Next Article in Journal
Comprehensive Analysis of Chloroplast Genomes in Leguminous Forage Species: Codon Usage, Phylogenetic Relationships, and Evolutionary Insights
Previous Article in Journal
The Half-Heading Stage May Represent the Optimal Harvest Time for the First Cut of Tall Wheatgrass
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 4
10.3390/agronomy15040764
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Biological Solutions for Higher Maize Yield and Reduced Stalk Damage Caused by the European Corn Borer, Ostrinia nubilalis (Hübner)

by
Filip Franeta
1,*,
Anja Đurić
1,
Dušan Dunđerski
1,
Dušan Stanisavljević
1,
Aleksandra Konjević
2,
Aleksandar Ivezić
3,
Tamara Popović
3 and
Željko Milovac
1
1
Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Serbia
2
Faculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovića 8, 21102 Novi Sad, Serbia
3
BioSense Institute, University of Novi Sad, 21000 Novi Sad, Serbia
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(4), 764; https://doi.org/10.3390/agronomy15040764
Submission received: 18 February 2025 / Revised: 13 March 2025 / Accepted: 19 March 2025 / Published: 21 March 2025
(This article belongs to the Section Pest and Disease Management)
Browse Figures
Versions Notes

Abstract

:
Maize (Zea mays L.) is one of the most important agricultural crops in the world; however, its production is often threatened by several harmful insects, one of which is the European corn borer, Ostrinia nubilalis (Hübner). This study aimed to examine the efficacy of several biological control methods against this pest. A randomized block design was used in the study, which included three treatments: parasitic wasps (Trichogramma brassicae), common green lacewings (Chrysoperla carnea), a combination of both agents and a control. The results showed that the treatment with T. brassicae wasps, as well as their combination with C. carnea predators, significantly improved the maize grain yield over the control treatment. The same combined treatment significantly improved the yield over treatments with only Trichogramma and only C. carnea predators, suggesting a higher efficacy of the combination of both agents in improving grain yield. Fewer tunnels and larvae in stalks, compared to other treatments, including the control, were also observed in this treatment. The reduced number of tunnels and larvae in stalks directly enabled plants to allocate more resources into grain development, which contributed to improved grain yield. In light of the growing focus on minimizing insecticide applications to mitigate environmental impacts, a combination of parasitic wasp and lacewings predator could be a suitable biological alternative to the use of chemical insecticides.

1. Introduction

Maize (Zea mays L.) represents a strategically significant agricultural crop and ranks first in production worldwide [1]. In Serbia, maize is the most economically significant agricultural crop, but its production encounters various challenges. One such challenge is the damage caused by harmful insects, particularly the European corn borer (ECB) Ostrinia nubilalis (Hübner, 1796), (Lepidoptera: Crambidae) [2]. The ECB has the potential to affect up to 60% of crops and cause yield losses ranging from 5% to 30% [3]. The larvae of this species can damage maize grains and burrow into maize stalks, causing physical damage, which can lead to stalk lodging, disrupting the flow of nutrients and water, and ultimately resulting in yield reductions [4,5]. In addition to this, ECB larvae create favorable conditions for the development of phytopathogenic and saprophytic fungi [6]. Species from the genera Aspergillus and Fusarium, which may develop on damaged kernels, further impact production by synthesizing mycotoxins that have serious negative effects on both human and animal health [7]. According to estimates, on a global level, 60% to 80% of food crops are contaminated with mycotoxins [8]. This represents a significant threat to food safety and public health, highlighting the urgent need for effective control strategies.
So far, maize protection strategies have relied on the use of insecticides, which significantly pollute the environment, are not selective, and have detrimental effects on human and animal health [9]. In this context, the European Union’s ‘Farm to Fork’ strategy aims to reduce the use and risk of chemical pesticides by 50%, and to cut the use of more toxic pesticides by 50% by 2030, stressing the importance of transitioning to sustainable agricultural practices [10]. Very often, the effectiveness of chemical insecticides demonstrates notable variability based on both the year of application and the specific type of insecticide used. In several trials in Croatia, it has been demonstrated that organophosphate (OP) insecticides and pyrethroids yielded moderate to good results in controlling ECB populations; conversely, imidacloprid was found to exhibit limited efficacy [11]. The same authors evaluated a combination of cypermethrin and chlorpyrifos ethyl in 2003 and 2004 and proved the effectiveness of the combination. However, if high temperatures occur, this may decrease the effectiveness of pyrethroids, making even insecticide control less efficient and difficult. Furthermore, most insecticides adversely affect the natural enemies in crops, even at low doses [12,13]. Additionally, considering that ECB larvae spend most of their life cycle inside the plant, chemical control poses a challenge, further highlighting the need for alternative control methods, such as biological control. Biological control is both environmentally sustainable and safe for human health, as it relies on the use of living organisms to manage pest insect populations [14]. The negative effects of insecticides and the vital role of natural enemies in maintaining ecological balance are firmly established within integrated pest management (IPM) practices. These practices effectively combine chemical and biological techniques adjusted for each crop, providing a sustainable approach to pest control.
A promising biological control method involves the use of parasitoid wasps from the genus Trichogramma (Hymenoptera: Trichogrammatidae). Female wasps use their ovipositors to make openings in the eggs of various species of Lepidoptera, including the ECB, where they lay their own eggs [15]. This process leads to the development of parasitic larvae within the eggs of the ECB, which eventually become recognizable by their distinctive black color. In a previous study [16], the parasitism rates varied between 63 and 83% in most of the experiments. These findings suggest that biological control can be an effective alternative in countries where insecticides are not commonly used to control this pest.
In addition to parasitoids, generalist predators from the family Chrysopidae (Neuroptera), such as lacewings, can also play a significant role in biological control. Lacewings are effective predators of numerous harmful insects, including the ECB [17]. Research has shown that these predators can target eggs and early larval stages of various lepidopteran pests [18,19], making them a promising option for biological control.
The aim of this research was to evaluate the effectiveness of different biological control strategies against the ECB in maize production. The study focused on the use of a parasitoid wasp species, Trichogramma brassicae (Bezdenko, 1968), and a predator, the common green lacewing Chrysoperla carnea (Stephens, 1836), as well as the possible advantage of combining these two biological agents to enhance pest management and improve crop protection.

2. Materials and Methods

To evaluate the efficacy of biological control methods, a field trial was set up in the experimental fields of the Institute of Field and Vegetable Crops at Rimski Šančevi in Novi Sad, Serbia (N 45°19′17.81″; E 19°50′1.857″). Sowing was carried out on 7 May 2024, using a Wintersteiger Plotking Planter (Wintersteiger Seedmech GmbH, Ried im Innkreis, Austria), which enabled precise and uniform seed distribution across the experimental plots. The hybrid NS 6000, which belongs to the 550 FAO maturity group, was used in the trial. Later sowing avoids the influence of the first ECB generation, since the seedlings are not attractive to ECB females for egg laying and are in ideal maturity stages (reproductive growth stage) for the egg laying of the second ECB generation. After sowing and prior to crop emergence, herbicide treatments were applied. These included Dual Gold 960EC (S-metolachlor 960 g/L) at a dose of 1.2 L ha−1 and Terbis (terbuthylazine 500 g/L) at 2 L ha−1. A corrective treatment followed, which included the application of Motivell Extra 6 OD (nicosulfuron 60 g/L) at 0.5 L ha−1, combined with Laudis (tembotrione 44 g/L) at 2.5 L ha−1. No insecticide treatments were applied to the trial.
In addition to chemical measures, inter-row cultivation was performed using a four-row cultivator, to improve the plant growth conditions and reduce weed competition. The trial was designed according to the principles of a completely randomized block design, consisting of four blocks that included three treatments and a control. Paths 2 m wide were established between the blocks. Each experimental plot (treatment area) measured 30 m × 6 m, with maize sown in eight rows, where the two central rows were treated.
The flight of the ECB was monitored using a commercial light trap, model “RO Agrobečej”. The trap was checked daily at 07:00, and the collected specimens were counted and sexed. The treatments were strategically timed to align with the peak flight of the second generation.
The following treatments were used in the trial:
T1—Control (no application of biological control agents)
T2—Application of parasitic wasps—T. brassicae
T3—Application of second-stage larvae of predatory lacewings—C. carnea
T4—Combination of parasitic wasps (T. brassicae) and predators (C. carnea)
The parasitic wasps were applied in the pupal stage, using specially designed capsules, while the predators were applied in the second larval stage in a commercially prepared formulation on 25 July 2024, targeting the second-generation eggs and early larval ECB stages. These products were acquired from Bioline AgroSciences Ltd. (Little Clacton, UK) In the treatments with Trichogramma wasps, four Tricholine capsules containing 2000 specimens of T. brassicae each were applied per treatment, placed on maize plants in the central row of the plot. In the treatments with lacewings, 500 L2 larvae of C. carnea (Chrysoline, Bioline AgroSciences Ltd.) were used, evenly distributed on the respective treatments, also in the central two rows of each plot. The treatments were repeated after seven days.
Assessments were conducted in early September, involving the dissection of 20 plants per treatment per replication. Each plant was cut in half, and the evaluations included the numbers of tunnels in stalks (NTS) and ears (NTE), tunnel lengths in stalks (TLS) and ears (TLE), and counts of surviving larvae in stems (NLS) and ears (NLE). The field trial was harvested on 24 September 2024, using a Wintersteiger Split Combine (Wintersteiger Seedmech GmbH, Ried im Innkreis, Austria), with the grain yield standardized to 14% moisture across all plots.

2.1. Weather Conditions

Temperature and precipitation data were collected from the nearest FieldClimate station (number 00000E89) throughout the growing season (May–September) of the experiment and compared to ideal conditions, defined as optimal daily temperatures between 16 °C and 22 °C and minimum monthly precipitation of 40–100 mm, depending on the month [20]. The observed temperatures were consistently higher than the ideal benchmarks, indicating suboptimal thermal conditions for maize growth. The analysis revealed temperature extremes and insufficient precipitation during critical growth phases, resulting in plant stress (Figure 1).

2.2. Statistical Analysis

The measured traits included the numbers of tunnels in ears and stalks (NTE and NTS, respectively), numbers of larvae in ears and stalks (NLE and NLS, respectively), tunnel lengths in centimeters in ears and stalks (TLE and TLS, respectively), and grain yields. Due to the weather conditions described in Section 2.1, many plants in some treatments were stunted and earless. To account for missing data from these earless plants, average trait values were calculated for each treatment and replication, ensuring consistency across all data points. This approach allowed the establishment of relationships between all measured traits without the impact of missing values.

2.2.1. Partial Least Squares Regression (PLSR)

Partial least squares regression (PLSR) was utilized as the first step to identify the most influential traits based on variable importance projection (VIP) scores greater than 0.8. In this analysis, the yield was used as the dependent variable, while the predictors included two factorial sets:
  • Full factorial interaction of traits from the ear (number of tunnels, number of larvae, and tunnel length).
  • Full factorial interaction of traits from the stalk (number of tunnels, number of larvae, and tunnel length).
The PLSR was performed with centered and scaled data, using the NIPALS algorithm. For model validation, we employed leave-one-out cross-validation. The root mean PRESS plot was used to determine the optimal number of components that minimized the PRESS statistics. Based on the PLSR results, traits with VIP > 0.8 were selected as the most relevant for further analyses. These selected traits were then used in a generalized regression model, with yield as the dependent variable and the selected traits and treatments as independent factors.

2.2.2. Comparison of Regression Models and Selection of Distribution

The generalized regression model was run using several response distributions, including normal, Cauchy, t-distribution (df = 5), exponential, gamma, Weibull and lognormal. The models were evaluated using Bayesian Information Criterion (BIC) and Generalized R2. Validation was performed using holdback validation, with holdback proportion of 0.2. The Best Subset method was applied to select the optimal number of predictors for the generalized regression model. Additionally, Tukey’s HSD test was implemented to compare the effects of different treatments on maize yield, identifying significant pairwise differences between the treatments.

2.2.3. Comparison of Treatments in Terms of Corn Borer Damage

A factor analysis was used for reduce the data dimensionality, using the varimax rotation method. The number of factors was chosen based on an eigenvalue > 1. Factors that were constructed on linear combinations of predictors were subsequentially used in a one-way ANOVA, to determine whether there was significant difference between treatments in terms of damage caused by ECB.

2.2.4. Descriptive Statistics

Descriptive statistics was used to evaluate the data quality and identify anomalies, ensuring consistent and reliable inferential analyses. This helped us build a strong foundation for our models and achieve more precise results with the analyzed traits and variables. Descriptive statistics has been used by many authors in their articles regarding corn borers [21,22,23].

2.2.5. Software and Tools

All statistical analyses were conducted using JMP Pro 17 (SAS Institute Inc., Cary, NC, USA).

3. Results

The experimental setup demonstrated that all treatments and their combinations significantly contributed to increased yield, except for the treatment with the predatory species C. carnea. Apart from the treatments, significant negative effects of the number of tunnels in the stalk (NTS) and the interaction of the numbers of tunnels and larvae in the stalk (NTS × NLS) on yield were found. Among all treatments, the combination of Trichogramma and Chrysoperla showed the lowest levels of stalk and ear damage, which likely contributed to achieving the highest yield in the experiment. It should be added that the precipitation levels were notably low in June, July, and August—three critical months for maize development. Insufficient rainfall during this period led to drought stress. Although the rainfall increased in September, it occurred too late to support grain development and filling (Figure 1). The described weather conditions may have contributed to an increased development of the ECB, resulting in a more pronounced yield reduction.

3.1. Descriptive Statistics

In this study, descriptive statistics were reported for the full dataset (combining all treatment groups) rather than stratified by treatment. This strategy provided an overview of the data and enabled us to check the data quality using the entire pool of observations. Verifying basic descriptive statistics for the whole dataset is as a crucial step to ensure the consistency and reliability of subsequent models [24]. Further in the analysis, we conducted detailed statistical comparisons between treatments for all traits, which evaluated the differences among treatments using appropriate statistical methods.
The number of larvae in the ear (NLE) had the highest coefficient of variation (CV) at 118.37%, followed by the yield and tunnel length in the ear (TLE), indicating substantial variability in these traits across treatments or replications (Table 1). As seen from Table 1, the average yield for the whole trial was just 1.97 t ha−1, with a maximum yield of just 3.60 t ha−1. The most likely reasons for this low yield were extremely high temperatures and a lack of precipitation during the critical growth period of maize (Figure 1), which affected pollination and the accumulation of grain dry matter. The maximum temperatures consistently exceeded 30 °C, peaking around 40 °C in July, August, and September. Such high temperatures likely caused heat stress, impairing pollen viability and disrupting pollination, particularly during the flowering period in July. This stress may explain the relatively low yields in the experiment, as temperatures above 32 °C increase the risk of pollen sterility [25]. The number of tunnels in the stalk (NTS) had the lowest CV, indicating greater consistency in this trait across treatments. The number of tunnels and tunnel length in the stalk (NTS and TLS) were generally higher than in the ear.

3.2. Initial Selection of Important Traits Impacting Yield Using PLSR

In this analysis, we included the examined traits in the PLSR as independent variables, with the yield as the dependent variable. Stalk and ear traits were used in the full factorial design, enabling the model to capture complex relationships that individual predictors might miss. The main idea behind this statistical analysis was to remove irrelevant traits or their interactions from further consideration, to reduce data multicollinearity and dimensionality. The root mean PRESS plot indicated that the optimal number of components needed to minimize the PRESS statistic was three (Supplementary Figure S1a), although the van der Voet T2 test showed no significant difference between two and three components (Supplementary Figure S1b). Therefore, the two-component model was selected for determination of the traits relevant for further analyses. These two factors explained 76.89% of X effects and 59.34% of Y responses.
The traits and their interaction that had VIP values below 0.8 are listed in Table 2. These traits included the number of larvae (ear); the interaction between the number of tunnels (ear) and the number of larvae (ear); the interaction between the number of tunnels (ear) and tunnel length (ear); the interaction between the number of larvae (ear) and tunnel length (ear); and the interaction involving the number of tunnels (ear), number of larvae (ear), and tunnel length (cm) (ear). The remaining traits had VIP values above 0.8 and their percent shares in factor 1 and factor 2 are shown in Supplementary Figure S2.

3.3. Final Selection of Important Traits Impacting Yield Using Generalized Regression Model

Based on the PLSR results, traits with VIP scores > 0.8 were selected as the most relevant predictors of yield. These traits were used as independent variables in the generalized regression model, with the yield as the dependent variable. Since we aimed to analyze the impacts of different treatments on the yield, we also included an independent variable for the treatment. To determine the best model, we employed the best subset estimation method along with holdback validation, using the Bayesian information criterion (BIC) and generalized R2 for evaluation. We concluded that the Cauchy distribution was the most appropriate for our data, as shown in Table 3. The Cauchy or Lorentz distribution is not commonly used in the context of yield estimations. In a previous review [26], the authors emphasized the challenge of modeling agricultural yields, given that they often deviate from normality, exhibiting skewness or heavy tails. The authors suggested that non-normal, heavy-tailed distributions (such as Cauchy distribution) are relevant for agricultural applications when traditional normality fails.
From the initial fourteen predictors (traits) included, we reduced their number to nine after the first stage of trait selection using the VIP filter method in the PLSR (Table 2). From nine, we further reduced the number to three significant predictors using a generalized regression model (Table 3). Those three significant predictors were the NTS, the interaction of NTS × NLS, and the treatment contrasts. The estimates for the NTS and NTS × NLS were negative, indicating a negative correlation between these traits and the yield. When comparing treatments, the generalized regression model showed significant differences between the combined treatment of Trichogramma + Chrysoperla versus Chrysoperla alone (1.234 t ha−1), Trichogramma + Chrysoperla combined versus the control treatment (0.994 t ha−1), and Trichogramma + Chrysoperla versus Trichogramma alone (0.707 t ha−1). These differences were confirmed by the Tukey test (Table 4). In addition to the combined Trichogramma + Chrysoperla treatment, a significantly higher yield was also obtained with Trichogramma compared to the control, while Chrysoperla alone did not show a significant difference from the control.

3.4. Factor and Discriminant Analyses Reveal Differences Between Treatments in Terms of Damage

To determine how the T4 treatment resulted in a significant increase in yield compared to the other treatments, a factor analysis was conducted to reduce the complexity of the data, which included numerous damage indicators. This analysis grouped the data into two main factors: ear damage and stalk damage. These factors collectively explained 60% of the variability in the data (Figure 2).
Subsequently, a one-way ANOVA was used to identify significant differences among treatments in terms of ear and stalk damage. The results showed that the p value was lower than 0.05 in terms of ear damage (Table 5). This significance means that the ear damage rates differed across the treatment group means. A further Tukey HSD test revealed that treatment T4 demonstrated the most effective results in reducing ear damage, achieving significantly better results than other treatments. With that said, the yield increase with T4 in comparison with the other treatments could have been the result of reduced ear damage.

4. Discussion

Agriculture is currently facing substantial challenges in terms of ecology and environmental sustainability. The detrimental effects of insecticide use on ecological systems and the essential role of natural predators in maintaining environmental balance have long been recognized [27]. Integrated pest management practiced in each crop aim to make both chemical and biological techniques compatible and synergistic [28]. However, with all techniques, pest monitoring is essential for determining the appropriate timing for insecticide treatments. Instead of concentrating exclusively on the intensity of the infestation, the monitoring process should focus on identifying the peak flight periods of lepidopteran pests and predicting when the first larvae will hatch [11]. The biological control of pests should be systematically applied, with continuous monitoring and careful timing of the release of biological agents, as implemented in our research.
Whether IPM or conventional methods are deployed for crop protection from insect pests, the use of Trichogramma species for controlling several lepidopteran pests in maize is well documented [29,30,31,32,33]. This parasitoid species has been used for the control of the ECB, Asian corn borer O. furnacalis (Lepidoptera: Crambidae), fall armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae), and other lepidopteran species. Both inundative and inoculative releases in field experiments have generally resulted in effective pest control.
A study assessing the effectiveness of Trichogramma wasps as biocontrol agents [34] reported a 28.2% increase in fresh maize yield. This result was achieved by applying three treatments of T. ostriniae, one for the first generation and two for the second generation, against the Asian corn borer, using a total of 750,000 individuals per hectare. However, Serbia is one of the few countries in Europe that does not currently utilize Trichogramma in its agricultural systems, despite several multiyear studies indicating high levels of Trichogramma (T. brassicae prevalently) parasitoid activity in maize crops ranging between 12.5% and 80.0% [35] and from 9.3% to 73.6% in sweet corn fields in north-west Serbia [36]. These findings suggest that while Trichogramma wasps were consistently present, their populations fluctuated significantly from year to year. However, they also justify and guide the rearing and augmentative release of these parasitoids in Serbia, considering the potential of their natural populations even without prior commercial application. Alongside direct individual application, Trichogramma wasps are frequently combined with other biocontrol agents, such as generalist predators, not only to suppress the targeted pests but also to modify the volatile chemical profile of host plants, making them more attractive to parasitoids [37].
Generalist predators such as lacewing larvae, particularly species from the genera Chrysoperla and Chrysopa, are documented to prey on many soft-bodied insects and have been reported as promising biocontrol agents [18,19]. They have successfully reduced populations of the green peach aphid Myzus persicae (Hemiptera: Aphididae) and the silverleaf whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) in tomatoes grown under screen-house conditions [38]. Moreover, they have been reported to effectively manage Acanthococcus lagerstroemiae (Hemiptera: Eriococcidae) in crapemyrtle, various aphid species, the grape mealybug Pseudochoccus maritimus (Hemiptera: Pseudococcidae), the greenhouse whitefly Trialeurodes vaporarium (Hemiptera: Aleyrodidae), the cotton bollworm Helicoverpa armigera (Lepidoptera: Noctuidae), and Heliothis spp. (Lepidoptera: Noctuidae) in important crops [39,40].
An example of an effective application of lacewing species from the Chrysoperla genus was shown in an experiment conducted in China, in which C. sinica was used for controlling H. armigera in cotton. Lacewing eggs were released at rates of 150,000, 300,000, and 450,000 per hectare in three separate releases at 7-day intervals. The efficacy of these treatments was measured at 66.7%, 77.8%, and 83.3%, respectively [39]. Another example of the successful application of Chrysopidae species for maize pest control was highlighted by a study investigating the effects of spraying corn plants with sucrose or molasses solutions. The results demonstrated that these sprays could alter the normal distribution patterns of certain members of the insect community on the plants. Specifically, the sprays led to an increase in the Chrysopidae population, which in turn reduced the number of corn borer larvae before they entered the stalk [41].
Given the natural potential of Trichogramma parasitoids in Serbia and the possibility of their combined use with other biocontrol agents, the objective of our study was to evaluate the efficacy of Trichogramma wasps and the predator C. carnea in the context of maize pest management. In the current study, two applications of both agents were performed in all treatments, with a one-week interval between the first and the second applications. Approximately 450,000 Trichogramma wasps and about 7000 lacewing larvae were released per hectare. The results showed that the treatment with Trichogramma wasps (treatment T2), as well as the combination of Trichogramma wasps with C. carnea predators (treatment T4), significantly improved the maize grain yield compared to the control treatment. Furthermore, treatment T4 demonstrated significant yield improvements over both treatments T2 (Trichogramma wasps) and T3 (C. carnea predators), suggesting that the combination of both agents is more effective in increasing grain yields. The grain yield with the T4 treatment (Table 4) was most likely improved through a reduction in ear damage since there was a high negative correlation between yield and ear damage along, with significant differences (Table 5). It was reasonable to expect that the combination of a predator and a parasitoid would lead to better results, although some studies have reported lower efficacy in treatments using two closely related parasitic species. Wang et al. [33] found that the synchronous release of two Trichogramma species (T. ostriniae and T. nubilale) resulted in a lower parasitism rate compared to single species releases, and particularly when compared with T. nubilale (20% higher parasitism rate recorded). However, different Trichogramma species compete for the same host stage (eggs), while lacewing larvae readily prey on first- and second-instar ECB larvae, reducing the potential competition among species.
Treatment T4 resulted in fewer tunnels (NTS) and larvae in stalks (NLS) compared to all other treatments, including the control. In contrast, treatments T2 and T3 did not show any statistically significant differences when compared to the control.
The parasitic and predatory effectiveness of natural enemies is affected by many variables, whereas environmental factors are crucial for determining their reproductive success, survival rates, host availability, and overall ability to control pest populations. Environmental factors such as temperature, humidity, and vegetation cover play a significant role in the activity and survival of biological control agents. Numerous studies have investigated the effects of suboptimal temperatures on the activity and survival of Trichogramma species. High temperatures, particularly above 33 °C, have been shown to negatively impact adult survival and female egg deposition [42,43]. Similarly, lower temperatures, particularly below 17 °C, reduce egg parasitism rates. For C. carnea, the optimal development temperature range is from approximately 30 to 32 °C [44]. In this study, capsules containing parasitic wasps and lacewing larvae were applied in late July, during a period of extreme heat (Figure 1). These conditions likely impaired the activity and reduced the survival of the released individuals, thereby diminishing the efficacy of the biological treatments. As seen from Table 1, the average yield for the whole trial was just 1.97 t ha−1, with a maximum yield of just 3.60 t ha−1. The most likely reasons for this low yield were the extremely high temperatures and a lack of precipitation during the critical growth period of the maize (Figure 1), which affected pollination and the accumulation of grain dry matter. The maximum temperatures consistently exceeded 30 °C, peaking around 40 °C in July, August, and September. High temperatures likely caused heat stress, impairing pollen viability and disrupting pollination, particularly during the flowering period in July. This stress may explain the relatively low yields in the experiment, as temperatures above 32 °C increase the risk of pollen sterility [25]. Further investigations and practices should take all of these factors into account when determining appropriate control methods, as considering a comprehensive range of influences will lead to more effective and sustainable pest management strategies.

5. Conclusions

The findings of this study demonstrated that the combined application of T. brassicae wasps and C. carnea larvae resulted in the highest maize grain yield, surpassing both the control group and all other treatments. This outcome is likely attributable to the significant reduction in ear damage observed within this treatment, highlighting the synergistic potential of combining parasitoids and predators for effective pest management. Notably, the treatment involving only lacewing larvae did not exhibit significant differences in yield compared to the control or other treatments across the examined parameters, emphasizing the importance of integrated approaches in pest control strategies. The extreme weather conditions during the experimental year negatively impacted the development of maize, as well as the performance of the biological control agents, particularly during critical periods of their life cycles. Such high temperatures, a limiting factor for Trichogramma and Chrysoperla species, likely reduced the effectiveness of the releases. These findings suggest that environmental conditions should be carefully considered when planning biological control interventions to maximize their success. Additionally, these results underscore the potential of integrating T. brassicae and C. carnea into maize pest management programs, particularly in regions such as Serbia, where natural populations of these species have been documented. However, the efficacy of such biological control strategies may be further optimized by aligning the release timings and conditions with favorable environmental parameters, ensuring the survival and performance of the released agents. Moreover, long-term studies assessing the economic feasibility and scalability of combining T. brassicae and C. carnea for widespread adoption in maize pest management would be valuable. The results of this study provide a foundation for considering T. brassicae and C. carnea for pest management programs aimed at reducing the reliance on chemical pesticides and promoting sustainable agricultural practices; however, more research is needed in order to confirm and optimize their possible implementation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15040764/s1, Figure S1: (a) Root Mean PRESS (Prediction Residual Sum of Squares) plot used to determine the optimal number of latent factors in the model. (b) Leave-One-Out Cross-Validation results using the NIPALS (Nonlinear Iterative Partial Least Squares) method with fast SVD (Singular Value Decomposition). Figure S2: (a) Percentage of variation in predictor variables (X) explained by extracted factors in the model. (b) Percentage of variation in response variable (Y), which represents yield, explained by the extracted factors.

Author Contributions

Conceptualization, F.F. and A.Đ.; methodology, F.F., Ž.M. and A.Đ.; validation, D.D.; formal analysis, D.D.; investigation, F.F., A.Đ. and A.I.; data curation, D.D.; writing—original draft preparation, F.F., D.D. and A.Đ.; writing—review and editing, F.F., D.D., A.Đ., Ž.M., D.S., A.K., A.I. and T.P.; visualization, D.D.; supervision, F.F.; project administration, F.F. and A.Đ.; funding acquisition, F.F. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the Autonomous Province of Vojvodina, Serbia, as part of a short-term project of special interest for sustainable development in the AP Vojvodina, “Biological Solutions for Controlling the European Corn Borer in Maize” (Project No. 000882611 2024 09418 003 000 000 001 01 001 04 002).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Food and Agriculture Organization of the United Nations. Available online: https://www.fao.org/faostat/en/#compare (accessed on 3 January 2025).
  2. Čamprag, D. Štetočine kukuruza. In Bolesti, Štetočine i Korovi Kukuruza i Njihovo Suzbijanje; Institut za kukuruz “Zemun Polje”: Novi Sad, Serbia, 2002; pp. 269–271. (In Serbian) [Google Scholar]
  3. Revillon, S.; Dillmann, C.; Galic, N.; Bauland, C.; Palaffre, C.; Malvar, R.A.; Butron, A.; Rebaudo, F.; Legrand, J. Effects of maize development and phenology on the field infestation dynamics of the European corn borer (Lepidoptera: Crambidae). J. Econ. Entomol. 2023, 117, 1913–1925. [Google Scholar] [CrossRef] [PubMed]
  4. Keszthelyi, S. An important corn enemy: The European corn borer (Ostrinia nubilalis Hbn.) in the new approximation. Hung. Agric. Res. 2010, 5, 20–23. [Google Scholar]
  5. Yanni, S.F.; Whalen, J.K.; Ma, B.L.; Gelinas, Y. European corn borer injury effects on lignin, carbon and nitrogen in corn tissues. Plant Soil 2011, 341, 165–177. [Google Scholar] [CrossRef]
  6. Tărău, A.; Păcurar, A.-M.; Mureșanu, F.; Șopterean, L.; Chețan, F.; Varga, A.; Porumb, I.; Russu, F.; Suciu, L. The research on the chemical control of Ostrinia nubilalis, in natural and artificial infestation conditions, an important link in integrated pest management. Manag. J. 2019, 19, 585–592. [Google Scholar]
  7. Alma, A.; Lessio, F.; Reyneri, A.; Blandino, M. Relationships between Ostrinia nubilalis (lepidoptera: Crambidae) feeding activity, crop technique and mycotoxin contamination of corn kernel in northwestern Italy. Int. J. Pest Manag. 2005, 51, 165–173. [Google Scholar] [CrossRef]
  8. Arias-Martín, M.; Haidukowski, M.; Farinós, G.P.; Patiño, B. Role of Sesamia nonagrioides and Ostrinia nubilalis as Vectors of Fusarium spp. and Contribution of Corn Borer-Resistant bt Maize to Mycotoxin Reduction. Toxins 2021, 13, 780. [Google Scholar] [CrossRef]
  9. Hashimi, M.H.; Hashimi, R.; Ryan, Q. Toxic effects of pesticides on humans, plants, animals, pollinators and beneficial organisms. Asian Plant Res. J. 2020, 5, 37–47. [Google Scholar] [CrossRef]
  10. European Commission—Questions and Answers: Farm to Fork Strategy—Building a Healthy and Fully Sustainable Food System. Available online: https://ec.europa.eu/commission/presscorner/detail/sr/qanda_20_885 (accessed on 3 January 2025).
  11. Bažok, R.; Igrc Barèiæ, J.; Kos, T.; Goltin Èuljak, T.; Šilović, M.; Jelovčan, S.; Kozina, A. Monitoring and efficacy of selected insecticides for European corn borer (Ostrinia nubilalis Hubn., Lepidoptera: Crambidae) control. J. Pest Sci. 2009, 82, 311–319. [Google Scholar] [CrossRef]
  12. Biddinger, D.J.; Hull, L.A. Effects of Several Types of Insecticides on the Mite Predator, Stethorus punctum (Coleoptera: Coccinellidae), Including Insect Growth Regulators and Abamectin. J. Econ. Entomol. 1995, 88, 358–366. [Google Scholar] [CrossRef]
  13. Bortolotti, L.; Porrini, C.; Micciarelli Sbrenna, A.; Giovanni, S. Ovicidal action of fenoxycarb on a predator, Chrysoperla carnea (Neuroptera: Chrysopidae). Appl. Entomol. Zool. 2000, 35, 265–270. [Google Scholar] [CrossRef]
  14. Baker, B.P.; Green, T.A.; Loker, A.J. Biological control and integrated pest management in organic and conventional systems. Biol. Control 2020, 140, 104095. [Google Scholar] [CrossRef]
  15. Ruberson, J.R.; Kring, T.J. Parasitism of developing eggs by Trichogramma pretiosum (Hymenoptera: Trichogrammatidae): Host age preference and suitability. Biol. Control 1993, 3, 39–46. [Google Scholar] [CrossRef]
  16. Razinger, J.; Vasileiadis, V.P.; Giraud, M.; van Dijk, W.; Modic, Š.; Sattin, M.; Urek, G. On-farm evaluation of inundative biological control of Ostrinia nubilalis (Lepidoptera: Crambidae) by Trichogramma brassicae (Hymenoptera: Trichogrammatidae) in three European maize-producing regions. Pest Manag. Sci. 2015, 71, 797–805. [Google Scholar] [CrossRef]
  17. Hudon, M.; LeRoux, E.J.; Harcourt, D.G. Seventy years of European corn borer (Ostrinia nubilalis) research in North America. In Agricultural Zoology Reviews; Russell, G.E., Ed.; Intercept: Wimborne, UK, 1989; Volume 3, pp. 53–96. [Google Scholar]
  18. Ismoilov, K.; Wang, M.; Jalilov, A.; Zhang, X.; Lu, Z.; Saidov, A.; Sun, X.; Han, P. First Report Using a Native Lacewing Species to Control Tuta absoluta: From Laboratory Trials to Field Assessment. Insects 2020, 11, 286. [Google Scholar] [CrossRef]
  19. Li, Y.Y.; Wang, Y.N.; Zhang, H.Z.; Zhang, M.S.; Wang, M.Q.; Mao, J.J.; Zhang, L.S. The green lacewing Chrysopa formosa as a potential biocontrol agent for managing Spodoptera frugiperda and Spodoptera litura. Bull. Entomol. Res. 2022, 113, 49–62. [Google Scholar] [CrossRef] [PubMed]
  20. Boćanski, J.; Jocković, Đ.; Radojičić, S.; Popov, R.; Čapelja, V. Seed production of NS corn hybrids: Possibilities for improvement. In Zbornik Radova Instituta za Ratarstvo i Povrtarstvo; Institut za ratarstvo i povrtarstvo: Novi Sad, Serbia, 2008; Volume 45, pp. 81–87. [Google Scholar]
  21. Thomas, M.D.; Moriarty, T.M.; Cook, D.D.; Mazzeo, B.A. Electrical Capacitance Measurements to Assess European Corn Borer Infestation in Maize. Comput. Electron. Agric. 2021, 187, 106305. [Google Scholar] [CrossRef]
  22. Abdel-Rahman, M.E.; Landmann, T.; Kyalo, R.; Ong’amo, G.; Mwalusepo, S.; Sulieman, S.; LeRu, B. Predicting stem borer density in maize using RapidEye data and generalized linear models. Int. J. Appl. Earth Obs. Geoinf. 2017, 57, 61–74. [Google Scholar] [CrossRef]
  23. Trnka, M.; Muška, F.; Semerádová, D.; Dubrovský, M.; Kocmánková, E.; Žalud, Z. European corn borer life stage model: Regional estimates of pest development and spatial distribution under present and future climate. Ecol. Model. 2007, 207, 61–84. [Google Scholar] [CrossRef]
  24. Piekutowska, M.; Hara, P.; Pentoś, K.; Lenartowicz, T.; Wojciechowski, T.; Kujawa, S.; Niedbała, G. Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study. Agronomy 2024, 14, 3010. [Google Scholar] [CrossRef]
  25. Herrero, M.P.; Johnson, R. High temperature stress and pollen viability of maize. Crop Sci. 1980, 20, 796–800. [Google Scholar] [CrossRef]
  26. Harri, A.; Erdem, C.; Coble, K.H.; Knight, T.O. Crop yield distributions: A reconciliation of previous research and statistical tests for normality. Appl. Econ. Perspect. Policy 2010, 31, 163–182. [Google Scholar] [CrossRef]
  27. Smith, R.F.; Reynolds, H.T. Principles, Definition and Scope of Integrated Pest Control. In Proceedings of the FAO Symposium on Integrated Pest Control, Rome, Italy, 11–15 October 1965; Food and Agriculture Organization of the United Nations: Rome, Italy, 1996; Volume 1, pp. 11–18. [Google Scholar]
  28. Deguine, J.P.; Aubertot, J.N.; Flor, R.J.; Lescourret, F.; Wyckhuys, K.A.; Ratnadass, A. Integrated pest management: Good intentions, hard realities. A review. Agron. Sustain. Dev. 2021, 41, 38. [Google Scholar]
  29. Wright, M.G.; Kuhar, T.P.; Hoffmann, M.P.; Chenus, S.A. Effect of inoculative releases of Trichogramma ostriniae on populations of Ostrinia nubilalis and damage to sweet corn and field corn. Biol. Control 2002, 23, 149–155. [Google Scholar] [CrossRef]
  30. Gagnon, A.E.; Audette, C.; Duval, B.; Boisclair, J. Can the Use of Trichogramma ostriniae (Hymenoptera: Trichogrammatidae) to Control Ostrinia nubilalis (Lepidoptera: Crambidae) Be Economically Sustainable for Processing Sweet Corn? J. Econ. Entomol. 2017, 110, 59–66. [Google Scholar] [CrossRef]
  31. Huang, N.X.; Jaworski, C.C.; Desneux, N.; Zhang, F.; Yang, P.Y.; Wang, S. Long-term and large-scale releases of Trichogramma promote pesticide decrease in maize in northeastern China. Entomol. Gen. 2020, 40, 331–335. [Google Scholar] [CrossRef]
  32. Jin, T.; Lin, Y.; Ma, G.; Liu, J.; Hao, Z.; Han, S.; Peng, Z. Biocontrol potential of Trichogramma species against Spodoptera frugiperda and their field efficacy in maize. Crop Prot. 2021, 150, 105790. [Google Scholar] [CrossRef]
  33. Wang, Y.; Hou, Y.-Y.; Benelli, G.; Desneux, N.; Ali, A.; Zang, L.-S. Trichogramma ostriniae is more effective than Trichogramma dendrolimi as a biocontrol agent of the Asian corn borer, Ostrinia furnacalis. J. Insects 2022, 13, 70. [Google Scholar] [CrossRef]
  34. Zhang, F.; Babendreier, D.; Wang, Z.; Il, K.S.; Zheng, L.; Pyon, Y.C.; Bai, S.; Song, K.; Ri, J.O.; Grossrieder, M.; et al. Mass releases of Trichogramma ostriniae increase maize production in DPR Korea. J. Appl. Entomol. 2010, 134, 481–490. [Google Scholar] [CrossRef]
  35. Ivezić, A.; Rugman-Jones, P.; Malausa, T.; Ris, N.; Ignjatović-Ćupina, A. Molecular identification of Trichogramma species parasitizing Ostrinia nubilalis in corn and pepper in south–east border of Europe. Int. J. Pest Manag. 2020, 67, 346–357. [Google Scholar] [CrossRef]
  36. Tancik, J. Natural parasitism of the second generation European corn borer eggs Ostrinia nubilalis (Hübner) (Lepidoptera, Pyralidae) by Trichogramma spp. in sweet corn fields in Vojvodina, Serbia—Short communication. CAAS Agric. J. 2017, 53, 50–54. [Google Scholar] [CrossRef]
  37. Cascone, P.; Tabebordbar, F.; Cencetti, G.; Michelozzi, M.; Shishehbor, P.; Guerrieri, E.; Giorgini, M. Phytophagy of Nesidiocoris tenuis triggers the response of Trichogramma achaeae to tomato plants infested by Tuta absoluta. J. Pest Sci. 2024, 97, 323–333. [Google Scholar] [CrossRef]
  38. Nair, I.J.; Sharma, S.; Kaur, R. Efficacy of the green lace wing, Chrysoperla zastrowi sillemi (Esben-Peterson) (Neuroptera: Chrysopidae), against sucking pests of tomato: An appraisal under protected conditions. Egypt. J. Biol. Pest Control 2020, 30, 74. [Google Scholar] [CrossRef]
  39. Wang, R.; Nordlund, D.A. Use of Chrysoperla spp. (Neuroptera: Chrysopidae) in augmentative release programmes for control of arthropod pests. Biocontrol News Inf. 1994, 15, 51–57. [Google Scholar]
  40. Wu, B.; Xie, R.; Gu, M.; Qin, H. Green lacewing Chrysoperla rufilabris (Neuroptera: Chrysopidae) is a potential biological agent for crapemyrtle bark scale (Hemiptera: Eriococcidae) pest management. Hortic. Sci. Technol. 2022, 2, 3. [Google Scholar]
  41. Carlson, R.E.; Chiang, H.C. Reduction of an Ostrinia nubilalis population by predatory insects attracted by sucrose sprays. Entomophaga 1973, 18, 205–211. [Google Scholar]
  42. Wang, B.; Ferro, D.; Hosmer, D. Effectiveness of Trichogramma ostriniae and T. nubilale for controlling the European corn borer Ostrinia nubilalis in sweet corn. Entomol. Exp. Appl. 1999, 91, 297–303. [Google Scholar] [CrossRef]
  43. Navik, O.; Yele, Y.; Kedar, S.C.; Sushil, S.N. Biological control of fall armyworm Spodoptera frugiperda (JE Smith) using egg parasitoids, Trichogramma species (Hymenoptera: Trichogrammatidae): A review. Egypt. J. Biol. Pest Control 2023, 33, 118. [Google Scholar] [CrossRef]
  44. Ranjbar Aghdam, H.; Nemati, Z. Modeling of the effect of temperature on developmental rate of common green lacewing, Chrysoperla carnea (Steph.)(Neuroptera: Chrysopidae). Egypt. J. Biol. Pest Control 2020, 30, 145. [Google Scholar] [CrossRef]
Agronomy 15 00764 g001
Figure 1. Weather conditions in 2024 when the experiment was conducted.
Figure 1. Weather conditions in 2024 when the experiment was conducted.
Agronomy 15 00764 g001
Agronomy 15 00764 g002
Figure 2. Biplot of the factor analysis illustrating the relationships between damage indicators. The first extracted factor (factor 1, 35.3% variance) represents “stalk damage”, with high loadings from NLS, TLS, and NTS. The second factor (factor 2, 24.1% variance) represents “ear damage”, with high loadings from NLE, NTE, and TLE. The plot shows how these damage indicators are grouped based on their contributions to different types of structural damage in maize stalks and ears.
Figure 2. Biplot of the factor analysis illustrating the relationships between damage indicators. The first extracted factor (factor 1, 35.3% variance) represents “stalk damage”, with high loadings from NLS, TLS, and NTS. The second factor (factor 2, 24.1% variance) represents “ear damage”, with high loadings from NLE, NTE, and TLE. The plot shows how these damage indicators are grouped based on their contributions to different types of structural damage in maize stalks and ears.
Agronomy 15 00764 g002
Table 1. Descriptive statistics table of examined traits.
Table 1. Descriptive statistics table of examined traits.
TraitMeanStd. DevCV (%)MinMax
Yield (t ha−1)1.970.7638.831.283.60
NTE0.540.1732.030.250.86
NLE0.160.19118.370.000.75
TLE3.491.3037.281.005.33
NTS2.380.3414.091.402.80
NLS1.120.2623.560.651.55
TLS22.754.3118.9410.9730.38
NTE—number of tunnels (ear); NLE—number of larvae (ear); TLE—tunnel length (cm) (ear); NTS—number of tunnels (stalk); TLS—tunnel length (cm) (stalk); NLS—number of larvae (stalk).
Table 2. Variable importance table, from which traits and their interactions that were below VIP value of 0.8 were removed from further analyses.
Table 2. Variable importance table, from which traits and their interactions that were below VIP value of 0.8 were removed from further analyses.
TraitsVIP
NTE1.2281
NLE0.2336
NTE × NLE0.1398
TLE1.5282
NTE × TLE0.1969
NLE × TLE0.3992
NTE × NLE × TLE0.2129
NTS1.3137
TLS1.5827
NTS × TLS1.0932
NLS0.9656
NTS × NLS1.1280
TLS × NLS1.0598
NTS × TLS × NLS1.0417
NTE—number of tunnels (ear); NLE—number of larvae (ear); NTE × NLE—number of tunnels (ear) × number of larvae (ear); TLE—tunnel length (cm) (ear); NTE × TLE—number of tunnels (ear) × tunnel length (cm) (ear); NLE × TLE—number of larvae (ear) × tunnel length (cm) (ear); NTE × NLE × TLE—number of tunnels (ear) × number of larvae (ear) × tunnel length (cm) (ear); NTS—number of tunnels (stalk); TLS—tunnel length (cm) (stalk); NTS × TLS—number of tunnels (stalk) × tunnel length (cm) (stalk); NLS—number of larvae (stalk); NTS × NLS—number of tunnels (stalk) × number of larvae (stalk); TLS × NLS—tunnel length (stalk) × number of larvae (stalk); NTS × TLS × NLS—number of tunnels (stalk) × tunnel length (stalk) × number of larvae (stalk).
Table 3. Model summary and parameter estimates of predictors for grain yield.
Table 3. Model summary and parameter estimates of predictors for grain yield.
ResponseYield
DistributionBest subset
Validation methodHoldback
Location model linkIdentity
Scale model linkIdentity
MeasureTrainingValidation
−LogLikelihood4.69−2.80
BIC26.772.10
Generalized R20.740.84
TermEstimateStd ErrorWald ChiSquareProb > ChiSquareLower 95%Upper 95%
Intercept9.2860.2411489.105<0.0001 **8.8149.757
NTE0.0000.0000.0001.0000.0000.000
TLE0.0000.0000.0001.0000.0000.000
NTS−2.5760.091798.593<0.0001 **−2.755−2.398
TLS0.0000.0000.0001.0000.0000.000
NTS × TLS0.0000.0000.0001.0000.0000.000
NLS0.0000.0000.0001.0000.0000.000
NTS × NLS−6.7310.307481.716<0.0001 **−7.332−6.130
TLS × NLS0.0000.0000.0001.0000.0000.000
NTS × TLS × NLS0.0000.0000.0001.0000.0000.000
Treatment [T3–T4]−1.2340.091182.328<0.0001 **−1.413−1.055
Treatment [T1–T4]−0.9940.073186.499<0.0001 **−1.137−0.852
Treatment [T2–T4]−0.7070.041292.813<0.0001 **−0.788−0.626
Note: **, significant (p < 0.01). T1—control (no application of biological control agents); T2—application of parasitic wasps from the genus Trichogramma as a biological control agent; T3—application of second-stage larvae of lacewing predators C. carnea in a commercially prepared formulation; T4—combination of parasitic wasps from the genus Trichogramma and lacewing predators C. carnea.
Table 4. Tukey HSD test of all pairwise comparisons between biological control treatments and effects on yield.
Table 4. Tukey HSD test of all pairwise comparisons between biological control treatments and effects on yield.
Treatment−TreatmentDifference (t ha−1)Std Errort RatioProb > |t|Lower 95%Upper 95%
T3T1−0.2400.105−2.2780.2053−0.6040.125
T3T2−0.5270.091−5.7880.0047 **−0.842−0.212
T3T4−1.2340.091−13.503<0.0001 **−1.550−0.918
T1T2−0.2870.070−4.1240.0239 *−0.528−0.046
T1T4−0.9940.073−13.656<0.0001 **−1.246−0.742
T2T4−0.7070.041−17.112<0.0001 **−0.851−0.564
Average yield (t ha−1)T1 = 1.988 (C)
T2 = 2.275 (B)
T3 = 1.748 (C)
T4 = 2.983 (A)
Note: **, significant (p < 0.01); *, significant (p < 0.05). Treatments not connected by the same letter are significantly different at 95% confidence interval.
Table 5. Effect of treatments on the variability of ear and stalk damage. Levels not connected by the same letter are significantly different.
Table 5. Effect of treatments on the variability of ear and stalk damage. Levels not connected by the same letter are significantly different.
Ear DamageStalk Damage
Prob > FProb > F
Treatment 0.0170 *0.2829
LevelT10.86A0.34A
T20.87A0.19A
T30.90A0.46A
T40.42B0.11A
Correlation with average yield r = −0.93r = −0.93
Note: *, significant (p < 0.05). Treatments not connected by the same letter are significantly different at 95% confidence interval.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Franeta, F.; Đurić, A.; Dunđerski, D.; Stanisavljević, D.; Konjević, A.; Ivezić, A.; Popović, T.; Milovac, Ž. Biological Solutions for Higher Maize Yield and Reduced Stalk Damage Caused by the European Corn Borer, Ostrinia nubilalis (Hübner). Agronomy 2025, 15, 764. https://doi.org/10.3390/agronomy15040764

AMA Style

Franeta F, Đurić A, Dunđerski D, Stanisavljević D, Konjević A, Ivezić A, Popović T, Milovac Ž. Biological Solutions for Higher Maize Yield and Reduced Stalk Damage Caused by the European Corn Borer, Ostrinia nubilalis (Hübner). Agronomy. 2025; 15(4):764. https://doi.org/10.3390/agronomy15040764

Chicago/Turabian Style

Franeta, Filip, Anja Đurić, Dušan Dunđerski, Dušan Stanisavljević, Aleksandra Konjević, Aleksandar Ivezić, Tamara Popović, and Željko Milovac. 2025. "Biological Solutions for Higher Maize Yield and Reduced Stalk Damage Caused by the European Corn Borer, Ostrinia nubilalis (Hübner)" Agronomy 15, no. 4: 764. https://doi.org/10.3390/agronomy15040764

APA Style

Franeta, F., Đurić, A., Dunđerski, D., Stanisavljević, D., Konjević, A., Ivezić, A., Popović, T., & Milovac, Ž. (2025). Biological Solutions for Higher Maize Yield and Reduced Stalk Damage Caused by the European Corn Borer, Ostrinia nubilalis (Hübner). Agronomy, 15(4), 764. https://doi.org/10.3390/agronomy15040764

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop