|C. Staver, G. Mora, J.J. Coria, E. Guzmán, O.E. Flores, G. Acevedo, D. Rengifo, A. Perez, A. Paulino, E. Perez, P. Suarez, J.C. Torres, J.C. Rojas Llanque, E. Nuñez, A. Bustamante, G. Espinoza, R.E. Corozo, W. Durango, S. Tiselema, G. Lara, M. Arias
|benchmarking, big data, Cavendish, digital agriculture, Musa
The export banana sector depends on weekly data to ensure that a perishable fruit reaches distant consumers regularly, meeting quality and ripeness standards.
Emerging organic and Fairtrade consumers have offered a window to small growers and their associations in dry tropical regions to export bananas, although with increased data demands to document production practices.
A survey among small growers and their associations in Dominican Republic, Peru and Ecuador showed that data are collected to ensure that contracted containers meet certification requirements primarily in paper forms, checklists, farm visits and phone calls.
Data are not managed and analyzed systematically to improve production efficiency and profitability or risk reduction.
FONTAGRO financed the development of an app for data collection and analysis for small growers and their organizations as a key strategy to scale promising innovations to reduce banana rejects from red rust and increase productivity through soil health.
Ma$ Banano, the name of the app, operates off-line with data uploaded opportunely to a central server.
Multiple users linked to production and fruit processing in each banana farm can enter data depending on their work responsibilities.
Data entry is organized in 2 sections aligned with the promising innovations.
For red rust, modules capture 1) bagging efficiency and timeliness and application of repellents and insecticides, and 2) quantification of rejects and their causes at processing.
For soil health, modules receive data on 1) mat density ha‑1, 2) plant vigor, 3) residue and fertilizer management and placement, 4) roots and soil biological, physical and chemical parameters, and 5) nutrient balance.
Two initial modules serve to identify the farm and field and to capture weekly reporting of flagged and harvested bunches and boxes processed.
A routine of app use begins with an assessment using all modules.
Monthly follow-up scouting of key practices and general quarterly monitoring orient continual improvement.
Data reports are generated through a web interface for individual growers and associations.
The overall base with data privacy mechanisms in place will be available for big-data research.
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