying fungi based on mycelium and propagule morphology.

3.2 Phytophthora capsici zoospore

The artificial infection method was successful in identifying the pathogenicity of Phytophthora. Zoospores were effectively produced, as shown in Figure 3.

Figure 3.

Zoospores release from sporangia, magnification 400X.

Trichoderma subcultures were successfully developed on PDA, with colonies identified as Trichoderma spp. based on their distinct morphological characteristics, such as colony color. The colonies displayed a diverse range of colors, from yellowish to dark green. Pure cultures of Trichoderma isolates were successfully obtained after multiple rounds of sub-culturing.

3.3 Phytophthora treatment in randomized complete block

The randomized complete block experiment showed that Trichoderma spp. root-dipping treatments significantly reduced the incidence of rots in pepper (P = 0.000). The results are summarized in Table 1, which shows the treatments and their corresponding DI% values.

Treatments	DI%*
CTRL+	100% ± 0.0a
CTRL-	0.0% ± 0.2d
Trichoderma	6.3% ± 0.1a

Table 1.

DI% for pepper plant inoculated with Trichoderma.

CTRL+: Pepper plant infected with Phytophthora, and CTRL-: non-inoculated healthy plant. The means of four replicates (16 replicates) in four blocks are represented by the standard deviation ±. Significant differences were determined using Tukey’s test grouping at p = 0.05 and letters were assigned accordingly.

Disease incidence in pepper plants was recorded by observing brown to black rots in roots, crowns, or stems. The Trichoderma treatment resulted in a DI% of 6.3%, compared to DI% = 100% in CTRL+ and DI% = 0.0% in healthy plant CTRL- (Figure 4).

Figure 4.

CTRL-; healthy pepper with no pathogen or Trichoderma inoculation, CTRL+; pepper root infected with Phytophthora capsici inoculum; Trich; Trichoderma as biocontrol treatment against Phytophthora infection.

Trichoderma exhibits effectiveness in treating infected pepper plants in each block group within every treatment. However, the random assignment of treatments (Trichoderma) or healthy plants’ controls (CTRL-) within each block constrains the exploration of all conceivable combinations of factor levels to a limited set, such as in the control (CTRL-) where there is no inoculation at all and CTRL+ (a high level of pathogen amount). This design may fall short in capturing interaction effects between factors, as it does not ensure a balanced representation of all possible combinations.

3.4 Deducing Phytophthora treatment in a full factorial design

3.4.1 2-factorial regression analysis

In this factorial experiment, a comprehensive regression analysis was conducted to investigate the effects of Phytophthora inoculum and Trichoderma treatment on disease incidence (DI%). The experimental design included three levels of pathogen inoculum (Pi); 0, 500,000, and 1000,000 CFU/ml and three levels of Trichoderma treatment (Tt); 0, 1000, and 10,000,000. The regression model effectively captures and explains the complex relationship between the studied factors and disease incidence. The coefficient of determination (R-sq) was significantly high at 98.84%, indicating that the model effectively explained a substantial portion of the variability in DI%. Further supporting the model’s effectiveness, the adjusted R-squared (R-sq(adj)) stood at 98.50%.

The statistical significance of the model was confirmed through analysis of variance (ANOVA) (p < 0.001). All main effects and interaction terms were significant, indicating a substantial influence of both inoculum and treatment factors, as well as their interaction, on DI%.

Eq. (2)’s coefficient reveals the impact of the pathogen inoculum on DI%, showing a significant positive effect at higher inoculum levels (500,000 and 1000,000). Treatment with Trichoderma at levels 0 and 1000 had a positive influence on DI%, while treatment at level 10,000,000 had a notable negative impact on DI%.

$$\begin{array}{ll}\mathrm{39,369}-& 39,37\text{Pinoculum}\_0+18,24\text{Pinoculum}\_500000+21,13\text{Pinoculum}\_1000000+\\ & 18,30\text{Ttreatment}\_0+17,13\text{Ttreatment}\_1000-35,43\text{Ttreatment}\_10000000\end{array}$$E2

(refer to the supplementary document for the complete regression equation).

3.4.2 Main and interaction effect

The results in the main effect of factors combination provide a comprehensive understanding of the relationships between Phytophthora capsici inoculum as a factor influencing DI% and the effective performance of Trichoderma treatment as a biocontrol (Figure 5). The interaction terms further clarified the combined effects of pathogen inoculum and treatment on DI% (Figure 6).

Figure 5.

Main effect plot of the Phytophthora inoculum (Pinoculum) and Trichoderma biocontrol (Ttreatment) on DI% represented by a non-horizontal line with a higher DI% mean in 500,000 and 1000,000 CFU/ml for (Pinoculum) and 1000 and 0 CFU/ml for (Ttreatment). The slope inclination of each line shows the magnitude of the DI% response.

Figure 6.

The graph of interaction effect shows the combined effects of Phytophthora inoculum (Pinoculum) and Trichoderma treatment (Ttreatment) on DI% which is in harmony with the main effect plots result. Notably, different intercepts are observed at all levels, indicating different effects of Pinoculum and Ttreatment on DI%. The highest levels 500,000 and 1000,000 CFU/ml of Phytophthora inoculum (Pinoculum) are both associated with the highest mean of DI%. Whereas, the lowest levels of Trichoderma treatment (Ttreatment) 1000 and 0 CFU/ml are the ones associated with the highest mean of DI%.

3.4.3 Multifactorial (4-factor) design 2x2x2x2

The study demonstrated that the multifactorial design, characterized by a 2x2x2x2 configuration, effectively explains the variation in DI% with a remarkable R-squared value of 100.00%. The adjusted R-squared, which considers the number of predictors, remained high, affirming the model’s robustness.

The analysis of variance (ANOVA) for the general factorial regression model yielded significant results, highlighting the collective impact of factors such as Phytophthora inoculum (Pi), Trichoderma treatment (Tt), temperature (T°), and relative humidity (RH%) on the response variable (DI%). The observed variation indicates that the linear main effects of Pi, Tt, T°, and RH% are all significant (p < 0.05), signifying the individual influence of each factor on DI%.

The main effects plot shows that the magnitude of the highest and lowest mean for each factor is strongly high, as observed by the slope lines. The impact on the response variable (DI%) is more pronounced with steeper slopes. All pairs of factors (Pi*Tt, Pi*T°, Pi*RH%, Tt*T°, Tt*RH%, T°*RH%) show significant interactions, highlighting the combined effects when considering two factors simultaneously. The interaction plot illustrates how the joint influence of two factors varies (Figure 7).

Figure 7.

Interaction effect plot of every pair of factors (Pi*Tt, Pi*T°, Pi*RH%, Tt*T°, Tt*RH%, T°*RH%) with simultaneous influence on the variable response (DI%) with the main effect plot highlighting the magnitude of response (DI%) at different factor level.

Interpreting the slopes and patterns in Figure 7 provides valuable insights into the combined effects of factors influencing DI%. For optimal control of DI%, it is recommended to monitor the temperature at its highest (40°C) and maintain the relative humidity at its lowest (30%). Even at the most favorable temperature condition of 20°C for Phytophthora, maintaining a relative humidity of 30% minimizes DI%.

The study found significant three-way interactions (PiTtT°, PiTtRH%, PiT°RH%, TtT°RH%) and a significant four-way interaction (PiTtT°*RH%), highlighting the collective impact of all four factors when considered together. It is important to note that the influence is primarily attributed to specific combinations, as shown in the Pareto chart in Figure 8. The Pareto chart identifies the ‘vital few’ as portions A and B, which significantly repress DI%. The cumulative line, crossing 80% with A, B, C, and D, underscores the substantial contribution of these major combinations to the overall impact on DI% evolution.

Figure 8.

Patero chart of portion with factors’ combinations with significant impact on DI%, adjacent Table 2 represents the factors’ combinations with a succession (Pi*Tt*T°*RH%) representing the portion A, B, C, and D.

Portion	Factors’ combinations
A	0*10000000*20*30
	0*0*40*80
	1000000*10000000*40*30
	0*10000000*40*30
	0*10000000*40*80
	0*0*20*80
	0*10000000*20*80
	0*0*20*30
	0*0*40*30
B	1000000*0*40*80
	1000000*10000000*20*30
C	1000000*10000000*40*80
D	1000000*0*40*30

Table 2.

Factors’ combinations with a succession (Pi*Tt*T°*RH%) representing the portions A, B, C, and D.

3.4.4 Multifactorial regression equation analysis

To ensure more effective solutions through the representation of the relationship between different factors and the predicted (DI%) response, it is crucial to prioritize coefficients with the highest impact. The examination of the interaction reveals coefficients of −3.563 (Pi*Tt*T°*RH%; 1000,000*10,000,000*40*30) and 5.687 (Pi*Tt; 0*10,000,000), indicating that the influence of Phytophthora inoculum (Pi) and Trichoderma treatment (Tt) on DI% varies depending on the levels of other factors, such as temperature (T°) and relative humidity (RH%). However, caution is necessary when interpreting significant interactions. For instance, when examining the Pi*Tt interaction with coefficients of −5.687 (Pi*Tt; 0*0) and 3.563 (Pi*Tt*T°*RH%; 1000,000*0*40*30), it is essential to acknowledge that combinations of factors and their interactions can result in subtle effects.

Coefficients may not apply universally to every situation, and general trends should be interpreted with caution. As shown in Figure 9, the biocontrol effect at this level may interact with other conditions.

Figure 9.

Multivariate chart: Factors’ combinations and dynamic of significant impact on DI%.

4. Discussion-conclusions

This research highlights the importance of using experimental factorial designs to study Phytophthora communities in nurseries, especially when resources are limited and greenhouse environments are controlled. The study demonstrates the crucial role of experimental factorial design in effectively managing the complexities inherent in the Phytophthora population. To comprehensively understand the interactions and variations that influence disease dynamics, researchers manipulate multiple factors simultaneously. In real-world conditions, external variations can impact study outcomes. Therefore, the incorporation of multiple-blocking factors, such as pairs of columns and rows, proves instrumental in controlling variability. This enhancement in precision enables a more accurate estimation of treatment effects, which is crucial for drawing confident conclusions about the true impacts of our treatments [6, 8, 9].

The experimental units are grouped into rows in a randomized complete block design, as shown in Figure 1, based on their similar characteristics or conditions within the greenhouse. The treatments, including Trichoderma, CTRL+, and CTRL-, are randomized within each block and represented in columns. This means that any observed differences in DI% are more likely to be due to the treatments (Trichoderma, CTRL+, and CTRL-) rather than block-specific factors [8, 25].

Blocking in factorial design enhances the precision of treatment effect estimation by minimizing the impact of external variations, similar to a completely randomized block design. However, factorial designs explicitly address interactions between factors, providing a more nuanced understanding of how different elements combine to affect DI%. This is especially important when multiple factors are involved, making it more efficient in terms of resource utilization compared to a completely randomized block design.

In a two-factor factorial design, such as pathogen inoculation and Trichoderma treatment, the main effects of each factor and their interaction are simultaneously investigated to understand how they may work together to influence DI%. The correlation between Phytophthora inoculum levels (500,000 and 1000,000 CFU/ml) and DI% is strong. This has important implications for biocontrol strategies, as it suggests that the effectiveness of biocontrol measures may be limited at these elevated inoculum levels. Therefore, treatment concentrations may need to be reassessed at higher concentrations. It is worth noting that the potential failure of biocontrol in suppressing root rot disease becomes more prominent when targeting inoculum concentrations below 1000 CFU/ml.

In 2x2x2x2 factorial design, the systematic variation of conditions is extended to include additional factors such as temperature and relative humidity. Monitoring relative humidity at its lowest point (30%) is effective in minimizing DI%, particularly in the context of favorable temperature conditions (<20°C) for Phytophthora. This provides insights into how these climate variables may influence disease dynamics, along with pathogen and treatment effects. Moreover, similar to the two-factor scenario, blocks in this factorial configuration can be defined based on sources of variation that are not the primary focus of the study but might impact DI%. For example, different sections of the greenhouse could be considered, which is a crucial aspect for developing targeted management strategies [8, 9, 14].

4.1 Implications for disease management in nurseries

In summary, the systematic planning of experiments with model-based factorial design optimizes resource utilization in nurseries. This is particularly important as characterizing Phytophthora communities can be labor-intensive. By facilitating controlled variation of multiple factors influencing inoculation or infection methods, experimental conditions that closely mimic real-world scenarios within nurseries are created through factorial design, providing more realistic insights into Phytophthora dynamics. That is, factorial design enhances the reproducibility of experiments, which is a crucial aspect in nurseries where consistency is paramount [6, 8, 10].

Additionally, a structured approach guides the implementation of cost-effective strategies, particularly when leveraging newer technologies. On the other hand, effective nursery management requires consideration of various factors beyond the conventional focus on temperature and humidity. While these factors are undoubtedly important, a holistic approach to greenhouse performance demands attention to dynamic environmental changes, energy efficiency, comprehensive control systems, water management strategies, and disease prevention caused by Oomycetes and in our case Phytophthora. Greenhouse efficiency depends on an integrated approach that goes beyond simplistic factorial models. It is important to recognize the multifaceted nature of factors that influence protected vegetable crop health and its overall production [14].

In conclusion, this study emphasizes the crucial role of model-based experimental factorial design in understanding the complexities of Phytophthora communities. The insights gained from this structured approach make a significant contribution to disease management strategies in nurseries, demonstrating the potential for impactful and resource-efficient research in controlled environments. The implications of our findings are significant for future research in Phytophthora control and monitoring, as we have developed the study to establish and model the stage for such endeavors using factorial design.