TRADEOFFS BETWEEN RURAL DEVELOPMENT POLICIES AND FOREST PROTECTION: SPATIALLY-EXPLICIT MODELING IN THE CENTRAL HIGHLANDS OF VIETNAM

Alleviating rural poverty remains an important objective of development policy in many areas of the world. However, traditional means of increasing rural livelihoods such as increased investments in agricultural intensification measures can have disastrous impacts on natural resources such as forests by greatly increasing incentives for clearing. This paper contains a spatially-explicit model of land use in the Dak Lak province in the Central Highlands of Vietnam. Land use is modeled using a reduced-form multinomial logit model, and policy simulations are conducted. These simulations demonstrate that the adoption of yield-increasing inputs requires concomitant forest protection policies, both in terms of forest area and spatial configuration.International Development,

Human African trypanosomiasis amongst urban residents in Kinshasa: a case-control study.

BACKGROUND: Increasing numbers of human African trypanosomiasis (HAT) cases have been reported in urban residents of Kinshasa, Democratic Republic Congo since 1996. We set up a case-control study to identify risk factors for the disease. METHODS: All residents of the urban part of Kinshasa with parasitologically confirmed HAT and presenting for treatment to the city's specialized HAT clinics between 1 August, 2002 and 28 February, 2003 were included as cases. We defined the urban part as the area with contiguous habitation and a population density >5000 inhabitants per square kilometre. A digital map of the area was drawn based on a satellite image. For each case, two serologically negative controls were selected, matched on age, sex and neighbourhood. Logistic regression models were fitted to control for confounding. RESULTS: The following risk factors were independently associated with HAT: travel, commerce and cultivating fields in Bandundu, and commerce and cultivating fields in the rural part of Kinshasa. No association with activities in the city itself was found. DISCUSSION: In 2002, the emergence of HAT in urban residents of Kinshasa appears mainly linked to disease transmission in Bandundu and rural Kinshasa. We recommend to intensify control of these foci, to target HAT screening in urban residents to people with contact with these foci, to increase awareness of HAT amongst health workers in the urban health structures and to strengthen disease surveillance

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.Comment: In Proc. 30th AAAI Conference on Artificial Intelligenc

Payments for environmental services : incentives through carbon sequestration compensation for cocoa-based agroforestry systems in Central Sulawesi, Indonesia

Up to 25 percent of all anthropogenic greenhouse gas emissions are caused by deforestation, and Indonesia is the third largest greenhouse gas emitter worldwide due to land use change and deforestation. On the island of Sulawesi in the vicinity of the Lore Lindu National Park (LLNP), many smallholders contribute to conversion processes at the forest margin as a result of their agricultural practices. Specifically the area dedicated to cocoa plantations has increased from zero (1979) to nearly 18,000 hectares (2001). Some of these plots have been established inside the 220,000 hectares of the LLNP. An intensification process is observed with a consequent reduction of the shade tree density. This study assesses which impact carbon sequestration payments for forest management systems have on the prevailing land use systems. Additionally, the level of incentives is determined which motivates farmers to desist from further deforestation and land use intensification activities. Household behaviour and resource allocation is analysed with a comparative static linear programming model. As these models prove to be a reliable tool for policy analysis, the output can indicate the adjustments in resource allocation and land use shifts when introducing compensation payments. The data was collected in a household survey in six villages around the LLNP. Four household categories are identified according to their dominant agroforestry systems. These range from low intensity management with a high degree of shading to highly intensified shade free systems. At the plot level, the payments from carbon sequestration are the highest for the full shade cocoa agroforestry system, but with low carbon prices of € 5 tCO2e-1 these constitute 5 percent of the cocoa gross margin. Focusing on the household level, however, an increase of up to 18 percent of the total gross margin can be realised. Furthermore, for differentiated carbon prices up to € 32 tCO2e-1 the majority of the households have an incentive to adopt the more sustainable shade intensive agroforestry system. A win-win situation seems to appear, whereby, when targeting only the shade intensive agroforestry systems with carbon payments, the poorest households economically benefit the most and land use systems with high environmental benefits are promoted.payments for environmental services, carbon sequestration, agroforestry systems, cocoa, linear programming, economic incentives, poverty, Environmental Economics and Policy, Land Economics/Use,

Could carbon payments be a solution to deforestation? Empirical evidence from Indonesia

Up to 25 percent of all anthropogenic greenhouse gas emissions are caused by deforestation, and Indonesia is the third largest emitter worldwide due to land use change and deforestation. On the island of Sulawesi in the vicinity of the Lore Lindu National Park, smallholders contribute to conversion processes at the forest margin as a result of their agricultural practices. Specifically the area dedicated to cocoa plantations has increased from zero in 1979 to nearly 18,000 hectares in 2001. Some of these plots have been established inside the 220,000 hectares of the National Park. An intensification process is observed with a consequent reduction of the shade tree density. This study focuses on the impact of carbon sequestration payments for forest management systems on smallholder households. The level of incentives is determined which motivates farmers to desist from further deforestation and land use intensification activities. Household behaviour and resource allocation is analysed with a comparative static linear programming model. As these models prove to be a reliable tool for policy analysis, the output can indicate the adjustments in resource allocation and land use shifts when introducing compensation payments. The data was collected in a household survey in six villages around the Lore Lindu National Park. Four household categories were identified according to their dominant agroforestry systems. With carbon credit prices up to €32 tCO2e-1 an incentive can be provided for the majority of the households to adopt the more sustainable shade intensive agroforestry systems. The results show that with current carbon prices the deforestation activities of the majority of households could be stopped. A win-win situation seems to appear, whereby, when targeting only the shade intensive agroforestry systems with carbon payments, the poorest households economically benefit the most, the vicious circle of deforestation can be interrupted and land use systems with high environmental benefits are promoted.Payments for Environmental Services, Avoided Deforestation, Linear Programming, Resource /Energy Economics and Policy,

GIS applications for poverty targeted aquaculture development in the lower Mekong Basin.

In the lower Mekong Basin, marginal socio-economic conditions prevail amongst rural small scale farming households which heavily depend on highly seasonal, rain-fed farming systems for their livelihood. Persistent rural poverty is aggravated by frequently occurring droughts and floods. A yearly flood-drought cycle, while essential to their household economy based on rice and fisheries, renders rural poor livelihoods vulnerable to recurrent periods of food insecurity. This research demonstrates how a combination of publicly accessible Remote Sensing imagery and disaggregated poverty maps, within a comprehensive rural development framework, can provide an effective method to target pro-poor aquaculture development interventions at the local level. An agro-ecosystems analysis is performed in order to capture the seasonal dynamics of water- and aquatic resource exploitation. A holistic farming systems approach emphasises the potential of ponds in integrated rural smallholder systems to reduce poverty and vulnerability under rain fed conditions. A Geographic Information System (GIS), an efficient spatial inventory tool and decision support system in resolving real world problems, is used to identify where rural poor households can potentially benefit from the integration of aquaculture into existing production systems. A time series of satellite derived vegetation index data reveals distinct agro-ecosystem seasonality over large parts of the study area, which is indicative for farming systems under rain fed conditions. The developed methodology is capable of identifying functionally different agro-ecosystems. Socio-economic indicators for Cambodian parts of the lowland areas point to widespread rural poverty and vulnerability to recurrent food insecurity, which is directly related to agro-ecosystems seasonality and annual climate variability. Dependence of farming households on low productivity rain fed rice agro-ecosystems in Cambodia’s southern provinces is in stark contrast to the highly productive farming systems directly bordering it, in the freshwater fluvial zone of the Vietnamese Mekong Delta. A rapid increase in rice productivity in this densely populated area went hand-in hand with a considerable reduction in rural poverty. In this flood-prone but fertile area, resource competition and falling market prices of rice may have prompted the development of a range of integrated farming systems. The incorporation of ponds on farm in these systems facilitates reuse of nutrients from farm by-products for low-input aquatic resource production. In Northeast Thailand, crop production and low-input aquaculture have been successfully integrated along a tradition of water- and living aquatic resources management in farmer managed systems under resource poor conditions. A spatially linked commune level rural development database for Sisaket province in Northeast Thailand provides a useful framework for planning of aquaculture development through systems that are appropriate and relevant to local socio-economic and agro-ecological conditions. It was concluded that the socio-economic and agro-ecological context of rural poverty in Southeast Cambodia offers scope for similar pathways to improve rural wellbeing and reduce vulnerability to poverty and food insecurity by integrating aquatic resources development in pond based systems as part of an interdisciplinary approach towards rural development

DETERMINING CONSERVATION PRIORITIES AND PARTICIPATIVE LAND USE PLANNING STRATEGIES IN THE MARINGA-LOPORI-WAMBA LANDSCAPE, DEMOCRATIC REPUBLIC OF THE CONGO

Deforestation and forest degradation driven largely by agricultural expansion are key drivers of biodiversity loss in the tropics. Achieving sustainable and equitable management of land and resources and determining priority areas for conservation activities are important in the face of these advancing pressures. The Congo Basin of Central Africa contains approximately 20% of the world's remaining tropical forest area and serves as important habitat for over half of Africa's flora and fauna. The Government of the Democratic Republic of the Congo (DRC) is currently laying the foundation for a national land use plan for conservation and sustainable use of its forests. Since 2004, the African Wildlife Foundation (AWF) has led efforts to develop a participatory land use plan for the Maringa-Lopori-Wamba (MLW) Landscape located in northern DRC. The landscape was recognized in 2002 as one of twelve priority landscapes in the Congo Basin targeted for the establishment of sustainable management plans. This dissertation focuses on the development of geospatial methods and tools for determining conservation priorities and assisting land use planning efforts in the MLW Landscape. The spatio-temporal patterns of recent primary forest loss are analyzed and complemented by the development of spatial models that identify the locations of 42 forest blocks and 32 potential wildlife corridors where conservation actions will be most important to promote future viability of landscape-wide terrestrial biodiversity such as the bonobo (Pan paniscus). In addition, the research explores three scenarios of potential agricultural expansion by 2050 and provides spatially-explicit information to show how trade-offs between biological conservation and human agricultural livelihoods might be balanced in land use planning processes. The research also describes a methodological approach for integrating spatial tools into participatory mapping processes with local communities and demonstrates how the resulting spatial data can be used to inform village-level agricultural land use for resource planning and management. Conclusions from the work demonstrate that primary forest loss is intensifying around agricultural complexes and that wildlife corridors connecting least-disturbed forest blocks are most vulnerable to future forest conversion. Conservation of these areas is possible with the development of land use plans in collaboration with local communities

Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. To address these challenges, we, in this work, provide three contributions. 1) A brand new machine learning data-set, purposely developed for informal settlement detection. 2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements.Comment: Published at the AAAI/ACM Conference on AI, ethics and society. Extended results from our previous workshop: arXiv:1812.0081

Workshop for annual review of Building Resilient Agro-sylvopastoral Systems in West Africa through Participatory Action Research (BRAS-PAR) Project and planning “Partnerships for Scaling Climate-Smart Agriculture (P4S) Phase II

Building Resilient Agro-sylvo-pastoral Systems in West Africa through Participatory Action Research (BRAS-PAR) is a CCAFS Flagship 2 funded four year (2015-2018) project coordinated by the World Agroforestry (ICRAF) and implemented in collaboration with partners namely national agricultural research institutions (INERA in Burkina Faso, SARI in Ghana, INRAN in Niger and ISRA in Senegal) and the International Union for Conservation of Nature (IUCN in Burkina Faso). BRAS-PAR sought to develop up-scalable technological and social innovations of climatesmart agriculture integrating tree-crop-livestock systems through improved understanding of farmer's perceptions and demands, by addressing barriers to adoption taking into consideration gender and social differentiation. The specific objectives include 1) testing, evaluating and validating with rural communities and other stakeholders, scalable climate-smart models of integrated tree-crop-livestock systems, the dominant farming systems in the region, that include climate-risk management strategies; 2) simulating options for improving water and tree-crop-livestock systems under different climate and socio-economic scenarios using models (WaNuLCAS, SWAT, etc.) for informed decision making; 3) assessing the conditions of success and failure of technological interventions on adaptation to climate change. The work here focus on research that evaluates climate-smart practices and technologies that are defined through participatory identification by multistakeholders in each site. Beyond these sites, the approach capitalizes lessons learnt from on-going climate resilient projects to encourage partners to add missing components to the climate-smart village model or initiate new activities when deemed appropriate. Started in 2015, BRAS-PAR targeted three main outcomes: (i) National agricultural research institutions institutionalize the principles of PAR through integration of non-traditional partners in technologies development to generate wider context specific information to be fed into programs and policies to create the enabling environment for the scaling of CSA technologies; (ii) National extension services, development projects and farmer’s organizations widely disseminate and ensure better access to information on best fit CSA portfolios to cope with climate change; and (iii) The private sector including NGOs (FNGN, Larwaal, ARCAD, Care international), microcredit institutions, agro-dealers, rural radios are scaling up/out relevant CSA portfolios through new incentive programs. This project has ended in December 2018 and the meeting review edthe main achievements. During the same first phase of CCAFS , the project “Partnerships for Scaling (P4S) Climate-Smart Agriculture (P56)” was implemented mainly in East Africa with a focus on supporting countries and partners to plan and program CSA actions. It developed new innovations (e.g., The Compendium and Climate Risk Profiles), refreshed and adapted others (e.g., Climate Wizard, mobile-based monitoring) and collaborated on tools (e.g., Rural Household Multi-Indicator Survey, CSA MRV Profile) to develop a comprehensive set of evidence and information to serve diverse stakeholder needs for situation analysis, targeting and prioritizing, program support and monitoring and evaluation (aka ‘CSA-Plan’, Girvetz et al. 2018). Merging the actions of BRAS-PAR and P4S I to become P4S II was done with the intention to use tools and evidence/lessons learned from the Climate-Smart Villages and other development activities, with existing and new partners through direct scientific support to decision makers (e.g., governments, civil society, and researchers) and capacity building to help bring CSA to scale. The scientific activities will be combined with dedicated communication activities such as photo essays, tweets, blog posts, etc. from field staff and partners to raise the visibility of the project and help show case of its successes in supporting countries and position of ICRAF, CIAT, and CCAFS as the go to research organization for the science of scaling up CSA. The key activity areas of P4S II will be around: supporting CSA investment and programming, de-risking agriculture, digital delivery and monitoring and, communauty based scaling of CSA. The present meeting was thought to plan the new activities around these areas for 2019 and beyond