|ARISTIZABAL, LUIS - Eg Consulting (SELF-EMPLOYED)|
|SHRINER, SUZANNE - Synergistic Hawaii Agriculture Council|
|HOLLINGSWORTH, ROBERT - Retired ARS Employee|
|MASCARIN, GABRIEL - Embrapa|
|CHAVEZ, BERNARDO - Washington State University|
|Matsumoto Brower, Tracie|
|ARTHURS, STEVEN - Texas A&M University|
Submitted to: International Journal of Tropical Insect Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/13/2018
Publication Date: 9/24/2018
Citation: Aristizabal, L.F., Shriner, S., Hollingsworth, R., Mascarin, G.M., Chavez, B., Matsumoto, T., Arthurs, S.P. (2018) "Field sampling strategies for coffee berry borer (Coleoptra: Curculionidae: Scolytinae) infesting berries in coffee farms in Hawaii," International Journal of Tropical Insect Science. Cambridge University Press,38(4),pp.418-426. doi: 10.1017/S174275841800022X.
Interpretive Summary: Field monitoring of coffee berry borer (CBB) is important to determine when control methods should be implemented. CBB infested berries were monitored every 2-4 weeks on 17 coffee farms in the Kona and Kau districts on the Big Island of Hawaii using the 30 tree/hectare method. Results showed that between 6 and 50 coffee branches per hectare (sample unit) are required to estimate infestation rates of 1.5–2.5% infested green berries (suggested economic threshold). In order make sampling more time efficient, an additional method was used on 14 farms which counted only green berries instead of all berries on a branch. Comparison of the two methods showed that both methods get similar infestation results but the modified method took 27% less time. Data from both methods also showed that the amount of CBB infested berries prior to harvest can predict infestation rates of the final coffee green bean sold to the market or used for roasting.
Technical Abstract: The coffee berry borer (CBB), Hypothenemus hampei Ferrari, a recent invader to Hawaii, is impacting coffee growers by reducing yields and quality and increasing production costs. Monitoring strategies are needed to assess infestations and where control operations are warranted, and evaluate their effectiveness. To develop and validate a fixed-precision sequential sampling plan, an intensive CBB sampling programme was conducted in 17 small farms in Kona and Kau districts in the Big Island in 2016/17. At each location, 30 trees/ha were monitored at 2–4 week intervals. Results show that the CBB has an aggregated spatial distribution based on Taylor's power law parameters. According to Green's stop line formula, between 6 and 50 coffee branches per ha (sample unit) are required to estimate infestation rates of 1.5–2.5% infested green berries (suggested economic threshold) with a precision fixed at 10 to 25%. Concurrently, a modified strategy was tested on 14 farms, in which only infested green berries (not total) was counted. The standard and modified sampling methods were highly correlated (R2 = 0.98), while the modified approach required on average only 35 min (27% less time) to complete, with an additional 24 min taken to observe the position of the CBB inside the berry. Our data also show that berry infestation rates of CBB prior to harvest were a good predictor of the total defects resulting in processed green coffee from these farms (Pearson's r coefficient of 0.82). Our findings support improved sampling for the CBB under Hawaiian conditions using a simpler and faster monitoring strategy based on counting green infested berries.