Mai Phuong Nguyen

Selection of shade tree species for agroforestry systems must take the complexity of these systems into account. Tree species selection should maximize the provision of ecosystem services while minimizing disservices. Selected species must be adapted to local agroecological conditions and cater to farmers' needs, while considering their preferences and constraints. The ShadeTreeAdvice methodology was developed to support said selection process using farmers' local ecological knowledge. It provides the steps to rapidly identify tree species and evaluate their impacts on a range of locally important ecosystem services. Results are uploaded to a decision support tool to tailor tree species recommendations to individual farmers' needs (www.shadetreeadvice.org). During the 5 year timeframe between 2016 and 2020, eight studies following this methodology were conducted in various coffee and cocoa growing regions across Africa, Asia and Central America. This article looks back at these studies to synthesize their findings and evaluate the methodology. We identified similarities in the use of tree species across different study areas, notably regarding leguminous and fruit tree species. We showed that the method was efficient to evaluate tree species' impacts on soil and climate regulation, crop production, and economic benefits. It was less efficient for evaluating impacts related to incidence of pests and diseases, often associated with knowledge gaps. The method also successfully allowed investigating the links between LEK and socio‐economic groups or environmental factors. Furthermore, we suggest a series of improvements in the methodology for future studies. These improvements include (i) broadening the scope of studies beyond tree species provision of ecosystem services to include tree species impact on farming practices; (ii) allowing the comparison of tree performances in agroforestry systems versus in full sun; (iii) providing a clear pathway for validation of the results; (iv) using tree species' functional traits to generalize the results. Read the free Plain Language Summary for this article on the Journal blog.

This article presents research about the influences of social and cultural norms on the adoption of agroforestry in the northwest mountainous region of Viet Nam. The farming systems practiced by various ethnic groups in the northwest mainly occur on sloping land, which extends over 70% of the land area in the region. Decades of intensive monoculture of annual crops has resulted in severe soil erosion, contributing to soil degradation and decline in crop yields. Integrating agroforestry practices on sloping land has the potential to halt and reverse soil degradation and improve local livelihoods, but its adoption is conditioned by the diverse social and cultural norms of different ethnic groups. This research applies knowledge-based system methods in order to understand local opportunities, preferences, and constraints influencing the adoption of agroforestry practices, using a purposive, gender-balanced sample of sixty farmers from six villages across three provinces in the northwest region comprising people from Kinh, Thai and H’mong ethnic groups. Our results show that although farmers from all groups are aware of the economic and ecological benefits of trees for soil conservation in general, they have different perceptions about the benefits of particular agroforestry practices. Behavioural norms controlling agroforestry adoption vary amongst ethnic groups, and farmers’ individual social and cultural preferences influence their aspirations and adoption decisions. We conclude that developing appropriate agricultural interventions in a culturally rich environment such as northwest Viet Nam requires understanding of the context-specific needs and interests of socially and culturally disaggregated populations. Policies supporting agroforestry are more likely to contribute to more sustainable livelihoods and ecological benefits where they are tailored to the requirements of different ethnic groups.

Estimates of carbon sequestration for timber trees is well documented, while fruit trees are understudied. The few existing estimates indicate that fruit trees and fertiliser management on them, can substantially sequester carbon in coffee monocultures, albeit unlikely to the same extent as timber trees. A carbon investor may thus favour timber. In this light, as programs for planting billions and trillion trees are launched “to save the climate”, a wide range of gender, social, justice and environmental concerns are voiced. To challenge the mitigation perspective, we contrasted two hypothetical tree planting strategies: a mitigation (carbon finance) perspective and a livelihoods-centred (local) perspective and explored what a rapid, gender and social inclusion-oriented livelihoods perspective could bring to the process of tree selection. The survey documents indigenous knowledge of trees’ potential (dis)benefits in coffee agroforestry systems among 106 female and male arabica-growers in northwest Vietnam. The results display many similarities between women and men in term of perceived benefits from trees. Women and men prioritized trees based on their economic benefits, impacts on coffee production and improved soil fertility. However, in determining the preferred species, women considered more factors, including consequences for pest and disease (on host tree or coffee), microclimate regulation and shade provision. These findings resemble those by others from the same region and demonstrate that consulting both women and men can result in a more diverse shortlist of potential trees for agroforestry/afforestation that reflect both genders’ economic and labour contributions to the household. Furthermore, tree planting projects would benefit from seeking collaboration for bundled ecosystem services, rather than merely from carbon finance. Conversely, carbon investors can rely on farmers’ preferences and rest assured that they also contribute to sequestering carbon.

The Nationally Determined Contributions (NDCs) of several non-Annex I countries mention agroforestry but mostly without associated mitigation target. The absence of reliable data, including on existing agroforestry practices and their carbon storage, partially constrains the target setting. In this paper, we estimate the mitigation potential of agroforestry carbon sequestration in Vietnam using a nationwide agroforestry database and carbon data from the literature. Sequestered carbon was estimated for existing agroforestry systems and for areas into which these systems can be expanded. Existing agroforestry systems in Vietnam cover over 0.83 million hectares storing a 1346 ± 92 million ton CO₂ equivalent including above-, belowground, and soil carbon. These systems could be expanded to an area of 0.93–2.4 million hectares. Of this expansion area, about 10% is considered highly suitable for production, with a carbon sequestration potential of 2.3–44 million ton CO₂ equivalent over the period 2021–2030. If neglecting agroforestry’s potential for modifying micro-climates, climate change can reduce the highly suitable area of agroforestry and associated carbon by 34–48% in 2050. Agroforestry can greatly contribute to Vietnam’s 2021–2030 NDC, for example, to offset the greenhouse gas emissions of the agriculture sector.

In recent decades in northwest Vietnam, Arabica coffee has been grown on sloping land in intensive, full sun monocultures that are not sustainable in the long term and have negative environmental impacts. There is an urgent need to reverse this negative trend by promoting good agricultural practices, including agroforestry, to prevent further deforestation and soil erosion on slopes. A survey of 124 farmers from three indigenous groups was conducted in northwest Vietnam to document coffee agroforestry practices and the ecosystem services associated with different tree species used in them. Trees were ranked according to the main ecosystem services and disservices considered to be locally relevant by rural communities. Our results show that tree species richness in agroforestry plots was much higher for coffee compared to non-coffee plots, including those with annual crops and tree plantations. Most farmers were aware of the benefits of trees for soil improvement, shelter (from wind and frost), and the provision of shade and mulch. In contrast, farmers had limited knowledge of the impact of trees on coffee quality and other interactions amongst trees and coffee. Farmers ranked the leguminous tree species Leucaena leucocephala as the best for incorporating in coffee plots because of the services it provides to coffee. Nonetheless, the farmers’ selection of tree species to combine with coffee was highly influenced by economic benefits provided, especially by intercropped fruit trees, which was influenced by market access, determined by the proximity of farms to a main road. The findings from this research will help local extension institutions and farmers select appropriate tree species that suit the local context and that match household needs and constraints, thereby facilitating the transition to a more sustainable and climate-smart coffee production practice.

Cropland mapping is very important for food security, policy development, land use planning, and environmental protection. Scientists have developed methods and techniques for cropland mapping with remote sensing image data. Both single date and multitemporal image data are used for generation of cropland and crop type maps. Multitemporal image data has advantages over single date image data from reliability and accuracy point of view because multitemporal image data allows to eliminate seasonality of vegetation. In this paper, the authors present new algorithm for cropland and crop type mapping with multitemporal Landsat image data. The algorithm requires for analysis of all Landsat scenes observed during one year and if needed, scenes in some years back to compensate clouds and cloud shadows. Phenology of land cover is constructed based on six bimonthly cloud free land covers that were automatically classified using the selected scenes. By grouping land covers within two months to six land covers of periods January–February, March–April, May-June, July–August, September–October, and November–December we create six bimonthly cloud free land covers that formulate a database for mapping cropland and crop types. By analysis of 50 Landsat scenes of path/row number 128/45 (northwest of Vietnam) observed mainly from 2017, 2016 and 2015 we success to map upland cropland and 14 crop types with area ranging from 145,143 ha to 3,373 ha per crop type. The study pointed out that phenology characterized by six bimonthly land covers is acceptable to identify cropland distribution and some specific crop types. For better results, apparently we need higher temporal resolution of image data. Due to uncertainty of the atmosphere, it is almost impossible to rely only on optical remote sensing data to achieve high temporality of data so application of multitemporal SAR data could be a way to overcome this obstacle.