International: A user-friendly tool for effective monitoring and management of forest conservation set-asides in oil palm landscapes

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Tropical forest ecosystems are at risk of clearance for agricultural expansion and the palm oil industry is a key driver of deforestation across the tropics. To tackle this, the Roundtable on Sustainable Palm Oil (RSPO) certification standard requires oil palm growers to set aside and manage forests within their plantations. However, these sites are often managed by companies or smallholder groups that have limited access to knowledge of forest ecology or conservation. A lack of guidance about how to monitor and manage these areas means that the full conservation potential of these policies is not currently being realised.Assessing and monitoring the quality of forest set asides is vital for applying appropriate management practices. However, existing methods for forest assessment are time-consuming, expensive and/ or require specialist expertise. As a result, monitoring of these forests is often done poorly, inconsistently, or not at all. Small farmers may be prohibited entirely from participating in certification schemes because of the burdens of management and monitoring. Thus, oil palm growers require a robust, inexpensive, quick and easy tool for monitoring forest quality and restoration efforts. They also need the knowledge to effectively interpret and act on the results of monitoring. The RSPO will benefit from a consistent, standardised method which will enable better auditing and assessment of the impact of certification on forest conservation.NERC-funded research has huge potential to inform the development of simplified conservation tools. As part of PI JML's NERC Knowledge Exchange fellowship we used data on biodiversity and ecosystem functioning to develop and test a Forest Integrity Assessment (FIA) survey tool for SE Asian forest. Relying only on yes-no answers, with no need for expensive equipment, measurements or taxonomic identification, the survey results of volunteers ranging from post-doctoral researchers to plantation managers revealed strong correlations with a variety of scientific forest quality metrics. These results, combined with positive feedback from non-specialist users of the survey, indicates that the FIA tool is ideal for overcoming the gap between policy and effective implementation.This project aims to convert the tool from its current paper form to a dynamic smartphone application. The benefits of this include 1) in-app training to improve accuracy, 2) the ability to quickly digitally record and store survey results, 3) improved portability and efficiency in the field, 4) integrated GPS mapping, 5) automated analysis of results 5) in-built decision-support to enable adaptive management. Digitalisation will provide opportunities to build in further capabilities such as remote sensed information, machine learning, long-term storage, archiving and the ability to expand the product for different regions and sectors. These benefits will transform the usability of the FIA tool, enabling widespread uptake and delivering high quality environmental management.A robust, evidence-based approach to conservation is necessary to ensure the best outcomes for conservation in vulnerable fragmented forest landscapes. However, the vast body of academic knowledge to inform the development of these approaches is largely inaccessible to end-users. The academic community is thus best placed to deliver this project, in close partnership with the key users of the tool, and in doing so build capacity in forest ecology and conservation expertise within the oil palm industry. Our partners include the RSPO certification standard, one of the largest oil palm growers globally, third sector organisations who work with growers and smallholders to implement sustainable practices, and key academic institutions. This consortium ensures that the tool will be incorporated into policy and guidance at the highest levels, be fit for purpose on the ground, and be supported by the latest world class science.