Comprehensive monitoring of coffee berry borer and host plant dynamics is essential for aggregating landscape-level data to improve management of this invasive pest. Here, we present a protocol for scientific monitoring of coffee berry borer movement, infestation, mortality, coffee plant phenology, weather, and farm management via a mobile electronic data recording application.
Coffee berry borer (CBB) is the most devastating insect pest for coffee crops worldwide. We developed a scientific monitoring protocol that is aimed at capturing and quantifying the dynamics and impact of this invasive insect pest as well as the development of its host plant across a heterogeneous landscape. The cornerstone of this comprehensive monitoring system is timely georeferenced data collection on CBB movement, coffee berry infestation, mortality by the fungus Beauveria bassiana, and coffee plant phenology via a mobile electronic data recording application. This electronic data collection system allows field records to be georeferenced through built-in global positioning systems, and is backed by a network of weather stations and records of farm management practices. Comprehensive monitoring of CBB and host plant dynamics is an essential part of an area-wide project in Hawaii to aggregate landscape-level data for research to improve management practices. Coffee agroecosystems in other parts of the world that experience highly variable environmental and socioeconomic factors will also benefit from implementing this protocol, in that it will drive the development of customized integrated pest management (IPM) to manage CBB populations.
Coffee Berry Borer (Hypothenemus hampei Ferrari) is an invasive insect pest that is found throughout major coffee growing regions of the world1,2. This tiny beetle spends most of its life cycle within the seed of a coffee berry, making it difficult to control with pesticide sprays. The adult female bores a hole into the coffee berry through the central disc, and into the seed where it builds galleries for reproduction. As the larvae develop, they feed on the endosperm, causing direct damage to the coffee bean and subsequent losses in yield and quality3. Indirect damage can also occur by the entry of fungi and pathogens into the bean, which can cause fermentation and alteration of the coffee flavor4.
CBB was first detected on Hawai'i Island in August 20105 and quickly spread to nearly all of the ~800 coffee farms in the Kona and Ka'u districts, two areas that are world-renowned for the premium quality of their coffee products6,7. Unmanaged and poorly managed farms can have infestation levels in excess of 90%, resulting in huge economic losses. In Hawaii, the estimated economy-wide impact due to CBB is approximately $21M annually8. CBB has continued to spread since its initial introduction to Hawaii Island, and was recently detected on the neighboring Hawaiian Islands Oahu (2014) and Maui (2016). Kauai is the only coffee-producing island in Hawaii that remains unaffected by CBB, but the island's 3,000 acres of coffee is extremely vulnerable to this highly dispersive pest.
Historically, synthetic insecticides such as endosulfan and chlorpyrifos have been used in many countries to control CBB. However, concerns regarding the toxicity of these insecticides to humans and the environment9, as well as evidence for insecticide resistance10, have resulted in these substances being banned from use in many countries. Currently, most coffee growing regions rely on an IPM approach to control CBB. IPMs typically involve a combination of sanitation practices (e.g., pruning and strip-picking), biological controls (e.g., the release of predatory beetles or parasitoids), and the application of biopesticides (e.g., the entomopathogenic fungus B. bassiana)11,12. Current recommendations for CBB management in Hawaii also suggest regular field monitoring using alcohol-baited traps and the "Thirty tree Sampling Method" developed by Cenicafé13,14. This sampling method involves randomly selecting a branch from the mid-canopy that has at least 45 green berries, and counting the number of infested and non-infested berries. This process is repeated in a zig-zag pattern across the field for a total of 30 trees per hectare (2.5 acres), and is used to estimate percent infestation.
While many of these IPM practices are being adopted by coffee growers in Hawaii, the extreme heterogeneity in climate, topography and cultural practices on the islands necessitate that IPM be customized to each location. The development of customized IPM will depend on a monitoring program that includes the essential elements of coffee agroecosystems, coffee pest biology, and the environment. We have implemented comprehensive monitoring of CBB and host plant dynamics as part of an area-wide project in Hawaii that aggregates landscape-level data to inform management practices. This protocol can be used in other coffee agroecosystems around the world, and will be especially useful in those that experience highly variable environmental and socioeconomic factors requiring customized IPM to manage CBB populations.
Note: A Spanish translation of the protocol is provided as Supplementary File 1.
1. Define Sampling Zones within Coffee Fields
2. Create a Data Collection Application in an Electronic System
3. Prepare and Deploy Traps for Monitoring CBB Movement
4. Service Traps
5. Service Zones for Plant Phenology
6. Service Zones for Damage Assessments of Green Berries
7. Count the Number of CBB in Each Trap
8. Score Phenology Photographs
9. Dissect Berries to Determine CBB Position
10. Service Manual Weather Stations
Note: Weather stations requiring manual data download may be serviced bi-weekly or monthly to download data and ensure all sensors are working properly. Weather variables that are important to consider for understanding CBB biology may include rainfall, humidity, air and soil temperature, solar radiation, photosynthetically active radiation (PAR), soil moisture, and wind speed/direction.
11. Record Management Practices
NOTE: Information on management practices may be used to understand patterns in CBB activity and population sizes. Relevant management practices may include (but are not limited to): spraying B. bassiana fungus, spraying pyrethins or other insecticides, pruning, weed management, strip picking, cherry picking, removing raisins from the ground, etc.
We report examples from several coffee farms that are representative of the type of results that may be obtained from the monitoring protocol described above. To determine CBB movement patterns within and among fields, the total catch for a given trap may be divided by the number of days since deployment to estimate the number of CBB caught per day. The number of CBB caught per day may then be averaged across all traps to determine the mean number of CBB caught per trap per day across the farm (mean ± SEM; Figure 2). Trap catch data may be used to infer periods of peak flight activity18, and can also be used to direct management activities such as pruning and B. bassiana sprays. Percent infestation obtained from berry damage assessments in the field may be compared with trap catch data to determine if periods of high infestation coincide with peak flight activity19. This information is essential for deciding if monitoring CBB activity through traps alone is sufficient to inform control measures. Berry dissections in the laboratory to determine CBB positions may be used to inform growers when to spray applications of B. bassiana (> 5% of CBB are in the AB position14). CBB position information can also be used in conjunction with hotspot maps generated from damage assessments in the field to inform growers of approximate locations within the field where B. bassiana should be sprayed (Figure 3).
A comprehensive view of the factors involved in CBB infestation may be obtained by compiling data on CBB positions, mortality by B. bassiana, plant phenology, and management practices. In the sample farm shown in Figure 4, the majority of infested berries dissected early in the growing season hosted CBB in the AB position, while the majority of berries dissected later in the season hosted CBB in the CD position. Following a peak in berry production, seven rounds of cherry harvesting were recorded from late July to December (Figure 4). Finally, seven applications of B. bassiana were conducted at approximately one month intervals throughout the season, with CBB mortality observed to range from 0 – 23% (Figure 4). Lastly, although weather data is not presented here, the addition of temperature, humidity, and rainfall information will likely provide further insights into factors driving CBB infestation patterns and B. bassiana effectiveness on coffee farms.
Figure 1. Mean (± SEM) CBB caught per trap per day for sampling done at weekly versus bi-weekly intervals. This average trap catch per day is for five funnel traps spread randomly across the farm. More extreme peaks and troughs are captured in the weekly sampling and these peaks appear slightly later in the bi-weekly sampling, although the general trends are comparable between the two intervals. Please click here to view a larger version of this figure.
Figure 2. Mean (± SEM) CBB caught per trap per day. This average trap catch per day is for nine funnel traps spread randomly across the farm. Two major peaks in CBB flight activity can be seen at this farm (March and December) during the 2016-2017 growing season. Please click here to view a larger version of this figure.
Figure 3. CBB infestation hotspots. This map of a sample coffee farm shows CBB infestation hotspots observed during a monitoring survey on June 14, 2017. The size of each red circle is proportional to the number of green infested berries on a sampled branch. In this sample farm, a total of 25 branches were sampled, and a range of 0 – 36 infested green berries was observed per branch. Please click here to view a larger version of this figure.
Figure 4. A comprehensive view of CBB infestation in a sample coffee farm. The position of CBB in dissected green berries is defined as AB (the female has initiated penetration into the berry but has not reached the endosperm) or CD (the female has entered the endosperm). Mortality of CBB (via the B. bassiana fungus), coffee plant phenology (the mean number of berries per branch), and farm management practices (B. bassiana sprays and cherry picks) are also displayed for the 2016 coffee-growing season. Please click here to view a larger version of this figure.
The monitoring protocol described here can serve as an essential part of research on CBB and control strategies against this invasive coffee pest. We have put this monitoring protocol into practice over the 2016 and 2017 coffee growing seasons on Hawaii Island in an effort to optimize every step of the process outlined in this article and the accompanying video. By doing this, we have ensured that important aspects of CBB population dynamics have been monitored and quantified, that the most effective low-cost materials have been determined for each step of the protocol, and that the data collected on CBB movement, infestation, mortality, coffee plant phenology, weather and farm management can be used to inform and improve current control strategies.
There are a number of critical steps in this protocol that must be followed to ensure optimum results. First, funnel traps must be set up at a uniform height and positioned between trees. This will ensure that the attractant is sufficiently diffused through the air, and that beetles can access the trap from all directions. Second, it is necessary to use sieves with the same mesh size (coarse-mesh sieve ≈ 1.5 mm, and fine-mesh sieve ≈ 1.0 mm) throughout the duration of monitoring to ensure consistent results for volumetric estimates of CBB. Third, the proportion of CBB versus other beetles in each trap can vary considerably among traps and over the growing season, and it is therefore necessary to estimate these proportions to minimize noise in trap count data. Fourth, infested berries must be stored in a cooler on ice until they can be transported to the lab, after which berries should be stored at 14 °C until dissection. Storage in a humid environment will result in CBB emergence from the berries20. Lastly, dissections must be conducted within 1 – 3 days of collection to ensure maximum survivorship of CBB. Mortality of CBB may occur if berries are stored at cold temperatures for prolonged periods.
Additional steps may be required for research initiatives that are not included here (e.g., monitoring CBB predator abundance). Modifications may also be made to this protocol if time, resources, and/or equipment are limiting factors. The trap attractant comprised of 3:1 methanol:ethanol may be changed to a 1:1 methanol:ethanol solution with comparable results21. Soapy water may also be substituted for propylene glycol as a kill solution in traps22. For estimates of large numbers of CBB (e.g., more than several hundred per trap), mass-based estimates of CBB may be substituted in place of volumetric estimates. For example, the average dry weight of a single CBB may be determined using a high-resolution scale. CBB collected in 70% ethanol may then be dried in an oven, and weighed to estimate the number of CBB per trap. A modified volumetric estimate can also be made by putting all the CBB from a trap into a graduated cylinder along with the kill solution, and allowing the contents to settle to the bottom22. Once settled, the volume of the cylinder filled by CBB may be noted, and the conversion factor for 1 mL may be determined to estimate the total number of CBB caught per trap. Lastly, coffee growers that have an intimate knowledge of their farms and are using this monitoring protocol to estimate CBB infestation and movement may wish to omit steps that involve documenting phenology and counting the number of raisins on branches.
Two potential limitations of this protocol are worth mentioning here. First, sampling of branches at chest height does not capture infestation in the early-flowering crop that may start higher in the tree canopy. However, the observations suggest that this early-flowering crop accounts for a very small percentage of the overall yield in coffee farms in Hawaii. Second, our protocol only accounts for infestation in green berries, and thus may not accurately capture estimates of berry damage when the number of color break and ripe berries is high (September – December in Hawaii).
The CBB monitoring protocol presented here has several distinct advantages over other monitoring protocols that are currently in use. First, the systematic random sampling design allows for more even sampling relative to sampling done in a zig-zag pattern. This sampling design allows for better estimates of berry damage throughout a given field, and increases the potential to detect hotspots. Second, the inclusion of elements in the monitoring protocol that are essential to coffee agroecosystems (e.g., phenology, weather variables, and management practices) will improve our understanding of the dynamics between invasive insect pests, their host plants, and various environmental factors. Third, the use of a mobile electronic data collection application during field surveys permits real-time data to be quickly and efficiently entered and organized into a database, and can also be related to other automated coffee monitoring methods such as detection via remote sensing23. Another important benefit of this method of data collection is that detailed infestation reports may be generated with ease, allowing timely management recommendations to be relayed to growers. Lastly, the real-time data collected on CBB biology, coffee plant phenology, weather, and management can be incorporated into the development of predictive models that can be used to customize management plans for a particular coffee growing location.
The authors have nothing to disclose.
We are grateful to Forest Bremer for providing drone imagery of coffee farms, as well as assistance with GIS methods. We thank Thomas Mangine, Matthew Mueller, Lindsey Hamilton, Shannon Wilson, Briana McCarthy and Mehana Sabado-Halpern for assistance with film production, and two anonymous reviewers for comments on an earlier draft. This work was funded by USDA-ARS. Opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the USDA. USDA is an equal opportunity provider and employer.
funnel trap | CIRAD | Brocap trap | |
propylene glycol | Better World Manufacturing, Inc. | ||
methanol | Fisher Scientific or similar supplier | CAUTION: Methanol is highly flammable, is toxic if inhaled or ingested, and is a skin and eye irritant. Wear gloves, eye protection, and protective clothing, and only use in well-ventilated rooms. | |
ethanol | Fisher Scientific or similar supplier | CAUTION: Ethanol is highly flammable, is toxic if inhaled or ingested, and is a skin and eye irritant. Wear gloves, eye protection, and protective clothing, and only use in well-ventilated rooms. | |
polypropylene resealable bags (2 Mil 3 x 4") | Uline or similar supplier | S-1292 | |
thumbtack | Widely available | For making drainage holes in funnel trap | |
paperclips | Widely available | For attaching lure bag to traps | |
galvanized wire (12 gauge) | Widely available | For attaching funnel trap to stakes | |
wire cutter | Widely available | ||
tomato stakes | Widely available | ||
permanent marker | Widely available | ||
mobile device | Apple or other supplier | iPad or smartphone equipped with camera | |
waterproof case | Widely available | For mobile device | |
data collection application | Fulcrum or similar software | ||
GNSS Surveyor | Bad Elf | ~1-meter positioning accuracy | |
1 mm mesh hand sieve | Widely available | ||
1.5 mm mesh hand sieve | Widely available | ||
20 mL glass scintillation vials | Widely available | ||
label maker | Widely available | ||
label tape | Widely available | ||
metal lab spatula | Widely available | ||
scrub brush | Widely available | ||
dish soap | Widely available | ||
binder clip | Widely available | ||
ruler | Widely available | ||
plastic tupperware | Widely available | ||
cooler | Widely available | ||
ice pack | Widely available | ||
wash bottle | Widely available | ||
papertowels | Widely available | ||
fine-tipped paintbrush | Widely available | ||
light microscope | Leica or similar supplier | ||
clear plastic lid | Widely available | ||
tally counter | Widely available | ||
10 mL syringe | Widely available | ||
fine-tipped forceps | Widely available | ||
scalpel or razor blade | Widely available | ||
freezer | Widely available | ||
waterproof data shuttle | HOBO by Onset Computer Corp. | U-DTW-1 | |
PAR Sensor with 3m Cable | HOBO by Onset Computer Corp. | S-LIA-M003 | |
Temp/RH Sensor (12-bit) w/ 2m Cable | HOBO by Onset Computer Corp. | S-THB-M002 | |
Solar Radiation Shield | HOBO by Onset Computer Corp. | RS3 | |
Extra-Large Solar Panel 6 Watts | HOBO by Onset Computer Corp. | SOLAR-6W | |
Rain Gauge (0.2mm) with 2m Cable | HOBO by Onset Computer Corp. | S-RGB-M002 | |
Smart Temp Sensor 12-bit w/ 2m Cable | HOBO by Onset Computer Corp. | S-TMB-M002 | |
Soil Moisture – 10HS | HOBO by Onset Computer Corp. | S-SMD-M005 | |
Silicon Pyranometer Sensor w/3m Cable | HOBO by Onset Computer Corp. | S-LIB-M003 | |
Light Sensor Bracket | HOBO by Onset Computer Corp. | M-LBB | |
NDVI Light Sensor Bracket | HOBO by Onset Computer Corp. | M-NDVI | |
Complete 3M Tripod kit | HOBO by Onset Computer Corp. | M-TPA-KIT | |
RX3000 3G Remote Monitoring Station | HOBO by Onset Computer Corp. | RX3003-00-01 | |
Global Limited Plan – RX3000 T2 4-hr | HOBO by Onset Computer Corp. | SP-806 |