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.
Cropland mapping in smallholder landscapes is challenged by complex and fragmented landscapes, labor-intensive and unmechanized land management causing high within-field variability, rapid dynamics in shifting cultivation systems, and substantial proportions of short-term fallows. To overcome these challenges, we here present a large-area mapping framework to identify active cropland and short-term fallows in smallholder landscapes for the 2020/2021 growing season at 4.77 m spatial resolution. Our study focuses on Northern Mozambique, an area comprising 381,698 km2. The approach is based on Google Earth Engine and time series of PlanetScope mosaics made openly available through Norwaýs International Climate and Forest Initiative (NICFI) data program. We conducted multi-temporal coregistration of the PlanetScope data using seasonal Sentinel-2 base images and derived consistent and gap-free seasonal time series metrics to classify active cropland and short-term fallows. An iterative active learning framework based on Random Forest class probabilities was used for training rare classes and uncertain regions. The map was accurate (area-adjusted overall accuracy 88.6% ± 1.5%), with the main error type being the commission of active cropland. Error-adjusted area estimates of active cropland extent (61,799.5 km2 ± 4,252.5 km2) revealed that existing global and regional land cover products tend to under-, or over-estimate active cropland extent, respectively. Short-term fallows occupied 28.9% of the cropland in our reference sample (13% of the mapped cropland), with consolidated agricultural regions showing the highest shares of short-term fallows. Our approach relies on openly available PlanetScope data and cloud-based processing in Google Earth Engine, which minimizes financial constraints and maximizes replicability of the methods. All code and maps were made available for further use.
Dam construction and operation modify land systems. We synthesized 178 observations of dam-induced land system changes from 54 peer-reviewed case studies. Changing extents of forests (23%), agricultural land (21%), and built-up areas (11%) were reported frequently, alongside alterations in land use intensity (23%). Land cover changes were mostly related to hydropower and multi-purpose dams, while irrigation dams dominantly caused land use intensity changes. While a significant share of the changes was caused by reservoir flooding (29%), indirect effects which interact with societal and environmental systems (42%) were of utmost importance. We suggested the distance to the dam and the time since commissioning as potential controls for the direction of land system changes. Our insights provide opportunities for future inductive investigations across large populations of dams at regional to global scales and highlight that multi-disciplinary research perspectives are imperative for the production of generalizable knowledge.
Agriculture , Rivers , Floods
