Mapping the consumer foodshed of the Kampala city region shows the importance of urban agriculture.

Due to rapid urbanisation, food systems in sub-Saharan African cities are increasingly under pressure. Through the lens of a foodshed, this paper quantitatively analyses the spatial extent of the food provisioning area for consumers of different socio-economic status in Kampala (Uganda). Based on a primary dataset of surveys with households and food vendors, we map the foodshed by registering where consumers obtain their food, and the origin of where it is grown. We show that 50% of the food consumed in the city originates from within a 120 km proximity to Kampala, including 10% from within the city itself. At present, urban agricultural activities are twice as important as international imports for the urban food provision. Established, high-income urban dwellers have a more local foodshed due to their broad participation in urban agriculture, while low-income newcomers rely heavily on retailers who source food from rural Uganda.

Modeling Production-Living-Ecological Space for Chengdu, China: An Analytical Framework Based on Machine Learning with Automatic Parameterization of Environmental Elements.

Cities worldwide are facing the dual pressures of growing population and land expansion, leading to the intensification of conflicts in urban productive-living-ecological spaces (PLES). Therefore, the question of "how to dynamically judge the different thresholds of different indicators of PLES" plays an indispensable role in the studies of the multi-scenario simulation of land space changes and needs to be tackled in an appropriate way, given that the process simulation of key elements that affect the evolution of urban systems is yet to achieve complete coupling with PLES utilization configuration schemes. In this paper, we developed a scenario simulation framework combining the dynamic coupling model of Bagging-Cellular Automata (Bagging-CA) to generate various environmental element configuration patterns for urban PLES development. The key merit of our analytical approach is that the weights of different key driving factors under different scenarios are obtained through the automatic parameterized adjustment process, and we enrich the study cases for the vast southwest region in China, which is beneficial for balanced development between eastern and western regions in the country. Finally, we simulate the PLES with the data of finer land use classification, combining a machine learning and multi-objective scenario. Automatic parameterization of environmental elements can help planners and stakeholders understand more comprehensively the complex land space changes caused by the uncertainty of space resources and environment changes, so as to formulate appropriate policies and effectively guide the implementation of land space planning. The multi-scenario simulation method developed in this study has offered new insights and high applicability to other regions for modeling PLES.

Future C loss in mid-latitude mineral soils: climate change exceeds land use mitigation potential in France.

Many studies have highlighted significant interactions between soil C reservoir dynamics and global climate and environmental change. However, in order to estimate the future soil organic carbon sequestration potential and related ecosystem services well, more spatially detailed predictions are needed. The present study made detailed predictions of future spatial evolution (at 250 m resolution) of topsoil SOC driven by climate change and land use change for France up to the year 2100 by taking interactions between climate, land use and soil type into account. We conclude that climate change will have a much bigger influence on future SOC losses in mid-latitude mineral soils than land use change dynamics. Hence, reducing CO2 emissions will be crucial to prevent further loss of carbon from our soils.

Controlling factors of the parental safety perception on children's travel mode choice.

The travel mode of children changed significantly over the last 20 years, with a decrease of children travelling as pedestrians or cyclists. This study focuses on six to twelve year old children. Parents determine to a large extent the mode choice of children in this age category. Based on the analysis of an extensive survey, the research shows that traffic infrastructure has a significant impact on parental decision making concerning children's travel mode choice, by affecting both the real and the perceived traffic safety. Real traffic safety is quantified in terms of numbers of accidents and road infrastructure. For the perceived traffic safety a parental allowance probability is calculated per road type to show that infrastructure characteristics influence parental decision making on the children's mode choice. A binary logistic model shows that this allowance is determined by age, gender and traffic infrastructure near the child's home or near destinations frequently visited by children. Since both real and perceived traffic safety are influenced by infrastructure characteristics, a spatial analysis of parental perception and accident statistics can be used to indicate the locations where infrastructure improvements will be most effective to increase the number of children travelling - safely - as pedestrians or cyclists.