A rapid tree diversity assessment method for cocoa agroforestry systems. (November 2021)

• Record Type: Journal Article
• Authors: Raneri, Jessica E.; Oliveira, Sandra; Demers, Nicole R.; Asare, Richard; Nuamah, Seth; Dalaa, Mustapha A.; Weise, Stephan
• Is Part Of: Ecological indicators. Volume 130(2021)
• Publication Date: 2021-11
• Subjects: Biodiversity -- Agroforestry -- Cocoa -- Rapid assessment -- Ghana; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2021.107993
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Three different tree biodiversity rapid assessment methods for cocoa fields tested. A stepwise approach with stakeholder engagement was used to evaluate results. Method with similar size plots set up on four directions was the most accurate. The method can be applied at large-scale with few resources to collect data. Farmers can report accurately on tree species and participate in data collection. Abstract: Biodiversity is recognized as an essential part of sustainable development efforts, however reducing biodiversity loss is a key global challenge that requires updated data on biodiversity status at different scales. Cocoa agroforests include tree species besides cocoa, a practice beneficial to biodiversity, ecosystem conservation and farming households. We present a stepwise procedure to test and select a method that rapidly assesses biodiversity in cocoa agroforests based primarily on species richness and counts of non-cocoa trees. Three rapid assessment methodologies (RapidBAM) with different sampling procedures were tested in three phases: calibration, testing and evaluation. Results showed the method using the lowest number of sample plots with a minimum area coverage and a consistent sampling time (regardless of farm context) provided the most accurate and straightforward assessment. Farmers accurately reported qualitatively on species, complimenting quantitative data produced by RapidBAM. Collecting biodiversity data with RapidBAM proved valuable to collect data at large-scales and is applicable to different landscapes. Monitoring biodiversity with fewer required resources than conventional methods is a relevant outcome, which can help defining efficient biodiversity-friendly farming practices.