Next Article in Journal
Transcriptome-Based Identification of the SaR2R3-MYB Gene Family in Sophora alopecuroides and Function Analysis of SaR2R3-MYB15 in Salt Stress Tolerance
Next Article in Special Issue
Influence of Chabazite Zeolite Foliar Applications Used for Olive Fruit Fly Control on Volatile Organic Compound Emission, Photosynthesis, and Quality of Extra Virgin Olive Oil
Previous Article in Journal
Differentially Expressed Genes Related to Isoflavone Biosynthesis in a Soybean Mutant Revealed by a Comparative Transcriptomic Analysis
Previous Article in Special Issue
A MaxEnt Model of Citrus Black Fly Aleurocanthus woglumi Ashby (Hemiptera: Aleyrodidae) under Different Climate Change Scenarios
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 13
Issue 5
10.3390/plants13050585
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Economic Injury Levels and Economic Thresholds for Leucoptera coffeella as a Function of Insecticide Application Technology in Organic and Conventional Coffee (Coffea arabica), Farms

by
Marcelo Coutinho Picanço Filho
1,
Eraldo Lima
1,
Daiane das Graças do Carmo
2,*,
Angelo Pallini
1,
Adriana Helena Walerius
1,
Ricardo Siqueira da Silva
3,
Letícia Caroline da Silva Sant’Ana
2,
Pedro Henrique Queiroz Lopes
1 and
Marcelo Coutinho Picanço
1,2
1
Department of Entomology, Federal University of Viçosa, Viçosa 36570-900, Brazil
2
Department of Agronomy, Federal University of Viçosa, Viçosa 36570-900, Brazil
3
Department of Agronomy, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina 39100-000, Brazil
*
Author to whom correspondence should be addressed.
Plants 2024, 13(5), 585; https://doi.org/10.3390/plants13050585
Submission received: 14 January 2024 / Revised: 8 February 2024 / Accepted: 20 February 2024 / Published: 21 February 2024
(This article belongs to the Special Issue Integrated Pest Management and Plants Health)
Browse Figures
Review Reports Versions Notes

Abstract

:
Leucoptera coffeella (Lepidoptera: Lyonetiidae) is one of the main pests in coffee crops. The economic injury level (EIL) is the lowest density of the pest at which economic damages match the costs of control measures. The economic threshold (ET) is the density of the pest at which control measures must be taken so that this population does not reach the EIL. These are the main indices used for pest control decision-making. Control of L. coffeella is carried out by manual, tractor, airplane or drone applications. This work aimed to determine EILs and ETs for L. coffeella as a function of insecticide application technology in conventional and organic Coffea arabica crops. Data were collected over five years in commercial C. arabica crops on seven 100 ha central pivots. The cost of control in organic crops was 16.98% higher than conventional. The decreasing order of control cost was manual > drone > airplane > tractor application. Coffee plants were tolerant to low densities (up to 15% mined leaves) of the pest that caused losses of up to 6.56%. At high pest densities (54.20% mined leaves), losses were high (85.62%). In organic and conventional crops and with the use of different insecticide application technologies, EIL and ET were similar. The EIL and ET were 14% and 11% of mined leaves, respectively. Therefore, these indices can be incorporated in integrated pest management programs in C. arabica crops. The indices determined as a function of insecticide application technology in organic and conventional coffee are important as they serve producers with different technological levels. Additionally, EILs and ETs can contribute to more sustainable production, as control methods will only be employed when the pest density reaches these indices.

1. Introduction

Coffee (Coffea spp.) is one of the most consumed drinks on the planet [1]. World coffee production in 2022 was 179.71 million 60 kg bags, comprising an area of 12.21 million hectares [2,3]. Coffee production involves 78 countries in the Americas (48.74% of production), Asia (32.39% of production), Africa (18.47% of production) and Oceania (0.40% of production). In addition to the economic aspects, coffee crops have social importance, as they create many direct and indirect jobs and are cultivated mainly by small farmers [2]. There are four species of coffee cultivated: Coffea arabica L., Coffea canephora L., Coffea dewevrei De Wild. & T. Durand and Coffea liberica Bull. Coffea arabica is responsible for around 60% of world coffee production [4].
The leaf miner Leucoptera coffeella (Guérin-Méneville) (Lepidoptera: Lyonetiidae) is one of the most important pests in coffee crops [5,6]. This pest is monophagous and attacks coffee crops in the Neotropical region and Africa [6,7,8,9]. During its life cycle, this insect progresses through the stages of egg, larva, pupa and adult. Its larvae undermine leaves, causing a reduction in plant photosynthesis and leaf senescence. It is reported that the attack by this pest can cause losses of up to 87% in the yield of coffee crops [6,9,10]. It has been verified that the densities of L. coffeella are higher in coffee crops grown in regions with higher air temperatures and a longer dry period, as occur in the Cerrado biome in Brazil [6,11]. Control of L. coffeella is carried out mainly with the application of insecticides. These applications can be carried out manually, or using a tractor or plane [12]. Recently, drones have also been used to apply pesticides to coffee crops [13,14].
The economic injury level (EIL) is the lowest density of the pest at which economic damages match the costs of control measures [15]. The economic threshold (ET) is the density of the pest at which control measures must be taken so that this population does not reach the EIL. EIL and ET are the main indices used in decision-making systems for integrated pest management programs. EIL is influenced by the value of production, cost and efficiency of pest control methods and the ability of these organisms to cause damage to plants [15,16]. EILs are inversely proportional to the value of production and directly proportional to the cost of pest control and the ability of these organisms to cause damage to plants [15,16,17]. The value of production is influenced by the crop yield and the value of the agricultural product. The cost of control is influenced by the cost of the products applied, the application technology and the number of applications [15,18,19]. Direct pests attack the marketed organs of plants, while indirect pests do not attack commercial organs. Therefore, L. coffeella is an indirect pest, as its larvae attack the leaves and not the beans, which are the part sold in coffee plantations [5,6]. The damage capacity of indirect pests is expressed by the type of curve that describes the yield of plants as a function of the density of these organisms [15,19].
In crops, pesticide application technology can influence pest control decision-making rates in two ways: due to their different costs and because they are used in different situations. In this context, pesticide applications using tractors generally have the lowest cost per hectare. Manual pesticide applications are the ones with the highest cost per hectare. Furthermore, when there is a need to control pests in large flat areas quickly, pesticide applications are carried out by plane [20,21]. Drones are a modern technology that can be used in both flat and mountainous areas [13,14].
The main production systems for C. arabica are conventional and organic. For a crop to be considered organic, it must be certified according to specific standards. Synthetic pesticides, fertilizers with high solubility (so-called chemical fertilizers) and genetically modified organisms cannot be used on these crops. In organic crops, chemical pest control is carried out through the application of natural insecticides, especially the use of botanical insecticides. To control L. coffeella in organic coffee crops, botanical insecticides could be applied in sprays [5]. In conventional crops, chemical pest control is carried out mainly through the application of organosynthetic insecticides [9,22,23]. To control this pest in conventional coffee crops, organosynthetic insecticides have been applied via soil and spray [9]. These products cause mortality during ingestion and can also have sublethal effects. Sublethal effects include reduction of oviposition by L. coffeella on treated plants and adult emergence [9,23,24,25].
The conventional agricultural production system, although widely adopted, raises several environmental and health concerns. The extensive use of chemical inputs such as synthetic fertilizers, pesticides and herbicides can lead to soil, water and even food contamination. In contrast, the organic production system, by adopting more sustainable methods, aims to promote soil health, biodiversity and the quality of water and food [26]. Measuring the EIL and ET indices for control decision-making L. coffeella in organic and conventional coffee plantations is crucial for more efficient and sustainable management of this pest. The use of these indices allows pest control to be carried out in the appropriate places and times. Furthermore, the use of these indices reduces production costs and the environmental impact of unnecessary use of control methods [22,27]. Despite the importance of coffee crops and L. coffeella, to date, no EILs nor ETs have been determined for this insect. The indices currently used in decision-making to control L. coffeella in coffee crops (20% to 30% of mined leaves) were suggested by researchers empirically [28]. Thus, the objective of this work was to determine EILs and ETs for L. coffeella as a function of insecticide application technology for conventional and organic C. arabica crops.

2. Material and Methods

2.1. Design

The data used in the work were collected over five years (2015/2016 to 2020/2021) in seven central pivot irrigation systems with commercial C. arabica crops. These crops were located in the Cerrado biome in Barreiras, Bahia, Brazil (45°30′29.44″ W, 12°18′16.04″ S). Each pivot had 100 ha and the plants were of the red catuaí variety. The spacing used was 3 × 1 m and irrigation was carried out using a central pivot according to the plants’ water needs. Fertilization was carried out according to the nutrient content of the soil [29]. Each experimental unit consisted of a central pivot of 100 hectares containing 333,333 coffee plants. Thus, in the five years this study was conducted, 11.67 million coffee plants were used. The work was carried out in three stages. First, the pest control costs were determined. In the second, the crop yield curve was determined depending on the intensity of the pest attack. In the third stage, the economic injury levels (EIL) and economic thresholds (ET) were determined.

2.2. Pest Control Costs

In order to determine the cost of control for L. coffeella, a survey was carried out with the producers and technicians responsible for managing the coffee crops to find out the products (insecticides and adjuvants) used for L. coffeella control. The insecticides selected were those registered and most used to control L. coffeella in coffee crops [30]. Research was carried out on the number of annual insecticide applications to control L. coffeella in coffee crops in the Cerrado biome. This was carried out because this is the region in which L. coffeella has caused the greatest problems [11].
Research was carried out with companies that provide pesticide application services, technicians and coffee producers on the costs of applying insecticide by tractor, manual, plane and drone. All costs were taken into account. Research into the costs of applications by tractor, manual and airplane were carried out due to these being the main technologies used in pesticide applications on coffee crops [31,32]. The research into the costs of applying insecticides by drones was carried out due to this being a modern and promising technology for applying pesticides [13]. In conventional crops, applications of insecticides via soil can be carried out manually or with a tractor, with only one application being made. Foliar sprays are carried out manually, by tractor, plane and drone, with five applications being made. In organic crops, products are applied only by foliar spraying manually, and by tractor, airplane and drone. Six applications are made annually.
Insecticide prices were researched in stores selling agricultural products in the Cerrado biome. The annual pest control costs were determined using Formula (1) [20,33]:
Cij = (CIi + CAj) × Ni
where C = annual control cost (USD ha−1), i = coffee cultivation system (1 = conventional and 2 = organic), j = coffee cultivation technology application (1 = manual, 2 = tractor, 3 = airplane and 4 = drone) of insecticide application, CI = average cost of insecticides (USD ha−1) used in an application, CA = cost of using each application technology and N = average annual number of insecticide applications.

2.3. Determination of the Crop Yield Curve Depending on the Intensity of Pest Attack

In the central pivots, the pest attack intensity was monitored in 100 plants, distributed in a regular grid. In each plant, four leaves belonging to the fourth-most apical pair of branches in the middle third of the plant canopy were evaluated. The presence of active mine (mine with live larvae) was assessed on each leaf (Figure 1). This was carried out because this is the methodology used to evaluate the intensity of L. coffeella attack on coffee crops [11,31].
The average monthly pest attack intensity was determined using Formula (2) [11]:
AIpam = (100 × NLpam) ÷ 400
where AI = attack intensity (%) of the pest, p = central pivot (1 to 7), a = year (1 to 5), m = month (1 to 12) and NL = number of leaves with active mines of L. coffeella. Subsequently, the average annual intensity of pest attack in each central pivot was calculated.
Mechanized harvesting was carried out annually on the coffee plantations in each central pivot. The harvested grains were processed and dried until they reached 11% moisture. The annual productivity was calculated in each central pivot (bags.ha−1). Productivity data (bags.ha−1) were transformed into yield (%) using Formula (3). This was performed due to requiring the yield value (%) of crops for the EIL calculation formula [15,34].
Ypa = (100 × Pdpa) ÷ MPd
where Y = coffee crop yield (%), p = central pivot (1 to 7), a = year (1 to 5), Pd = crop productivity of coffee (bags.ha−1) and MPd = maximum coffee productivity during the period of work (82 bags·ha−1).
Data analyses were performed using R-4.1.3 and RStudio software 2023.12.0+369 [35]. Yield data (%) as a function of the average annual intensity of L. coffeella attack (percentage of mined leaves) were subjected to regression analysis at p < 0.05. The significant model (p < 0.05) was selected, based on which had the highest coefficient of determination (R2) and which had biological significance to describe the studied phenomenon [36,37]. The total degrees of freedom of this analysis were 34; the number of degrees of freedom of the model was 1 and the number of degrees of freedom of the error was 33. The confidence interval of the selected model at 95% probability was also determined.

2.4. Determination of Economic Injury Levels and Economic Thresholds

Economic injury levels and economic thresholds for L. coffeella were determined, depending on the insecticide application technology for organic and conventional C. arabica crops. The application technologies studied were manual, tractor, airplane and drone. The study of these first three technologies was due to them being those used by farmers in the application of insecticides on coffee crops. The study of applications using drones was due to this technology being promising for carrying out pesticide applications on coffee crops [13,14]. The production systems studied were conventional and organic. This was due to them being the two main coffee production systems worldwide [29].
Using the equation of the yield curve of coffee crops as a function of the intensity of attack by L. coffeella, Equation (4) was generated to calculate the EIL [15]:
EILij = (Lij ÷ 0.029) × 0.5
where EIL = economic injury level, i = coffee cultivation system (1 = conventional and 2 = organic), j = insecticide application technology (1 = manual, 2 = tractor, 3 = plane and 4 = drone) and L = losses caused by the pest (%) when its density is equal to the EIL.
Lij values were determined using Formula (5) [33]:
Lij = (100 × Cij) ÷ (k × V0)
where L = losses caused by the pest (%) when its density is equal to the EIL, i = cultivation system (1 = conventional and 2 = organic), j = insecticide application technology (1 = manual, 2 = tractor, 3 = airplane and 4 = drone), C = annual control cost (USD·ha−1), k = pest control efficiency and V0 = value average production of coffee crops without pest attack (USD·ha−1).
The value k = 0.8 was used due to 80% being the control efficiency required for the registration of insecticides in Brazil [38]. The V0 value was determined using Formula (6) [15]:
V0 = APd × CP
where V0 = production value of coffee crops without pest attack (USD·ha−1), APd = average productivity of coffee crops (40 bags·ha−1) and CP = price of coffee (USD 187.05·bag−1).
The CP price value was the average price of coffee received by farmers from 2019 to 2023 [39]. These prices were corrected for October 2023 according to the inflation that occurred in Brazil during this period using the IPCA index [40].
ET were determined using Formula (7), according to [15,41]:
ETti = EIL ti × 0.75
where ET = economic threshold for the pest, t = cultivation system (1 = conventional and 2 = organic), i = insecticide application technology (1 = manual, 2 = tractor, 3 = airplane and 4 = drone) and EIL = economic injury level. In Formula (7), the coefficient of 0.75 was used due to the value of 25% (1.00 − 0.25 = 0.75) being the maximum error allowed in the determinations of the components in the decision-making systems of integrated pest management programs [42,43]. This allows decision-making related to L. coffeella control to be performed early, so that natural methods and applied products control the pest before it causes economic damage. In this context, Pereira et al. [6] report that natural agents (natural enemies, climatic elements, plant resistance and physiological disturbances during the life cycle) are responsible for mortalities of 89% to 94.3% of L. coffeella in commercial C. arabica crops.
The percentage values of the confidence intervals of the yield curve of coffee crops as a function of the attack intensity of L. coffeella were used to determine the confidence intervals of the EIL and ET (Figure 2A). When there was an intersection between these confidence intervals, it was considered that the compared decision-making indices did not present a significant difference (p < 0.05).
In the economic injury level and economic threshold recommendations, coffee crops were divided into three groups. In the first group were crops with flat topography (up to 12% slope), where the adoption of mechanization is possible. In the second group were crops in mountainous regions, where the adoption of mechanization is not possible. In the third group were the crops of family farmers with low purchasing power. This division was carried out due to these groups of crops being in situations that use different insecticide application technologies [12,20,21].

3. Results

3.1. Pest Control Costs

3.1.1. Costs in Conventional Crops

To control L. coffeella in conventional C. arabica crops, insecticides are applied to the soil and in foliar sprays. On average, one application of insecticides is made to the soil and five foliar sprays. The active ingredients used the most in soil applications are imidacloprid and thiamethoxam. Insecticide applications to the soil can be carried out manually or using a tractor. The total cost of manual application of insecticide to the soil is USD 154.14 per hectare, with 19.95% of this cost due to manual application and 80.05% due to insecticide expenditure. The total cost of applying insecticide to the soil using a tractor is USD 140.85, with 12.37% of this cost due to the use of the tractor and 87.63% due to the expense of the insecticide (Table 1).
The insecticides used the most in foliar sprays to control L. coffeella in C. arabica crops are Cartap, Chlorantraniliprole, flupyradifurone and mixtures of abamectin and chlorantraniliprole as well as lufenuron and profenofos. The total cost of manual spraying is USD 314.15; 32.80% of this cost is due to manual spraying and 67.20% is due to insecticide costs. The total cost of spraying carried out using a tractor is USD 247.70; 25.97% of this value is due to the use of the tractor and 74.03% due to insecticide costs. The total cost of applications carried out with an airplane is USD 257.95; 27.36% of this value is due to the use of the airplane and 72.64% due to the insecticides applied. The total cost of applications carried out with a drone is USD 298.85; 31.60% of this value is due to the use of the drone and 68.40% due to insecticides (Table 2).

3.1.2. Costs in Organic Crops

The two natural products registered in Brazil for the control of L. coffeella in coffee crops are the botanical insecticides Sophora flavescens and azadaractin. These insecticides are applied in approximately six sprays annually. The total cost of control using natural manual applications is USD 536.76. Of this cost, 25.53% is due to manual spraying activities and 74.47% is due to expenses with natural insecticides. The total control cost using a tractor is USD 457.02. Of this cost, 18.58% is due to the use of the tractor and 81.42% due to expenses with natural insecticides. The total control cost using an airplane is USD 469.32. Of this cost, 19.89% is due to the use of the plane and 80.11% is due to expenses with natural insecticides. The total cost of control using a drone is USD 518.40. Of this cost, 24.21% is due to the use of the drone and 75.79% is due to expenses with natural insecticides (Table 3).

3.2. Crop Yield as a Function of Pest Attack Intensity

The regression curve of the yield of C. arabica crops as a function of the percentage of leaves mined by L. coffeella was significant (F 1, 33 = 41.60 and p < 0.0001) and it presented a coefficient of determination of 59%. This model was quadratic, with concavity facing downwards. At low intensities of attack by L. coffeella, yield losses in coffee crops were low. This can be exemplified by the fact that, when the percentages of leaves mined by L. coffeella were 5%, 10% and 15%, losses in crop yield were 0.73%, 2.91% and 6.56%, respectively. At the maximum intensity of the pest attack (54.20% of mined leaves), the losses caused by the pest in the yield of coffee crops were high, that is, 85.62% (Figure 2A).
Figure 2B represents the residual analysis of the data from the regression model determined in this work. The average error of the regression model estimates was 8.72%, with a maximum of 27.03%. In the attack intensity range of up to 15% of leaves mined by L. coffeella, the average error of the model estimates was 8.37%, with a maximum error of 15.65%. In the range of attack intensity from 15.01% to 30% of leaves mined by L. coffeella, the average error of the model estimates was 8.77%, with a maximum error of 27.09%. In the attack intensity range of more than 30% of leaves mined by L. coffeella, the average error of the model estimates was 5.53%, with a maximum error of 20.96%.

3.3. Economic Injury Levels and Economic Thresholds

For conventional C. arabica crops, the EILs were 14.65%, 13.34%, 13.52% and 14.19% of leaves mined by L. coffeella for manual, tractor, airplane and drone applications, respectively. The average ET values were 10.99%, 10.01%, 10.14% and 10.65% of leaves mined by L. coffeella for manual, tractor, airplane and drone applications, respectively (Table 4).
For organic C. arabica crops, the EILs were 15.69%, 14.48%, 14.67% and 15.42% of leaves mined by L. coffeella for manual, tractor, airplane and drone applications, respectively. The average ETs were 11.77%, 10.86%, 11.00% and 11.56% of leaves mined by L. coffeella for manual, tractor, airplane and drone applications, respectively (Table 4).
Examining the confidence intervals at 95% probability of EIL and ET, it can be seen that these indices did not differ significantly (p < 0.05) with the insecticide application technology and coffee cultivation systems. The average EIL value was around 14% of leaves mined by L. coffeella. The average ET value was 11% of leaves mined by L. coffeella (Table 4).

4. Discussion

The fact that the four insecticide application technologies have different costs has implications for integrated L. coffeella management programs. The use of a tractor was the lowest-cost application technology due to the lower cost of using this machine (USD 34.76·h−1) and its speed, as it only takes 30 min to spray 1 hectare of coffee with a tractor. Application by plane was the second application technology in terms of cost due to its speed (26 s·ha−1), despite its high cost per hour of use (USD 2689.95·h−1). Pesticide applications by plane are used in large areas with flat topography [20,21].
The cost of using a drone was the third application technology in terms of cost due to the high cost per hour of using this machine (USD 110.43·h−1), although this application is carried out quickly (15 min·ha−1). Due to the high cost of using a drone, it should only be used in special situations such as coffee plantations in mountainous regions [13], where it can carry out spraying to control L. coffeella quickly and with less cost than manual applications. Manual application was the application technology with the highest cost, due to the longer time spent on these applications (8 h·ha−1). This implies that small coffee farmers who apply pesticides manually, especially in mountainous regions, have the highest cost of controlling L. coffeella.
The component that most influenced the cost of controlling L. coffeella was the price of insecticides. In conventional coffee crops, the cost of insecticides corresponds to 71.45% of the cost of controlling the pest. In conventional crops, insecticides were applied to the soil (29.69% of this cost) and in spraying (70.31% of this cost). This happened since there was only one application to the soil but there were five annual sprays. In organic production systems, the cost of natural insecticides represented 87.50% of the cost of controlling L. coffeella, and, in these systems, insecticides are applied by spraying.
Crop yield curves depending on the intensity of pest attack indicate the degree of susceptibility of plants to these herbivores. There are three types of curves that describe crop yields as dependent on the intensity of pest attack. The first type of curve has an upward concavity, and it represents plants that are highly susceptible to insect pests. The second type is a straight line with a negative angular coefficient, and it represents plants susceptible to insect pests. The third type of curve has a downward concavity, and it represents plants tolerant to insect herbivory [36,44]. Therefore, coffee plants showed tolerance to L. coffeella since the yield curve of these plants as a function of the attack intensity of the pest had a downward concavity. L. coffeella injuries are due to the formation of mines in the leaves, which reduces photosynthesis and increases leaf senescence [45]. In this work, it was found that, when the percentage of leaves mined by L. coffeella was up to 15%, there was a reduction in the yield of coffee plants of only 6.56% Plant tolerance to insect attack is related to physiological mechanisms in plants. These mechanisms allow attacks by these herbivores to affect plant yield less [46].
Based on EIL, ET and control costs, decision-making systems for L. coffeella in coffee crops can be divided into three groups. The first group contains decision-making systems for crops with flatter topography and where the adoption of mechanization is possible. The second group has the decision-making systems for coffee plantations located in mountainous regions where the adoption of mechanization is not possible. The third group has the decision-making systems for the crops of family farmers with low purchasing power.
In crops with flatter topography, control of L. coffeella must be carried out with the application of insecticides by tractor. This minimizes the cost of controlling the pest, as this application technology has a lower cost. In this first group of crops, if there are some fields missing where it was not possible to control the pest, insecticide applications must be carried out using an airplane. This must be performed because the use of airplanes is quick to execute and this application technology had the second-lowest cost. In this group of crops, L. coffeella must be controlled when the density of this pest reaches 10.01% or 11.86% of mined leaves in conventional or organic crops, respectively, since these are the ETs in these production systems with the application of insecticides using tractors.
In coffee plantations in mountainous regions, control of L. coffeella must be carried out with the application of insecticides using a drone. This must be performed because this application technology can be used on crops in mountainous regions [13]. Furthermore, the use of a drone was less expensive than the manual application of insecticides. In this group of crops, L. coffeella must be controlled when the density of this pest reaches 10.65% or 11.56% of mined leaves in conventional or organic crops, respectively, since these are the ETs in these production systems with insecticide application by drone.
In the coffee plantations of family farmers with low purchasing power, control of L. coffeella is carried out manually using family labor [47]. In this group of crops, L. coffeella must be controlled when the density of this pest reaches 10.99% or 11.77% of mined leaves in conventional or organic crops, respectively. This must be performed because these are the ETs for these production systems with manual application of insecticides.
The economic injury levels (EIL) and economic thresholds (ET) were similar between production systems (conventional and organic) and insecticide application technologies (manual, tractor, airplane and drone), which has implications for the decision-making process for control of L. coffeella. In this context, the average EIL and ET values were 14% and 11% of leaves with active mines (with live larvae) of L. coffeella. Therefore, in both organic and conventional coffee crops, when the L. coffeella population reaches 11% of leaves with active mines, this pest must be controlled. If this decision is not adopted, this pest will cause economic damage. On the other hand, when the density of L. coffeella is less than 11% of leaves with active mines, insecticides (natural or organosynthetic) should not be applied to control this pest. In this situation, if insecticides are applied, production costs will increase and there will be an unnecessary environmental impact [15,22,48].
The EIL and ET determined in this work are important for coffee farming. These decision-making indices are the first determined for this pest using appropriate scientific criteria. These indices are robust and representative of the phenomena studied because they were determined in commercial coffee plantations over a long period (five years). Furthermore, the size of the area used to conduct the work (700 hectares) is representative for the determinations made and it is located in the region where L. coffeella causes the greatest damage, which is the cerrado biome [11]. Currently, the rates used in decision-making to control L. coffeella in coffee crops are 20% to 30% of mined leaves, as suggested by researchers empirically [28]. The indices defined in this work for organic and conventional production were much lower than those determined empirically. Making a comparison between these indices, coffee producers were carrying out L. coffeella control late, which resulted in productivity losses. In this way, using the appropriate control indices determined in this study, producers will make a safe and efficient control decision, reducing losses due to L. coffeella attack. Furthermore, with these indices, there is no excessive use of chemicals, ensuring sustainability in production and the preservation of the environment, including fauna, soil and water [18].

5. Conclusions

The economic injury levels (EILs) and economic thresholds (ETs) determined in this work for Leucoptera coffeella should be incorporated into integrated management programs in coffee crops. This must be carried out because these decision-making indices are robust, are representative of reality and have been determined using appropriate scientific methodology. In organic and conventional crops and with the use of different insecticide application technologies, EIL and ET were similar. The EIL and ET were 14% and 11% of mined leaves, respectively.

Author Contributions

Conceptualization, M.C.P.F., E.L. and M.C.P.; methodology, M.C.P.; formal analysis, M.C.P.F., M.C.P.; investigation, M.C.P.F., D.d.G.d.C., A.H.W., P.H.Q.L. and L.C.d.S.S.; resources, M.C.P., E.L., A.P. and R.S.d.S.; data curation, M.C.P.F., M.C.P. and D.d.G.d.C.; writing—original draft preparation, M.C.P.F.; writing—review and editing, D.d.G.d.C., E.L. and M.C.P.; visualization, M.C.P.F.; supervision, D.d.G.d.C., A.P., R.S.d.S., E.L. and M.C.P.; project administration, A.P. and M.C.P.; funding acquisition, M.C.P. and A.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by ‘Conselho Nacional de Desenvolvimento Científico e Tecnológico’ (CNPq), ‘Fundação de Amparo à Pesquisa de Minas Gerais’ (FAPEMIG) and ‘Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil’ (CAPES)—Finance Code 001.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We are grateful to members of the Integrated Pest Management Laboratory at the Universidade Federal de Viçosa, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fun-dação de Amparo à Pesquisa de Minas Gerais (FAPEMIG), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES) and INCT Semioquímicos na Agricultura, Process 465511/2014-7.

Conflicts of Interest

The authors declare no conflicts of interest. The funders 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.

References

  1. Ruta, L.L.; Farcasanu, I.C. Coffee and Yeasts: From Flavor to Biotechnology. Fermentation 2021, 7, 9. [Google Scholar] [CrossRef]
  2. FAO Food and Agriculture Organization of the United Nations. Available online: https://www.fao.org/faostat/en/#data/QCL (accessed on 4 January 2024).
  3. USDA United States Department of Agriculture. Available online: https://apps.fas.usda.gov/psdonline/circulars/coffee.pdf (accessed on 2 December 2023).
  4. ICO International Coffee Organization. Available online: https://www.ico.org/pt/botanical_p.asp (accessed on 4 January 2024).
  5. Venzon, M. Agro-Ecological Management of Coffee Pests in Brazil. Front. Sustain. Food Syst. 2021, 5, 721117. [Google Scholar] [CrossRef]
  6. Pereira, E.J.G.; Picanço, M.C.; Bacci, L.; Crespo, A.L.B.; Guedes, R.N.C. Seasonal Mortality Factors of the Coffee Leafminer, Leucoptera Coffeella. Bull. Entomol. Res. 2007, 97, 421–432. [Google Scholar] [CrossRef] [PubMed]
  7. Green, D.S. A Proposed Origin of the Coffee Leaf-Miner, Leucoptera coffeella (Guerin-Meneville) (Lepidoptera: Lyonetiidae). Bull. Entomol. Soc. Am. 1984, 30, 30–31. [Google Scholar] [CrossRef]
  8. Pantoja-Gomez, L.M.; Corrêa, A.S.; De Oliveira, L.O.; Guedes, R.N.C. Common Origin of Brazilian and Colombian Populations of the Neotropical Coffee Leaf Miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae). J. Econ. Entomol. 2019, 112, 924–931. [Google Scholar] [CrossRef] [PubMed]
  9. Dantas, J.; Motta, I.O.; Vidal, L.A.; Nascimento, E.F.M.B.; Bilio, J.; Pupe, J.M.; Veiga, A.; Carvalho, C.; Lopes, R.B.; Rocha, T.L.; et al. A Comprehensive Review of the Coffee Leaf Miner Leucoptera coffeella (Lepidoptera: Lyonetiidae)—A Major Pest for the Coffee Crop in Brazil and Others Neotropical Countries. Insects 2021, 12, 1130. [Google Scholar] [CrossRef] [PubMed]
  10. Bacca, T.; Lima, E.R.; Picanço, M.C.; Guedes, R.N.C.; Viana, J.H.M. Optimum Spacing of Pheromone Traps for Monitoring the Coffee Leaf Miner Leucoptera Coffeella. Entomol. Exp. Appl. 2006, 119, 39–45. [Google Scholar] [CrossRef]
  11. Walerius, A.H.; Pallini, A.; Venzon, M.; Santana Júnior, P.A.; Costa, T.L.; Paes, J.d.S.; Pimentel, E.d.S.; Picanço, M.C. Use of Geostatistics as a Tool to Study Spatial-Temporal Dynamics of Leucoptera coffeella in Coffee Crops. Agriculture 2023, 13, 438. [Google Scholar] [CrossRef]
  12. Viana, R.G.; Ferreira, L.R.; Teixeira, M.M. Tecnologia de Aplicação de Defensivos Agrícolas Em Café. In Estratégias para Produção de Café com Qualidade e Sustentabilidade; Suprema: Visconde de Rio Branco, Brazil, 2010; pp. 165–217. [Google Scholar]
  13. Souza, F.G.; Portes, M.F.; Silva, M.V.; Teixeira, M.M.; Júnior, M.R.F. Impact of Sprayer Drone Flight Height on Droplet Spectrum in Mountainous Coffee Plantation. Rev. Bras. Eng. Agríc. Ambient. 2022, 26, 901–906. [Google Scholar] [CrossRef]
  14. Mogili, U.R.; Deepak, B.B.V.L. Review on Application of Drone Systems in Precision Agriculture. Procedia Comput. Sci. 2018, 133, 502–509. [Google Scholar] [CrossRef]
  15. Pedigo, L.P.; Hutchins, S.H.; Higley, L.G. Economic Injury Levels in Theory and Practice. Annu. Rev. Entomol. 1986, 31, 341–368. [Google Scholar] [CrossRef]
  16. Pereira, P.S.; Sarmento, R.A.; Galdino, T.V.S.; Lima, C.H.O.; dos Santos, F.A.; Silva, J.; dos Santos, G.R.; Picanço, M.C. Economic Injury Levels and Sequential Sampling Plans for Frankliniella Schultzei in Watermelon Crops. Pest Manag. Sci. 2017, 73, 1438–1445. [Google Scholar] [CrossRef]
  17. Higley, L.G.; Pedigo, L.P. Environmental Aspects of Integrated Pest Management Economic Injury Level Concepts and Their Use in Sustaining Environmental Quality. Ecosyst. Environ. 1993, 46, 233–243. [Google Scholar] [CrossRef]
  18. Lopes, M.C.; Farias, E.S.; Costa, T.L.; Arcanjo, L.P.; Santos, A.A.; Ribeiro, A.V.; Santos, R.C.; Picanço, M.C. Economic Injury Level and Sequential Sampling Plan for Liriomyza Huidobrensis Management in Tomato Crops. Crop Prot. 2019, 124, 104848. [Google Scholar] [CrossRef]
  19. da Silva, P.R.; Istchuk, A.N.; Foresti, J.; Hunt, T.E.; Alves de Araújo, T.; Fernandes, F.L.; Rodrigues de Alencar, E.; Bastos, C.S. Economic Injury Levels and Economic Thresholds for Diceraeus (Dichelops) melacanthus (Hemiptera: Pentatomidae) in Vegetative Maize. Crop Prot. 2021, 143, 105476. [Google Scholar] [CrossRef]
  20. Arcanjo, L.d.P.; da Silva, É.M.; Araújo, T.A.; Crespo, A.L.B.; Santana Júnior, P.A.; Gomes, G.B.P.; Picanço, M.C. Decision-Making Systems for Management of the Invasive Pest Neoleucinodes elegantalis (Guenée) (Lepidoptera: Crambidae) in Commercial Tomato Crops According to Insecticide Spray Method and Plant Stage. Crop Prot. 2021, 140, 105408. [Google Scholar] [CrossRef]
  21. Matthews, G.; Bateman, R.; Miller, P. Pesticide Application Methods; John Wiley & Sons: Hoboken, NJ, USA, 2014. [Google Scholar]
  22. Santos, J.L.; Pereira, P.S.; Reis, K.H.B.; Freitas, D.R.; Picanço Filho, M.C.; Peluzio, J.M.; Sarmento, R.A.; Guedes, R.N.C.; Picanço, M.C. Decision-Making for Thrips Control in Soybean Fields Using Precision Agriculture Principles. J. Appl. Entomol. 2023, 148, 140–149. [Google Scholar] [CrossRef]
  23. Gusmão, M.R.; Picanço, M.; Gonring, A.H.R.; Moura, M.F. Seletividade Fisiológica de Inseticidas a Vespidae Predadores Do Bicho-Mineiro-Do-Cafeeiro. Pesqui. Agropecu. Bras. 2000, 35, 681–686. [Google Scholar] [CrossRef]
  24. Venzon, M.; Rosado, M.C.; Fadini, M.A.M.; Ciociola, A.I.; Pallini, A. The Potential of NeemAzal for the Control of Coffee Leaf Pests. Crop Prot. 2005, 24, 213–219. [Google Scholar] [CrossRef]
  25. Leite, S.A.; Dos Santos, M.P.; Resende-Silva, G.A.; Da Costa, D.R.; Moreira, A.A.; Lemos, O.L.; Guedes, R.N.C.; Castellani, M.A. Area-Wide Survey of Chlorantraniliprole Resistance and Control Failure Likelihood of the Neotropical Coffee Leaf Miner Leucoptera coffeella (Lepidoptera: Lyonetiidae). J. Econ. Entomol. 2020, 113, 1399–1410. [Google Scholar] [CrossRef]
  26. Carvalho, F.P. Pesticides, Environment, and Food Safety. Food Energy Secur. 2017, 6, 48–60. [Google Scholar] [CrossRef]
  27. Fernandes, F.L.; Picanço, M.C.; Campos, S.O.; Bastos, C.S.; Chediak, M.; Guedes, R.N.C.; Da Silva, R.S. Economic Injury Level for the Coffee Berry Borer (Coleoptera: Curculionidae: Scolytinae) Using Attractive Traps in Brazilian Coffee Fields. J. Econ. Entomol. 2011, 104, 1909–1917. [Google Scholar] [CrossRef] [PubMed]
  28. Reis, P.R.; Júlio, C. Souza Manejo Integrado Do Bicho-Mineira Perileucoptera coffeella (Guérin-Produção de Café. An. Soc. Entomol. Bras. 1996, 25, 77–82. [Google Scholar] [CrossRef]
  29. Sakiyama, N.; Martinez, H.; Tomaz, M.; Borém, A. Café Arábica Do Plantio à Colheita; UFV: Viçosa, Brazil, 2015. [Google Scholar]
  30. MAPA Ministério Da Agricultura Pecuária e Abastecimento. Available online: https://agrofit.agricultura.gov.br/agrofit_cons/principal_agrofit_cons (accessed on 18 May 2023).
  31. Picanco, M.C.; Santana, P.A., Jr.; Silva, G.A.; Lopes, M.C.; Araújo, T.A.; Silva, G.A.R. Manejo Integrado de Pragas. In Café Arábica: Do Plantio à Colheita; Sakiyama, N., Martinez, H., Tomaz, M., Borém, A., Eds.; UFV: Viçosa, Brazil, 2015; pp. 151–173. [Google Scholar]
  32. SENAR. Pragas, Doenças e Plantas Daninhas; SENAR: Brasília, Brazil, 2017; ISBN 9788576641513. [Google Scholar]
  33. Lima, C.H.O.; Sarmento, R.A.; Pereira, P.S.; Galdino, T.V.S.; Santos, F.A.; Silva, J.; Picanço, M.C. Feasible Sampling Plan for Bemisia tabaci Control Decision-Making in Watermelon Fields. Pest Manag. Sci. 2017, 73, 2345–2352. [Google Scholar] [CrossRef] [PubMed]
  34. Costa, T.L.; Sarmento, R.A.; Araújo, T.A.d.; Pereira, P.S.; Silva, R.S.; Lopes, M.C.; Picanço, M.C. Economic Injury Levels and Sequential Sampling Plans for Bemisia tabaci (Hemiptera: Aleyrodidae) Biotype B on Open-Field Melon Crops. Crop Prot. 2019, 125, 104887. [Google Scholar] [CrossRef]
  35. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2022. [Google Scholar]
  36. Lima, C.H.d.O.; Sarmento, R.A.; Pereira, P.S.; Ribeiro, A.V.; Souza, D.J.; Picanço, M.C. Economic Injury Levels and Sequential Sampling Plans for Control Decision-Making Systems of Bemisia tabaci Biotype B Adults in Watermelon Crops. Pest Manag. Sci. 2019, 75, 998–1005. [Google Scholar] [CrossRef] [PubMed]
  37. Johnson, J.B.; Omland, K.S. Model Selection in Ecology and Evolution. Trends Ecol. Evol. 2004, 19, 101–108. [Google Scholar] [CrossRef]
  38. Pereira, R.R.; Picanço, M.C.; Santana, P.A.; Moreira, S.S.; Guedes, R.N.C.; Corrêa, A.S. Insecticide Toxicity and Walking Response of Three Pirate Bug Predators of the Tomato Leaf Miner Tuta Absoluta. Agric. For. Entomol. 2014, 16, 293–301. [Google Scholar] [CrossRef]
  39. IBGE (Instituto Brasileiro de Geografia e Estatística). Available online: https://www.ibge.gov.br/estatisticas/economicas/agricultura-e-pecuaria/9117-producao-agricola-municipal-culturas-temporarias-e-permanentes.html?=&t=resultados (accessed on 29 December 2023).
  40. IBGE (Instituto Brasileiro de Geografia e Estatística). Available online: https://sidra.ibge.gov.br/pesquisa/snipc/ipca/quadros/brasil/dezembro-2023 (accessed on 28 December 2023).
  41. Chasen, E.M.; Undersander, D.J.; Cullen, E.M. Revisiting the Economic Injury Level and Economic Threshold Model for Potato leafhopper (Hemiptera: Cicadellidae) in Alfalfa. J. Econ. Entomol. 2015, 108, 1748–1756. [Google Scholar] [CrossRef]
  42. Moura, M.F.; Picanço, M.C.; Guedes, R.N.C.; Barros, E.C.; Chediak, M.; Morais, E.G.F. Conventional Sampling Plan for the Green Leafhopper Empoasca Kraemeri in Common Beans. J. Appl. Entomol. 2007, 131, 215–220. [Google Scholar] [CrossRef]
  43. Pinto, C.B.; Almeida Sarmento, R.; Visintin da Silva Galdino, T.; Silvestre Pereira, P.; Gomes Barbosa, B.; Henrique Oliveira Lima, C.; Rodrigues da Silva, N.; Coutinho Picanço, M. Standardized Sampling Plan for the Thrips Frankliniella schultzei (Thysanoptera: Thripidae) on Watermelon Crops. J. Econ. Entomol. 2017, 110, 748–754. [Google Scholar] [CrossRef]
  44. Moura, M.F.; Lopes, M.C.; Pereira, R.R.; Parish, J.B.; Chediak, M.; de Paulo Arcanjo, L.; das Graças do Carmo, D.; Picanço, M.C. Sequential Sampling Plans and Economic Injury Levels for Empoasca Kraemeri on Common Bean Crops at Different Technological Levels. Pest Manag. Sci. 2018, 74, 398–405. [Google Scholar] [CrossRef]
  45. Ramiro, D.A.; Guerreiro-Filho, O.; Queiroz-Voltan, R.B.; Matthiesen, S.C. Melhoramento Genético Vegetal. Bragantia 1842, 63, 363–372. [Google Scholar] [CrossRef]
  46. Tiffin, P. Mechanisms of Tolerance to Herbivore Damage: What Do We Know? Evol. Ecol. 2000, 14, 523–536. [Google Scholar] [CrossRef]
  47. Garcia, R.D.C.; Castro, L.F.; Borel, R.M.A. Cafeicultura: Base da Economia Familiar na Região de Montanha no Espírito Santo. In Simpósio de Pesquisa dos Cafés do Brasil; Embrapa Café: Brasília, Brazil; Minasplan: Belo Horizonte, Brazil, 2000; pp. 327–331. Available online: http://www.sbicafe.ufv.br/handle/123456789/772 (accessed on 4 January 2024).
  48. Picanço, M.C.; Bacci, L.; Crespo, A.L.B.; Miranda, M.M.M.; Martins, J.C. Effect of Integrated Pest Management Practices on Tomato Production and Conservation of Natural Enemies. Agric. For. Entomol. 2007, 9, 327–335. [Google Scholar] [CrossRef]
Plants 13 00585 g001
Figure 1. Thirds of the canopy of a Coffea arabica plant, pair of leaves used to evaluate attack intensity, active mine (with live caterpillar) and Leucoptera coffeella caterpillar.
Figure 1. Thirds of the canopy of a Coffea arabica plant, pair of leaves used to evaluate attack intensity, active mine (with live caterpillar) and Leucoptera coffeella caterpillar.
Plants 13 00585 g001
Plants 13 00585 g002
Figure 2. (A) Regression curve of the yield of Coffea arabica crops as a function of the attack intensity of Leucoptera coffeella and (B) residue analysis of the regression model data in (A); each circle represents a central pivot of 100 ha containing 333,333 coffee plants and the vertical straight segments represent the model’s confidence interval at 95% probability.
Figure 2. (A) Regression curve of the yield of Coffea arabica crops as a function of the attack intensity of Leucoptera coffeella and (B) residue analysis of the regression model data in (A); each circle represents a central pivot of 100 ha containing 333,333 coffee plants and the vertical straight segments represent the model’s confidence interval at 95% probability.
Plants 13 00585 g002
Table 1. Costs of applying insecticide to the soil to control Leucoptera coffeella in conventional coffee crops.
Table 1. Costs of applying insecticide to the soil to control Leucoptera coffeella in conventional coffee crops.
ItemsUnitUnit Cost
(USD)		QtyTotal Cost
(USD ha−1)
Insecticides
 Imidacloprid 700 WGkg143.151.13161.76
 Thiamethoxam 250 WGkg50.101.7085.17
 (1) Average cost of insecticides applied to the soil123.47
Manual application
 (2) Application cost 30.67
 (3) Total cost of a manual application = (1) + (2)154.14
Tractor application
 (4) Application cost 17.38
 (5) Total cost of an application with a tractor = (1) + (4)140.85
In the header: Qty = quantity of the item used per hectare in an application.
Table 2. Costs of insecticide spraying to control Leucoptera coffeella in conventional coffee crops.
Table 2. Costs of insecticide spraying to control Leucoptera coffeella in conventional coffee crops.
ItemsUnitUnit Cost
(USD)		QtyTotal Cost
(USD ha−1)
Insecticides
 Abamectin 18 SC + Chlorantraniliprole 45 SCL81.800.5040.90
 Chlorantraniliprole 350 WGkg153.370.0913.80
 Cartap 500 PSkg38.830.9034.95
 Flupyradifurone 200 SLL60.530.7545.40
 Lufenuron 50 EC + Profenofos 500 ECL36.810.7025.77
 (1) Average cost of insecticides 32.16
Manual application
 (2) Application cost 30.67
 (3) Cost of an application = (1) + (2) 62.83
 (4) Total cost = (3) × 5 sprays 314.15
Tractor application
 (5) Application cost 17.38
 (6) Cost of an application = (1) + (5) 49.54
 (7) Total cost = (6) × 5 sprays 247.70
Airplane applications
 (8) Application cost 19.43
 (9) Cost of an application = (1) + (8) 51.59
 (10) Total cost = (9) × 5 sprays 257.95
Drone applications
 (11) Application cost 27.61
 (12) Cost of an application = (1) + (11) 59.77
 (13) Total cost = (12) × 5 sprays 298.85
In the header: Qty = quantity of the item used per hectare in an application.
Table 3. Costs of controlling Leucoptera coffeella in organic coffee crops.
Table 3. Costs of controlling Leucoptera coffeella in organic coffee crops.
ItemsUnitUnit Cost
(USD)		QtyTotal Cost
(USD ha−1)
Insecticides
 Sophora flavescens 190.5 SLL32.711.1035.98
 Azadaractin 12 ECL30.222.7081.59
 (1) Average cost of insecticides 58.79
Manual application
 (2) Application cost 30.67
 (3) Cost of an application = (1) + (2) 89.46
 (4) Total cost = (3) × 6 sprays 536.76
Tractor application
 (5) Application cost 17.38
 (6) Cost of an application = (1) + (5) 76.17
 (7) Total cost = (3) × 6 sprays 457.02
Airplane applications
 (8) Application cost 19.43
 (9) Cost of an application = (1) + (8) 78.22
 (10) Total cost = (6) × 6 sprays 469.32
Drone applications
 (11) Application cost 27.61
 (12) Cost of an application = (1) + (11) 86.40
 (13) Total cost = (12) × 6 sprays 518.40
In the header: Qty = quantity of the item used per hectare in an application.
Table 4. Control costs, economic injury levels (EILs) and economic thresholds (ETs) for Leucoptera coffeella in conventional and organic coffee crops using different insecticide application technologies.
Table 4. Control costs, economic injury levels (EILs) and economic thresholds (ETs) for Leucoptera coffeella in conventional and organic coffee crops using different insecticide application technologies.
Insecticide Application TechnologyPest Control Costs (USD ha−1 Year−1) *Mined Leaves (%) §
Soil ApplicationSpraysTotalEILET
Conventional crops
Manual (soil application and sprays)752.001536.412288.4114.65 (13.76–15.54)10.99 (10.32–11.66)
Tractor (soil application and sprays)687.001211.411898.4113.34 (12.51–14.17)10.01 (9.38–14.17)
Tractor (soil application) and airplane (sprays)687.001261.411948.4113.52 (12.68–14.36)10.14 (9.51–14.36)
Tractor (soil application) and drone (sprays)687.001461.412148.4114.19 (13.32–15.06)10.65 (9.99–15.06)
Organic crops
Manual (sprays)-2625.062625.0615.69 (14.75–16.63)11.77 (11.06–16.63)
Tractor (sprays)-2235.062235.0614.48 (13.59–15.37)10.86 (10.19–15.37)
Airplane (sprays)-2295.062295.0614.67 (13.77–15.57)11.00 (10.33–15.57)
Drone (sprays)-2535.062535.0615.42 (14.49–16.35)11.56 (10.87–16.35)
* These values are from Table 1, Table 2 and Table 3. § These determinations were carried out using the curve in Figure 2, the EIL determination formula and the production value of USD 7482.13 per hectare. The numbers before the parentheses are the average EIL and ET values. The numbers in parentheses are the 95% probability confidence intervals for these indices.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Picanço Filho, M.C.; Lima, E.; Carmo, D.d.G.d.; Pallini, A.; Walerius, A.H.; da Silva, R.S.; Sant’Ana, L.C.d.S.; Lopes, P.H.Q.; Picanço, M.C. Economic Injury Levels and Economic Thresholds for Leucoptera coffeella as a Function of Insecticide Application Technology in Organic and Conventional Coffee (Coffea arabica), Farms. Plants 2024, 13, 585. https://doi.org/10.3390/plants13050585

AMA Style

Picanço Filho MC, Lima E, Carmo DdGd, Pallini A, Walerius AH, da Silva RS, Sant’Ana LCdS, Lopes PHQ, Picanço MC. Economic Injury Levels and Economic Thresholds for Leucoptera coffeella as a Function of Insecticide Application Technology in Organic and Conventional Coffee (Coffea arabica), Farms. Plants. 2024; 13(5):585. https://doi.org/10.3390/plants13050585

Chicago/Turabian Style

Picanço Filho, Marcelo Coutinho, Eraldo Lima, Daiane das Graças do Carmo, Angelo Pallini, Adriana Helena Walerius, Ricardo Siqueira da Silva, Letícia Caroline da Silva Sant’Ana, Pedro Henrique Queiroz Lopes, and Marcelo Coutinho Picanço. 2024. "Economic Injury Levels and Economic Thresholds for Leucoptera coffeella as a Function of Insecticide Application Technology in Organic and Conventional Coffee (Coffea arabica), Farms" Plants 13, no. 5: 585. https://doi.org/10.3390/plants13050585

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

Article Metrics

Back to TopTop