A project dubbed the “Development of a Detection System for Pest and Disease Resistance in Philippine Coffee Varieties” was released by the Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development of the Department of Science and Technology (DOST-PCAARRD). The study will employ molecular markers to distinguish indigenous coffee kinds that are resistant from others that are prone to illnesses and insect pests.
The experiment is also being implemented by Dr Ernelea P. Cao of the Institute of Biology at the University of the Philippines Diliman (UPD), who claims that the influence of coffee pests is only apparent when they are common. Through the study, early detection of coffee seedlings using the detection system can help to lower the incidences of insect pests and diseases.
The berry borer and coffee leaf rust disease have had the biggest negative effects on coffee production. Given this, a detection method will help farmers, researchers, and coffee growers by allowing them to identify the vulnerable coffee kinds using the system.
On the other hand, the project created and synthesised primers for the detection system during its first year. To detect coffee white stem borer, coffee leaf rust, and coffee berry disease, Coffea Arabica and C. Canephora types were subjected to loop-mediated isothermal amplification (LAMP) technology.
In the future, the project will collect leaf samples from C. arabica varieties registered with the National Seed Industry Council (NSIC) at the Bureau of Plant Industry (BPI) in Baguio City -one of the main sources of coffee in the Philippines.
The berry borer and coffee leaf rust disease have had the biggest negative effects on coffee production. Given this, a detection method will help farmers, researchers, and coffee growers by allowing them to identify the vulnerable coffee kinds using the kit.
To validate resistant and susceptible genes identified through RNA profiling, the team will compare healthy coffee leaves with coffee leaf rust and white stem borer-infested ones. The team’s LAMP primers will also be validated in coffee plantations in Batangas and Cavite.
To introduce the resistant genes to other coffee kinds, the study stressed the significance of incorporating a breeding component. The early resistance screening of local coffee types made possible by the detection system that uses molecular markers is anticipated to help prevent the distribution and planting of impacted coffee plants, which could result in further losses for coffee producers.
By doing this, researchers want to support the local coffee sector and aid in the rehabilitation of coffee estates in various Philippine regions, like Batangas and Cavite, which were severely damaged by significant ashfalls from the Taal volcano eruption in 2020.
The Remote Online Surveillance for Banana (ROSANNA) mobile application improved banana production while lowering disease control costs. This app was developed by the University of South-eastern Philippines (USeP) and is a mobile agricultural disease surveillance system capable of gathering and disseminating disease-related data at the banana farm level. It enables farm managers and field workers to monitor disease prevalence in the field in near real-time.
The Collaborative Research and Creation to Leverage Philippine Economy (CRADLE) Programme of the DOST provided funding for the development of the app. The software mostly addressed Banana Bunchy Top Disease (BBTD), Black Leaf Streak (BLS), and other widespread banana diseases.
The ROSANNA app is currently being used by HRC to keep tabs on banana diseases in the field. With HRC, continuity plans, and the procedures required for its deployment and expansion to other locations have also been explored.
