Temporal correlation coefficient for directed networks

Explaining the stickiness of supply chain relations in the Brazilian soybean trade

The global trade of agricultural commodities has profound social-ecological impacts, from potentially increasing food availability and agricultural efficiency, to displacing local communities, and to incentivizing environmental destruction. Supply chain stickiness, understood as the stability in trading relationships between supply chain actors, moderates the impacts of agricultural commodity production and the possibilities for supply-chain interventions. However, what factors determine stickiness, that is, how and why farmers, traders, food processors, and consumer countries, develop and maintain trading relationships with specific producing regions, remains unclear. Here, we use data on the Brazilian soy supply chain, a mixed methods approach based on extensive actor-based fieldwork, and an explanatory regression model, to identify and explore the factors that influence stickiness between places of production and supply chain actors. We find four groups of factors to be important: economic incentives, institutional enablers and constraints, social and power dimensions, and biophysical and technological conditions. Among the factors we explore, surplus capacity in soy processing infrastructure, (i.e., crushing and storage facilities) is important in increasing stickiness, as is export-oriented production. Conversely, volatility in market demand expressed by farm-gate soy prices and lower land-tenure security are key factors reducing stickiness. Importantly, we uncover heterogeneity and context-specificity in the factors determining stickiness, suggesting tailored supply-chain interventions are beneficial. Understanding supply chain stickiness does not, in itself, provide silver-bullet solutions to stopping deforestation, but it is a crucial prerequisite to understanding the relationships between supply chain actors and producing regions, identifying entry points for supply chain sustainability interventions, assessing the effectiveness of such interventions, forecasting the restructuring of trade flows, and considering sourcing patterns of supply chain actors in territorial planning.

"HerdGPS-Preprocessor"-A Tool to Preprocess Herd Animal GPS Data; Applied to Evaluate Contact Structures in Loose-Housing Horses

Sensors delivering information on the position of farm animals have been widely used in precision livestock farming. Global Positioning System (GPS) sensors are already known from applications in military, private and commercial environments, and their application in animal science is increasing. However, as trade-offs between sensor cost, battery life and sensor weight have to be made, GPS based studies scheduling long data collection periods and including a high number of animals, have to deal with problems like high hardware costs and data disruption during recharging of sensors. Furthermore, human-animal interaction due to sensor changing at the end of battery life interferes with the animal behaviour under analysis. The present study thus proposes a setting to deal with these challenges and offers the software tool "HerdGPS-Preprocessor", because collecting position data from multiple animals nonstop for several weeks produces a high amount of raw data which needs to be sorted, preprocessed and provided in a suitable format per animal and day. The software tool "HerdGPS-Preprocessor" additionally outputs contact lists to enable a straight analysis of animal contacts. The software tool was exemplarily deployed for one month of daily and continuous GPS data of 40 horses in a loose-housing boarding facility in northern Germany. Contact lists were used to generate separate networks for every hour, which are then analysed with regard to the network parameter density, diameter and clique structure. Differences depending on the day and the day time could be observed. More dense networks with more and larger cliques were determined in the hours prior to the opening of additional pasture.