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Article

Temporal Analysis of the Relationship between Black Bean Aphid (Aphis fabae) Infestation and Meteorological Conditions in Faba Bean (Vicia faba)

by
Mohammad Almogdad
*,
Karolina Lavrukaitė
and
Roma Semaškienė
Lithuanian Research Centre for Agriculture and Forestry, Instituto al. 1, Kėdainiai Distr., LT-58344 Akademija, Lithuania
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(6), 1182; https://doi.org/10.3390/agronomy14061182
Submission received: 15 April 2024 / Revised: 16 May 2024 / Accepted: 28 May 2024 / Published: 30 May 2024
(This article belongs to the Section Pest and Disease Management)
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Abstract

:
Faba beans (Vicia faba L.) face threats from pests like the black bean aphid (Aphis fabae S.). By understanding the intricate interactions between environmental factors and pest dynamics, we aim to enhance pest management practices in leguminous crop production for improved efficiency and sustainability. A field experiment spanning three growing seasons (2021–2023) explored the link between meteorological parameters and A. fabae abundance in V. faba. Weekly field inspections documented aphid levels alongside concurrent meteorological data. Correlation and multiple linear regression were used to evaluate these relationships. Aphid infestation varied annually, appearing in 2021 and 2023 but not in 2022. Peak density aligned with specific growth stages, indicating temporal variability. In 2023, a significant surge of 1157.4% to 2126.0% compared to 2021 levels highlighted population dynamics in response to environmental factors. Negative correlations with total rainfall were consistent in both years, while positive correlations with maximum temperature and relative humidity were observed. Multiple linear regression attributed 67.1% to 99.9% of aphid abundance variance to the meteorological parameters, emphasizing their role in predicting aphid populations. Our study sheds light on the complex relationship between meteorological parameters and A. fabae dynamics, offering valuable insights into factors impacting aphid abundance in V. faba.

1. Introduction

Faba bean (Vicia faba L.) is an important cool-season legume crop cultivated worldwide due to its high nutritional value and ability to fix atmospheric nitrogen. However, faba bean production faces numerous challenges, including various insect pests that can significantly impact yield and quality. Among these pests, the black bean aphid (Aphis fabae S.) (Hemiptera: Aphididae) stands out as a major threat, causing substantial economic losses in many faba-bean-growing regions [1,2].
The population dynamics of aphids are influenced by a multitude of factors, including the prevailing environmental conditions, host plant characteristics, and natural enemies [3,4,5]. Of these factors, environmental weather conditions play a pivotal role in shaping aphid populations by affecting their reproduction, development, dispersal, and survival. Understanding the population dynamics of A. fabae and its interactions with the environment is crucial for developing effective pest management strategies. In recent years, there has been growing interest in investigating the impact of meteorological conditions on aphid populations, as it provides valuable insights into their population dynamics and can guide the development of targeted control measures [6,7]. Climate may have a major impact on aphid migratory rates, reproduction, and survival [8]. Aphid population rates and the density on their hosts are influenced by abiotic variables such as temperature, relative humidity, and rainfall [9]. The range, activity, and number of natural enemies—which are crucial for controlling crop pests that consume herbivores—are impacted by climate change. Increased average temperature may cause interactions between predators and prey to be disrupted [10]. There was a substantial impact of climatic conditions on the population density and infestation incidence percentages of the cowpea aphid, Aphis craccivora (Koch) [11]. Insects’ daily basic activities are directly impacted by warming [12]. According to Jovičić et al. [13], there is a significant correlation between the total daily air temperatures (ideal or maximum) for aphid development and the number of aphid peaks. When the combined impact of meteorological factors and aphid incidence was calculated, it was shown that the weather parameters contributed to 62 to 74% of the change in incidence [14]. Several studies have explored the relationship between weather parameters and the abundance, distribution, and behavior of A. fabae in faba bean fields. These investigations have highlighted the importance of temperature, humidity, rainfall, and other climatic variables in shaping the population dynamics and infestation patterns of A. fabae [15,16,17].
Temperature is a crucial environmental factor affecting the development rates and reproductive capacity of aphids. Studies have shown that both high and low temperatures can have significant impacts on the population growth and dispersal of A. fabae [18]. Similarly, humidity levels can influence aphid survival, fecundity, and movement [19]. Wind speed and direction play a role in the dispersal of aphids, facilitating their movement between different host plants and fields [20]. Furthermore, rainfall patterns can affect aphid colonization and survival, as population-level effects may arise from the behavioral responses of biocontrol agents to rainfall [21].
Due to the importance of A. fabae as a major pest in V. faba, it is necessary to understand better how weather factors regulate its density, as well as its abundance dynamics throughout the crop. Our objective was to determine A. fabae population seasonality in faba bean fields and to figure out abiotic factors (maximum temperature, minimum temperature, mean temperature, total rainfall, and relative humidity) regulating its populations and the temporal distribution of this pest. By providing an in-depth investigation of over-time patterns, this study fills knowledge gaps and improves the relevance of research findings to practical agricultural operations.

2. Materials and Methods

2.1. Study Design

This comprehensive research was executed during the active vegetative phase, spanning the months of May through August, across three successive agricultural seasons: 2021, 2022, and 2023. The experimental site was the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, located in the agricultural field of Akademija, Kėdainiai district, Lithuania, precisely positioned at 55°38′ N latitude and 23°85′ E longitude. The selection of two specific faba bean cultivars, ‘Vertigo’ and ‘Fuego’, was underpinned by their adaptability to the nuanced local growing conditions and their prevalence in the regional market. The planned experimental area encompassed 590 m2, with each cultivar allocated 295 m2. This was further subdivided into four replicates, each measuring 74 m2. The spatial arrangement of replicates for each cultivar adhered to the principles of a completely randomized design. The individual plots were delineated with dimensions of 3.0 m × 10.0 m, maintaining spacings of 2.5 m between blocks and 1.0 m between adjacent plots. The sowing of faba bean seeds was carried out using a drilling machine at the recommended seeding rate of 60 plants per square meter on 29 April in the agricultural seasons of 2021 and 2022 and on 5 May in 2023. The drilling machine was a Wintersteiger (Austria). The spacing between rows and plants was systematically set at 15 cm and 5 cm, respectively. The overarching experimental strategy prioritized strict adherence to recommended cultural practices, including control measures for diseases and weeds, intervening judiciously based on the evolving necessities observed during the growing seasons. Remarkably, insecticide applications were deliberately excluded with the explicit aim of creating conditions conducive to the assessment and observation of insect infestation dynamics within the experimental framework.

2.2. Aphis fabae Population Dynamics

After the emergence of seedlings, a weekly field inspection was instituted to document the abundance of aphids. Each inspection session involved the random selection of twenty plants from each plot. The evaluation entailed a thorough counting of both winged and wingless aphids present throughout the entire plant, thereby facilitating a nuanced and holistic representation of the prevailing infestation levels. This surveillance of aphid populations commenced from the initial germination phase and persisted throughout the entirety of the growth cycle until the harvest phase, ensuring a thorough assessment of the trends in aphid abundance. During each assessment, plant growth stages were identified using the phenological growth stage key developed by the Biologische Bundesanstalt, Bundessortenamt, and Chemical industry (BBCH) as described by Knott [22].

2.3. Weather Data Collection

Meteorological data were acquired through the utilization of the Dotnuva weather station positioned approximately 2.5 km from the experimental plots, ensuring a precise representation of the indigenous climatic conditions. The study encompassed a multifaceted set of meteorological parameters, including temperature metrics (maximum, minimum, and mean) recorded in degrees Celsius, average relative humidity expressed as a percentage, and rainfall quantification in millimeters. The daily collection protocol afforded a comprehensive set of intricate meteorological dynamics throughout the entire duration of the study.

2.4. Statistical Analysis

The collected aphid population data were recorded and statistically analyzed using SAS statistical software 9.4 (SAS Institute Inc., Cary, NC, USA). Graphs were prepared using Microsoft Excel 365 to visually represent the data. The assessment of the influence exerted by various weather parameters on the aphid population involved the computation of Pearson correlation coefficients. Employing established methodologies as outlined by Gomez and Gomez [23], weather data from aphid population assessments conducted at 3-, 7-, and 14-day intervals were integrated into the correlation analysis. The resulting coefficients reveal the magnitude and direction of the relationship between meteorological variables and aphid populations over these timeframes. Factors that increase aphid abundance are indicated by positive coefficients, while those that decrease it are suggested by negative coefficients. Subsequently, the obtained correlation coefficients underwent rigorous significance testing at a five percent level to ascertain the reliability of the observed relationships. Furthermore, to gain a comprehensive understanding of the collective impact of all-weather parameters on the aphid population, a multiple regression equation was developed. This statistical method allowed for the quantification of the individual contributions made by each weather factor to the variations observed in the aphid population.

3. Results

3.1. Weather Patterns during the Growing Season (May to August)

3.1.1. Temperature Trends

The three years under scrutiny, 2021, 2022, and 2023, exhibited distinctive temperature dynamics during their respective growing seasons (Figure 1). In 2021, May marked the coolest conditions among the three years, with temperatures dipping 1 °C below the mean temperature. June experienced the warmest and driest weather, with temperatures soaring 3.8 °C above the mean temperature. July showcased the hottest temperatures, surpassing the mean temperature by 5 °C, while August reverted to cool and windy conditions, with temperatures 0.5 °C below the mean temperature. Moving to 2022, the growing season exhibited diverse temperature patterns. May began with the most changeable and windy weather. Warm and rainy conditions prevailed in June. July experienced the most variable weather and August brought a blend of cooler and hotter days. May, June, July, and August had average temperatures 1.5, 1.6, 0.1, and 3.8 °C above the long-term mean temperature, respectively. In the 2023 growing season, May commenced with the most variable conditions, registering temperatures 0.3 °C above the mean temperature. June showcased the warmest weather, with temperatures 1.5 °C above the mean temperature. July displayed the epitome of variability, with temperatures 0.1 °C below the mean temperature, and August culminated in the zenith of humidity and warmth, featuring temperatures 3 °C above the mean temperature. Overall, each year exhibited its unique temperature characteristics, emphasizing the importance of analyzing the nuanced climatic variations within the context of the growing seasons.
The weather data show a clear temperature increase during the study year. A higher temperature than the long-term mean was associated with the risk of aphid abundance increasing.

3.1.2. Rainfall Patterns

Figure 2 illustrates distinct variations in rainfall patterns and relative humidity across the three consecutive years during their respective growing seasons. In 2021, the precipitation dynamics showcased notable fluctuations, and May stood out as the wettest, receiving a substantial 100.9 mm of rainfall, surpassing the mean precipitation by an impressive 210.2% of the long-term mean. June, however, experienced minimal precipitation at 30.1 mm, marking a shift to drier conditions. July registered the lowest precipitation levels at 21.4 mm, representing 30% of the precipitation mean. Concluding the season, August witnessed the most substantial monthly rainfall at 150.8 mm, exceeding the mean by 243.2%. In the following year, 2022, precipitation patterns displayed diverse trends. May saw rainfall at 78.5 mm. June emerged as the wettest month, with a notable 151.5 mm of precipitation, equivalent to 248.4% of the precipitation mean. July and August presented varied precipitation levels, with July receiving 96.8 mm (138.3% of the mean) and August registering 43.2 mm (69.7% of the mean). Shifting to the 2023 growing season, May remained notably dry with 9.6 mm of precipitation. June featured a climatic blend of elevated temperatures and substantial rainfall, registering 33.5 mm. July exhibited diverse precipitation levels, with 48.7 mm observed (69.6% of the mean precipitation). August concluded with the most substantial monthly rainfall at 96.2 mm (217% of the mean precipitation). This comparative analysis underscores the nuanced variations in rainfall across the three years, providing valuable insights into the evolving climatic conditions during each growing season.
Quantitatively comparing the years, 2022 emerged as the year with the highest overall precipitation, characterized by extremes in both excess and scarcity. The faba bean fields experienced an extraordinary deluge, with rainfall surging to 248.4% above the long-term mean in one month (June). In contrast, 2021 exhibited notable disparities between wet and dry months. Remarkably, 2023 demonstrated a balanced distribution of precipitation events, portraying a harmonious blend of dry and wet periods. These intricate variations underscore the necessity of considering not only total precipitation but also the temporal distribution of rainfall.

3.2. Population Dynamics of Aphis fabae

Figure 3 illustrates the dynamic trend of Aphis fabae populations, providing a comprehensive visual representation of the fluctuations over the observed period. In 2021, the examination of A. fabae infestation dynamics within the ‘Vertigo’ and ‘Fuego’ faba bean cultivars unveiled a synchronized initiation of infestation on the 31st of May at growth stage BBCH 13, persisting until the 21st of June at growth stage BBCH 61. Noteworthy climacteric points include the pinnacle of infestation occurring on the 14th of June at growth stage BBCH 50, wherein ‘Vertigo’ and ‘Fuego’ exhibited aphid populations of 182.5 and 222.7 individuals on 20 plants, respectively. In contrast, 2022 showed an interesting absence of aphids for the whole growth season. Even though it was unusual, the lack of A. fabae in 2022 gave important information about possible environmental conditions that could lead to aphid exclusion. Fast-forwarding to 2023, an intriguing recurrence of the 2021 pattern emerged, with A. fabae making its appearance and vanishing concurrently in both cultivars. However, a notable extension in the incidence period was observed, spanning 6 weeks, compared to the 4-week interval in 2022. The onset of aphid presence in 2023 was marked by its first detection on the 23rd of May at growth stage BBCH 12, persisting until the 4th of July at growth stage BBCH 69. Elevating the narrative to the population dynamics, the peak of the total aphid population on 20 plants for ‘Vertigo’ and ‘Fuego’ materialized on the 27th of June at growth stage BBCH 65, unveiling populations of 3880.0 and 2577.5 aphid individuals, respectively. This numerical surge in 2023 indicated a substantial escalation ranging from 1157.4% to 2126.0% when compared to the aphid populations observed in 2021, irrespective of the cultivar. Remarkably, across both 2021 and 2023, a discernible deceleration in aphid populations from their peaks to complete eradication within a succinct 2-week interval highlights the dynamic nature of A. fabae infestation and underscores its temporal nuances in response to evolving environmental conditions.

3.3. Meteorological Conditions’ Impact on Aphis fabae Population

To assess the influence of individual abiotic factors on Aphis fabae population dynamics in ‘Vertigo’ and ‘Fuego’ faba bean cultivars, correlation coefficients were computed between aphid populations’ abundance and weather parameters (Table 1). Schober et al. [24] presented that there is a correlation between two variables that indicates how one variable affects the other, either positively (positive correlation) or negatively (negative correlation). The term “correlation” refers to the Pearson’s product-moment correlation, which is a linear relationship between two continuous variables. The range of correlation values is −1 to +1. There is no straight-line correlation if it is 0 while the association is stronger the closer the number is to +1 or –1. In 2021, correlation analyses for the preceding 3 days revealed a significant negative and very strong correlation (r = −0.959) between total rainfall and A. fabae population in the ‘Fuego’ cultivar, while a non-significant negative strong correlation (r = −0.754) was observed in the ‘Vertigo’ cultivar. Aphis fabae populations on both cultivars showed non-significant negative weak correlations with maximum and mean temperature and negligible correlations with minimum temperature. Positive correlations varied from weak in ‘Vertigo’ to moderate in ‘Fuego’ concerning relative humidity. Over the preceding 14 days, correlations were generally non-significant, ranging from weak to moderate for relative humidity or total rainfall with aphid population. Positive correlations were non-significant and varied between negligible and moderate for maximum, minimum, and mean temperature. In 2023, a similar pattern emerged over the preceding 3 days regarding the total rainfall, revealing a noteworthy and significant negative moderate correlation (r = −0.658) between total rainfall and A. fabae population in ‘Vertigo’, while ‘Fuego’ exhibited a negative moderate correlation that did not reach significance. Over the preceding 7 and 14 days, correlations were not significant but positive, varying between weak and moderate. Notably, compared to 2021, several weather parameters exhibited increased significance. Over the preceding 7 and 14 days, a strong positive correlation was evident between maximum temperature and A. fabae population, ranging from (r = 0.738) to (r = 0.826). However, in the preceding 3 days, correlations were not significant and moderate in both cultivars. Concerning relative humidity, a significant positive strong correlation (r ranging between 0.791 and 0.834) was observed for the average of 3 days before aphid inspection, while over the preceding 7 and 14 days, correlations were not significant, displaying a range between weak and moderate correlations.
The interplay between weather parameters and A. fabae populations in faba bean during 2021 is presented in Table 2. Employing multiple linear regression analysis, the contributions of maximum temperature, minimum temperature, mean temperature, total rainfall, and relative humidity were elucidated, revealing nuanced impacts ranging from 0.1% to 6.3%, 0.8% to 4.7%, 12.5% to 78.9%, 6.0% to 59.6%, and 0.2% to 35.8%, respectively. This comprehensive exploration collectively attributed 67.1% to 99.9% of the variance in A. fabae abundance to the amalgamated influence of these weather parameters. Remarkably, the temporal dynamics manifested distinctive effects, with the preceding 14 days showcasing the most substantial influence and the preceding 3 days exhibiting the least. Within this web of climatic factors, the multiple linear regression underscored the preeminent impact of mean temperature on A. fabae abundance. The pivotal year of 2022, marked by the conspicuous absence of A. fabae, serves as an intriguing anomaly. Transitioning to 2023, Table 3 delineates the effects of weather parameters on A. fabae populations. An amalgamation of maximum, minimum, and mean temperature yielded a statistically significant 30.6% contribution to A. fabae abundance in the preceding 7 days of monitoring, particularly evident in the ‘Vertigo’ cultivar. Individually, the significant influence of maximum temperature on A. fabae populations unfolded, showing significant impacts of 63.2% and 68.2% for the preceding 7 and 14 days of monitoring, respectively. These climatic variables mirrored analogous patterns in the ‘Fuego’ cultivar, where maximum temperature wielded significant influence, contributing 54.4% and 58.2% to A. fabae populations for the preceding 7 and 14 days of monitoring, respectively.

4. Discussion

The integrated analysis of weather patterns and Aphis fabae population dynamics provides a comprehensive understanding of the temporal influences on aphid infestation. The synchronicity observed in 2021 between temperature variations and aphid initiation, climaxing in mid-June, aligns with existing literature on the correlation between warmer temperatures and increased aphid activity during this growth stage [25]. This synchronized initiation in both ‘Vertigo’ and ‘Fuego’ cultivars suggests a potential influence of temperature cues on aphid incidence. It can be inferred that the considerable variations in precipitation, characterized by May being the wettest and June encountering minimal rainfall, are presumed to have influenced the abundance of aphids during this timeframe. This aligns with the findings of the previous study, which indicated a positive relationship between the variation in growing season precipitation and soybean aphid abundance [26]. It can be asserted that the peak of aphid populations on 14 June at growth stage 18 correlated with distinct temperature and precipitation conditions, underscoring the sensitivity of A. fabae to the climatic intricacies of that particular year. This observation aligns with the findings of the previous study, which demonstrated that the green spruce aphid is similarly highly influenced by factors such as temperature, carbon dioxide concentration, and other climate variables [27]. The intriguing absence of A. fabae in 2022, amidst diverse temperature and precipitation patterns, presents a unique epidemiological landscape. This absence, though atypical, is crucial for elucidating potential environmental factors contributing to aphid exclusion. The literature suggests that extreme weather events and the presence of natural predators can play a role in aphid population dynamics [28,29]. The anomalously precipitation-abundant conditions, characterized by extremes in both excess and scarcity, may have created an environment inhospitable to A. fabae. This corresponds with the results of the earlier investigation, in which intense rain caused insects to be displaced from the plants, resulting in a decrease in their population in the field [30,31]. The atypical conditions in 2022 present an opportunity to delve deeper into the intricate interplay of these factors. The recurrence of the 2021 pattern in 2023, with an extended incidence period and numerical surge underscores the dynamic nature of A. fabae infestation. The longer duration of aphid presence in both cultivars during 2023, compared to the 4-week interval in 2021, suggests heightened environmental favorability. The substantial escalation in aphid populations, ranging from 1157.4% to 2126.0% when compared to 2021, emphasizes the sensitivity of A. fabae to evolving environmental conditions. This numerical surge may be attributed to a combination of factors, including favorable weather conditions and potential carryover effects from the previous season. The density of A. fabae has been noted to rise with increasing temperatures during growth seasons [32]. Warmer temperatures can accelerate insect development rates and increase the number of generations per year [33]. Some aphid species, like Myzus persicae, have been observed to become more abundant with elevated temperatures [34]. The precipitation variations in 2023, with both deficiency and scarcity, coincide with the intriguingly high incidence of aphids. Periods of drought may lead to stress in plants, potentially influencing the presence of aphids. The limited precipitation occurrences in 2023 prolonged the occurrence duration and led to a numerical increase in aphid populations. It can also be inferred that dry conditions will diminish the impact of predators on aphids, aligning with the findings of the previous study which indicated that drought can intricately alter interactions between natural enemies and herbivores [35]. Dry conditions are anticipated to elevate aphid populations, building on the insights from the previous study that linked the observed aphid response to diminished plant vigor and heightened chemical defense in plants experiencing drought stress [36]. Plant phenology plays a crucial role in influencing the extent of aphid species infestation. For instance, it determines the growth stages which are susceptible to aphid invasions and the crops that will most likely be severely affected [37]. Future research endeavors should focus on elucidating the specific environmental factors influencing aphid exclusion and unraveling the complex interplay between weather patterns, crop characteristics, and natural ecological dynamics within agricultural systems.
Aphids and climate have been the subject of many studies. For instance, the protein structure and behavior of potato aphid (Macrosiphum euphorbiae) morphs were investigated in response to daily variations in temperature and UV radiation [38]. It was discovered that an average day of four hours at 35 °C (heat stress) had more harmful impacts on aphids. Furthermore, it was observed that various exoskeletal proteins were either activated or their abundance increased under high temperatures and that the performance of aphids under heat stress was decreased. It is also connected with the lower abundance of several enzymes in crucial pathways of energy metabolism. The location, activity, and number of natural enemies—which are crucial for keeping aphids in check—are all impacted by climate change [38]. Increased average temperatures could lead to disturbances in predator–prey relationships [10]. Temperature changes affect the phenologies of insects, which can cause a temporal and regional mismatch between insect pests and predators or plants [39].
In this study the discussed knowledge can be used for new forecast models in regional conditions and can be recommended for validation in countries with similar environmental conditions.
The correlation analyses between A. fabae population dynamics and weather parameters for the years 2021 and 2023 offer nuanced insights into the intricate interplay within the agroecosystem. In 2021, our results suggest that higher rainfall might contribute to a reduction in aphid populations. Comparing our findings with previous studies, the observed negative correlation between total rainfall and A. fabae population in 2021 aligns with research emphasizing the impact of precipitation on aphid dynamics. Studies by Pathipati et al. [17] have suggested that increased rainfall may reduce aphid populations by influencing their reproductive success [40] and movement patterns [41]. However, the variability in this correlation across cultivars underscores the need for nuanced interpretations, as demonstrated by Saljoqi et al. [42] and Abbas et al. [43] who found differential responses among aphid populations in different wheat varieties. In 2023, positive correlations over the preceding 7 and 14 days, although not significant, highlight the multifaceted nature of longer-term weather influences on aphid dynamics. Compared to 2021, 2023 reveals increased significance in several weather parameters. Notably, strong positive correlations between maximum temperature and A. fabae population suggest a potential role of temperature in influencing aphid dynamics over the longer term. Relative humidity, particularly the average of 3 days before aphid inspection, demonstrates a significant positive strong correlation, underscoring the importance of humidity as a factor influencing A. fabae populations. The significance of correlations, especially with maximum temperature and relative humidity, resonates with the broader literature on aphid responses to climate. Sun et al. [3] and Wains et al. [44] highlighted the positive correlation between temperature and aphid population growth, while Kishor et al. [45] underscored the importance of relative humidity in shaping aphid abundance. The findings of this study align with prior research, particularly in confirming the significant influence of mean temperature on A. craccivora abundance [46]. This reaffirms the pivotal role of temperature in shaping pest populations, which has been consistently documented in Sharmin et al. [47]. According to their findings, multiple regression equations indicated that temperature exerted the most significant impact on the population of A. craccivora. This consistency underscores the importance of temperature regulation in shaping pest populations and aligns with previous research highlighting the sensitivity of aphid species to temperature fluctuations. However, the current study expands upon these findings by elucidating the nuanced impacts of additional weather parameters, such as maximum temperature, minimum temperature, total rainfall, and relative humidity, which collectively account for a substantial portion of the variance in Brachycaudus helichrysi [48], cotton aphid [49], Lipaphis erysimi [50], Myzus persicae [17], and wheat aphid [51]. The pivotal year of 2022, marked by the absence of A. fabae, contrasts with previous studies that consistently reported the presence of aphids in faba bean fields. Wellings et al. [52] and Wu et al. [25] conducted long-term studies on aphid populations, and their findings contradict our observation. The anomaly of 2022 presents an opportunity for further investigation into potential environmental factors that contributed to the exclusion of A. fabae during that specific growing season. In 2023, the significant influence of maximum temperature on A. fabae populations, especially in the preceding 7 and 14 days, aligns with studies highlighting temperature as a key driver of aphid dynamics. The previous studies demonstrated that warmer temperatures positively accelerate development by amplifying aphid reproduction rates [53,54] or by altering population dynamics [55]. Aphid incidence was impacted significantly by the environmental parameters according to a multiple regression model [56]. Our findings were relatively comparable with Hammad et al. [57] who found that the total effect of weather parameters on the cowpea aphid (A. craccivora) population was 50.42–98.98%.
Our study on the relationship between weather parameters and aphid populations in faba bean crops offers actionable insights for practitioners in agricultural pest management. By integrating our findings into on-the-ground practices, farmers can better anticipate and respond to aphid outbreaks. Utilizing weather-monitoring systems alongside pest surveillance allows for timely interventions, leveraging knowledge of temperature and humidity trends to implement targeted control measures. The importance of proactive pest monitoring and the implementation of integrated pest management practices tailored to local conditions are emphasized.

5. Conclusions

The population dynamics of A. fabae in ‘Vertigo’ and ‘Fuego’ faba bean cultivars across 2021, 2022, and 2023 reveal synchronized infestation initiation, a unique absence in 2022, and a notable numerical surge in 2023, emphasizing the dynamic nature of pest infestation responses to environmental conditions.
Significant negative correlations with total rainfall were found, indicating rainfall’s suppression of aphid populations in short-term periods. Maximum temperature showed strong positive correlations in 2023, suggesting its promotion of aphid growth over longer periods. Varying correlations with relative humidity highlight its nuanced relationship with aphid infestation across different timeframes and cultivars. The specific correlation analyses reveal that total rainfall and maximum temperature play significant roles in influencing A. fabae population dynamics, with varying impacts observed across different years and timeframes.
The multiple linear regression analysis revealed nuanced impacts of maximum temperature, minimum temperature, mean temperature, total rainfall, and relative humidity on A. fabae abundance, collectively explaining 67.1% to 99.9% of the variance in population dynamics. Distinctive temporal effects were observed, with the preceding 14 days exerting the most substantial influence. Mean temperature emerged as the preeminent factor influencing A. fabae abundance, while the absence of A. fabae in 2022 presented an intriguing anomaly. In 2023, maximum temperature significantly influenced aphid populations, with notable impacts observed over different monitoring periods.

Author Contributions

Idea, implementation, investigation, analysis, and writing: M.A.; correction and recommendations for improvement: R.S.; review and editing: K.L. All authors have read and agreed to the published version of the manuscript.

Funding

Part of this research was promoted by the project “Stepping-up IPM decision support for crop protection”.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors express their gratitude to the technical team of the Department of Plant Pathology and Protection at the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, for their valuable contributions to this research.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 14 01182 g001aAgronomy 14 01182 g001b
Figure 1. Temporal dynamics of maximum, minimum, and mean temperature (°C) during the growing seasons from 2021–2023.
Figure 1. Temporal dynamics of maximum, minimum, and mean temperature (°C) during the growing seasons from 2021–2023.
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Figure 2. Temporal variation of daily rainfall (mm/day) and relative humidity (%) during the growing seasons from 2021–2023. Note: R: Daily rainfall. RH: Relative humidity.
Figure 2. Temporal variation of daily rainfall (mm/day) and relative humidity (%) during the growing seasons from 2021–2023. Note: R: Daily rainfall. RH: Relative humidity.
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Figure 3. The total count of Aphis fabae individuals collected from twenty plants throughout the faba-bean-growing seasons in 2021 and 2023. Note: Graphs exhibit varying y-axis ranges. ND: The data for the year 2022 indicate the absence of Aphis fabae.
Figure 3. The total count of Aphis fabae individuals collected from twenty plants throughout the faba-bean-growing seasons in 2021 and 2023. Note: Graphs exhibit varying y-axis ranges. ND: The data for the year 2022 indicate the absence of Aphis fabae.
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Table 1. Temporal associations of Aphis fabae population with varied weather parameters (3, 7, and 14 days prior to monitoring) in the years 2021 and 2023 ND: Pearson’s correlation coefficient.
Table 1. Temporal associations of Aphis fabae population with varied weather parameters (3, 7, and 14 days prior to monitoring) in the years 2021 and 2023 ND: Pearson’s correlation coefficient.
Weather ParametersVertigoFuego
3 Days Prior7 Days Prior14 Days Prior3 Days Prior7 Days Prior14 Days Prior
2021
Temperature (°C)Maximum−0.2680.0680.074−0.1910.2000.341
Minimum−0.0030.5160.2050.0660.5930.501
Mean−0.2580.0970.078−0.1630.2270.349
Total Rainfall (mm)−0.7540.242−0.332−0.99 *−0.206−0.552
Relative Humidity (%)0.151−0.058−0.6110.4350.155−0.668
2023
Temperature (°C)Maximum0.6720.795 *0.826 *0.6140.738 *0.763 *
Minimum0.711 *0.7380.7060.811 *0.751 *0.631
Mean0.7180.738 *0.777 *0.793 *0.7160.719
Total Rainfall (mm)−0.658 *0.5950.482−0.5350.6060.372
Relative Humidity (%)0.791 *0.6920.4630.834 *0.6840.302
* Statistically significant at p-value ≤ 0.05. ND: The data for the year 2022 indicate the absence of Aphis fabae.
Table 2. Multiple linear regression modeling of weather parameters and Aphis fabae populations on faba bean, unveiling the coefficient of determination (R2) for the year 2021.
Table 2. Multiple linear regression modeling of weather parameters and Aphis fabae populations on faba bean, unveiling the coefficient of determination (R2) for the year 2021.
Days Prior to MonitoringRegression EquationR2100.R2Role of Individual Factor (%)p-Value
Vertigo Cultivar
3Y = 97.297 − 1.721X10.0131.31.30.805
Y = 47.403 + 5.417X1 − 9.719X20.0606.04.70.883
Y = −845.432 + 214.560X1 + 52.887X2 − 272.856X30.58958.952.90.387
Y = −670.824 + 190.534X1 + 49.673X2 − 247.721X3 − 0.898X40.66666.67.70.556
Y = −3215.277 + 439.868X1 + 65.847X2 − 514.707X3 + 13.603X4 + 16.614X50.85685.619.00.598
7Y = 79.936 − 1.001X10.0040.40.40.891
Y = 47.458 + 4.217X1 − 7.433X20.0313.12.70.938
Y = −778.522 + 290.440X1 + 89.404X2 − 400.172X30.82082.078.90.122
Y = −426.702 + 242.617X1 + 89.197X2 − 354.394X3 − 1.862X40.98698.616.60.026
Y = −619.331 + 269.507X1 + 92.197X2 − 385.453X3 − 1.022X4 + 0.925X50.99999.91.30.016
14Y = 157.285 − 4.379X10.0636.36.30.587
Y = 67.501 + 8.244X1 − 17.185X20.10210.23.90.806
Y = −339.457 + 152.181X1 + 46.944X2 − 212.624X30.22722.712.50.829
Y = 146.462 + 138.430X1 + 86.440X2 − 243.302X3 − 3.607X40.82382.359.60.322
Y = 1930.188 − 177.576X1 − 6.536X2 + 176.855X3 − 7.234X4 − 8.177X50.89389.37.00.525
Fuego Cultivar
3Y = 87.146 − 1.529X10.0080.80.80.841
Y = 65.532 + 1.562X1 − 4.211X20.0161.60.80.967
Y = −573.105 + 151.161X1 + 40.571X2 − 195.172X30.24324.322.70.811
Y = −391.469 + 126.168X1 + 37.228X2 − 169.025X3 − 0.934X40.31331.37.00.902
Y = −4202.241 + 499.589X1 + 61.451X2 − 568.885X3 + 20.785X4 + 24.882X50.67167.135.80.821
7Y = 39.569 + 0.477X10.0010.10.10.952
Y = 19.855 + 3.645X1 − 4.512X20.0090.90.80.981
Y = −876.292 + 314.182X1 + 100.552X2 − 434.166X30.78878.877.90.154
Y = −646.591 + 282.959X1 + 100.417X2 − 404.278X3 − 1.216X40.84884.86.00.281
Y = −1167.638 + 355.693X1 + 108.532X2 − 488.291X3 + 1.056X4 + 2.503X50.93093.08.20.432
14Y = 77.204 − 1.146X10.0030.30.30.897
Y = −0.146 + 9.728X1 − 14.805X20.0272.72.40.945
Y = −734.871 + 269.594X1 + 100.974X2 − 383.872X30.36936.934.20.663
Y = −242.003 + 255.646X1 + 141.035X2 − 414.989X3 − 3.659X40.88488.454.20.217
Y = 64.291 + 201.382X1 + 125.069X2 − 342.841X3 − 4.282X4 − 1.404X50.88688.60.20.540
Note: Y—total count of Aphis fabae individuals collected from twenty plants; X1—maximum temperature (°C); X2—minimum temperature (°C); X3—mean temperature (°C); X4—total rainfall (mm); X5—relative humidity (%).
Table 3. Multiple linear regression modeling of weather parameters and Aphis fabae populations on faba bean, unveiling the coefficient of determination (R2) for the year 2023.
Table 3. Multiple linear regression modeling of weather parameters and Aphis fabae populations on faba bean, unveiling the coefficient of determination (R2) for the year 2023.
Days Prior to MonitoringRegression EquationR2100.R2Role of Individual Factor (%)p-Value
Vertigo Cultivar
3Y = −14007 + 664.223X10.45245.245.20.098
Y = −9583.079 + 381.082X1 + 216.690X20.60160.114.90.159
Y = −3082.638 + 1235.462X1 + 1055.654X2 − 2090.387X30.70970.910.80.241
Y = −6830.942 + 870.302X1 + 562.424X2 − 1080.944X3 + 92.527X40.72672.61.70.472
Y = −42561 − 1409.684X1 − 2794.035X2 + 5469.363X3 + 203.268X4 + 204.677X50.94794.722.10.379
7Y = −12190 + 579.957X10.63263.263.20.032
Y = −10122 + 443.276X1 + 108.772X20.64564.51.30.125
Y = −15847 + 2777.473X1 + 1262.681X2 − 3608.479X30.95195.130.60.018
Y = −14878 + 2888.786X1 + 1534.861X2 − 3961.851X3 − 55.771X40.95795.70.60.083
Y = −22818 + 2067.945X1 + 755.545X2 − 2321.839X3 − 139.117X4 + 131.470X50.99499.43.70.129
14Y = −15105 + 708.023X10.68268.268.20.022
Y = −29503 + 1581.267X1 − 596.56X20.77377.39.10.051
Y = −30251 + 2048.346X1 − 347.953X2 − 758.318X30.77577.50.20.167
Y = −27352 + 4063.086X1 + 2278.938X2 − 5122.338X3 − 238.558X40.93393.315.80.129
Y = −37094 + 4092.076X1 + 1841.458X2 − 4680.104X3 − 276.463X4 + 107.728X50.95395.32.00.357
Fuego Cultivar
3Y = −12520 + 594.275X10.37737.737.70.142
Y = −6291.349 + 195.620X1 + 305.094X20.68468.430.70.099
Y = −2797.058 + 654.889X1 + 756.077X2 − 1123.681X30.71771.73.30.231
Y = 515.4857 + 977.598X1 + 1191.966X2 − 2015.772X3 − 81.771X40.73173.11.40.465
Y = −33977 − 1223.423X1 − 2048.244X2 + 4307.671X3 + 25.134X4 + 197.588X50.94594.521.40.384
7Y = −11085 + 527.136X10.54454.454.40.050
Y = −6966.246 + 254.931X1 + 216.624X20.59859.85.40.161
Y = −11939 + 2282.296X1 + 1218.850X2 − 3134.141X30.83983.924.10.104
Y = −9890.675 + 2517.630X1 + 1794.282X2 − 3881.225X3 − 117.909X40.86886.82.90.246
Y = −13678 + 2126.049X1 + 1422.512X2 − 3098.861X3 − 157.670X4 + 62.717X50.87687.60.80.559
14Y = −13667 + 640.613X10.58258.258.20.045
Y = −30224 + 1644.838X1 − 686.046X20.70870.812.60.085
Y = −27061 − 328.461X1 − 1736.378X2 + 3203.724X30.74774.73.90.198
Y = −24467 + 1473.501X1 + 613.086X2 − 699.409X3 − 213.364X40.87887.813.10.228
Y = −22522 + 1467.712X1 + 700.442X2 − 787.714X3 − 205.795X4 − 21.511X50.87987.90.10.554
Note: Y—total count of Aphis fabae individuals collected from twenty plants; X1—maximum temperature (°C); X2—minimum temperature (°C); X3—mean temperature (°C); X4—total rainfall (mm); X5—relative humidity (%).
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Almogdad, M.; Lavrukaitė, K.; Semaškienė, R. Temporal Analysis of the Relationship between Black Bean Aphid (Aphis fabae) Infestation and Meteorological Conditions in Faba Bean (Vicia faba). Agronomy 2024, 14, 1182. https://doi.org/10.3390/agronomy14061182

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Almogdad M, Lavrukaitė K, Semaškienė R. Temporal Analysis of the Relationship between Black Bean Aphid (Aphis fabae) Infestation and Meteorological Conditions in Faba Bean (Vicia faba). Agronomy. 2024; 14(6):1182. https://doi.org/10.3390/agronomy14061182

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Almogdad, Mohammad, Karolina Lavrukaitė, and Roma Semaškienė. 2024. "Temporal Analysis of the Relationship between Black Bean Aphid (Aphis fabae) Infestation and Meteorological Conditions in Faba Bean (Vicia faba)" Agronomy 14, no. 6: 1182. https://doi.org/10.3390/agronomy14061182

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