# Increase of _Trichoderma harzianum_ Production Using Mixed-Level Fractional Factorial Design
## 5 Discussion  
In this study, a group of different organic materials available in the region was investigated to determine its viability for the growth and multiplication of _Trichoderma harzianum_ and to determine the most suitable organic materials for generating a high amount of conidial biomass with prolonged viability. _Trichoderma harzianum_ production generates added value and has been proven to be a good approach through which multiple low-cost growth media can be used to formulate bioproducts. The environment can be the main beneficiary, given that environmental pollution can be reduced by promoting the reduction of chemical products used for treating plant diseases [49]. Finally, many more in-depth studies like the present are required to select substrates that provide large, stable, and efficient microbial populations. The NOBA method has proven effective in dealing with mixed levels designs that require a large number of experiments. It is easy to apply, does not require complex programming, and significantly reduces the number of runs. One can use the current real data set as a basis and select the appropriate levels based on the existing literature and then apply the NOBA method to obtain two benefits, optimization of the response variable and minimization of costs. Our results demonstrated the effectiveness of the NOBA method as an optimization tool; it was possible to double the production of spores, a result above (10\({}^{6}\)) conidia/g was obtained, which is the minimum requirement for the application of biopesticides in agriculture. Different optimization models and methodologies are available like one factor at a time (OFAT) until complex statistical designs such as two-level fractional factorial designs (FFD), the Box-Behnken design (BBD), Taguchi Design (TD) or Placket-Burmann design (PB). OFAT is a traditional screening method. It consists of selecting a starting point, or baseline of the levels, for each factor, and then successively varying each factor in its range [10]. FFD are experiments where many factors are considered with two levels; the objective is to identify those factors that have large effects through a fraction of the factorial design [10]. BBD is a second-order multivariate technique based on a partial three-level design; it allows the estimation of parameters in a quadratic model and the evaluation of the lack of fit of the model [50]. TD emphasizes the appropriate selection of control factor levels to minimize factor-borne variability of noise and, in this way, generate a robust product or process [10]. PB is one of the most applied screening methods for the recognition of the most significant factors among a large number of variables [51]. The NOBA method presents several advantages in relation to the mentioned methodologies. Considerably reduces the number of runs, therefore, resources and time. The fraction obtained comes from a mixed-level factorial design; you can choose as many factorsor levels as needed and still obtain a balanced orthogonal/semi-orthogonal economic size fraction. Orthogonality makes the effects of the factors independent; therefore, each column provides different information to the design. The balance allows a uniform distribution of information for each level and causes the intersection column to become orthogonal to the main effects. The NOBA method considers the entire factorial design to obtain the fraction, whereas BBD does not. BBD is a spherical response surface, which includes a central point and midpoints between the corners; therefore, the extreme values of the factors are not included in the runs. The NOBA method does consider the extreme values of the variables, and therefore the effects can be estimated more efficiently since it considers the entire region.