Socio-Spatial Relationships in Dairy Cows

Farm animals may serve as models for evaluating social networks in a controlled environment. We used an automated system to track, at fine temporal and spatial resolution (once per minute, +/- 50 cm) every individual in six herds of dairy cows (Bos taurus). We then analysed the data using social network analyses. Relationships were based on non-random attachment and avoidance relationships in respect to synchronous use and distances observed in three different functional areas (activity, feeding and lying). We found that neither synchrony nor distance between cows was strongly predictable among the three functional areas. The emerging social networks were tightly knit for attachment relationships and less dense for avoidance relationships. These networks loosened up from the feeding and lying area to the activity area, and were less dense for relationships based on synchronicity than on median distance with respect to node degree, relative size of the largest cluster, density and diameter of the network. In addition, synchronicity was higher in dyads of dairy cows that had grown up together and shared their last dry period. This last effect disappeared with increasing herd size. Dairy herds can be characterized by one strongly clustered network including most of the herd members with many non-random attachment and avoidance relationships. Closely synchronous dyads were composed of cows with more intense previous contact. The automatic tracking of a large number of individuals proved promising in acquiring the data necessary for tackling social network analyses.

Virtue, Law and Mercy

The attribution of responsibility in world society is increasingly a field of contestation. On the one hand, the perception of causal and moral links reaching far in space and time are ever more explicitly pronounced; on the other hand, the very complexity of these links often engenders a fragmentation of responsibility both in law (Veitch 2007) as well as in moral commitment. Moreover, those institutions of legal responsibility attempting to reflect some of these interrelations are often criticised as insufficient by those who follow alternative narratives of causation and moral community. Current institutions of responsibility in law appear to abstract from what could be called enabling contexts; they perform their cuts in the chains of enabling interactions at very brief intervals (Strathern 2001). The result is often “organised irresponsibility” (Veitch 2007; Beck 1996), producing appeals to a global community of concern in time and space without corresponding obligatory commitments. This talk explores alternative conceptualisations of responsibility, and enquires into their notion of the person, their temporal and socio-spatial dimensions, and their notion of liability.

Mortality from road traffic accidents in Switzerland: longitudinal and spatial analyses

Road traffic accidents (RTA) are an important cause of premature death. We examined socio-demographic and geographical determinants of RTA mortality in Switzerland by linking 2000 census data to RTA mortality records 2000-2005 (ICD-10 codes V00-V99). Data from 5.5 million residents aged 18-94 years, 1744 study areas, and 1620 RTA deaths were analyzed, including 978 deaths (60.4%) in motor vehicle occupants, 254 (15.7%) in motorcyclists, 107 (6.6%) in cyclists, and 259 (16.0%) in pedestrians. Weibull survival models and Bayesian methods were used to calculate hazard ratios (HR), and standardized mortality ratios (SMR) across study areas. Adjusted HR comparing women with men ranged from 0.04 (95% CI 0.02-0.07) in motorcyclists to 0.43 (95% CI 0.32-0.56) in pedestrians. There was a u-shaped relationship with age in motor vehicle occupants and motorcyclists. In cyclists and pedestrians, mortality increased after age 55 years. Mortality was higher in individuals with primary education (HR 1.53; 95% CI 1.29-1.81), and higher in single (HR 1.24; 95% CI 1.05-1.46), widowed (HR 1.31; 95% CI 1.05-1.65) and divorced individuals (HR 1.62; 95% CI 1.33-1.97), compared to persons with tertiary education or married persons. The association with education was particularly strong for pedestrians (HR 1.87; 95% CI 1.20-2.91). RTA mortality increased with decreasing population density of study areas for motor vehicle occupants (test for trend p<0.0001) and motorcyclists (p=0.0021) but not for cyclists (p=0.39) or pedestrians (p=0.29). SMR standardized for socio-demographic and geographical variables ranged from 82 to 190. Prevention efforts should aim to reduce inequities across socio-demographic and educational groups, and across geographical areas, with interventions targeted at high-risk groups and areas, and different traffic users, including pedestrians.

Accessibility as a determinant of environmental dynamics in socio-economic disparities in mainland Southeast Asia

Access and accessibility are important determinants of people’s ability to utilise natural resources, and have a strong impact on household welfare. Physical accessibility of natural resources, on the other hand, has generally been regarded as one of the most important drivers of land-use and land-cover changes. Based on two case studies, this article discusses evidence of the impact of access to services and access to natural resources on household poverty and on the environment. We show that socio-cultural distances are a key limiting factor for gaining access to services, and thereby for improved household welfare. We also discuss the impact of socio-cultural distances on access to natural resources, and show that large-scale commercial exploitation of natural resources tends to occur beyond the spatial reach of socio-culturally and economically marginalised population segments. We conclude that it is essential to pay more attention to improving the structural environment that presently leaves social minority groups marginalised. Innovative approaches that use natural resource management to induce poverty reduction – for example, through compensation of local farmers for environmental services – appear to be promising avenues that can lead to integration of the objectives of poverty reduction and sustainable environmental stewardship.

Spatial assessment of carbon stocks of living vegetation at the national level in Lao PDR

The international mechanism for Reducing Greenhouse Gas Emissions from Deforestation and Forest Degradation (REDD) supposedly offers new opportunities for combining climate mitigation, conservation of the environment, and socio-economic development for development countries. In Laos REDD is abundantly promoted by the government and development agencies as a potential option for rural development. Yet, basic information for carbon management is missing: to date no knowledge is available at the national level on the quantities of carbon stored in the Lao landscapes. In this study we present an approach for spatial assessment of vegetation-based carbon stocks. We used Google Earth, Landsat and MODIS satellite imagery and refined the official national land cover data to assess carbon stocks. Our study showed that more than half (52%) of carbon stock of Laos is stored in natural forests, but that 70% of this stock is located outside of national protected areas. On the basis of two carbon-centered land use scenarios we calculated that between 30 and 40 million tons of carbon could be accumulated in shifting cultivation areas; this is less than 3% of the existing total stock. Our study suggests that the main focus of REDD in Laos should be on the conservation of existing carbon stocks, giving highest priority to the prevention of deforestation outside of national protected areas.

From Statistical Data to Spatial Knowledge - informing decision making in Vietnam

Vietnam has developed rapidly over the past 15 years. However, progress was not uniformly distributed across the country. Availability, adequate visualization and analysis of spatially explicit data on socio-economic and environmental aspects can support both research and policy towards sustainable development. Applying appropriate mapping techniques allows gleaning important information from tabular socio-economic data. Spatial analysis of socio-economic phenomena can yield insights into locally-specifi c patterns and processes that cannot be generated by non-spatial applications. This paper presents techniques and applications that develop and analyze spatially highly disaggregated socioeconomic datasets. A number of examples show how such information can support informed decisionmaking and research in Vietnam.

Socio-economic atlas of Kenya: Depicting the national population census by county and sub-location

The Socio-Economic Atlas of Kenya is the first of its kind to offer high-resolution spatial depictions and analyses of data collected in the 2009 Kenya Population and Housing Census . The combination of geographic and socio-eco - nomic data enables policymakers at all levels, development experts, and other interested readers to gain a spatial understanding of dynamics affecting Kenya. Where is the informal economic sector most prominent? Which areas have adequate water and sanitation? Where is population growth being slowed effectively? How do education levels vary throughout the country? And where are poverty rates lowest? Answers to questions such as these, grouped into seven broad themes, are visually illustrated on high-resolution maps. By supplying precise information at the sub-location level and summarizing it at the county level, the atlas facilitates better planning that accounts for local contexts and needs. It is a valuable decision-support tool for government institutions at different administrative levels, educational institutions, and others. Three organizations – two in Kenya and one in Switzerland – worked together to create the atlas: the Kenya National Bureau of Statistics (KNBS), the Nanyuki-based Centre for Training and Integrated Research in ASAL Development (CETRAD), and the Centre for Development and Environment (CDE) at the University of Bern. Close cooperation between KNBS, CETRAD, and CDE maximized synergies and knowledge exchange.

The geography of large-scale land acquisitions: Analysing socio-ecological patterns of target contexts in the global South

This paper analyses local geographical contexts targeted by transnational large-scale land acquisitions (>200 ha per deal) in order to understand how emerging patterns of socio-ecological characteristics can be related to processes of large-scale foreign investment in land. Using a sample of 139 land deals georeferenced with high spatial accuracy, we first analyse their target contexts in terms of land cover, population density, accessibility, and indicators for agricultural potential. Three distinct patterns emerge from the analysis: densely populated and easily accessible croplands (35% of land deals); remote forestlands with lower population densities (34% of land deals); and moderately populated and moderately accessible shrub- or grasslands (26% of land deals). These patterns are consistent with processes described in the relevant case study literature, and they each involve distinct types of stakeholders and associated competition over land. We then repeat the often-cited analysis that postulates a link between land investments and target countries with abundant so-called “idle” or “marginal” lands as measured by yield gap and available suitable but uncultivated land; our methods differ from the earlier approach, however, in that we examine local context (10-km radius) rather than countries as a whole. The results show that earlier findings are disputable in terms of concepts, methods, and contents. Further, we reflect on methodologies for exploring linkages between socioecological patterns and land investment processes. Improving and enhancing large datasets of georeferenced land deals is an important next step; at the same time, careful choice of the spatial scale of analysis is crucial for ensuring compatibility between the spatial accuracy of land deal locations and the resolution of available geospatial data layers. Finally, we argue that new approaches and methods must be developed to empirically link socio-ecological patterns in target contexts to key determinants of land investment processes. This would help to improve the validity and the reach of our findings as an input for evidence-informed policy debates.

Contextualizing local-scale point sample data using global-scale spatial datasets: Lessons learnt from the analysis of large-scale land acquisitions

This paper examines how the geospatial accuracy of samples and sample size influence conclusions from geospatial analyses. It does so using the example of a study investigating the global phenomenon of large-scale land acquisitions and the socio-ecological characteristics of the areas they target. First, we analysed land deal datasets of varying geospatial accuracy and varying sizes and compared the results in terms of land cover, population density, and two indicators for agricultural potential: yield gap and availability of uncultivated land that is suitable for rainfed agriculture. We found that an increase in geospatial accuracy led to a substantial and greater change in conclusions about the land cover types targeted than an increase in sample size, suggesting that using a sample of higher geospatial accuracy does more to improve results than using a larger sample. The same finding emerged for population density, yield gap, and the availability of uncultivated land suitable for rainfed agriculture. Furthermore, the statistical median proved to be more consistent than the mean when comparing the descriptive statistics for datasets of different geospatial accuracy. Second, we analysed effects of geospatial accuracy on estimations regarding the potential for advancing agricultural development in target contexts. Our results show that the target contexts of the majority of land deals in our sample whose geolocation is known with a high level of accuracy contain smaller amounts of suitable, but uncultivated land than regional- and national-scale averages suggest. Consequently, the more target contexts vary within a country, the more detailed the spatial scale of analysis has to be in order to draw meaningful conclusions about the phenomena under investigation. We therefore advise against using national-scale statistics to approximate or characterize phenomena that have a local-scale impact, particularly if key indicators vary widely within a country.