Exploring the effects of tree presence on maize pest damages, seed health, grain yield, nutritive value, and nutraceutical compounds

Exploring the effects of tree presence on maize pest damages, seed health, grain yield, nutritive value, and nutraceutical compounds | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the effects of tree presence on maize pest damages, seed health, grain yield, nutritive value, and nutraceutical compounds Matteo Finocchi, Alice Ripamonti, Alberto Mantino, Fabrizio Giuseppe Cella, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6115397/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The present study evaluates the effects of tree presence in an agroforestry system on maize performance, focusing on yield, nutritive value, fatty acid composition, and resistance to biotic stresses. Two maize hybrids, FAO C300 and FAO C500, were cultivated (i) in the narrow alleys of an agroforestry alley-cropping system based on SRC poplar tree rows and (ii) in a treeless control system. In 2023, the results indicate that tree presence does not reduce the average grain yield. However, a significant interaction between systems and maize hybrids was observed. FAO C500 demonstrated superior adaptability to the agroforestry environment, likely due to its longer growth cycle and more developed leaf and root systems, which allow for better resource utilization under water stress conditions. Tree presence did not affect the fatty acid profile of the grains, although FAO C500 accumulated more linoleic acid, while FAO C300 showed higher levels of oleic acid. The agroforestry environment reduced seed infestation by Aspergillus niger , particularly in the FAO C500 hybrid, but increased damage by insects on ear surfaces. These findings highlight the variability in maize hybrid suitability for agroforestry; however, since these results are based on only one year of experimentation, further trials over multiple years are necessary to validate these findings and gain a deeper understanding of tree effects on maize performance. agroforestry fatty acid profile mycotoxin contamination tocopherol profile Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The regeneration of farming systems requires the adoption of innovative agricultural practices that ensure farmers’ incomes and social benefits while minimizing environmental impacts. At the same time, these practices should enhance agro-ecosystem services and promote biodiversity (Burgess et al. 2022 ). In the context of climate change, climate-smart adaptation strategies are essential in temperate areas for key crops like maize ( Zea mays L.). Simultaneously, reducing the environmental impacts of cropping systems is crucial for mitigating the causes of global warming. The “green revolution” and the intensification of cropping systems, particularly for commodities like maize, have resulted in significant environmental impacts, making the development of viable alternatives essential (Aneja et al., 2009 ; Paris et al., 2019). The FAOstat data (2021) indicates that 17.6 M ha of agricultural land is used for maize cultivation (Erenstein et al. 2022 ), making maize one of the most important crops for both food and feed supply chains (Abrol & Shankar, 2014 ; Hristov et al., 2020 ; Revilla et al., 2022 ). It is not only a key crop for human consumption but also serves as a high-energy starch component in livestock feeding (Rouf Shah et al., 2016 ; Saldivar & Perez-Carrillo, 2016 ; Serna-Saldivar & Perez-Carrillo, 2019; Sudheesh et al., 2022 ), for example in Europe 75% of the maize production is used as feed (Erenstein et al. 2022 ). However, the environmental impacts associated with maize production are increasingly recognized. Intensive farming practices, which rely heavily on agrochemicals, fertilizers, and diesel fuel, contribute to greenhouse gas emissions, soil degradation, and water pollution (Holka & Bieńkowski, 2020 ). In this context, agroforestry - the practice of deliberately integrating woody species within agricultural fields (Burgess and Rosati 2018) - stands out among carbon farming practices as a promising approach. It aims to reduce the carbon footprint of agricultural products and sequester carbon in both soil (Riedell et al., 2009 ) and woody plants (Kay et al., 2019 ), while simultaneously ensuring food security (Leifeld, 2023 ; Paustian et al., 2019 ; Tang et al., 2019 ). In maize-dominated systems, agroforestry could provide additional benefits by buffering crops against variability in weather conditions during the summer months (van Noordwijk et al. 2014 ). Understanding the effects of tree presence on maize performance is essential, requiring consideration of multiple factors beyond grain yield. Recent reviews have examined the potential future impacts of climate change on pathogen infections in maize crops, which could limit their profitability (Juroszek & Von Tiedemann, 2013 ; Pumariño et al., 2015 ). These studies highlight the importance of understanding how changing microclimatic conditions may alter the interactions between crops and pathogens, potentially increasing disease pressure. Current challenges include the attack on maize by the key pest Ostrinia nubilalis Hübner and its impact on mycotoxin contamination by Aspergillus flavus Link.. These issues emphasize the need for low-input management strategies in agriculture (Magagnoli et al., 2021 ). Although mycotoxin contamination of maize grain is primarily a post-harvest concern, preventive strategies should be applied in the field to mitigate the risk of aflatoxin contamination (Sarrocco et al., 2019 ). The nutritional value of maize grain is critical for maintaining its viability as a high-value crop. Although total lipids in maize are a minor component, the fatty acid composition significantly influences its nutritional and nutraceutical properties (Ray et al. 2019 ). Moreover, the nutritional profile of maize is closely linked to its content of phenolic compounds and flavonoids, which are bioactive compounds with well-documented antioxidant properties. Phenolic acids, the primary type of phenolic compounds in maize, contribute to resistance against oxidative stress and microbial attacks, enhancing the grain’s quality and shelf life. Flavonoids, meanwhile, exhibit anti-inflammatory, antiviral, and anticancer effects, further adding to the health benefits of maize consumption (Liu et al., 2024 ; Slika et al., 2022 ). The levels of these compounds vary depending on maize hybrids, growth conditions, and processing methods (Elisa et al. 2022). Importantly, phenolic compounds and flavonoids also benefit animal health and improve the quality of animal-derived products (Ponnampalam et al., 2022 ). Based on this, tailored solutions based on field experiments are essential to address the concerns of farmers who are hesitant to adopt agroforestry systems on their farms, often due to insufficient information and limited access to extension services (Tranchina et al., 2024a; 2024b). This study aims to assess the performance of maize in agroforestry systems by investigating the influence of tree presence on yield, nutritional quality, pest dynamics, and the presence of mycotoxigenic fungi. The research is conducted under Mediterranean conditions within a poplar short-rotation coppice (SRC) agroforestry field trial. Materials and methods Site description, experimental design and crop management The field trial was conducted in 2023 at the "E. Avanzi" Centre for Agro-Environmental Research of the University of Pisa, Italy (43° 40′ 49″ N, 10° 20′ 47″ E; elevation: 1 m above sea level, 0% slope). The site is characterized by a Mediterranean climate, with an annual rainfall of 920 mm and a long-term mean temperature of 15°C (1987–2017). The experiment took place in an alley-cropping experimental field featuring SRC poplar rows-oriented NW–SE (357° N-NW) and maize of FAO maturity classes 300 and 500. The experimental field originated from a 0.7 ha poplar SRC plantation ( Populus × canadensis Moench, clone AF2, and Populus × generosa Henry × nigra L., clone Monviso) established in 2009. From 2009 to 2021, the poplar trees were coppiced biennially (Mantino et al., 2023). The tree row distance is 13.5m. Meteorological data were collected using a weather station (Vantage Pro2, Davis Instruments Corporation, Hayward, California, USA) located 50 m from the experimental trial site (43° 40′ 51.6″ N, 10° 20′ 27.6″ E). On April 26, 2023, maize belonging to FAO classes 300 and 500 (C300 and C500, respectively) was sown across 16 experimental plots arranged into four vertical blocks (Blocks) and four horizontal blocks (Longs) for testing two treatments: control (CRL) and agroforestry (AGR) (Fig. 1). The control treatment consisted of two adjacent vertical blocks, each 6 m wide—one dedicated to C300 and the other to C500. While the agroforestry treatment consists in two vertical blocks, each 12 m wide and representing an alley between two tree rows. The two maize classes were randomized in the within each vertical block. Figure 1 illustrates the experimental layout in detail. The maize was sown at a density of 8.3 plants per square meter, and a phosphorus fertilization rate of 150 kg P₂O₅ ha⁻¹ was applied at sowing. During the growing season, nitrogen fertilization was performed, with a total of 200 kg N ha⁻¹ applied uniformly across both treatments. Weed control was achieved through weeding and herbicide application in mid-May, targeting broadleaf weeds. Harvesting was carried out on September 7, 2023, when the grain reached a moisture content of 10%. Samples collection The grain yield harvest was carried out on September 7, 2023, following a structured sampling scheme. For the control plots, eight plants were collected from each plot. In the agroforestry plots, 15 plants were sampled per plot, distributed as follows: five plants from the east side, 3 meters from the row of poplars; five plants from the center of the plot, within the alley; and five plants from the west side, 3 meters from the trees. A total of 184 plants were sampled across all plots. Post-harvest measurements included: number of ears per plant, plant height, leaf biomass weight, leaf-to-stem ratio, ear weight. The ear was further divided into the weight of the chaff and the husk. The dry matter content was determined by drying the samples in an oven at 60°C for 96 hours. The entomological assessments focused on identifying pest damage by recording the presence or absence of pest-related injuries and calculating the percentage of the ear affected by pests. In particular, the level damage of maize ear surface caused by Helicoverpa sp. (Lepidoptera: Noctuidae ), or Ostrinia nubilalis (Lepidoptera: Pyralidae ) was visual measured on the 184 samples. Determination and quantification of phenolic compounds and flavonoids present in maize grain The same type of grouped samples used for the identification of mycotoxin-producing fungi was employed to investigate the quantification of phenolic compounds and flavonoids in maize grain from both the control and agroforestry systems. Sample extraction was carried out using 80% (v/v) aqueous methanol, following the procedure described by Tavarini et al. ( 2019 ). Briefly, the samples were mixed with 80% methanol, sonicated for 30 minutes, stirred for an additional 30 minutes, and then centrifuged at 6000 rpm for 30 minutes. The supernatant was collected, and the pellet was re-extracted twice. The resulting hydroalcoholic extracts were used to quantify total phenolic and flavonoid content and to evaluate antioxidant and antiradical activity. Total phenolic content was determined using the Folin–Ciocalteu method (Alonso-Borbolan et al., 2003 ). Absorbance was measured at 750 nm, and a standard curve of gallic acid (0–250 mg L⁻¹) was used to express phenolic concentrations as milligrams of gallic acid equivalents (GAE) per gram of dry matter (DM). Flavonoid concentration was assessed using the aluminum chloride colorimetric method based on Kim et al. ( 2003 ). Absorbance was measured at 510 nm, and results were expressed as milligrams of catechin equivalents (CE) per gram of dry matter (DM), using a standard curve of catechin (0–250 mg L⁻¹). Determination of antioxidant activity of maize grain extracts. To evaluate antioxidant and antiradical activity, the ABTS (2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid)), DPPH (1,1-diphenyl-2-picrylhydrazyl), and FRAP (ferric reducing antioxidant power) assays were performed. In the ABTS assay, absorbance was measured at 734 nm, and antioxidant activity was expressed as micromoles of Trolox equivalents per gram of dry matter (DM), using a standard curve of Trolox (0–200 µmol L⁻¹), as described by Re et al. ( 1999 ). The DPPH assay was conducted following the method outlined by Brand-Williams et al. ( 1995 ), with absorbance recorded at 517 nm. Antioxidant activity was expressed as milligrams of Trolox equivalents per gram of dry matter (DM), using a standard curve of Trolox (0–1 mg mL⁻¹). For the FRAP assay, absorbance was measured at 593 nm, and antioxidant activity was expressed as micromoles of Fe(II) per gram of dry matter (DM), using a standard curve of FeSO₄, as detailed by Benzie and Strain ( 1996 ). Seed health test for the detection of mycotoxigenic fungi To assess the presence of mycotoxigenic fungi, particularly A. flavus , dried grain samples were grouped by plot for the control treatment (a composite of grains from the eight sampled plants) and by position relative to the trees for the agroforestry treatment (grains grouped from the five sampled plants at different positions within each plot). These grouped samples were used for all subsequent analyses, including the determination of mycotoxigenic fungi, quantification of tocopherols, grain composition, and fatty acid profiling. Special attention was given to A. flavus , a major contributor, along with Aspergillus parasiticus Speare, to aflatoxin contamination in maize. Due to insufficient seed quantities in these grouped samples to perform the health analyses, additional grouping was carried out. This resulted in six control plates (three for class 300 and three for class 500) and 18 agroforestry treatment plates, with no distinction made regarding distance from the trees (nine plates each for class 300 and class 500 in agroforestry). For each grouped sample, 100 seeds were surface sterilized using an aqueous solution containing sodium hypochlorite (1% active chlorine) in 50% ethanol, followed by three washes in sterile water (10 minutes each). The sterilized seeds were then plated on Potato Dextrose Agar (PDA, 34 g L⁻¹; Difco, Milan, Italy) supplemented with antibiotics (streptomycin, 300 ppm; bacitracin, 150 ppm) and hymexazol (1.0 mL L⁻¹) to limit the growth of Mucoraceae . Plates were incubated for 10 days at 22 ± 2°C under a 12-hour light/12-hour dark photoperiod, with daily monitoring of fungal colony development. Fungal colonies were transferred to new PDA plates with antibiotics to obtain single-spore colonies. Each isolate was identified at the genus level based on morphological macro- and microscopic features, including colony color, conidiophore structure, and conidial shape and size. Observations were made using a Laica MZ FLIII stereomicroscope and a Leitz Dialux 22 microscope. Due to the low incidence of A. flavus detected in this study, no further analyses on aflatoxin presence or quantification were conducted. Statistical analysis Statistical analysis was conducted to determine the presence of significant differences between treatments: CRL and AGR; and maize class: C300 and C500; and their interaction. A liner mixed model was used for evaluating the presence of significant difference on grain yield, chemical composition, fatty acids profile, and nutraceutical compounds among treatments and maize classes. The analysis was conducted using the R software (R core team, 2024) with the lme4 package and the “lmer” function (Bates et al., 2014) where treatments, maize classes and their interaction were the fixed effects, and the two random factors were: “blocks” nested in treatment and “longs” nested in treatment. HSD Tukey’s post hoc test was carried out by using the “emmeans” package (Lenth, 2025 ). $$\:{Y}_{ikjz}=\mu\:+Clas{s}_{i}+Trea{t}_{k}+Clas{s}_{i}\times\:Trea{t}_{k}+[Treat/Long{s}_{j}]+[Treat/Block{s}_{z}]+{\epsilon\:}_{ikjz}$$ Where: µ = mean of variable Class = maize FAO class (C300, C500, n = 2) Treat = treatment: CRL and AGR (n = 2) Longs: blocks from East to West (n = 4) Blocks: blocks from North to South (n = 4) Secondly, to check differences among treatments and maize classes on the level damage of ear surface caused by Helicoverpa sp. (Lepidoptera: Noctuidae ), or O. nubilalis (Lepidoptera: Pyralidae ), a Kruskal–Wallis non-parametric model followed by the Steel–Dwass multiple comparison (p < 0.05) was used because data were not normally distributed. Lastly, significant differences on fungal pathogen seed infection among treatments and maize classes was performed using the non-parametric Kruskal-Wallis test because data were not normally distributed. The post hoc analysis was conducted using the Dunn test, adjusted for Bonferroni correction. Results and discussion Limitation of the experiment The findings are specific to the 2023 growing season and its climatic conditions and should not be generalized. Additionally, limited replications and constrained randomization due to farm- and field-specific conditions restrict broader applicability. These results provide initial insights from a case study that should be complemented by further field trials and larger research projects. Meteorological conditions Figure 2 illustrates the monthly average temperature, total rainfall, and evapotranspiration. During the trial period (April–September), cumulative precipitation reached 450 mm. July was the driest month, with only 8 mm of rainfall, while August recorded the highest monthly total at 105 mm. In May and June, during the crop’s exponential and linear growth phases, rainfall exceeded 255 mm. This, combined with mild temperatures, promoted rapid crop growth and development. Reference evapotranspiration (ET₀), estimated using the Penman-Monteith method, exceeded 816 mm over the trial period. Maize grain yield, chemical composition and fatty acids profile Results of the Type III ANOVA indicated no significant differences for the system treatment (p = 1.00) and maize class (p = 0.38). However, their interaction was statistically significant (p = 0.0028), suggesting that the effect of treatment differs depending on the maize classes (Fig. 3 ). Significant differences in grain yield occurred between C300 and C500 in AGR. Specifically, the pairwise multiple comparisons test indicated a p-level of 0.0195. These findings suggest that agroforestry systems based on poplar SRC rows have a differential impact on the performance of C300 and C500 maize classes, which may reflect the adaptability of the maize hybrids to the agroforestry environment (Baier et al., 2023 ). While the main effects of treatment and maize class were not significant on their own, their interaction suggests that the benefits of agroforestry systems may be influenced by the specific maize hybrid. At the harvest time, differences in moisture content between C300 (7% of moisture) and C500 (11% of moisture) showed significant differences while no significant difference between treatments was recorded (p = 0.96). The analysis of the yield and moisture content data showed that the agroforestry system did not result in significant overall differences compared to the control. However, the C500 performed better, likely due to its longer growing cycle demonstrating a higher moisture content at the harvest time. Moreover, the potential C500 greater leaf and root systems compared to C300 played a key role. These traits allowed C 500 to optimize resource use, particularly under water stress conditions that became apparent from June onwards until the end of the cycle. The higher ability of C500 to exploit soil water and nutrients due to a greater root system may explain its higher grain production under limited resource conditions. As observed by Baier et al. ( 2023 ) in a meta-analysis evaluating maize grain yield in agroforestry systems, the crop productivity is generally higher in agroforestry systems, or at least not significantly lower compared to conventional systems. Our findings are consistent with these studies, confirming that it is possible to integrate trees into maize-based cropping systems. This supports the growing body of literature suggesting that agroforestry practices can maintain or even enhance crop productivity while contributing to environmental sustainability (Mouratiadou et al., 2024 ). Regarding nutritive value and chemical composition, a significant difference emerged only for crude protein (CP) content between the two treatment levels, CRL and AGR (p = 0.04), with a significant interaction effect (p = 0.023). Specifically, the mean CP content in class C300 was 16% higher than in class C500. Notably, no significant differences were observed between the agroforestry treatment and the control group in any of the analyses, suggesting that, under these conditions, agroforestry did not significantly impact grain composition compared to open-field cultivation. A detailed analysis of the fatty acid profile focused on the most abundant and relevant fatty acids in the maize grain lipid fraction: palmitic acid (C16:0), oleic acid (C18:1c9), and linoleic acid (C18:2n6). Fatty acid composition was influenced by maize class. Stearic acid (C16:0) content was significantly higher in class C500 than in class C300 (p = 0.00047), with an 18% increase. Oleic acid (C18:1c9) showed a highly significant difference (p < 0.001), with class C300 having a higher content (28.30 g 100g⁻¹ of fatty acids) compared to class C500 (23.42 g 100g⁻¹). Similarly, linoleic acid (C18:2n6), the most abundant fatty acid in maize grain lipids, differed significantly between classes (p < 0.001), with class C500 showing 6% higher levels than class C300 (Table 1 ). These differences are also reflected in the broader fatty acid groupings: SFA (saturated fatty acids), PUFA (polyunsaturated fatty acids), PUFA_n6 (n6-family polyunsaturated fatty acids), and MUFA (monounsaturated fatty acids), as they are influenced by variations in the key fatty acids mentioned above (C16:0, C18:1c9, and C18:2n6).Therefore, the differences observed in the individual fatty acids are directly reflected in the overall categories, confirming the presence of a significant effect on the lipid profiles of the different classes. The agroforestry system does not appear to significantly affect the lipid profile of maize. The observed differences in the fatty acid composition, such as levels of oleic acid and linoleic acid, are solely attributed to the genetic characteristics of the hybrids as previous study describes (Zhang et al., 2024 ). Specifically, C500 is distinguished by a higher accumulation of linoleic acid, while C300 has higher levels of oleic acid. This result confirms that the accumulation of specific fatty acids is linked to the genetic traits of the hybrid and not to the adopted cropping system (Zhang et al., 2024 ). It is well known that the lipid composition of maize is skewed towards triglycerides, and in terms of fatty acids, it leans towards omega-6 (linoleic acid), which is primarily accumulated in the endosperm, cotyledons, and aleurone layer (Golijan et al., 2019 ). Table 1 Means and statical analysis results of chemical composition and fatty acid profile of maize grain content. Variable Factor C300 C500 p-value Crude Protein Class 8.24% 7.10% 0.04 Treatment * Class - - 0.023 Treatment - - ns C16:0 Class 12.32 g100 g − 1 14.90 g100 g − 1 < 0.001 C18:1c9 Class 28.30 g100 g − 1 23.42 g100 g − 1 < 0.001 C18:2n6 Class 53.13 g100 g − 1 56.52 g100 g − 1 < 0.001 PUFA Class 54.94 g100 g − 1 58.31 g100 g − 1 < 0.001 PUFA_n6 Class 53.13 g100 g − 1 56.52 g100 g − 1 < 0.001 MUFA Class 29.42 g100 g − 1 24.58 g100 g − 1 < 0.001 SFA Class 15.55 g100 g − 1 17.02 g100 g − 1 < 0.001 Phenolic concentration The results for phenolic concentration indicated that only the Class factor had a statistically significant effect (p = 0.0473). Class C300 exhibited the highest concentration, approximately 24% higher than in C500 grains (Fig. 4 ). In contrast, no significant effect was observed for the agroforestry treatment compared to the control (p = 0.6870) or for the interaction between Treatment and Class (p = 0.4803). Moreover, no significant differences were observed for treatment and maize class in flavonoid concentration (Fig. 5 ). However, their interaction was statistically significant (p = 0.0069). Specifically, the post-hoc test revealed a significant difference between maize classes in the agroforestry system (p = 0.0401). C500 accumulated approximately 44% more flavonoids than C300, indicating that the effect of agroforestry treatments varies depending on the maize hybrids (Fig. 5 ). The antioxidant activity, regardless of the assay carried out (ABTS, DPPH or FRAP) was not statistically influenced by the maize hybrid and the agroforestry treatment, as well as by their interaction (p-value > 0.05). These results indicate that maize class plays a key role in determining phenolic content and suggest that C300 may have greater potential for producing these bioactive compounds. Phenolic compounds are primarily defensive molecules that tend to accumulate in plants under stress or suboptimal growing conditions. Indeed, C300 performed worse than C500 under the specific climatic conditions of this trial. However, C500 exhibited higher flavonoid levels, which are often associated with antioxidant activity and health benefits, and appeared better suited to the growing environment, as it achieved a higher overall yield (Mouradov and Spangenberg, 2024). The greater flavonoid accumulation in C500 under the agroforestry system (AGR) suggests that the response to agroforestry is hybrid-dependent and highlights the potential for tailoring agroforestry practices to specific maize hybrids to optimize flavonoid production. Based on previous results, maize class 500 appears more resilient and better adapted to the environmental conditions of this study, making it a more suitable choice for maximizing productivity. Additionally, it yields higher flavonoid levels, which, due to their antioxidant properties, can neutralize reactive oxygen species generated under stress conditions, thereby contributing to cellular protection and plant adaptation (Shomali et al., 2022 ). Seed health, phytopathological assessment A seed health test isolated several important plant-pathogenic and mycotoxigenic fungi from maize grain, including Fusarium spp., Penicillium spp., and, to a lesser extent, Aspergillus flavu s and A. niger . The Kruskal-Wallis test revealed a statistically significant difference in the percentage of grains infected by A. niger across treatment levels (p = 0.0037). This suggests that the different treatments significantly influence A. niger infestation, as shown in Fig. 6 , where the medians of the distributions are represented. Other fungal genera and species, particularly A. flavus , showed no significant differences (p > 0.05). To further investigate differences between treatment groups, a Dunn post hoc test with Bonferroni correction was performed following the Kruskal-Wallis test, as described in the previous paragraph. This test compares pairwise differences while adjusting p-values for multiple comparisons to control for Type I errors. The results of the Dunn test are summarized in Table 2 . The Bonferroni correction was applied to mitigate the risk of Type I errors. The results indicate significant differences between certain treatment groups: specifically, between CRL C300 and AGR C500 (p = 0.010) and between CRL C300 and CRL C500 (p = 0.036). No significant differences were found between the other group pairs (adjusted p-values > 0.05). Table 2 Results from Kruskall – Wallis statistical analysis for A. niger presence in maize grain. AGR means agroforestry, CRL means control system, C300 means maize FAO class 300 and C500 means maize FAO class 500. Comparison Z-value Adjusted p-value AGR C300 vs. CRL C300 -1.54 0.74 AGR C300 vs. AGR C500 2.25 0.14 CRL C300 vs. AGR C500 3.14 0.01 AGR C300 vs. CRL C500 1.82 0.41 CRL C300 vs. CRL C500 2.74 0.036 AGR C500 vs . CRL C500 0.22 1.00 Additionally, the mean percentage of grains infected by A. niger in each treatment group was as follows: AGR C300: 12.44%, CRL C300: 26.67%, AGR C500: 0.22%, CRL C500: 0.00%. These findings suggest that the CRL C300 group had the highest infection levels, while the AGR C500 and CRL C500 groups showed minimal to no infection (Fig. 6 ). Despite the very low number of seeds infected by A. flavus (as expected) both in control and treated samples, the reduction in Aspergillus niger infection of seeds collected from plants AGR system, especially in the C500 FAO class, is an interesting finding. This could be due to the microclimatic conditions created by the presence of trees, which might limit the proliferation of pathogens. The scientific literature lacks experiments on maize in agroforestry specifically analyzing kernel health. Numerous studies have highlighted the influence of microclimatic conditions on fungal growth and mycotoxin production (Wemheuer et al., 2020 ; Medina et al., 2014 ). However, no significant differences were observed for other fungal species, suggesting that the effect of agroforestry on seed health may be pathogen-specific. Overall, the results indicate that seed health depends on both the genetic characteristics of the hybrids and the favorable environmental conditions created by the agroforestry system. Entomological section Considering the impact of the two checked moth species ( Helicoverpa sp. and O. nubilalis ) on ear surface area, plants in the AGR system had significantly greater ear damage (χ² = 50.452; d.f. = 3; p < 0.001) than those grown in CRL (Fig. 7 ). Considering the damaged ear area related to the location of the plant within the theses (distance from the trees), no significant differences were found between the ears of plants grown in the AGR, while a significant difference (χ 2 = 58.034; d.f.=7; p < 0.001) is confirmed with respect to the maize C300 and C500 ears grown in the CRL (Fig. 8 ). Agroforestry could help overcome pest management problems providing habitat for wildlife (Favor et al. 2024 ). On the other hand, in the experimental context considered, plants grown in agroforestry systems showed a larger surface area of ears damaged by insect attacks compared to the control. This could be due to higher humidity conditions, which could promote the development of these pests, and the still low biodiversity of an extremely young and small agroforestry system in a low- variegated agricultural context (Isaac et al., 2024 ). However, no significant differences were observed regarding the position of plants relative to the trees, suggesting that the damage was evenly distributed within the agroforestry system. Conclusions In summary, under our experimental conditions, the poplar SRC agroforestry system has proven to be a suitable agricultural practice, capable of maintaining high production levels without compromising maize quality. Specifically, the FAO 500 hybrid, with its longer growing cycle and extended development of leaf and root systems compared to the FAO 300 hybrid, appears better suited to agroforestry conditions, enhancing resource utilization under water stress. Furthermore, its higher linoleic acid concentration confirms its potential application in animal feed, where a lipid profile rich in polyunsaturated fatty acids is preferred due to their nutraceutical activity. On the other hand, the agroforestry system did not have a significant impact on the fatty acid composition, confirming that the observed differences are due to the genetic characteristics of the hybrids. The selection of hybrids remains a crucial factor in optimizing the nutritional quality of maize. While agroforestry reduced infection by some pathogens, such as Aspergillus niger , the increased damage caused by pests highlights the need for integrated pest management to fully capitalize on the benefits of agroforestry implementation. Long-term experiments are needed to validate the results and refine agronomic practices to maximize both environmental and production benefits. However, a precision agriculture approach integrated within agroforestry could offer a sustainable path forward, reducing the ecological footprint of maize while maintaining productivity levels necessary for food and livestock feed systems. It can also help manage inputs more efficiently, reducing excess nitrogen and other agrochemical runoff, while optimized irrigation systems address maize’s high water demands and mitigate drought stress, enhancing crop resilience (Bongiovanni, 2004 ). The implementation of integrated pest management techniques, the use of resistant maize hybrids, and careful monitoring of soil quality are some of the approaches that can reduce the impact of pests and plant pathogens and mitigate the risk of mycotoxin contamination. Declarations Author Contributions Conceptualization, A.M., M.M., A.S., R.R., S.S., A.D., A.C.; methodology, M.F., A.R., F.C. A.M., M.M., A.S., R.R., S.S., formal analysis, M.F., M.C., A.R., F.C., A.M., M.M., A.S and A.C.; investigation, M.F., A.R., F.C. A.M., M.C., R.R., and A.C.; data curation, M.F., R.R., S.S., A.C., and A.M.; writing—original draft preparation, M.F., A.R.; writing—review and editing, A.M., M.M., A.S., R.R., S.S., A.C.; supervision, M.M., and A.S. All authors have read and agreed to the published version of the manuscript. Funding This research was carried out within the framework of the project “PRA_2022_39 - Influenza della consociazione erbacea–arborea (agroforestazione) sulla produttività e qualità della granella del mais, inclusa la presenza della piralide e l’insorgenza di micotossine,” funded by the University of Pisa. Data Availability Statement The data will be made available upon reasonable request to the corresponding author. Acknowledgments The authors would like to acknowledge the Centre for Agri-environmental Research “Enrico Avanzi” for hosting the trials and providing technical support. Conflicts of Interest The authors declare no conflict of interest. References Abrol, D. P., & Shankar, U. (2014). Pesticides, food safety and integrated pest management. In Integrated Pest Management: Pesticide Problems, Vol.3 (pp. 167–199). 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Different letters indicate significance for the HSD Tukey’s test (p-value \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6115397/v1/99dbeec3df51d39736e256b3.jpeg"},{"id":77408329,"identity":"5836e575-a021-4a55-9272-2f85d4cc3bf5","added_by":"auto","created_at":"2025-02-28 09:47:46","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":166304,"visible":true,"origin":"","legend":"\u003cp\u003eFlavonoids concentration expressed as mg Catechin equivalents (CAE) in g of dry weight (DW). Different letters indicate significance for the HSD Tukey’s test (p-value \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6115397/v1/d00eeba5edaf5216b14ef769.jpeg"},{"id":77407757,"identity":"6b636974-2d2f-4240-aed5-75ea1931318f","added_by":"auto","created_at":"2025-02-28 09:39:47","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":181636,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of distribution of \u003cem\u003eA. niger \u003c/em\u003eamong treatments and maize classes. AGR means agroforestry, CRL means control system, C300 means maize FAO class 300 and C500 means maize FAO class 500.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6115397/v1/ad9cf8daab9488a77ce594b9.jpeg"},{"id":77409308,"identity":"7d9aec11-75f3-4f68-a90b-07c62d090471","added_by":"auto","created_at":"2025-02-28 09:55:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":244646,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots showing differences in the percentage of ear surface area damaged among maize plants grown in CRL and AGR system. Red box plots indicate the median (line) within each box and the range of dispersion (lower and upper quartiles and outliers) of the median capture parameter. Green lines and blue vertical bars indicate mean values and standard errors, respectively. Kruskal–Wallis test followed by the Steel–Dwass multiple comparison (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6115397/v1/83194aa8711b85060b93fa01.png"},{"id":77407744,"identity":"5509161b-c1fa-41b1-935f-7234edbeabf1","added_by":"auto","created_at":"2025-02-28 09:39:46","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":491818,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots showing differences in the percentage of ear surface area damaged among maize plants grown under CRL system and AGR system considering the position within the plot in the AGR system (west side of the plot; east side of the plot; central part of the plot). Red box plots indicate the median (line) within each box and the range of dispersion (lower and upper quartiles and outliers) of the median capture parameter. Green lines and blue vertical bars indicate mean values and standard errors, respectively. Kruskal–Wallis test followed by the Steel–Dwass multiple comparison (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6115397/v1/bc41d0a0751a32063982d730.jpeg"},{"id":77436100,"identity":"a2fdce61-c7d2-4cd5-9a1e-433e8ae4b0e1","added_by":"auto","created_at":"2025-02-28 15:01:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3118579,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6115397/v1/c7f818da-1b9b-4d75-bfa2-1837d7e0f7a0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the effects of tree presence on maize pest damages, seed health, grain yield, nutritive value, and nutraceutical compounds","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe regeneration of farming systems requires the adoption of innovative agricultural practices that ensure farmers\u0026rsquo; incomes and social benefits while minimizing environmental impacts. At the same time, these practices should enhance agro-ecosystem services and promote biodiversity (Burgess et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the context of climate change, climate-smart adaptation strategies are essential in temperate areas for key crops like maize (\u003cem\u003eZea mays\u003c/em\u003e L.). Simultaneously, reducing the environmental impacts of cropping systems is crucial for mitigating the causes of global warming. The \u0026ldquo;green revolution\u0026rdquo; and the intensification of cropping systems, particularly for commodities like maize, have resulted in significant environmental impacts, making the development of viable alternatives essential (Aneja et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Paris et al., 2019).\u003c/p\u003e \u003cp\u003eThe FAOstat data (2021) indicates that 17.6 M ha of agricultural land is used for maize cultivation (Erenstein et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), making maize one of the most important crops for both food and feed supply chains (Abrol \u0026amp; Shankar, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hristov et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Revilla et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is not only a key crop for human consumption but also serves as a high-energy starch component in livestock feeding (Rouf Shah et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Saldivar \u0026amp; Perez-Carrillo, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Serna-Saldivar \u0026amp; Perez-Carrillo, 2019; Sudheesh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), for example in Europe 75% of the maize production is used as feed (Erenstein et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the environmental impacts associated with maize production are increasingly recognized. Intensive farming practices, which rely heavily on agrochemicals, fertilizers, and diesel fuel, contribute to greenhouse gas emissions, soil degradation, and water pollution (Holka \u0026amp; Bieńkowski, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, agroforestry - the practice of deliberately integrating woody species within agricultural fields (Burgess and Rosati 2018) - stands out among carbon farming practices as a promising approach. It aims to reduce the carbon footprint of agricultural products and sequester carbon in both soil (Riedell et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and woody plants (Kay et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while simultaneously ensuring food security (Leifeld, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Paustian et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In maize-dominated systems, agroforestry could provide additional benefits by buffering crops against variability in weather conditions during the summer months (van Noordwijk et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Understanding the effects of tree presence on maize performance is essential, requiring consideration of multiple factors beyond grain yield.\u003c/p\u003e \u003cp\u003eRecent reviews have examined the potential future impacts of climate change on pathogen infections in maize crops, which could limit their profitability (Juroszek \u0026amp; Von Tiedemann, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pumari\u0026ntilde;o et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These studies highlight the importance of understanding how changing microclimatic conditions may alter the interactions between crops and pathogens, potentially increasing disease pressure. Current challenges include the attack on maize by the key pest \u003cem\u003eOstrinia nubilalis\u003c/em\u003e H\u0026uuml;bner and its impact on mycotoxin contamination by \u003cem\u003eAspergillus flavus\u003c/em\u003e Link.. These issues emphasize the need for low-input management strategies in agriculture (Magagnoli et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although mycotoxin contamination of maize grain is primarily a post-harvest concern, preventive strategies should be applied in the field to mitigate the risk of aflatoxin contamination (Sarrocco et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe nutritional value of maize grain is critical for maintaining its viability as a high-value crop. Although total lipids in maize are a minor component, the fatty acid composition significantly influences its nutritional and nutraceutical properties (Ray et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, the nutritional profile of maize is closely linked to its content of phenolic compounds and flavonoids, which are bioactive compounds with well-documented antioxidant properties. Phenolic acids, the primary type of phenolic compounds in maize, contribute to resistance against oxidative stress and microbial attacks, enhancing the grain\u0026rsquo;s quality and shelf life. Flavonoids, meanwhile, exhibit anti-inflammatory, antiviral, and anticancer effects, further adding to the health benefits of maize consumption (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Slika et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The levels of these compounds vary depending on maize hybrids, growth conditions, and processing methods (Elisa et al. 2022). Importantly, phenolic compounds and flavonoids also benefit animal health and improve the quality of animal-derived products (Ponnampalam et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on this, tailored solutions based on field experiments are essential to address the concerns of farmers who are hesitant to adopt agroforestry systems on their farms, often due to insufficient information and limited access to extension services (Tranchina et al., 2024a; 2024b).\u003c/p\u003e \u003cp\u003eThis study aims to assess the performance of maize in agroforestry systems by investigating the influence of tree presence on yield, nutritional quality, pest dynamics, and the presence of mycotoxigenic fungi. The research is conducted under Mediterranean conditions within a poplar short-rotation coppice (SRC) agroforestry field trial.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSite description, experimental design and crop management\u003c/h2\u003e \u003cp\u003eThe field trial was conducted in 2023 at the \"E. Avanzi\" Centre for Agro-Environmental Research of the University of Pisa, Italy (43\u0026deg; 40\u0026prime; 49\u0026Prime; N, 10\u0026deg; 20\u0026prime; 47\u0026Prime; E; elevation: 1 m above sea level, 0% slope). The site is characterized by a Mediterranean climate, with an annual rainfall of 920 mm and a long-term mean temperature of 15\u0026deg;C (1987\u0026ndash;2017). The experiment took place in an alley-cropping experimental field featuring SRC poplar rows-oriented NW\u0026ndash;SE (357\u0026deg; N-NW) and maize of FAO maturity classes 300 and 500. The experimental field originated from a 0.7 ha poplar SRC plantation (\u003cem\u003ePopulus \u0026times; canadensis\u003c/em\u003e Moench, clone AF2, and \u003cem\u003ePopulus \u0026times; generosa\u003c/em\u003e Henry \u0026times; \u003cem\u003enigra\u003c/em\u003e L., clone Monviso) established in 2009. From 2009 to 2021, the poplar trees were coppiced biennially (Mantino et al., 2023). The tree row distance is 13.5m. Meteorological data were collected using a weather station (Vantage Pro2, Davis Instruments Corporation, Hayward, California, USA) located 50 m from the experimental trial site (43\u0026deg; 40\u0026prime; 51.6\u0026Prime; N, 10\u0026deg; 20\u0026prime; 27.6\u0026Prime; E).\u003c/p\u003e \u003cp\u003eOn April 26, 2023, maize belonging to FAO classes 300 and 500 (C300 and C500, respectively) was sown across 16 experimental plots arranged into four vertical blocks (Blocks) and four horizontal blocks (Longs) for testing two treatments: control (CRL) and agroforestry (AGR) (Fig.\u0026nbsp;1). The control treatment consisted of two adjacent vertical blocks, each 6 m wide\u0026mdash;one dedicated to C300 and the other to C500. While the agroforestry treatment consists in two vertical blocks, each 12 m wide and representing an alley between two tree rows. The two maize classes were randomized in the within each vertical block. Figure\u0026nbsp;1 illustrates the experimental layout in detail.\u003c/p\u003e \u003cp\u003eThe maize was sown at a density of 8.3 plants per square meter, and a phosphorus fertilization rate of 150 kg P₂O₅ ha⁻\u0026sup1; was applied at sowing. During the growing season, nitrogen fertilization was performed, with a total of 200 kg N ha⁻\u0026sup1; applied uniformly across both treatments. Weed control was achieved through weeding and herbicide application in mid-May, targeting broadleaf weeds. Harvesting was carried out on September 7, 2023, when the grain reached a moisture content of 10%.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSamples collection\u003c/h3\u003e\n\u003cp\u003eThe grain yield harvest was carried out on September 7, 2023, following a structured sampling scheme. For the control plots, eight plants were collected from each plot. In the agroforestry plots, 15 plants were sampled per plot, distributed as follows: five plants from the east side, 3 meters from the row of poplars; five plants from the center of the plot, within the alley; and five plants from the west side, 3 meters from the trees. A total of 184 plants were sampled across all plots.\u003c/p\u003e \u003cp\u003ePost-harvest measurements included: number of ears per plant, plant height, leaf biomass weight, leaf-to-stem ratio, ear weight. The ear was further divided into the weight of the chaff and the husk. The dry matter content was determined by drying the samples in an oven at 60\u0026deg;C for 96 hours.\u003c/p\u003e \u003cp\u003eThe entomological assessments focused on identifying pest damage by recording the presence or absence of pest-related injuries and calculating the percentage of the ear affected by pests. In particular, the level damage of maize ear surface caused by \u003cem\u003eHelicoverpa\u003c/em\u003e sp. (Lepidoptera: \u003cem\u003eNoctuidae\u003c/em\u003e), or \u003cem\u003eOstrinia nubilalis\u003c/em\u003e (Lepidoptera: \u003cem\u003ePyralidae\u003c/em\u003e) was visual measured on the 184 samples.\u003c/p\u003e\n\u003ch3\u003eDetermination and quantification of phenolic compounds and flavonoids present in maize grain\u003c/h3\u003e\n\u003cp\u003eThe same type of grouped samples used for the identification of mycotoxin-producing fungi was employed to investigate the quantification of phenolic compounds and flavonoids in maize grain from both the control and agroforestry systems.\u003c/p\u003e \u003cp\u003eSample extraction was carried out using 80% (v/v) aqueous methanol, following the procedure described by Tavarini et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Briefly, the samples were mixed with 80% methanol, sonicated for 30 minutes, stirred for an additional 30 minutes, and then centrifuged at 6000 rpm for 30 minutes. The supernatant was collected, and the pellet was re-extracted twice. The resulting hydroalcoholic extracts were used to quantify total phenolic and flavonoid content and to evaluate antioxidant and antiradical activity. Total phenolic content was determined using the Folin\u0026ndash;Ciocalteu method (Alonso-Borbolan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Absorbance was measured at 750 nm, and a standard curve of gallic acid (0\u0026ndash;250 mg L⁻\u0026sup1;) was used to express phenolic concentrations as milligrams of gallic acid equivalents (GAE) per gram of dry matter (DM). Flavonoid concentration was assessed using the aluminum chloride colorimetric method based on Kim et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Absorbance was measured at 510 nm, and results were expressed as milligrams of catechin equivalents (CE) per gram of dry matter (DM), using a standard curve of catechin (0\u0026ndash;250 mg L⁻\u0026sup1;).\u003c/p\u003e \u003cp\u003e \u003cem\u003eDetermination of antioxidant activity of maize grain extracts.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo evaluate antioxidant and antiradical activity, the ABTS (2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid)), DPPH (1,1-diphenyl-2-picrylhydrazyl), and FRAP (ferric reducing antioxidant power) assays were performed.\u003c/p\u003e \u003cp\u003eIn the ABTS assay, absorbance was measured at 734 nm, and antioxidant activity was expressed as micromoles of Trolox equivalents per gram of dry matter (DM), using a standard curve of Trolox (0\u0026ndash;200 \u0026micro;mol L⁻\u0026sup1;), as described by Re et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe DPPH assay was conducted following the method outlined by Brand-Williams et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), with absorbance recorded at 517 nm. Antioxidant activity was expressed as milligrams of Trolox equivalents per gram of dry matter (DM), using a standard curve of Trolox (0\u0026ndash;1 mg mL⁻\u0026sup1;).\u003c/p\u003e \u003cp\u003eFor the FRAP assay, absorbance was measured at 593 nm, and antioxidant activity was expressed as micromoles of Fe(II) per gram of dry matter (DM), using a standard curve of FeSO₄, as detailed by Benzie and Strain (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSeed health test for the detection of mycotoxigenic fungi\u003c/h3\u003e\n\u003cp\u003eTo assess the presence of mycotoxigenic fungi, particularly \u003cem\u003eA. flavus\u003c/em\u003e, dried grain samples were grouped by plot for the control treatment (a composite of grains from the eight sampled plants) and by position relative to the trees for the agroforestry treatment (grains grouped from the five sampled plants at different positions within each plot). These grouped samples were used for all subsequent analyses, including the determination of mycotoxigenic fungi, quantification of tocopherols, grain composition, and fatty acid profiling.\u003c/p\u003e \u003cp\u003eSpecial attention was given to \u003cem\u003eA. flavus\u003c/em\u003e, a major contributor, along with \u003cem\u003eAspergillus parasiticus\u003c/em\u003e Speare, to aflatoxin contamination in maize. Due to insufficient seed quantities in these grouped samples to perform the health analyses, additional grouping was carried out. This resulted in six control plates (three for class 300 and three for class 500) and 18 agroforestry treatment plates, with no distinction made regarding distance from the trees (nine plates each for class 300 and class 500 in agroforestry).\u003c/p\u003e \u003cp\u003eFor each grouped sample, 100 seeds were surface sterilized using an aqueous solution containing sodium hypochlorite (1% active chlorine) in 50% ethanol, followed by three washes in sterile water (10 minutes each). The sterilized seeds were then plated on Potato Dextrose Agar (PDA, 34 g L⁻\u0026sup1;; Difco, Milan, Italy) supplemented with antibiotics (streptomycin, 300 ppm; bacitracin, 150 ppm) and hymexazol (1.0 mL L⁻\u0026sup1;) to limit the growth of \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eMucoraceae\u003c/span\u003e. Plates were incubated for 10 days at 22\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C under a 12-hour light/12-hour dark photoperiod, with daily monitoring of fungal colony development.\u003c/p\u003e \u003cp\u003eFungal colonies were transferred to new PDA plates with antibiotics to obtain single-spore colonies. Each isolate was identified at the genus level based on morphological macro- and microscopic features, including colony color, conidiophore structure, and conidial shape and size. Observations were made using a Laica MZ FLIII stereomicroscope and a Leitz Dialux 22 microscope.\u003c/p\u003e \u003cp\u003eDue to the low incidence of \u003cem\u003eA. flavus\u003c/em\u003e detected in this study, no further analyses on aflatoxin presence or quantification were conducted.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted to determine the presence of significant differences between treatments: CRL and AGR; and maize class: C300 and C500; and their interaction.\u003c/p\u003e \u003cp\u003eA liner mixed model was used for evaluating the presence of significant difference on grain yield, chemical composition, fatty acids profile, and nutraceutical compounds among treatments and maize classes. The analysis was conducted using the R software (R core team, 2024) with the lme4 package and the \u0026ldquo;lmer\u0026rdquo; function (Bates et al., 2014) where treatments, maize classes and their interaction were the fixed effects, and the two random factors were: \u0026ldquo;blocks\u0026rdquo; nested in treatment and \u0026ldquo;longs\u0026rdquo; nested in treatment. HSD Tukey\u0026rsquo;s post hoc test was carried out by using the \u0026ldquo;emmeans\u0026rdquo; package (Lenth, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{ikjz}=\\mu\\:+Clas{s}_{i}+Trea{t}_{k}+Clas{s}_{i}\\times\\:Trea{t}_{k}+[Treat/Long{s}_{j}]+[Treat/Block{s}_{z}]+{\\epsilon\\:}_{ikjz}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e\u0026micro;\u0026thinsp;=\u0026thinsp;mean of variable\u003c/p\u003e \u003cp\u003eClass\u0026thinsp;=\u0026thinsp;maize FAO class (C300, C500, n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003cp\u003eTreat\u0026thinsp;=\u0026thinsp;treatment: CRL and AGR (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003cp\u003eLongs: blocks from East to West (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003cp\u003eBlocks: blocks from North to South (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003cp\u003eSecondly, to check differences among treatments and maize classes on the level damage of ear surface caused by \u003cem\u003eHelicoverpa\u003c/em\u003e sp. (Lepidoptera: \u003cem\u003eNoctuidae\u003c/em\u003e), or O. \u003cem\u003enubilalis\u003c/em\u003e (Lepidoptera: \u003cem\u003ePyralidae\u003c/em\u003e), a Kruskal\u0026ndash;Wallis non-parametric model followed by the Steel\u0026ndash;Dwass multiple comparison (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used because data were not normally distributed.\u003c/p\u003e \u003cp\u003eLastly, significant differences on fungal pathogen seed infection among treatments and maize classes was performed using the non-parametric Kruskal-Wallis test because data were not normally distributed. The post hoc analysis was conducted using the Dunn test, adjusted for Bonferroni correction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLimitation of the experiment\u003c/h2\u003e \u003cp\u003eThe findings are specific to the 2023 growing season and its climatic conditions and should not be generalized. Additionally, limited replications and constrained randomization due to farm- and field-specific conditions restrict broader applicability. These results provide initial insights from a case study that should be complemented by further field trials and larger research projects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeteorological conditions\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the monthly average temperature, total rainfall, and evapotranspiration. During the trial period (April\u0026ndash;September), cumulative precipitation reached 450 mm. July was the driest month, with only 8 mm of rainfall, while August recorded the highest monthly total at 105 mm. In May and June, during the crop\u0026rsquo;s exponential and linear growth phases, rainfall exceeded 255 mm. This, combined with mild temperatures, promoted rapid crop growth and development. Reference evapotranspiration (ET₀), estimated using the Penman-Monteith method, exceeded 816 mm over the trial period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMaize grain yield, chemical composition and fatty acids profile\u003c/h2\u003e \u003cp\u003eResults of the Type III ANOVA indicated no significant differences for the system treatment (p\u0026thinsp;=\u0026thinsp;1.00) and maize class (p\u0026thinsp;=\u0026thinsp;0.38). However, their interaction was statistically significant (p\u0026thinsp;=\u0026thinsp;0.0028), suggesting that the effect of treatment differs depending on the maize classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant differences in grain yield occurred between C300 and C500 in AGR. Specifically, the pairwise multiple comparisons test indicated a p-level of 0.0195.\u003c/p\u003e \u003cp\u003eThese findings suggest that agroforestry systems based on poplar SRC rows have a differential impact on the performance of C300 and C500 maize classes, which may reflect the adaptability of the maize hybrids to the agroforestry environment (Baier et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the main effects of treatment and maize class were not significant on their own, their interaction suggests that the benefits of agroforestry systems may be influenced by the specific maize hybrid.\u003c/p\u003e \u003cp\u003eAt the harvest time, differences in moisture content between C300 (7% of moisture) and C500 (11% of moisture) showed significant differences while no significant difference between treatments was recorded (p\u0026thinsp;=\u0026thinsp;0.96).\u003c/p\u003e \u003cp\u003eThe analysis of the yield and moisture content data showed that the agroforestry system did not result in significant overall differences compared to the control. However, the C500 performed better, likely due to its longer growing cycle demonstrating a higher moisture content at the harvest time. Moreover, the potential C500 greater leaf and root systems compared to C300 played a key role. These traits allowed C 500 to optimize resource use, particularly under water stress conditions that became apparent from June onwards until the end of the cycle. The higher ability of C500 to exploit soil water and nutrients due to a greater root system may explain its higher grain production under limited resource conditions. As observed by Baier et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in a meta-analysis evaluating maize grain yield in agroforestry systems, the crop productivity is generally higher in agroforestry systems, or at least not significantly lower compared to conventional systems. Our findings are consistent with these studies, confirming that it is possible to integrate trees into maize-based cropping systems. This supports the growing body of literature suggesting that agroforestry practices can maintain or even enhance crop productivity while contributing to environmental sustainability (Mouratiadou et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding nutritive value and chemical composition, a significant difference emerged only for crude protein (CP) content between the two treatment levels, CRL and AGR (p\u0026thinsp;=\u0026thinsp;0.04), with a significant interaction effect (p\u0026thinsp;=\u0026thinsp;0.023). Specifically, the mean CP content in class C300 was 16% higher than in class C500. Notably, no significant differences were observed between the agroforestry treatment and the control group in any of the analyses, suggesting that, under these conditions, agroforestry did not significantly impact grain composition compared to open-field cultivation.\u003c/p\u003e \u003cp\u003eA detailed analysis of the fatty acid profile focused on the most abundant and relevant fatty acids in the maize grain lipid fraction: palmitic acid (C16:0), oleic acid (C18:1c9), and linoleic acid (C18:2n6). Fatty acid composition was influenced by maize class. Stearic acid (C16:0) content was significantly higher in class C500 than in class C300 (p\u0026thinsp;=\u0026thinsp;0.00047), with an 18% increase. Oleic acid (C18:1c9) showed a highly significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with class C300 having a higher content (28.30 g 100g⁻\u0026sup1; of fatty acids) compared to class C500 (23.42 g 100g⁻\u0026sup1;). Similarly, linoleic acid (C18:2n6), the most abundant fatty acid in maize grain lipids, differed significantly between classes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with class C500 showing 6% higher levels than class C300 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese differences are also reflected in the broader fatty acid groupings: SFA (saturated fatty acids), PUFA (polyunsaturated fatty acids), PUFA_n6 (n6-family polyunsaturated fatty acids), and MUFA (monounsaturated fatty acids), as they are influenced by variations in the key fatty acids mentioned above (C16:0, C18:1c9, and C18:2n6).Therefore, the differences observed in the individual fatty acids are directly reflected in the overall categories, confirming the presence of a significant effect on the lipid profiles of the different classes.\u003c/p\u003e \u003cp\u003eThe agroforestry system does not appear to significantly affect the lipid profile of maize. The observed differences in the fatty acid composition, such as levels of oleic acid and linoleic acid, are solely attributed to the genetic characteristics of the hybrids as previous study describes (Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, C500 is distinguished by a higher accumulation of linoleic acid, while C300 has higher levels of oleic acid. This result confirms that the accumulation of specific fatty acids is linked to the genetic traits of the hybrid and not to the adopted cropping system (Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is well known that the lipid composition of maize is skewed towards triglycerides, and in terms of fatty acids, it leans towards omega-6 (linoleic acid), which is primarily accumulated in the endosperm, cotyledons, and aleurone layer (Golijan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeans and statical analysis results of chemical composition and fatty acid profile of maize grain content.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC300\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC500\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment * Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC16:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.32 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.90 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC18:1c9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.30 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.42 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC18:2n6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.13 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.52 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.94 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.31 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUFA_n6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.13 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.52 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.42 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.58 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.55 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.02 g100 g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePhenolic concentration\u003c/h2\u003e \u003cp\u003eThe results for phenolic concentration indicated that only the Class factor had a statistically significant effect (p\u0026thinsp;=\u0026thinsp;0.0473). Class C300 exhibited the highest concentration, approximately 24% higher than in C500 grains (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, no significant effect was observed for the agroforestry treatment compared to the control (p\u0026thinsp;=\u0026thinsp;0.6870) or for the interaction between Treatment and Class (p\u0026thinsp;=\u0026thinsp;0.4803).\u003c/p\u003e \u003cp\u003eMoreover, no significant differences were observed for treatment and maize class in flavonoid concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, their interaction was statistically significant (p\u0026thinsp;=\u0026thinsp;0.0069). Specifically, the post-hoc test revealed a significant difference between maize classes in the agroforestry system (p\u0026thinsp;=\u0026thinsp;0.0401). C500 accumulated approximately 44% more flavonoids than C300, indicating that the effect of agroforestry treatments varies depending on the maize hybrids (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe antioxidant activity, regardless of the assay carried out (ABTS, DPPH or FRAP) was not statistically influenced by the maize hybrid and the agroforestry treatment, as well as by their interaction (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThese results indicate that maize class plays a key role in determining phenolic content and suggest that C300 may have greater potential for producing these bioactive compounds. Phenolic compounds are primarily defensive molecules that tend to accumulate in plants under stress or suboptimal growing conditions. Indeed, C300 performed worse than C500 under the specific climatic conditions of this trial.\u003c/p\u003e \u003cp\u003eHowever, C500 exhibited higher flavonoid levels, which are often associated with antioxidant activity and health benefits, and appeared better suited to the growing environment, as it achieved a higher overall yield (Mouradov and Spangenberg, 2024). The greater flavonoid accumulation in C500 under the agroforestry system (AGR) suggests that the response to agroforestry is hybrid-dependent and highlights the potential for tailoring agroforestry practices to specific maize hybrids to optimize flavonoid production.\u003c/p\u003e \u003cp\u003eBased on previous results, maize class 500 appears more resilient and better adapted to the environmental conditions of this study, making it a more suitable choice for maximizing productivity. Additionally, it yields higher flavonoid levels, which, due to their antioxidant properties, can neutralize reactive oxygen species generated under stress conditions, thereby contributing to cellular protection and plant adaptation (Shomali et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSeed health, phytopathological assessment\u003c/h2\u003e \u003cp\u003eA seed health test isolated several important plant-pathogenic and mycotoxigenic fungi from maize grain, including \u003cem\u003eFusarium\u003c/em\u003e spp., \u003cem\u003ePenicillium\u003c/em\u003e spp., and, to a lesser extent, \u003cem\u003eAspergillus flavu\u003c/em\u003es and \u003cem\u003eA. niger\u003c/em\u003e. The Kruskal-Wallis test revealed a statistically significant difference in the percentage of grains infected by \u003cem\u003eA. niger\u003c/em\u003e across treatment levels (p\u0026thinsp;=\u0026thinsp;0.0037). This suggests that the different treatments significantly influence \u003cem\u003eA. niger\u003c/em\u003e infestation, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, where the medians of the distributions are represented. Other fungal genera and species, particularly \u003cem\u003eA. flavus\u003c/em\u003e, showed no significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eTo further investigate differences between treatment groups, a Dunn post hoc test with Bonferroni correction was performed following the Kruskal-Wallis test, as described in the previous paragraph. This test compares pairwise differences while adjusting p-values for multiple comparisons to control for Type I errors. The results of the Dunn test are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Bonferroni correction was applied to mitigate the risk of Type I errors. The results indicate significant differences between certain treatment groups: specifically, between CRL C300 and AGR C500 (p\u0026thinsp;=\u0026thinsp;0.010) and between CRL C300 and CRL C500 (p\u0026thinsp;=\u0026thinsp;0.036). No significant differences were found between the other group pairs (adjusted p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults from Kruskall \u0026ndash; Wallis statistical analysis for \u003cem\u003eA. niger\u003c/em\u003e presence in maize grain. AGR means agroforestry, CRL means control system, C300 means maize FAO class 300 and C500 means maize FAO class 500.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR C300 \u003cem\u003evs.\u003c/em\u003e CRL C300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR C300 \u003cem\u003evs.\u003c/em\u003e AGR C500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRL C300 \u003cem\u003evs.\u003c/em\u003e AGR C500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR C300 \u003cem\u003evs.\u003c/em\u003e CRL C500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRL C300 \u003cem\u003evs.\u003c/em\u003e CRL C500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR C500 \u003cem\u003evs\u003c/em\u003e. CRL C500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the mean percentage of grains infected by \u003cem\u003eA. niger\u003c/em\u003e in each treatment group was as follows: AGR C300: 12.44%, CRL C300: 26.67%, AGR C500: 0.22%, CRL C500: 0.00%. These findings suggest that the CRL C300 group had the highest infection levels, while the AGR C500 and CRL C500 groups showed minimal to no infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the very low number of seeds infected by \u003cem\u003eA. flavus\u003c/em\u003e (as expected) both in control and treated samples, the reduction in \u003cem\u003eAspergillus niger\u003c/em\u003e infection of seeds collected from plants AGR system, especially in the C500 FAO class, is an interesting finding. This could be due to the microclimatic conditions created by the presence of trees, which might limit the proliferation of pathogens. The scientific literature lacks experiments on maize in agroforestry specifically analyzing kernel health. Numerous studies have highlighted the influence of microclimatic conditions on fungal growth and mycotoxin production (Wemheuer et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Medina et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, no significant differences were observed for other fungal species, suggesting that the effect of agroforestry on seed health may be pathogen-specific. Overall, the results indicate that seed health depends on both the genetic characteristics of the hybrids and the favorable environmental conditions created by the agroforestry system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEntomological section\u003c/h2\u003e \u003cp\u003eConsidering the impact of the two checked moth species (\u003cem\u003eHelicoverpa\u003c/em\u003e sp. and \u003cem\u003eO. nubilalis\u003c/em\u003e) on ear surface area, plants in the AGR system had significantly greater ear damage (χ\u0026sup2; = 50.452; d.f. = 3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those grown in CRL (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the damaged ear area related to the location of the plant within the theses (distance from the trees), no significant differences were found between the ears of plants grown in the AGR, while a significant difference (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;58.034; d.f.=7; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) is confirmed with respect to the maize C300 and C500 ears grown in the CRL (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgroforestry could help overcome pest management problems providing habitat for wildlife (Favor et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, in the experimental context considered, plants grown in agroforestry systems showed a larger surface area of ears damaged by insect attacks compared to the control. This could be due to higher humidity conditions, which could promote the development of these pests, and the still low biodiversity of an extremely young and small agroforestry system in a low- variegated agricultural context (Isaac et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, no significant differences were observed regarding the position of plants relative to the trees, suggesting that the damage was evenly distributed within the agroforestry system.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, under our experimental conditions, the poplar SRC agroforestry system has proven to be a suitable agricultural practice, capable of maintaining high production levels without compromising maize quality. Specifically, the FAO 500 hybrid, with its longer growing cycle and extended development of leaf and root systems compared to the FAO 300 hybrid, appears better suited to agroforestry conditions, enhancing resource utilization under water stress. Furthermore, its higher linoleic acid concentration confirms its potential application in animal feed, where a lipid profile rich in polyunsaturated fatty acids is preferred due to their nutraceutical activity.\u003c/p\u003e \u003cp\u003eOn the other hand, the agroforestry system did not have a significant impact on the fatty acid composition, confirming that the observed differences are due to the genetic characteristics of the hybrids. The selection of hybrids remains a crucial factor in optimizing the nutritional quality of maize. While agroforestry reduced infection by some pathogens, such as \u003cem\u003eAspergillus niger\u003c/em\u003e, the increased damage caused by pests highlights the need for integrated pest management to fully capitalize on the benefits of agroforestry implementation. Long-term experiments are needed to validate the results and refine agronomic practices to maximize both environmental and production benefits.\u003c/p\u003e \u003cp\u003eHowever, a precision agriculture approach integrated within agroforestry could offer a sustainable path forward, reducing the ecological footprint of maize while maintaining productivity levels necessary for food and livestock feed systems. It can also help manage inputs more efficiently, reducing excess nitrogen and other agrochemical runoff, while optimized irrigation systems address maize\u0026rsquo;s high water demands and mitigate drought stress, enhancing crop resilience (Bongiovanni, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The implementation of integrated pest management techniques, the use of resistant maize hybrids, and careful monitoring of soil quality are some of the approaches that can reduce the impact of pests and plant pathogens and mitigate the risk of mycotoxin contamination.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, A.M., M.M., A.S., R.R., S.S., A.D., A.C.; methodology, M.F., A.R., F.C. A.M., M.M., A.S., R.R., S.S., formal analysis, M.F., M.C., A.R., F.C., A.M., M.M., A.S and A.C.; investigation, M.F., A.R., F.C. A.M., M.C., R.R., and A.C.; data curation, M.F., R.R., S.S., A.C., and A.M.; writing\u0026mdash;original draft preparation, M.F., A.R.; writing\u0026mdash;review and editing, A.M., M.M., A.S., R.R., S.S., A.C.; \u0026nbsp;supervision, M.M., and A.S. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was carried out within the framework of the project \u0026ldquo;PRA_2022_39 - Influenza della consociazione erbacea\u0026ndash;arborea (agroforestazione) sulla produttivit\u0026agrave; e qualit\u0026agrave; della granella del mais, inclusa la presenza della piralide e l\u0026rsquo;insorgenza di micotossine,\u0026rdquo; funded by the University of Pisa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be made available upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Centre for Agri-environmental Research \u0026ldquo;Enrico Avanzi\u0026rdquo; for hosting the trials and providing technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrol, D. 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(2024). Fatty acid de novo biosynthesis in plastids: Key enzymes and their critical roles for male reproduction and other processes in plants. In Plant Physiology and Biochemistry (Vol. 210). Elsevier Masson s.r.l. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plaphy.2024.108654\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2024.108654\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"agroforestry, fatty acid profile, mycotoxin contamination, tocopherol profile","lastPublishedDoi":"10.21203/rs.3.rs-6115397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6115397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study evaluates the effects of tree presence in an agroforestry system on maize performance, focusing on yield, nutritive value, fatty acid composition, and resistance to biotic stresses. Two maize hybrids, FAO C300 and FAO C500, were cultivated (i) in the narrow alleys of an agroforestry alley-cropping system based on SRC poplar tree rows and (ii) in a treeless control system. In 2023, the results indicate that tree presence does not reduce the average grain yield. However, a significant interaction between systems and maize hybrids was observed. FAO C500 demonstrated superior adaptability to the agroforestry environment, likely due to its longer growth cycle and more developed leaf and root systems, which allow for better resource utilization under water stress conditions. Tree presence did not affect the fatty acid profile of the grains, although FAO C500 accumulated more linoleic acid, while FAO C300 showed higher levels of oleic acid. The agroforestry environment reduced seed infestation by \u003cem\u003eAspergillus niger\u003c/em\u003e, particularly in the FAO C500 hybrid, but increased damage by insects on ear surfaces. These findings highlight the variability in maize hybrid suitability for agroforestry; however, since these results are based on only one year of experimentation, further trials over multiple years are necessary to validate these findings and gain a deeper understanding of tree effects on maize performance.\u003c/p\u003e","manuscriptTitle":"Exploring the effects of tree presence on maize pest damages, seed health, grain yield, nutritive value, and nutraceutical compounds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-28 09:39:41","doi":"10.21203/rs.3.rs-6115397/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47b42775-0df4-4488-bbf2-1de8941a57ac","owner":[],"postedDate":"February 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-28T14:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-28 09:39:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6115397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6115397","identity":"rs-6115397","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}