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Article

Nitrogen and Potassium Fertilization Modulate Dalbulus maidis (HEMIPTERA: CICADELLIDAE) Abundance and Corn Stunt Disease Severity

by
Ademar Novais Istchuk
1,*,
Matheus Henrique Schwertner
1,
Matheus Luis Ferrari
1,
Luiz Henrique Marques
2 and
Vanda Pietrowski
3
1
Corteva Agriscience, Linha Marreco Km 2, Toledo 85900-970, Brazil
2
Corteva Agriscience, Avenida Tambore, 267, Alphaville 06460-000, Brazil
3
Departamento de Agronomia, Universidade Estadual do Oeste do Paraná, Rua Pernambuco, 82, Marechal Cândido Rondon 85960-000, Brazil
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(19), 2086; https://doi.org/10.3390/agriculture15192086
Submission received: 10 September 2025 / Revised: 30 September 2025 / Accepted: 2 October 2025 / Published: 7 October 2025
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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Abstract

Corn stunt complex, transmitted by the corn leafhopper (Dalbulus maidis), poses significant yield risks to corn production. This study evaluated the effects of two corn hybrids and top-dressed nitrogen (N) and potassium (K) fertilization on D. maidis incidence and corn stunt symptom expression under field conditions. Eighteen treatments were tested in a randomized complete block design with six replications over two seasons. Leafhopper populations were monitored using yellow sticky traps, and symptom incidence and severity were assessed at R1 and R3 stages, respectively. While D. maidis populations varied substantially between seasons, neither N nor K fertilization, nor hybrid selection, significantly affected vector abundance. Importantly, symptom frequency and severity were not directly proportional to leafhopper density. Top-dressed fertilization, particularly with K, reduced the visual expression of corn stunt symptoms although it did not prevent infection. Hybrid responses to fertilization varied, with a genotype exhibiting greater symptom mitigation. Grain yield was not significantly influenced by nutrient rates or hybrid choice. These findings suggest that balanced N and K fertilization enhances crop resilience to corn stunt disease without directly suppressing vector populations. Integrating nutritional management with hybrid selection presents a promising strategy to add in corn stunt control and deepens our understanding of the environmental factors that mitigate severe symptoms.

1. Introduction

Corn (Zea mays L.) is a staple crop of global economic and food security importance, yet its productivity is increasingly threatened by complex phytosanitary challenges [1]. Among these, the corn stunt complex, caused by a group of pathogens such as Corn stunt spiroplasma (CSS), Maize bushy stunt phytoplasma (MBSP), and transmitted primarily by the corn leafhopper (Dalbulus maidis) (DeLong & Wolcott) (Hemiptera: Cicadellidae) has emerged as a major challenge in many corn-producing regions [2,3,4,5]. Yield losses associated with this disease complex can reach up to 100%, posing significant risks to both commercial agriculture and smallholder livelihoods [6]. This threat is particularly acute in regions such as Latin America, where corn is a dietary and economic cornerstone; Brazil alone produced over 120 million tons in the last season [7].
Current management strategies for the corn stunt complex rely heavily on controlling D. maidis populations, primarily through chemical insecticides and agronomic practices. However, the efficacy of these approaches is often limited by the high mobility of the vector, the persistence of volunteer corn, and alternative host plants [8,9,10,11,12]. Furthermore, the genetic resistance of commercial hybrids to the corn stunt complex remains insufficient, necessitating the development of integrated approaches that combine chemical and biological control, cultural practices, and the use of more tolerant hybrids.
Environmental factors such as temperature and rainfall, as well as crop nutrition, can influence both vector dynamics and disease expression, further complicating effective management [13,14,15,16,17]. Climate change is expected to further alter pest and pathogen pressures, emphasizing the need for adaptive and sustainable management strategies [18]. For a broader integrated strategy, it is critical to understand the effects of crop fertilization [19] on both corn leafhopper population and disease symptom severity. Although some studies have investigated the effect of plant nutrition on D. maidis populations and the incidence of corn stunt [14,20,21,22], the research focus, tested nutrients, and results obtained have been inconsistent across studies. This highlights the need for further research addressing the interactions between hybrid genotype, nutrient management, and vector abundance under field conditions.
Essential nutrients such as nitrogen (N) and potassium (K) play fundamental roles in key physiological processes, including cell wall strengthening, hormonal regulation, and vascular system efficiency. As a result, they may help mitigate the negative impacts associated with impaired sap flow caused by mollicutes. However, the extent to which nutrient management can enhance hybrid tolerance or alter vector–pathogen dynamics remains unclear.
In this context, the present study aimed to evaluate the effects of hybrid selection and top-dressed N and K fertilization on D. maidis incidence, corn stunt symptom expression, grain yield and other agronomic traits under field conditions. By describing these interactions, we can inform more resilient and integrated approaches for management of the corn stunt complex and protect corn yields under evolving agronomic and environmental challenges.

2. Materials and Methods

2.1. Trial Conditions and Experimental Design

Two field experiments were conducted under natural rainfall in Toledo, Paraná, Brazil (24°40′21.30″ S; 53°45′35.80″ W; elevation: 540 m), during the 2022/2023 (Experiment 1) and 2023/2024 (Experiment 2) summer corn seasons. Total rainfall was 865 mm in the first season and 860 mm in the second season (Figure S1). In both seasons, the preceding crop was black oats (Avena strigosa Schreb.), and there were no other corn crops or volunteer corn plants in the vicinity of the experimental area.
Soil chemical and physical properties were characterized in the 0–20 cm layer. The soil exhibited a clay texture, with 651 g kg−1 of clay. Organic matter content was 32 g dm−3, and base saturation was 78.2%. The pH in CaCl2 was 6.1, with low-acidity indicators (H+Al = 2.0 cmolc dm−3; Al3+ = 0.0 cmolc dm−3). Available phosphorus (P) and K levels were 16.8 mg dm−3 and 0.3 cmolc dm−3, respectively. Calcium (Ca2+) and magnesium (Mg2+) contents were 5.4 and 1.5 cmolc dm−3, respectively, resulting in a sum of bases of 7.2 cmolc dm−3 and a cation exchange capacity of 9.1 cmolc dm−3.
The experiments followed a randomized complete block design with 18 treatments and six replications. Treatments consisted of a factorial combination of two commercial Pioneer® Brand hybrids, (“Hybrid A” and “Hybrid B”), three N rates (0, 50, and 100 kg N ha−1) and three K2O rates (0, 40, and 80 kg K2O ha−1), all applied as top-dressed fertilization. Fertilization rates were determined based on an expected yield of 7.5 t ha−1, corresponding to a total requirement (sowing + top dressing) of 100–120 kg ha−1 of N and 50 –70 kg ha−1 of K2O. For simplicity, K2O rates are hereafter referred to as K rates. Each plot consisted of eight rows (0.50 m row spacing) each 5 m in length, totaling 20 m2.
Hybrid A is characterized by high fertility requirements, an early maturity cycle (CRM 135), an average plant height of 2.7 m, excellent stalk and root quality, and good grain quality. Hybrid B exhibits a strong response to fertilization, a super-early maturity cycle (CRM 131), an average height of 2.3 m, good stalk and root quality, excellent grain quality, and good leaf health. Both hybrids exhibit average tolerance to corn stunt complex, as observed in the Brazilian market, considering their maturity and adaptation zone (Internal data).

2.2. Trial Conduction and Maintenance

The experiments were sown at a density of 70,000 seeds ha−1 on 16 December 2022 (Experiment 1) and 10 November 2023. Seeds had an insecticidal treatment containing Dermacor® (Chlorantraniliprole—625 g L−1) at a dose of 48 mL kg−1 of seeds, and Poncho® (Clothianidin—600 g L−1) at a dose of 70 mL kg−1 of seeds. Basal fertilization at planting, regardless of hybrid and top-dressing treatment, consisted of 200 kg ha−1 of formulated fertilizer Unifértil 07-39-06 (N, P2O5, K2O + Ca—3.2%).
Weed management was conducted using a two-phase approach. In the pre-emergence phase, Dual Gold® (S-Metolacloro—960 g L−1) at a dose of 1.75 L ha−1 was used. During post-emergence, a combination of Atrazine (500 g L−1) at 3.0 L ha−1 and Soberan® (Tembotrione—420 g L−1) at 0.25 L ha−1 was used. Seven insecticide applications were made in the first season, and six in the second, targeting stink bugs (Diceraeus spp.) and D. maidis. The insecticides used encompassed multiple chemical groups, including sulfoximines, pyrethroids, organophosphates, carbamates, neonicotinoids, and their combinations.
Top-dressing fertilization was applied at the V3 growth stage (third leaf). This stage occurred eight and eleven days after emergence in the first and second experiments, respectively. Nitrogen was supplied using the formulated fertilizer Sulfammo MeTA 29 (29% N, 4% Ca, 2% Mg, and 6% S—TIMAC Agro), while K was supplied as potassium chloride (60% K2O—BRFértil). Fertilizers were manually applied to each row, with each product applied separately.

2.3. Dalbulus maidis Abundance

Corn leafhopper populations were monitored using one yellow sticky trap (BIOTRAP—Biocontrole) installed at the center of each plot. Monitoring was carried out throughout the vegetative period, from crop emergence (VE) until tasseling (VT). Although D. maidis populations in corn fields typically peak around physiological maturity [23,24], our monitoring was limited to the VT stage, as this period represents the most susceptible phase of the crop to injury caused by the corn stunt pathogens [9].
Traps were replaced weekly, and their height was adjusted as needed to match the average plant height in each plot. A total of seven weekly collections were performed in each season, totaling 49 days of monitoring each season. On each collection date, a subsample of ten adult leafhoppers was randomly selected from the traps and sent for molecular analysis using polymerase chain reaction (PCR) to detect the presence of MBSP and CSS in the vectors. The PCR analysis followed the published methodology [25], using a Rotor-Gene Q® thermal cycler (QIAGEN, Hilden, Germany). The universal primer pairs CSSF2/CSSR6 were employed to detect CSS while the R16F2n/R16R2 were used for MBSP detection [26,27]. The DNA of a plant infected with the pathogens was used as positive control, while plain water served as a negative control.

2.4. Corn Stunt Symptoms

Visual symptoms of the corn stunt complex were assessed on all plants within the six central rows of each plot. At full flowering (R1), plants were classified according to the presence of mild symptoms (foliar symptoms only) or severe symptoms (internode shortening and multiple ears). Additionally, six leaf samples were collected per plot (totaling 36 samples per treatment) for quantification of MBSP and CSS presence in the plants using PCR. For sampling, the middle third of the second leaf below the tassel was collected. Samples were stored in 50 mL Falcon® tubes containing 98% alcohol and subsequently sent to the laboratory (Corteva Agriscience, Porto Nacional, Tocantins, Brazil).
At the milk stage (R3) approximately 30 days after flowering, a second visual assessment was conducted using a 9–1 vigor score to evaluate each plant’s response to the pathogens transmitted by D. maidis, following the methodology proposed by [9].

2.5. Agronomic Evaluations

Fifteen days after fertilizer application, leaf samples were collected for nutrient analysis. Each composite sample consisted of 30 leaves, with 15 leaves taken from each hybrid. The most recent emerged leaf, including the sheath, was sampled from the first and fourth rows of the plots. After collection, samples were placed in paper bags, labeled, and sent to the laboratory for chemical analysis (PriomorLab, Assis Chateaubriand, Paraná, Brazil).
Prior to harvest, all plants from the four central rows were counted and classified according to their integrity as follows: stalk lodged (broken stalk below the main ear), root lodged (plant that leans from a vertical axis at an approximate 30° angle or greater), or normal. The percentage of plants in each category was calculated based on the total number of plants per plot.
At harvest, when grain moisture was approximately 25%, all ears were manually collected regardless of plant integrity. Harvested ears were dried to reduce and standardize moisture content. Ears were then counted and classified by quality as follows: malformed ears (pollination issues resulting in few set kernels, reduced size, or were twisted in shape), poorly filled ears (normal pollination but with shriveled kernels in parts of the ear), or normal ears. The percentage of ears in each category was calculated based on the total number of harvested ears per plot. Harvested grain weight (kg), thousand-kernel weight (g) and moisture content (%) were measured, and grain yields were standardized to a moisture content of 13%.

2.6. Statistical Analysis

All statistical analyses and graphical designs were performed using R software version 4.4.2 and RStudio version 2025.05.0 [28]. The distribution of each variable was assessed by fitting probabilistic models to normal, log-normal, gamma, Poisson, and negative binomial distributions (function fitdist, package fitdistrplus). Model fits were compared based on theoretical cumulative distribution functions (CDF) (function cdfcomp, package fitdistrplus), and goodness-of-fit metrics, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), were calculated (function gofstat, package fitdistrplus). The distribution with the lowest AIC and BIC values was selected as the best fit for the data.
Depending on the distribution, either generalized linear mixed models or linear mixed models were fitted to the response variables (functions glmer.nb, lmer, glmmTMB; packages lme4, glmmTMB). These models were used to address non-normally distributed variables, account for the hierarchical structure of repeated measures (e.g., corn leafhopper abundance), and appropriately model the correlation among observations within the same block or week of sampling. Fixed effects included Season, Hybrid, N, and K, as well as all possible interactions (Season × Hybrid × N × K). Block was included as a random effect (1|Block) to account for variation between experimental blocks. For D. maidis abundance, weekly collections nested within blocks were included as a random effect (1|Week/Block) to capture variation both within and between blocks over time.
The singularity of random effects was assessed using the function isSingular (package lme4). Model residuals were validated using the function simulateResiduals (package DHARMa) to verify assumptions of normality, homoscedasticity, and independence. Model predictions and confidence intervals (95%) were generated with the function ggpredict (package ggeffects), and graphical visualizations were created using ggplot2.
When a significant interaction effect was observed (alpha = 0.05) between fertilization rates, response surfaces were generated by estimating 100 intermediate values for each factor (function surf3D, function plot3D). Visual symptom scores were grouped by treatment, and individual plant scores were presented as frequency based on the total number of plants in the plot. For group comparisons, score frequencies were further aggregated by season, hybrid, N rate, and K rate, and the resulting categories were compared to the scoring scale.

3. Results

3.1. Dalbulus maidis Abundance

The total number of leafhoppers collected during the 2022/2023 season (65,711) was nearly double that collected during the 2023/2024 season (33,781), indicating a substantial difference in D. maidis pressure between the two years. As shown in Table 1 and Figure 1, D. maidis abundance differed significantly between the two seasons, with distinct temporal patterns (Figure 2).
Specifically, in the 2022/2023 season (Figure 2a), a peak in leafhopper abundance was observed at the V9 growth stage, following relatively low populations during the early vegetative stages. A secondary peak occurred at V13, and the overall abundance was higher compared to the second season. In contrast, the 2023/2024 season (Figure 2b) exhibited a peak at V5, with a more gradual population increase during the early vegetative stages and a lower overall abundance.
The analytical detection of mollicutes in leafhoppers, determined by PCR analysis, is presented in Figure 3. Across both seasons, the prevalence of MBSP in leafhoppers was consistently higher than that of CSS. The peak prevalence of MBSP occurred in leafhopper samples collected at the V7 growth stage in both seasons, reaching 50% in 2022/2023 and 60% in 2023/2024.

3.2. Corn Stunt Symptoms

An average of 79 plants per plot were evaluated and classified for the presence of corn stunt symptoms. The percentage of asymptomatic plants and those exhibiting mild symptoms was significantly affected by the interaction between season and N rate (Table S1). Additionally, the frequency of plants exhibiting severe symptoms, including shortened internodes, reduced plant height, and multiple ears, was influenced by corn hybrid, N rate, and the interaction between season and N rate.
In the absence of top-dressed N, the percentage of asymptomatic plants at R1 was similar between seasons (Figure 4a). However, increasing N rates led to a higher percentage of asymptomatic plants in the 2022/2023 season, while it reduced the percentage in 2023/2024. A similar contrasting trend was observed for the percentage of plants exhibiting mild symptoms (Figure 4b): higher N rates reduced the percentage of plants with mild symptoms in 2022/2023, but increased it in 2023/2024. Overall, the frequency of mild corn stunt symptoms was lower in the 2022/2023 season compared to 2023/2024.
Regardless of the N rate, corn plants receiving top-dressed fertilization exhibited a lower incidence of severe stunt symptoms compared to those without N fertilization, with this reduction being more pronounced in Hybrid B (Figure 4c). Compared to the first experiment, the 2023/2024 season showed a lower frequency of severe symptoms in both unfertilized plants and those receiving 50 kg ha−1 of top-dressed N (Figure 4d).
PCR analysis at R1 revealed a high rate of mollicute infection in plants, even in the absence of apparent visual symptoms (Table S2). The incidence of CSS and MBSP in plants, as determined by PCR, was not significantly affected by fertilization levels or corn hybrid.
In general, corn stunt symptom severity at R3 was lower in the first season (Figure 5a). During the 2022/2023 season, 91.3% of plants exhibited vigor scores of seven or higher, characterized by foliar chlorosis (reddish or yellowish) and slight delays in growth and development. In contrast, only 84% of plants received this classification in the subsequent season, which also had a higher frequency of plants scoring below five, including those with shortened internodes, abnormal proliferation, and ear malformation.
When isolating the effect of corn hybrids across seasons, Hybrid B exhibited a higher percentage of plants with vigor scores of seven or higher (91.7%) compared to the Hybrid A (84.1%), a difference of 7.6 percentage points (Figure 5b).
The effect of N rates differed between the hybrids (Figure 6a). In Hybrid A, symptom severity increased with increasing N rates across all symptom categories except for a score of nine (absence of symptoms), where the frequency of asymptomatic plants increased with 100 kg ha−1 of N. Conversely, Hybrid B exhibited reduced corn stunt symptom severity with increasing top-dressed N rates, attributed to both an increased number of asymptomatic plants and a decreased frequency of plants scoring below five.
Similar to N, increasing top-dressed K rates also reduced corn stunt symptoms (Figure 6b). Hybrid A showed a 2.5% increase in symptom-free plants at the highest K rate, though the frequency of plants scoring seven or higher remained similar across all K rates. In contrast, the Hybrid B exhibited a more pronounced reduction in symptom severity with increasing K rates, mirroring the response to increasing N rates. This difference was evident in the frequency of plants scoring eight or lower.

3.3. Agronomic Evaluations

Foliar N and K concentrations in corn leaf samples were significantly influenced by the interaction between top-dressed nutrient rate and season (Table S3). Specifically, foliar concentrations following fertilization were higher in the 2022/2023 season than in 2023/2024 (Figure 7a,b). Regardless of season, foliar concentrations of both nutrients increased with increasing fertilizer rates.
The percentage of normal plants at harvest was significantly influenced by the interactions between season and hybrid, hybrid and N rate, and season and K rate (Table S4). The 2022/2023 season exhibited a lower percentage of normal plants, with Hybrid A showing a greater percentage compared to Hybrid B (Figure 8a). Across all top-dressed N rates, Hybrid B had a lower percentage of intact plants compared to Hybrid A (Figure 8b). The hybrids also responded differently to increasing N rates: Hybrid A showed an increase in normal plants at higher N rates, while Hybrid B exhibited a reduction. The effect of increasing K rates on plant integrity also differed between seasons. In 2022/2023, higher K rates increased the number of normal plants, whereas in the second experiment, plots receiving top-dressed K showed a reduction (Figure 8c).
The percentage of broken plants was higher in the 2022/2023 season, and in this season, Hybrid A had more stalk-lodged plants compared to Hybrid B (Figure 9a). A similar trend was observed for the percentage of root-lodged plants, but in this case, Hybrid B had a higher number of root-lodged plants in the 2022/2023 season (Figure 9b).
The proportion of normal ears differed between seasons and corn hybrids, while the percentage of malformed ears at harvest was significantly influenced by the interaction between season and hybrid (Table S5). The percentage of poorly filled ears was not significantly influenced by N or K rates, hybrids, or season. Hybrid B exhibited a higher proportion of normal ears compared to Hybrid A (Figure 10a). The 2023/2024 season had a higher number of normal ears than the 2022/2023 season (Figure 10b). The percentage of malformed ears was higher in Hybrid A compared to Hybrid B across both seasons and was generally higher in the 2022/2023 season compared to the 2023/2024 season (Figure 10c).
Thousand-kernel weight varied among the tested hybrids and between seasons (Table S6). In the second experimental season, Hybrid B exhibited a greater thousand-kernel weight than Hybrid A. Furthermore, the thousand-kernel weight of Hybrid B in the second season was also higher than that recorded for the same hybrid in the 2022/2023 season (Figure 11). The harvested yield per plot was affected by the interaction between cropping season and N rates (Table S6). Differences between seasons were observed only in the absence of top-dressed N fertilization (Figure 12); at other rates, yields did not differ between experiments. During the 2022/2023 season, yield remained constant across increasing N rates, whereas in the 2023/2024 season, higher N rates resulted in lower grain yield.

4. Discussion

4.1. Dalbulus Maidis Abundance

Monitoring of D. maidis populations with sticky traps proved effective for capturing temporal fluctuations in pest abundance, consistent with previous studies [16,29,30]. The greater number of D. maidis individuals collected during the first season may be attributed to increased insect migration and lower precipitation during early crop development, conditions known to favor leafhopper population growth (Figure 1 and Figure S1) [16]. These results underscore the importance for farmers of implementing regular pest monitoring, particularly during critical crop periods, to optimize management decisions and resource allocation.
Despite evidence that D. maidis can discriminate among plants with different nutritional levels, preferentially oviposits on those with higher pre-plant nitrogen fertilization, and increases the survival, longevity, and its fecundity [14,17], our study did not detect significant differences in vector populations among corn plants subjected to varying N and K top-dressing rates or between hybrids. The consistently high leafhopper populations observed in both seasons may have masked subtle treatment effects. This finding contrasts with previous reports of increased leafhopper populations following nitrogen [21,22], suggesting that, under high vector pressure, adjustments in N and K fertilization alone may not be sufficient to reduce D. maidis abundance. This highlights the need to adopt integrated management strategies that combine pest monitoring, hybrid selection, and targeted insecticide applications.
The higher incidence of MBSP relative to CSS observed in this study is consistent with previous reports of greater MBSP frequency in cooler regions of Brazil [31]. Leafhoppers carrying pathogens at the onset of crop establishment are likely migrants following the safrinha harvest, having reproduced on volunteer corn, other summer or silage corn plantings, or having survived for some period on alternative host plants [10,32,33]. The elevated frequency of MBSP detected at the V7 growth stage may explain the increase in infected plants observed between the V10 and VT stages, as recently described by [34].
The observed peak in infection likely reflects the acquisition and spread of MBSP by new generations of leafhoppers feeding on asymptomatic corn plants within the experimental area (Figure 2). This is supported by evidence that the first infected plants begin transmitting MBSP to new leafhoppers with moderate efficiency as early as five days after inoculation [35]. Subsequent reductions in the proportion of infected leafhoppers are probably attributable to sampling limitations, population increases resulting from reduced insecticide applications, and the emergence of new, uninfected vector generations. This dilution effect of infected individuals within the overall population tends to persist until harvest [13]. These findings highlight the importance of early-season vector management and the need for integrated strategies, including timely insecticide applications and volunteer corn control, to limit the initial establishment and subsequent corn stunting spread.

4.2. Corn Stunt Symptoms

The reduced incidence of corn stunt symptoms at the R1 stage during the first season may be attributed to elevated foliar N levels resulting from fertilizer application, despite the higher abundance of D. maidis (Figure 1, Figure 4a and Figure 7a). This reduction was observed across all top-dressed N rates, aligning with previous findings that N fertilization can decrease the expression of stunting [22]. Notably, the incidence of severe symptoms in N-fertilized plants differed between hybrids, suggesting that top-dressed N application amplifies genotypic differences in the symptom expression and facilitates the identification of hybrid susceptibility to the corn stunt complex. Nevertheless, N fertilization reduced the number of symptomatic plants in both hybrids, particularly under conditions of lower overall symptom frequency.
Adequate N fertilization supports physiological parameters such as photosynthetic efficiency, gas exchange, chlorophyll content, and water use, while also reducing oxidative stress, all of which may contribute to symptom mitigation and improved plant health [36,37].
The likely random and uniform colonization of plots by infective leafhoppers combined with high vector populations and their capacity to infect multiple plants throughout the crop cycle may explain the lack of significant differences in PCR-detected infection rates across treatments. Additionally, early detection of mollicute infections via PCR likely contributed to underestimation of the true number of affected plants [22,35,38].
Interestingly, despite the higher D. maidis populations in the first experiment, symptom severity at the R3 stage was lower compared to the subsequent season (Figure 1 and Figure 5a). This finding reinforces the fact that damage caused by vector-borne pathogens is not necessarily proportional to vector abundance, but rather to the infectivity rate within the vector population [15,39,40]. Thus, economic injury levels and vector control thresholds cannot be based solely on population abundance, underscoring the need for effective vector management regardless of density.
Environmental factors also played a critical role influencing both D. maidis occurrence and its infection by mollicutes [13,15,16]. The higher temperatures and reduced rainfall during the 2023/2024 season, particularly during the grain-filling stage (Figure S1), may have exacerbated the damage caused by the corn stunt complex [4]. These results highlight the importance of considering seasonal weather patterns in disease risk assessment and management plans.
Consistent with previous reports, hybrid responses to corn stunt pathogens varied, emphasizing the importance of hybrid selection [29,41,42,43]. Both hybrids exhibited a greater proportion of asymptomatic plants at the highest N fertilization rate, suggesting that adequate N can help mitigate mild stunt symptoms (Figure 6a). Hybrid B showed reduced severe symptoms at higher N rates, while Hybrid A exhibited increased severe symptoms with higher rates of top-dressed N, highlighting the need for genotype-specific nutrient management.
Importantly, this is the first report indicating that top-dressed K fertilization can reduce the visual symptoms of the corn stunt complex at the R3 stage (Figure 6b). The physiological basis for symptom mitigation observed with K fertilization can be attributed to its central role in cell wall formation and vascular function. As reviewed by [44], K is essential for the synthesis of cellulose and hemicellulose, activation of cell wall-modifying enzymes, and maintenance of cellular turgor. These processes collectively strengthen plant tissues and improve water and nutrient transport, which likely underlie the observed mitigation of symptom severity in our trials.
However, increased fertilization did not prevent infection but rather limited symptom expression, likely due to enhanced plant immune responses at higher nutrient levels. The observed differences between hybrids may be attributed to improved nutrient use efficiency, with Hybrid B demonstrating a particularly strong response to fertilization.

4.3. Agronomic Evaluations

Top-dressed fertilization effectively increased foliar N and K concentrations, confirming improved plant nutrition and the efficacy of the treatments (Table S3, Figure 7). Across all top-dressing rates, foliar nutrient concentrations remained within the recommended range for corn development [45], and no deficiencies were observed. The lower foliar concentrations in the second season likely resulted from higher rainfall, which may have increased nutrient leaching and reduced availability.
Plant integrity at harvest differed between seasons, likely due to greater precipitation before harvest in the first experiment, which may have compromised stalk strength (Figure 8 and Figure S1). Increased rainfall during grain filling also resulted in heavier ears, making plants more susceptible to lodging. Susceptibility to lodging and stalk breakage varied between hybrids was strongly influenced by environmental conditions [46]. Lodging and stalk breakage can also be associated with stunt diseases, which impair phloem transport and may promote fungal infections, further reducing stalk integrity [47,48].
Interestingly, a higher concentration of intact plants was observed in plots with lower average plant vigor at R3; however, this metric should not be interpreted in isolation as an indicator of severe corn stunt symptoms. Plants exhibiting milder stunt symptoms often have a greater capacity for ear development and grain filling, which can make them more prone to lodging due to the increased weight they must support. Additionally, precipitation near physiological maturity in the 2022/2023 season likely reduced root stability and increased plant weight. Both factors may have contributed to greater lodging risk. Winds associated with rainfall may have further exacerbated lodging, and the delayed harvest in this season prolonged plant exposure to these adverse conditions.
The percentage of normal ears was negatively correlated with corn stunt symptoms, explaining the higher number of normal ears in the hybrid with fewer symptoms (Hybrid B). This correlation was stronger for symptoms observed at R1, likely due to more systemic effects in plants infected earlier [9]. Conversely, the highest number of malformed ears occurred in the hybrid with more severe symptoms, especially under adverse climatic conditions, such as those observed during the 2022/2023 season (Figure 10c).
In the first season, Hybrid B exhibited a high number of broken and lodged plants at harvest, which may have led to poor grain filling and reduced dry matter accumulation. The thousand-grain weight, which is directly correlated with yield [49], varied accordingly.
Despite differences in corn stunt symptom severity between seasons, grain yield remained relatively constant (Figure 4 and Figure 5a). This may reflect hybrid-specific responses to infection, which can vary with environmental conditions and infection timing [48]. Thus, hybrids may display different symptom levels without yield loss, or suffer yield reductions even with lower symptom incidence, depending on the context.
Although previous studies have reported higher yields in plants with greater vigor scores [9,40,41,42], only a weak correlation between yield and plant vigor at R3 was observed in this study. This may be due to the manual harvest method, which included all plants regardless of integrity; mechanical harvesting, which often excludes broken and lodged plants, might reveal a stronger correlation between symptom severity and yield.
Conversely, a significant negative correlation was found between yield and D. maidis population, with the highest yields obtained in plots with lower vector abundance. This relationship warrants further investigation, as under normal water availability, the direct damage caused by D. maidis is typically minimal [50].
These findings highlight the importance of integrated management strategies that combine balanced fertilization, hybrid selection, and effective pest monitoring. While optimal nutrition can improve plant health and mitigate some effects of the corn stunt complex, it cannot fully compensate for high vector pressure or adverse environmental conditions. In contrast, high N fertilization potentially promoting vector outbreaks [17]. Overall, farmers should consider both agronomic and pest management practices to maintain yield stability and crop integrity under variable conditions.

4.4. Limitations and Future Research

Field trials provide valuable insights under real-world conditions, offering direct applicability for farmers and enabling assessment of the economic impacts of management practices. However, the considerable variation observed between seasons in this study complicates the formulation of broader conclusions, given the complex interactions among plants, vectors, and pathogens. This inherent variability highlights the need for complementary approaches.
To more precisely elucidate the effects of plant nutrition on D. maidis and corn stunt symptom expression, future research should incorporate laboratory or controlled-environment experiments, which can help isolate specific factors and reduce environmental noise. Additionally, evaluating plant fertilization responses under more nutrient-deficient soil conditions could better reveal the impacts of nutrition on both vector populations and disease symptoms.

5. Conclusions

No effect of top-dressed N and K fertilization on the population of D. maidis was observed. High populations of D. maidis do not necessarily correlate with frequency and severity of corn stunt symptoms. Fertilization does not prevent plant infection but can limit symptom expression, especially under conditions of milder symptoms. Top-dressed K fertilization reduces the visual presence of corn stunt symptoms at the R3 stage.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture15192086/s1, Figure S1: Meteorological data recorded during the conduct of Experiment 1 (a) and Experiment 2 (b) in Toledo, Paraná; Table S1: Summary of linear mixed models (LMMs) for the percentage of asymptomatic plants and mild symptoms, and of a generalized linear mixed model (GLMM; gamma distribution, log link, Laplace approximation) for the percentage of plants exhibiting severe symptoms, as functions of corn hybrid and nitrogen and potassium rates across two seasons; Table S2: Results of PCR-based detection of MBSP and CSS in corn plants at the R1 reproductive stage, and visual assessment of corn stunting symptoms in two hybrids under varying top-dressed nitrogen and potassium rates across two seasons in Toledo, Paraná; Table S3: Summary of linear mixed models (LMMs) for foliar nitrogen and potassium concentrations in corn leaf samples under varying top-dressed nutrient rates across two seasons; Table S4: Summary of linear mixed models (LMMs) for percentage of normal plants, stalk lodged, and root lodged plants as functions of corn hybrid and nitrogen and potassium rates across two seasons; Table S5: Summary of linear mixed models (LMMs) for percentage of normal ears, malformed, and poorly filled ears as functions of corn hybrid and nitrogen and potassium rates across two seasons; Table S6: Summary of linear mixed models (LMMs) for thousand-kernel weight (g) and harvested grain (kg) as functions of corn hybrid and nitrogen and potassium rates across two seasons.

Author Contributions

Conceptualization, A.N.I., L.H.M. and V.P.; methodology, A.N.I., L.H.M. and V.P.; validation, A.N.I., M.H.S., M.L.F., L.H.M. and V.P.; formal analysis, A.N.I.; investigation, A.N.I., M.H.S. and M.L.F.; resources, L.H.M.; writing—original draft preparation, A.N.I.; writing—review and editing, A.N.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Corteva™ Agriscience.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed towards the corresponding author.

Acknowledgments

The authors acknowledge all the effort and support of all researchers and partners from Corteva™ Agriscience.

Conflicts of Interest

The authors Ademar N. Istchuk, Matheus H. Schwertner, Matheus L. Ferrari and Luiz H. Marques are currently employed by Corteva™ Agriscience or an affiliated corporate legal entity of Corteva™ Agriscience. The authors declare that the research related to the manuscript was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
AICAkaike Information Criterion
BICBayesian Information Criterion
CRMComparative Relative Maturity
CSSCorn stunt spiroplasma
KPotassium
NNitrogen
MBSPMaize bushy stunt phytoplasma
PCRPolymerase chain reaction
R1Corn full flowering stage
R3Corn milk stage
VTCorn tasseling stage
V“x”Corn growth stage, “x” leaf

References

  1. Dowswell, C.R.; Paliwal, R.L.; Cantrell, R.P. Maize in the Third World, 1st ed.; CRC Press: Boca Raton, FL, USA, 2020. [Google Scholar]
  2. Nault, L.R. Maize Bushy Stunt and Corn Stunt: A Comparison of Disease Symptoms, Pathogen Host Ranges, and Vectors. Phytopathology 1980, 70, 659–662. [Google Scholar] [CrossRef]
  3. Whitcomb, R.F.; Chen, T.A.; Williamson, D.L.; Liao, C.; Tully, J.G.; Bove, J.M.; Mouches, C.; Rose, D.L.; Coan, M.E.; Clark, T.B. Spiroplasma kunkelii Sp. Nov.: Characterization of the Etiological Agent of Corn Stunt Disease. Int. J. Syst. Bacteriol. 1986, 36, 170–178. [Google Scholar] [CrossRef]
  4. de Oliveira, C.M.; Frizzas, M.R. Eight Decades of Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae) in Brazil: What We Know and What We Need to Know. Neotrop. Entomol. 2022, 51, 1–17. [Google Scholar] [CrossRef]
  5. Faris, A.M.; Rodrigues Duffeck, M.; Olson, J.D.; Espindola, A.S.; Muller, L.; Velasco, S.E.; Zambiasi, J.M. First Report of Dalbulus maidis (DeLong and Wolcott) (Hemiptera: Cicadellidae) in Oklahoma. Insects 2024, 15, 778. [Google Scholar] [CrossRef]
  6. de Oliveira, E.; de Oliveira Resende, R.; de la Paz Giménez Pecci, M.; Laguna, I.G.; Herrera, P.; Cruz, I. Incidência de Viroses e Enfezamentos e Estimativa de Perdas Causadas Por Molicutes Em Milho No Paraná. Pesqui. Agropecu. Bras. 2003, 38, 19–25. [Google Scholar] [CrossRef]
  7. USDA United States Department of Agriculture, Production—Corn. Available online: https://fas.usda.gov/data/production/commodity/0440000 (accessed on 10 April 2024).
  8. Oliveira, C.M.; Sabato, E.d.O. Doenças Em Milho: Insetos-Vetores, Molicutes e Vírus, 1st ed.; Embrapa: Brasília, Brazil, 2017. [Google Scholar]
  9. Neves, T.N.; Foresti, J.; Silva, P.R.; Alves, E.; Rocha, R.; Oliveira, C.; Picanço, M.C.; Pereira, E.J. Insecticide Seed Treatment against Corn Leafhopper: Helping Protect Grain Yield in Critical Plant Growth Stages. Pest Manag. Sci. 2022, 78, 1482–1491. [Google Scholar] [CrossRef]
  10. Istchuk, A.N.; Da Silva, P.R.; Borges, A.R.P.; das Neves, T.C.N.; Ramos Pereira, R.; Schwertner, M.H.; Ishizuka, T.K.; Pietrowski, V. Does the Corn Leafhopper Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae) Reproduce in Hosts Other than Maize? EntomoBrasilis 2023, 16, e1044. [Google Scholar] [CrossRef]
  11. Machado, E.P.; Souza, E.V.; Dias, G.S.; Sacilotto, M.G.; Omoto, C. Is Insecticide Resistance a Factor Contributing to the Increasing Problems with Dalbulus maidis (Hemiptera: Cicadellidae) in Brazil? Pest Manag. Sci. 2024, 80, 5120–5130. [Google Scholar] [CrossRef]
  12. Moritz, E.O.; Plassa, W.; Bordin, I.; Bianco, R.; Telles, T.S. Dynamics of Expenditure on Insecticides in the Management and Control of Corn Leafhoppers (Dalbulus maidis). Pest Manag. Sci. 2025, 81, 7077–7089. [Google Scholar] [CrossRef]
  13. Istchuk, A.N.; Sá Farias, E.; Foresti, J.; Santana Júnior, P.A.; Pereira, R.R.; Ishizuka, T.K.; da Silva, P.R.; Schwertner, M.H.; Pietrowski, V. Forewarning the Seasonal Dynamics of Corn Leafhopper and Mollicutes through Neural Networks. Int. J. Biometeorol. 2025, 69, 1383–1394. [Google Scholar] [CrossRef]
  14. Melchert, N.A.; Manzano, C.; Virla, E.G.; Luft-Albarracin, E. On How Nitrogen Fertilization Affects the Host Election and Oviposition Behavior of the Corn Stunt Spiroplasma Vector, Dalbulus maidis (Hemiptera: Cicadellidae). Arthropod Plant Interact. 2023, 17, 621–628. [Google Scholar] [CrossRef]
  15. da Cunha, T.G.; Von dos Santos Veloso, R.; de Araújo, M.M.M.; Tavares, L.G.; Ribeiro, L.F.B.; Tormen, G.P.; Campos, D.S.; Picanço, M.C.; Lopes, E.A.; Pereira, R.R.; et al. Distribution of Dalbulus maidis (DeLong) (Hemiptera: Cicadellidae) and Incidence of Maize Rayado Fino Virus and Candidatus Phytoplasma Asteris in Corn Succession Planting Systems. Pest Manag. Sci. 2023, 79, 2325–2337. [Google Scholar] [CrossRef]
  16. Foresti, J.; Pereira, R.R.; Santana, P.A.; Neves, T.N.C.; Silva, P.R.; Rosseto, J.; Istchuk, A.N.; Ishizuka, T.K.; Harter, W.; Schwertner, M.H.; et al. Spatial–Temporal Distribution of Dalbulus maidis (Hemiptera: Cicadellidae) and Factors Affecting Its Abundance in Brazil Corn. Pest Manag. Sci. 2022, 78, 2196–2203. [Google Scholar] [CrossRef]
  17. Melchert, N.A.; Manzano, C.; Virla, E.G.; Luft-Albarracín, É. High Levels of Nitrogen Fertilization Enhance the Fitness of the Vector of Corn Stunt Disease, Dalbulus maidis. Entomol. Exp. Appl. 2025, 173, 737–746. [Google Scholar] [CrossRef]
  18. Santana, P.A.; Kumar, L.; Da Silva, R.S.; Pereira, J.L.; Picanço, M.C. Assessing the Impact of Climate Change on the Worldwide Distribution of Dalbulus maidis (DeLong) Using MaxEnt. Pest Manag. Sci. 2019, 75, 2706–2715. [Google Scholar] [CrossRef] [PubMed]
  19. Han, P.; Desneux, N.; Becker, C.; Larbat, R.; Le Bot, J.; Adamowicz, S.; Zhang, J.; Lavoir, A.V. Bottom-up Effects of Irrigation, Fertilization and Plant Resistance on Tuta absoluta: Implications for Integrated Pest Management. J. Pest Sci. 2019, 92, 1359–1370. [Google Scholar] [CrossRef]
  20. Power, A.G. Influence of Plant Spacing and Nitrogen Fertilization in Maize on Dalbulus maidis (Homoptera: Cicadellidae), Vector of Corn Stunt. Environ. Entomol. 1989, 18, 494–498. [Google Scholar] [CrossRef]
  21. Bastos, C.S.; Galvão, J.C.C.; Picanço, M.C.; Pereira, P.R.G.; Cecon, P.R. Nutrient Content Affecting Spodoptera frugiperda and Dalbulus maidis Occurrence in Corn. Insect Sci. 2007, 14, 117–123. [Google Scholar] [CrossRef]
  22. Virla, E.G.; Albarracín, E.B.L.; Díaz, C.; Van Nieuwenhove, G.A.; Fernández, F.D.; Aráoz, M.V.C.; Melchert, N.A.; Conci, L.R.; Pecci, M.P.G. Bottom-up Effect of Nitrogen Fertilization on the Density of the Corn Leafhopper and Its Impact on Both Disease Incidence and Natural Parasitism. J. Pest Sci. 2023, 96, 93–104. [Google Scholar] [CrossRef]
  23. De Oliveira, C.M.; De Oliveira, E.; De Souza, I.R.P.; Alves, E.; Dolezal, W.; Paradell, S.; Lenicov, A.M.M.D.R.; Frizzas, M.R. Abundance and Species Richness of Leafhoppers and Planthoppers (Hemiptera: Cicadellidae and Delphacidae) in Brazilian Maize Crops. Fla. Entomol. 2013, 96, 1470–1481. [Google Scholar] [CrossRef]
  24. Meneses, A.R.; Querino, R.B.; Oliveira, C.M.; Maia, A.H.N.; Silva, P.R.R. Seasonal and Vertical Distribution of Dalbulus maidis (Hemiptera: Cicadellidae) in Brazilian Corn Fields. Fla. Entomol. 2016, 99, 750–754. [Google Scholar] [CrossRef]
  25. Lee, I.M.; Hammond, R.W.; Davis, R.E.; Gundersen, D.E. Universal Amplification and Analysis of Pathogen 16S RDNA for Classification and Identification of Mycoplasmalike Organism. Phytopathology 1993, 83, 834–842. [Google Scholar] [CrossRef]
  26. Barros, T.S.L.; Davis, R.E.; Resende, R.O.; Dally, E.L. Design of a Polymerase Chain Reaction for Specific Detection of Corn Stunt Spiroplasma. Plant Dis. 2001, 85, 475–480. [Google Scholar] [CrossRef] [PubMed]
  27. Gundersen, D.E.; Lee, I.M. Ultrasensitive Detection of Phytoplasmas by Nested-PCR Assays Using Two Universal Primer Pairs. Phytopathol. Mediterr. 1996, 35, 144–151. [Google Scholar]
  28. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023. [Google Scholar]
  29. Canale, M.C.; Nesi, C.N.; Castilhos, R.V. Abundance of Dalbulus maidis and Impact of Maize Rayado Fino Disease on Different Genotypes in Field Conditions in Santa Catarina, Brazil. Trop. Plant Pathol. 2023, 48, 675–684. [Google Scholar] [CrossRef]
  30. Canale, M.C.; Lopes, J.R.S.; Nesi, C.N.; Prado, S.d.S. Role of Dalbulus maidis (Hemiptera: Cicadellidae) Gender on Maizebushy Stunt Phytoplasma Transmission. Phytopathog. Mollicutes 2018, 8, 32. [Google Scholar] [CrossRef]
  31. Galvão, S.R.; Sabato, E.O.; Bedendo, I.P. Occurrence and Distribution of Single or Mixed Infection of Phytoplasma and Spiroplasma Causing Corn Stunting in Brazil. Trop. Plant Pathol. 2021, 46, 152–155. [Google Scholar] [CrossRef]
  32. Oliveira, C.M.; Frizzas, M.R.; de Oliveira, E. Overwintering Plants for Dalbulus maidis (DeLong and Wolcott) (Hemiptera: Cicadellidae) Adults during the Maize off-Season in Central Brazil. Int. J. Trop. Insect Sci. 2020, 40, 1105–1111. [Google Scholar] [CrossRef]
  33. Oliveira, C.M.; Lopes, J.R.S.; Nault, L.R. Survival Strategies of Dalbulus maidis during Maize off-Season in Brazil. Entomol. Exp. Appl. 2013, 147, 141–153. [Google Scholar] [CrossRef]
  34. Toloy, R.S.; Nose, N.P.; Giongo, M.V.; Cangussu, A.S.R.; Campos, F.S.; Ootani, M.A.; Ribeiro, J.M.; Aguiar, R.W.S.; Viteri Jumbo, L.O.; Oliveira, E.E.; et al. Exploring Natural Infection in Alternative Hosts of Corn Stunt Spiroplasma and Tracking the Temporal Dynamics of Corn Stunt Disease in the Brazilian Cerrado Biome. Agronomy 2024, 14, 2341. [Google Scholar] [CrossRef]
  35. García Gonzalez, J.; Giraldo Jaramillo, M.; Roberto Spotti Lopes, J. Undetected Infection by Maize Bushy Stunt Phytoplasma Enhances Host-Plant Preference to Dalbulus maidis (Hemiptera: Cicadellidae). Environ. Entomol. 2018, 47, 396–402. [Google Scholar] [CrossRef]
  36. Galindo, F.S.; Pagliari, P.H.; da Silva, E.C.; de Lima, B.H.; Fernandes, G.C.; Thiengo, C.C.; Bernardes, J.V.S.; Jalal, A.; Oliveira, C.E.S.; de Sousa Vilela, L.; et al. Impact of Nitrogen Fertilizer Sustainability on Corn Crop Yield: The Role of Beneficial Microbial Inoculation Interactions. BMC Plant Biol. 2024, 24, 268. [Google Scholar] [CrossRef]
  37. Marchezan, C.; Ferreira, P.A.A.; Silva, L.S.; Bacca, A.; Krug, A.V.; Nicoloso, F.T.; Tarouco, C.P.; Tiecher, T.L.; Brunetto, G.; Ceretta, C.A. Nitrogen Availability and Physiological Response of Corn After 12 Years with Organic and Mineral Fertilization. J. Soil. Sci. Plant Nutr. 2020, 20, 979–989. [Google Scholar] [CrossRef]
  38. Ramos, A.; Esteves, M.B.; Cortés, M.T.B.; Lopes, J.R.S. Maize Bushy Stunt Phytoplasma Favors Its Spread by Changing Host Preference of the Insect Vector. Insects 2020, 11, 600. [Google Scholar] [CrossRef] [PubMed]
  39. Goodwin, P.H.; Mahuku, G.S.; Liu, H.; Xue, B.G. Monitoring Phytoplasma in Populations of Aster Leafhoppers from Lettuce Fields Using the Polymerase Chain Reaction. Crop Prot. 1999, 18, 91–99. [Google Scholar] [CrossRef]
  40. Toffanelli, C.M.; Bedendo, I.P. Efeito Da População Infetiva de Dalbulus maidis Na Produção de Grãos e No Desenvolvimento de Sintomas Do Enfezamento Vermelho Do Milho. Fitopatol. Bras. 2002, 27, 82–86. [Google Scholar] [CrossRef]
  41. da Costa, R.V.; da Silva, D.D.; Cota, L.V.; Campos, L.J.M.; de Almeida, R.E.M.; Bernardes, F.P. Incidence of Corn Stunt Disease in Off-Season Corn Hybrids in Different Sowing Seasons. Pesqui. Agropecu. Bras. 2019, 54, e00872. [Google Scholar] [CrossRef]
  42. Castilhos, R.V.; Parizotto, C.; Bermudez, F.; Ribeiro, L.D.P.; Canale, M.C. Severity of Corn Stunt Disease in the Midwest of the Brazilian of Santa Catarina. Rev. Bras. Milho Sorgo 2022, 21, e1278. [Google Scholar] [CrossRef]
  43. Maia, P.T.N.; Santos, N.M.; Mendes, S.M.; Marques, V.M.; Fadini, M.A.M.; Silva, B.L.S.; Araujo, D.D.S.; Cota, L.V.; Oliveira, I.R. Sustainable Control of Dalbulus maidis: A Field Study with Entomopathogenic Fungi. Entomol. Exp. Appl. 2025. [Google Scholar] [CrossRef]
  44. Wang, M.; Zheng, Q.; Shen, Q.; Guo, S. The Critical Role of Potassium in Plant Stress Response. Int. J. Mol. Sci. 2013, 14, 7370–7390. [Google Scholar] [CrossRef]
  45. Pauletti, V.; Motta, A.C.V. Manual de Adubação e Calagem para o Estado do Paraná, 2nd ed.; SBCS—Sociedade Brasileira de Ciência do Solo: Curitiba, Brazil, 2019; ISBN 9788569146070. [Google Scholar]
  46. Gomes, L.S.; Brandão, A.M.; de Brito, C.H.; de Moraes, D.F.; Lopes, M.T.G. Resistência Ao Acamamento de Plantas e Ao Quebramento Do Colmo Em Milho Tropical. Pesqui. Agropecu. Bras. 2010, 45, 140–145. [Google Scholar] [CrossRef]
  47. Cota, L.V.; Silva, D.D.; Aguiar, F.M.; Costa, R.V. Resistência de Genotipos de Milho Aos Enfezamentos; Embrapa: Sete Lagoas, Brazil, 2018. [Google Scholar]
  48. Silva, D.D.; Aguiar, F.M.; Cota, L.V.; Costa, R.V.; Mendes, S.M. Molicutes Em Milho: A Diversificação de Sistemas de Produção Pode Ser a Solução? In Novos Sistemas de Produção; Medeiros, F.H.V., Pedroso, L.A., Guimarães, M.R.F., Silva, B.A.A.S., Almeida, L.G.F., Silva, F.J., Silva, R.L.M., Ferreira, L.C., Pereira, A.K.M., Couto, T.B.R., et al., Eds.; Universidade Federal de Lavras: Lavras, Brazil, 2017; pp. 32–52. [Google Scholar]
  49. Souza, V.T.; Ribeiro, C.M.; Scalon, J.D.; Guedes, F.L. Relações Entre Componentes de Rendimento e Características Morfológicas de Milho. Magistra 2014, 26, 495–506. [Google Scholar]
  50. Virla, E.G.; Coll Araoz, M.V.; Luft Albarracin, E. Estimation of Direct Damage to Maize Seedlings by the Corn Leafhopper, Dalbulus maidis (Hemiptera: Cicadellidae), under Different Watering Regimes. Bull. Entomol. Res. 2021, 111, 438–444. [Google Scholar] [CrossRef]
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Figure 1. Distribution and predicted abundance of Dalbulus maidis per plot during the 2022/2023 and 2023/2024 seasons in Toledo, PR. Shaded areas represent the distribution of observed data, while points and lines depict predicted values and their confidence intervals.
Figure 1. Distribution and predicted abundance of Dalbulus maidis per plot during the 2022/2023 and 2023/2024 seasons in Toledo, PR. Shaded areas represent the distribution of observed data, while points and lines depict predicted values and their confidence intervals.
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Figure 2. Weekly population fluctuation of Dalbulus maidis collected using sticky traps during the 2022/2023 (a) and 2023/2024 (b) seasons; * = fertilizer application.
Figure 2. Weekly population fluctuation of Dalbulus maidis collected using sticky traps during the 2022/2023 (a) and 2023/2024 (b) seasons; * = fertilizer application.
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Figure 3. Mollicute prevalence in Dalbulus maidis collected from corn at different vegetative stages during the 2022/2023 (a) and 2023/2024 (b) seasons in Toledo, PR.
Figure 3. Mollicute prevalence in Dalbulus maidis collected from corn at different vegetative stages during the 2022/2023 (a) and 2023/2024 (b) seasons in Toledo, PR.
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Figure 4. Predicted percentage of asymptomatic plants by season (a), plants exhibiting mild symptoms by season (b), plants exhibiting severe symptoms by corn hybrids (c), and plants exhibiting severe symptoms by season (d) at the R1 stage as a function of nitrogen rate (kg ha−1). Shaded areas represent 95% confidence intervals for the predictions.
Figure 4. Predicted percentage of asymptomatic plants by season (a), plants exhibiting mild symptoms by season (b), plants exhibiting severe symptoms by corn hybrids (c), and plants exhibiting severe symptoms by season (d) at the R1 stage as a function of nitrogen rate (kg ha−1). Shaded areas represent 95% confidence intervals for the predictions.
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Figure 5. Frequency of vigor scores (symptoms) used to assess plant response to corn stunt complex pathogens across two seasons (a) and two corn hybrids (b) in Toledo, PR.
Figure 5. Frequency of vigor scores (symptoms) used to assess plant response to corn stunt complex pathogens across two seasons (a) and two corn hybrids (b) in Toledo, PR.
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Figure 6. Frequency of vigor scores (symptoms) used to assess the response of two corn hybrids to corn stunt complex pathogens under varying top-dressed nitrogen (a) and potassium (b) rates.
Figure 6. Frequency of vigor scores (symptoms) used to assess the response of two corn hybrids to corn stunt complex pathogens under varying top-dressed nitrogen (a) and potassium (b) rates.
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Figure 7. Predicted foliar nitrogen (a) and potassium (b) concentrations in corn leaf samples as a function of top-dressed nutrient rate (kg ha−1) across two seasons. Shaded areas represent 95% confidence intervals for the predictions.
Figure 7. Predicted foliar nitrogen (a) and potassium (b) concentrations in corn leaf samples as a function of top-dressed nutrient rate (kg ha−1) across two seasons. Shaded areas represent 95% confidence intervals for the predictions.
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Figure 8. Percentage of intact plants in two corn hybrids across two seasons (a), in two hybrids under varying N rates (b), and across two seasons under varying K rates (c).
Figure 8. Percentage of intact plants in two corn hybrids across two seasons (a), in two hybrids under varying N rates (b), and across two seasons under varying K rates (c).
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Figure 9. Distribution and predicted percentage of stalk lodged (a) and root lodged (b) plants in two corn hybrids across two seasons. Shaded areas represent the distribution of observed data, while points and lines depict predicted values and their confidence intervals.
Figure 9. Distribution and predicted percentage of stalk lodged (a) and root lodged (b) plants in two corn hybrids across two seasons. Shaded areas represent the distribution of observed data, while points and lines depict predicted values and their confidence intervals.
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Figure 10. Distribution and predicted percentage of normal ears in two corn hybrids (a) and normal ears across two seasons (b), and percentage of malformed ears in two corn hybrids across two seasons (c). Shaded areas represent the distribution of observed data, while points and lines depict predicted values and their confidence intervals.
Figure 10. Distribution and predicted percentage of normal ears in two corn hybrids (a) and normal ears across two seasons (b), and percentage of malformed ears in two corn hybrids across two seasons (c). Shaded areas represent the distribution of observed data, while points and lines depict predicted values and their confidence intervals.
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Figure 11. Distribution and predicted thousand-kernel weight (g) in two corn hybrids across two seasons. Shaded areas represent the distribution of observed data, while points and lines depict predicted values and their confidence intervals.
Figure 11. Distribution and predicted thousand-kernel weight (g) in two corn hybrids across two seasons. Shaded areas represent the distribution of observed data, while points and lines depict predicted values and their confidence intervals.
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Figure 12. Predicted harvested corn grain weight per plot (kg) as a function of nitrogen application rate (kg ha−1) across two seasons. Shaded areas represent 95% confidence intervals for the predictions.
Figure 12. Predicted harvested corn grain weight per plot (kg) as a function of nitrogen application rate (kg ha−1) across two seasons. Shaded areas represent 95% confidence intervals for the predictions.
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Table 1. Summary of the generalized linear mixed model (GLMM; negative binomial distribution, log link, Laplace approximation) for Dalbulus maidis abundance as a function of corn hybrid and nitrogen and potassium rates across two seasons.
Table 1. Summary of the generalized linear mixed model (GLMM; negative binomial distribution, log link, Laplace approximation) for Dalbulus maidis abundance as a function of corn hybrid and nitrogen and potassium rates across two seasons.
Response (y)TermSlope ± SEzP
Dalbulus maidis abundanceIntercept4.312 ± 0.19122.536<0.001
Season−0.631 ± 0.020−32.071<0.001
Hybrid−0.015 ± 0.019−0.7780.437
Nitrogen0.001 ± 0.0010.2500.802
Potassium0.001 ± 0.0010.4750.635
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Istchuk, A.N.; Schwertner, M.H.; Ferrari, M.L.; Marques, L.H.; Pietrowski, V. Nitrogen and Potassium Fertilization Modulate Dalbulus maidis (HEMIPTERA: CICADELLIDAE) Abundance and Corn Stunt Disease Severity. Agriculture 2025, 15, 2086. https://doi.org/10.3390/agriculture15192086

AMA Style

Istchuk AN, Schwertner MH, Ferrari ML, Marques LH, Pietrowski V. Nitrogen and Potassium Fertilization Modulate Dalbulus maidis (HEMIPTERA: CICADELLIDAE) Abundance and Corn Stunt Disease Severity. Agriculture. 2025; 15(19):2086. https://doi.org/10.3390/agriculture15192086

Chicago/Turabian Style

Istchuk, Ademar Novais, Matheus Henrique Schwertner, Matheus Luis Ferrari, Luiz Henrique Marques, and Vanda Pietrowski. 2025. "Nitrogen and Potassium Fertilization Modulate Dalbulus maidis (HEMIPTERA: CICADELLIDAE) Abundance and Corn Stunt Disease Severity" Agriculture 15, no. 19: 2086. https://doi.org/10.3390/agriculture15192086

APA Style

Istchuk, A. N., Schwertner, M. H., Ferrari, M. L., Marques, L. H., & Pietrowski, V. (2025). Nitrogen and Potassium Fertilization Modulate Dalbulus maidis (HEMIPTERA: CICADELLIDAE) Abundance and Corn Stunt Disease Severity. Agriculture, 15(19), 2086. https://doi.org/10.3390/agriculture15192086

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