1. Introduction
In recent years, the yellow mealworm,
Tenebrio molitor L. (Coleoptera: Tenebrionidae) has been considered as a potential source of animal protein in feeds for fish [
1,
2,
3,
4,
5,
6] and livestock [
7,
8,
9,
10,
11,
12,
13,
14]. An increasing number of companies have been founded every year since 2013 that focus on insect mass production as animal feed [
15]. One of the most important aspects of mass production involves the formulation of inexpensive yet effective diets that maximize biomass productivity over time. Recent research has focused on the potential use of agricultural by-products as insect food to reduce production costs of insect biomass [
16,
17,
18,
19]. However, current studies on
T. molitor have focused on the effects of single ingredients on biological and food utilization parameters. No attempts have been made to evaluate combinations of multiple by-products as ingredients with the aim to develop diets for
T. molitor. Self-selection studies incorporating by-products have been used to develop complete diets for the house cricket,
Acheta domesticus L. [
20].
Developing adequate diet formulations for insects using multiple undefined (oligidic) ingredients is a complex procedure and can take multiple years of research, particularly in insect species with long life cycles such as
T. molitor. Optimal diets can be obtained from multiple oligidic ingredients by allowing insects to select the optimal ratios of each ingredient in a multiple-choice experimental setting. This method is known as self-selection and was first proposed by Waldbauer and Friedman (1991) [
21]. The objectives of this study were to (1) determine the ingredients with the highest potential for formulation of insect diets using the self-selection method, (2) establish the optimal macro-nutrient intake ratios of
T. molitor based on the self-selected intake of 20 ingredients, and (3) explore the impact of intake of macro-nutrients, neutral detergent fiber (NDF), phytosterol, and minerals including Fe, Mg, Ca, Zn, Cu, and Mn on the biomass gain, food assimilation and efficiency of conversion of ingested food (ECI).
3. Results
The means of consumption of each of the ingredients within each of the combination treatments are presented in
Table 2. The relative consumption of each ingredient within combination treatments is illustrated as percentages in
Figure 2. The ingredients that were consumed in higher proportion were dry potato in treatment 8 (41.01%); crude rice bran in treatments 3, 4, 5, 6, and 7 (40.47%, 34.87%, 30.37%, 32.27% and 33.18%, respectively); wheat bran in treatments 1 and 9 (30.49% and 37.1%, respectively); and corn dry distiller’s grain with solubles (DDGS)in treatment 2 (34.63%). The least consumed ingredients were rice hulls in treatments 1 and 2 (0.31% and 0.13%, respectively); coffee chaff in treatment 8 (1.48%); peanut hulls in treatments 3, 6 and 7 (1.66%, 2.77%, and 3.19%, respectively); soybean meal in treatment 9 (2.06%); olive meal in treatment 4 (2.27%); and sunflower meal in treatment 5 (2.58%) (
Figure 2). In general, highly consumed ingredients had a high carbohydrate content. The ingredients consumed in low percentages generally contained high amounts of fiber at the expense of other nutrients, such as rice hulls, coffee chaff and peanut hulls or have a combination of high fiber and high protein contents like meals of olive, soybean and sunflower.
Despite the great diversity observed in the relative consumption of ingredients between treatments, the intake ratios of macro nutrients (lipid + protein + carbohydrate = 1) tended to converge close to a set of ranges between 0.03 to 0.16 of lipid, 0.21 to 0.25 of protein and 0.62 to 0.74 of carbohydrate (
Table 3,
Figure 3) with overall means of 0.1 ± 0.04, 0.24 ± 0.04, and 0.66 ± 0.06 for lipid, protein, and carbohydrate, respectively. The only exception was treatment 2 which showed significantly higher protein (0.36) (
F = 379.1; df 8, 81;
p < 0.0001) and lower carbohydrate (0.55) (
F = 460.8; df 8, 81;
p < 0.0001) intake ratio than all the other treatments (
Table 3) outlying visibly in the graph of
Figure 3. However, the rest of the treatments showed some significant differences among them in the macro nutrient intake ratios (
F = 304, 379.1, and 460.8 for lipid, protein and carbohydrate, respectively; df 8, 81;
p < 0.0001) that were less obvious in
Figure 3 (
Table 3).
These differences in the intake ratios of macro nutrients resulted in significant differences in group live biomass gain (
F = 10.15; df = 8, 81;
p < 0.0001), overall dry-weight food consumption (
F = 10.91; df = 8, 81;
p < 0.0001), food assimilation (
F = 29.13; df = 8, 81;
p < 0.0001), and ECI (
F = 28.41; df = 8, 81;
p < 0.0001) among choice treatments (
Figure 4). The highest live biomass gain was observed in treatment 5 (7.3 ± 0.28 g), followed by treatments 1 (6.91 ± 0.41 g) and 7 (6.83 ± 0.74 g). The highest assimilation was observed in treatment 8 (55.25 ± 2.03%) followed by treatment 5 (50.86 ± 1.86%). The highest ECI was observed in treatment 5 (9.87 ± 0.45%) followed by treatment 8 (9.48 ± 0.64%). In general, the best performing treatments were 5, 8, and 1 (
Figure 4). Treatment 2 was the worst performer among choice treatments, showing the lowest live biomass gain (5.45 ± 0.49 g), the lowest food assimilation (39.19 ± 2.11%), and the lowest ECI (7.18 ± 0.5%) (
Figure 4). The low performance of larvae groups of treatment 2 may be associated with the significant deviations in macronutrient intake ratios observed in this treatment (
Figure 3). The optimal macro-nutrient ratios for
T. molitor may be closer to those observed in average for treatments 1, 5, and 8, which were 0.06 ± 0.03, 0.23 ± 0.01, and 0.71 ± 0.03 for lipid, protein, and carbohydrate, respectively.
Live biomass gain was significantly impacted by efficiency of food conversion (ECI) (R2 = 0.53; F = 100.74; df = 1, 88; p < 0.0001) and food assimilation (R2 = 0.13; F = 13.35; df = 1, 88; p = 0.0004) in a positive way. Consumption of some ingredients have significant effects on biomass gain, food assimilation and ECI. For instance, consumption of potato had a significant positive effect on food assimilation (β = 0.01; R2 = 0.57; F = 116.64; df = 1, 88; p < 0.0001), but consumption of corn DDGS had the opposite effect on food assimilation (β = −0.007; R2 = 0.21; F = 23.0; df = 1, 88; p < 0.0001).
Ingredients that had a mean consumption percentage of at least 10% in any given choice treatment were considered relevant ingredients (RI). Relevant ingredients included potato, cabbage, wheat bran, crude rice bran, defatted rice bran, corn DDGS, spent brewery DG, canola meal, and sunflower meal. Multiple regression analysis indicated that the consumption of all the relevant ingredients had a significant positive effect on live biomass gain (R2 = 0.7; F = 20.75; df = 9, 80; p < 0.0001). Only consumption of potato, cabbage, rice bran whole, and spent brewery DG had a significant positive effect on food assimilation (partial F Ratios = 49.47, 12.17, 6.62, and 8.55; df = 9, 80; p < 0.0001, = 0.0008, = 0.0119, and = 0.0045, respectively). Significant negative effects on food assimilation were observed with consumption of canola and sunflower meals (partial F Ratios = 6.39 and 4.49; df 9, 80; p = 0.0135 and 0.0371, respectively). The resulting optimized model for assimilation (after stepwise) agreed with the full model analysis including the 6 variables that showed significant effects on food assimilation (R2 = 0.75; p = 42.6; df = 6, 83; p < 0.0001). In the full model (9 independent variables) the efficiency of food conversion (ECI) was only affected significantly by the consumption of potato, and this effect was positive (partial F Ratio = 13.31; df 9, 80; p = 0.0005). However, when this model was analyzed with the stepwise method, an optimized 3-variable model resulted that included potato, rice bran, and canola meal all affecting ECI significantly and positively (R2 = 0.64; p = 49.9; df = 3, 88; p < 0.0001). Significant quadratic effects on live biomass gain were observed from consumption of potato (β1 = 0.146, β2 = −0.023; R2 = 0.18; F = 9.47; df = 2, 87; p = 0.0002), corn DDGS (β1 = 0.077, β2 = −034; R2 = 0.38; F = 26.2; df = 2, 87; p < 0.0001) and spent brewery DG (β1 = −0.105, β2 = −0.024; R2 = 0.27; F = 16.02; df 2, 87; p < 0.0001). Biomass gain was maximized at an intermediate level of consumption of these three ingredients.
Intake ratios of some nutrients had a significant impact on food assimilation and efficiency of food conversion (ECI). The optimal multiple regression models obtained after stepwise and backwards elimination procedures consisted of only 2 dependent variables explaining food assimilation and 4 variables explaining ECI. Models are valid only within the ranges observed for these variables, presented in
Table 4. Food assimilation was impacted significantly by carbohydrate and neural detergent fiber (
R2 = 0.73;
F = 117.93; df = 2, 87;
p < 0.0001) (
Table 5). These two variables also impacted ECI in addition to the minerals Mg and Mn (
R2 = 0.73;
F = 57.48; df = 4, 85;
p < 0.0001) (
Table 6).
4. Discussion
It is apparent by the results presented in this study that
T. molitor larvae tend to balance their intake of macro nutrients by selecting among a variety of ingredients when feeding. This agrees with previous studies confirming the ability of
T. molitor to self-select for optimal macro-nutrient intake ratios [
41,
42,
43,
44]. The intake ratios of macro nutrients by
T. molitor larvae converged within a narrow range of values among eight of the nine combination treatments of different food ingredients. Treatment 2 was the exception showing excess intake of protein and reduced intake of carbohydrate. Deviation of macro-nutrient intake ratios observed in treatment 2 coincided with a low performance of growth and food utilization of the larvae grown in this treatment. The reason for the deviations in macro-nutrient intake ratios observed in treatment 2 may have been the absence of an additional ingredient with low protein content besides defatted rice bran. There was an unusually high consumption of corn DDGS (34.66 ± 4.13%) and spent brewery DG (29.75 ± 1.46%) in this treatment resulting in a combined mean consumption of 64.41% of these two ingredients from the mean total food consumption in treatment 2. In the other three treatments where these two ingredients were present together (treatments 5, 6, and 7), their combined consumption did not exceed 26% of the total food consumed. Additionally, consumption of corn DDGS and spent brewery DG did not exceed 21.5% when presented alone within the food choices (treatments 1, 3, 4, and 8). The high consumption of these two distilled grain ingredients in treatment 2 is itself an anomaly and may have been driven by the need for lipid intake, which was extremely low (lower than 3.6%) in the rest of the ingredients presented in treatment 2: two defatted ingredients (canola meal and rice bran defatted), alfalfa pellets, the hulls of peanut and rice, and coffee chaff [
26,
27,
33,
34]. The lipid content of corn DDGS and spent brewery DG is reported to be higher than 8% [
26,
27,
30,
34,
35,
37].
The optimal macro nutrient ratios for
T. molitor may be those observed in the best performing treatments (1, 5, and 8): 0.06 ± 0.03 (max 0.12 min 0.03), 0.23 ± 0.01 (max 0.25 min 0.2), and 0.71 ± 0.03 (max 0.75 min 0.65) for lipid, protein, and carbohydrate, respectively. Rho and Lee (2016) [
45] determined that an equal ratio of protein and carbohydrate was the best for
T. molitor based on adult fecundity and longevity. However, this study is not comparable with ours because both studies were done on different life stages and measured different life cycle parameters.
Ingredients that were considered relevant based on relative consumption percentage (over 10%) included potato, cabbage, wheat bran, crude rice bran, defatted rice bran, corn DDGS, spent brewery DG, canola meal, and sunflower meal. Multiple regression analyses of consumption of relevant ingredients versus live biomass gain showed significant positive effects. These results can be interpreted as evidence that such ingredients are suitable for inclusion in diets for
T. molitor, especially when biomass production is one of the main priorities. However, consumption of relevant ingredients did not always have positive effects on food assimilation. For instance, canola and sunflower meals had significant negative effects on assimilation. Food assimilation is not necessarily critical for biomass production when the food provided has a low cost, as in this case where agricultural by-products are used. Analysis of nutrient intake ratios showed that intake of fiber negatively affects food assimilation. This may explain the negative effects of canola and sunflower meals on assimilation, since both meals have a relatively high fiber content. Food conversion efficiency (ECI) was impacted positively by the consumption of potato, rice bran and canola meal. Because both food assimilation and ECI significantly impacted biomass gain in a positive way, we may consider the ingredients that impact both parameters in a positive way as highly suitable for inclusion in insect diets. Potato, rice bran, cabbage, spent brewery DG, and canola meal seem to be highly suitable as ingredients in
T. molitor diets, but defatted rice bran, corn DDGS, and sunflower meal are promising if provided in the correct proportions. Wheat bran, potato, and cabbage have been used and are currently used regularly in
T. molitor diets for mass production [
46]. The rest of the ingredients are not currently used in commercial production, but some studies have assessed their potential, such as on spent brewery DG [
32].
Macro-nutrient intake ratios were an important factor affecting live biomass gain, food assimilation and ECI. Macro-nutrient ratios were optimal for
T. molitor within ranges of 0.06 ± 0.03, 0.23 ± 0.01, and 0.71 ± 0.03 for lipid, protein, and carbohydrate, respectively. Nutrient intake analyses showed that the intake of carbohydrate significantly and positively impacted live biomass gain, food assimilation and ECI. The intake of protein did not impact these three parameters within the ranges observed in this study. It appears that protein intake was strongly regulated by self-selection in most treatments, with the only exception of treatment 2. Other studies have reported that high protein intake reduce development time and pupal size [
42] and increased adult longevity and fecundity [
43]. In this study the impact of high intake of protein on biomass productivity and food utilization was negative. High intake levels of fiber also had a negative impact on food assimilation and ECI. Li et al. (2015) [
47] reported that the optimal intake levels of crude fiber for
T. molitor is within a range of 5 to 10%. In this study we did not compare intakes of crude fiber, but the self-selected percentages of ND fiber were between 22.52 ± 0.62% in treatment 5 and 34.94 ± 0.94% in treatment 9.
5. Conclusions
The macro-nutrient intake ratios resulting from ingredient self-selection by T. molitor fell within narrow margins: Lipid intake was between 0.12 and 0.03, protein between 0.25 and 0.2, and carbohydrate between 0.75 and 0.65. Deviations from these ranges of macro nutrient intake ratios resulted in a diminished performance in larval growth and food utilization.
The relevant ingredients, based on their relative consumption by T. molitor larvae included potato, cabbage, wheat bran, crude rice bran, defatted rice bran, corn DDGS, spent brewery DG, canola meal, and sunflower meal. Consumption of relevant ingredients significantly affected live biomass production in a positive way in T. molitor larvae.
Both food assimilation and efficiency of conversion of ingested food were positively impacted by ingestion of carbohydrate and negatively impacted by ingestion of fiber. Ingredients that enhanced both of these parameters had relatively high carbohydrate and low fiber content such as potato. However, levels of carbohydrate and fiber should not depart from the self-selected ranges observed, because excessive or deficient intake of those nutrients can have a detrimental impact on growth and food utilization in T. molitor larvae.