Population Genetic Structure in a Social Landscape: Barley in a Traditional Ethiopian Agricultural System

Conservation strategies are increasingly driven by our understanding of the processes and patterns of gene flow across complex landscapes. The expansion of population genetic approaches into traditional agricultural systems requires understanding how social factors contribute to that landscape, and thus to gene flow. This study incorporates extensive farmer interviews and population genetic analysis of barley landraces (Hordeum vulgare) to build a holistic picture of farmer-mediated geneflow in an ancient, traditional agricultural system in the highlands of Ethiopia. We analyze barley samples at 14 microsatellite loci across sites at varying elevations and locations across a contiguous mountain range, and across farmer-identified barley types and management strategies. Genetic structure is analyzed using population-based and individual-based methods, including measures of population differentiation and genetic distance, multivariate Principal Coordinate Analysis, and Bayesian assignment tests. Phenotypic analysis links genetic patterns to traits identified by farmers. We find that differential farmer management strategies lead to markedly different patterns of population structure across elevation classes and barley types. The extent to which farmer seed management appears as a stronger determinant of spatial structure than the physical landscape highlights the need for incorporation of social, landscape, and genetic data for the design of conservation strategies in human-influenced landscapes

2014 Future Earth Young Scientists Conference on Integrated Science and Knowledge Co-Production for Ecosystems and Human Well-Being

Effective integration in science and knowledge co-production is a challenge that crosses research boundaries, climate regions, languages and cultures. Early career scientists are crucial in the identification of, and engagement with, obstacles and opportunities in the development of innovative solutions to complex and interconnected problems. On 25-31 May 2014, International Council for Science and International Social Science Council, in collaboration with the International Network of Next-Generation Ecologists and Institute for New Economic Thinking: Young Scholars Initiative, assembled a group of early career researchers with diverse backgrounds and research perspectives to reflect on and debate relevant issues around ecosystems and human wellbeing in the transition towards green economy, funded by the German Research Foundation, at Villa Vigoni, Italy. As a group of young scientists, we have come to a consensus that collaboration and communication among a diverse group of peers from different geographic regions could break down the barriers to multi-disciplinary research designed to solve complex global-scale problems. We also propose to establish a global systematic thinking to monitor global socio-ecological systems and to develop criteria for a “good” anthropocene. Finally, we aim to bridge gaps among research, the media, and education from a governance perspective linking with “sustainable development goals”

Reimagining the potential of Earth observations for ecosystem service assessments

The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management, conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. This will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide

Genotypes for farmers' varieties of barley in the Gamo Highlands of Ethiopia at 13 microsatellite loci

Barley samples were collected in the field in Ethiopia, through leaf samples in situ, or seeds planted in the greenhouse at the University of Montana. Genotyping was performed on an ABI Genetic Analyzer, and genotypes were determined using ABI Genemapper software

Subnational distribution of average farm size and smallholder contributions to global food production

Smallholder farming is the most prevalent form of agriculture in the world, supports many of the planet’s most vulnerable populations, and coexists with some of its most diverse and threatened landscapes. However, there is little information about the location of small farms, making it difficult both to estimate their numbers and to implement effective agricultural, development, and land use policies. Here, we present a map of mean agricultural area, classified by the amount of land per farming household, at subnational resolutions across three key global regions using a novel integration of household microdata and agricultural landscape data. This approach provides a subnational estimate of the number, average size, and contribution of farms across much of the developing world. By our estimates, 918 subnational units in 83 countries in Latin America, sub-Saharan Africa, and South and East Asia average less than five hectares of agricultural land per farming household. These smallholder-dominated systems are home to more than 380 million farming households, make up roughly 30% of the agricultural land and produce more than 70% of the food calories produced in these regions, and are responsible for more than half of the food calories produced globally, as well as more than half of global production of several major food crops. Smallholder systems in these three regions direct a greater percentage of calories produced toward direct human consumption, with 70% of calories produced in these units consumed as food, compared to 55% globally. Our approach provides the ability to disaggregate farming populations from non-farming populations, providing a more accurate picture of farming households on the landscape than has previously been available. These data meet a critical need, as improved understanding of the prevalence and distribution of smallholder farming is essential for effective policy development for food security, poverty reduction, and conservation agendas

Automated Plantation Mapping in Southeast Asia Using Remote Sensing Data

Plantation mapping is critical for understanding and addressing deforestation, a key driver of climate change and ecosystem degradation. Unfortunately, most plantation maps are limited to small areas for specific years because they rely on visual inspection of imagery. Here we propose an automated approach for annual mapping of plantations using remote sensing data. Due to the heterogeneity of land cover classes, we propose a novel ensemble learning method that simultaneously uses training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. With the experiments on MODIS data, we demonstrate the superiority of the proposed method over multiple baselines. In addition, we conduct extensive validation by comparing the detected plantation by our approach with the existing datasets developed through visual interpretation by expert observers. Based on the random sampling and the comparison with high-resolution images, the precision (i.e. user’s accuracy) and recall (i.e. producer’s accuracy) of our generated map are around 85.53% and 81.51%, respectively, and the overall accuracy is 95.20%

Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is technically challenging. Thus, most available plantation maps rely on visual inspection of imagery, and many of them are limited to small areas for specific years. Here, we present an automated approach, which we call Plantation Analysis by Learning from Multiple Classes (PALM), for mapping plantations on an annual basis using satellite remote sensing data. Due to the heterogeneity of land cover classes, PALM utilizes ensemble learning to simultaneously incorporate training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. We implement the proposed automated approach using MODIS data in Sumatra and Indonesian Borneo (Kalimantan). To validate the classification, we compare plantations detected using our approach with existing datasets developed through visual interpretation. Based on random sampling and comparison with high-resolution images, the user’s accuracy and producer’s accuracy of our generated map are around 85% and 80% in our study region

Smallholder Agriculture and Climate Change

Hundreds of millions of the world's poorest people directly depend on smallholder farming systems. These people now face a changing climate and associated societal responses. We use mapping and a literature review to juxtapose the climate fate of smallholder systems with that of other agricultural systems and population groups. Limited direct evidence contrasts climate impact risk in smallholder agricultural systems versus other farming systems, but proxy evidence suggests high smallholder vulnerability. Smallholders distinctively adapt to climate shocks and stressors. Their future adaptive capacity is uncertain and conditional upon the severity of climate change and socioeconomic changes from regional development. Smallholders present a greenhouse gas (GHG) mitigation paradox. They emit a small amount of CO2 per capita and are poor, making GHG regulation unwarranted. But they produce GHG-intensive food and emit disproportionate quantities of black carbon through traditional biomass energy. Effectively accounting for smallholders in mitigation and adaption policies is critical and will require innovative solutions to the transaction costs that enrolling smallholders often imposes. Together, our findings show smallholder farming systems to be a critical fulcrum between climate change and sustainable development

Addendum: Future Earth Young Scientists Conference on Integrated Science and Knowledge Co-Production for Ecosystems and Human Well-Being (vol 11, pg 11553, 2014)

The authors would like to add the following affiliation for Peter Søgaard Jørgensen of paper [1]:   8 International Network of Next-Generation Ecologists, Universitetsparken 15, Building 3, Copenhagen 2100, Denmark[...

2014 future earth young scientists conference on integrated science and knowledge co-production for ecosystems and human well-being

Effective integration in science and knowledge co-production is a challenge that crosses research boundaries, climate regions, languages and cultures. Early career scientists are crucial in the identification of, and engagement with, obstacles and opportunities in the development of innovative solutions to complex and interconnected problems. On 25–31 May 2014, International Council for Science and International Social Science Council, in collaboration with the International Network of Next-Generation Ecologists and Institute for New Economic Thinking: Young Scholars Initiative, assembled a group of early career researchers with diverse backgrounds and research perspectives to reflect on and debate relevant issues around ecosystems and human wellbeing in the transition towards green economy, funded by the German Research Foundation, at Villa Vigoni, Italy. As a group of young scientists, we have come to a consensus that collaboration and communication among a diverse group of peers from different geographic regions could break down the barriers to multi-disciplinary research designed to solve complex global-scale problems. We also propose to establish a global systematic thinking to monitor global socio-ecological systems and to develop criteria for a “good” anthropocene. Finally, we aim to bridge gaps among research, the media, and education from a governance perspective linking with “sustainable development goals”