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Decision and Policy Analysis Research Area – DAPA

Participatory GIS training launched in Vietnam

Written by: Louis Parker (CIAT)

Edited by: Georgina Smith (CIAT) and Manon Koningstein (CIAT).

Smiling faces, laughter and a huge crowd of people greeted us as we arrived in the Climate-Smart Village of Ma. An organised cooking competition was in full swing to celebrate World Food Day, Vietnamese Women’s Day and of course, importantly, the start of the Participatory GIS (PGIS) training and survey.

As the festivities quietened down, the PGIS team quickly got to work to tackle the mammoth task ahead of them – visiting a total of 100 farmers, recording each of their fields with GPS points, and conducting a short, simple and interactive survey to extract information about crops grown, varieties used, incidence of pests and diseases, and the climate pressures faced at the field scale.

Participatory GIS training in Vietnam. Photocredit: Louis Parker (CIAT)

Participatory GIS training in Vietnam. Photocredit: Louis Parker (CIAT)

Complex topography

As Ma Village has a complex topography, with forested mountain areas, low lying paddy fields, and a patchwork of small cultivated islands on the lake, taking GPS points of the fields and asking farmers questions required a large team with understanding of the local context.

As such, CIAT’s PGIS team were accompanied by two experienced  staff from the local Northern Mountainous And Forestry Science Institute, NOMAFSI), two commune staff, and two local farmers.

Day 1 – The survey was thoroughly reviewed. It was even adapted slightly to include an extra question on the costs and benefits. For the local village staff this was the first time they had undertaken a survey.

Day 2 – The GPS training was undertaken. Information on assessing the accuracy of the GPS, taking the way points and recording the farmer and respective plots were discussed. After this, training was put to practice, as each of the numerators had a chance to undertake the survey and map a field.

Day 3 – The PGIS team split into groups and headed to undertake the survey with local farmers.

Participatory GIS training in Vietnam. Photocredit: Louis Parker (CIAT)

Participatory GIS training in Vietnam. Photocredit: Louis Parker (CIAT)

So how does PGIS differ to other GIS data collection methodologies? A vital step in the planning and monitoring of future projects, PGIS can help prioritise spatially which Climate-Smart Agricultural (CSA) practices are appropriate for particular communities and areas.

Unlike land-use maps which have a fixed resolution, PGIS maps can be downscaled, allowing future changes to be monitored based on solid baseline data and key metrics from key localities that serve as benchmark sites for timeline comparison in the Climate Smart Villages.

Stef de Haan, CIAT’s program manager in Asia, said:  “PGIS is unique in that it provides an opportunity to extract fine resolution (field level information), spatial data based on local knowledge and perceptions using a short, simple and interactive survey. It can feed into the development of land use maps and remote sensing activities, and facilitates scaling up”.

The team. Photocredit: Louis Parker (CIAT)

PGIS to identify climate-smart practices

The outputs of the PGIS study will enable researchers to pilot prioritized climate-smart practises, identifying which practises could be piloted in a spatially explicit manner, and which farmers are best-equipped to undertake these practises is vital for future success.

As Huong Tham, CIAT’s Agricultural Economist notes: “A successful survey requires fitting into farmers’ busy schedule, visiting the fields when they are undertaking required activities, such as planting or weeding. As a GIS team we need to be flexible and responsive to farmers’ needs”.

Results from the survey are expected to give more reasons to smile –  not just for CIAT’s GIS team, but also for farmers. With the information from these maps, it will be possible to introduce agricultural practises which are better suited to a changing climate in the long-term.

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