Implementing smallholder carbon projects: building local institutional capacity through participatory action research

Two smallholder agricultural carbon projects in East Africa engaged in a participatory action research process to identify ways local actors could take on larger management roles within the projects. Key lessons from this process were: * Community-based intermediaries can play a leading role in land- management trainings and supportive roles in carbon measurement and marketing. * Local government participation is critical to project success. * Local NGOs and private-sector actors can play central roles in training, providing agricultural inputs and linking farmers to markets. * Standardized training and curricula are important for scaling up. * Women’s roles in projects can grow if project benefits are aligned with their needs and trainings are made more accessible. * Agricultural benefits are more important than carbon payments for participating farmers. * Strengthened local and national policies in support of sustainable agricultural land management are needed to scale up project benefits

Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis

To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these ‘source’ populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8–28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa

Lessons and Implications for REDD+ Implementation Experiences from Tanzania

A book chapter 10Reduced deforestation and forest degradation ‘plus’ the role of conservation, sustainable management of forests and enhanced carbon stock (REDD+) has been singled out as one of the core strategies against climate change. At the same time, forests offer important livelihoods. To acquire experience on how to establish REDD+ ‘on the ground’, REDD+ pilot projects were established in Tanzania. The pilots were expected to provide valuable insights on many issues that will likely be encountered by both the government and local communities as REDD+ develops to assist in future REDD+ initiative. This study was conducted to draw lessons from two REDD+ pilot projects in Kondoa and Rungwe districts in Dodoma and Mbeya regions, respectively. Structured questionnaires for households with both closed and open ended questions were used to collect socio-economic, institutional and livelihoods-related information. Participatory rural appraisal (PRA) techniques, participant observation and focus group discussions (FGDs) were also employed. Results show that land and forests are the main livelihood assets in the two pilot project areas. Although REDD+ was generally accepted by most communities in the pilots, there were some levels of scepticism based on their past land use history. For example, the introduction of REDD+ in Kondoa faced rejection from some villages due to fears over land grabbing and exclusion from forest access. On the contrary, villages which depend solely on state-owned forests did not object to REDD+ as they are used to resource use exclusion mechanisms from such tenure systems. Assessment of the trial payments showed that most of the people would consider stopping deforestation and forests degradation if they get compensation relative to the losses of income they will encounter. Communities prefer payments in form of community investments rather than paying cash to individuals. It was observed as well that at the local level parallel governance structures for REDD+ have increasingly become a source of intra-village conflicts. In fact, the livelihood of the poor inhabitants is directly hooked to surrounding forests and natural services with growing future needs of land per household that threaten the future of REDD+. On the other hand, land use plans go through a relatively too long process and are costly. Thus, the government should consider preparing plans for all villages to reduce the costs of planning for natural resource management and use

Using machine learning for image-based analysis of sweetpotato root sensory attributes

The sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance, taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore, measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for breeders. In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as highthroughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye imaging system, data pre-processing for background elimination and feature extraction to develop machine learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties. For flesh-colour the trained Linear Regression and Random Forest Regression models attained 2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained 2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable 2 threshold of 0.80 for acceptable comparability to the human sensory panel showing that this approach can be used for the prediction of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers