Why has farming in Europe changed? A farmers' perspective on the development since the 1960s.

Farming in Europe has been the scene of several important socio-economic and environmental developments and crises throughout the last century. Therefore, an understanding of the historical driving forces of farm change helps identifying potentials for navigating future pathways of agricultural development. However, long-term driving forces have so far been studied, e.g. in anecdotal local case studies or in systematic literature reviews, which often lack context dependency. In this study, we bridged local and continental scales by conducting 123 oral history interviews (OHIs) with elderly farmers across 13 study sites in 10 European countries. We applied a driving forces framework to systematically analyse the OHIs. We find that the most prevalent driving forces were the introduction of new technologies, developments in agricultural markets that pushed farmers for farm size enlargement and technological optimisation, agricultural policies, but also cultural aspects such as cooperation and intergenerational arrangements. However, we find considerable heterogeneity in the specific influence of individual driving forces across the study sites, implying that generic assumptions about the dynamics and impacts of European agricultural change drivers hold limited explanatory power on the local scale. Our results suggest that site-specific factors and their historical development will need to be considered when addressing the future of agriculture in Europe in a scientific or policy context. Supplementary Information: The online version contains supplementary material available at 10.1007/s10113-023-02150-y.

Farmer surveys in Europe suggest that specialized, intensive farms were more likely to perceive negative impacts from COVID-19.

It has been shown that the COVID-19 pandemic affected some agricultural systems more than others, and even within geographic regions, not all farms were affected to the same extent. To build resilience of agricultural systems to future shocks, it is key to understand which farms were affected and why. In this study, we examined farmers' perceived robustness to COVID-19, a key resilience capacity. We conducted standardized farmer interviews (n = 257) in 15 case study areas across Europe, covering a large range of socio-ecological contexts and farm types. Interviews targeted perceived livelihood impacts of the COVID-19 pandemic on productivity, sales, price, labor availability, and supply chains in 2020, as well as farm(er) characteristics and farm management. Our study corroborates earlier evidence that most farms were not or only slightly affected by the first wave(s) of the pandemic in 2020, and that impacts varied widely by study region. However, a significant minority of farmers across Europe reported that the pandemic was "the worst crisis in a lifetime" (3%) or "the worst crisis in a decade" (7%). Statistical analysis showed that more specialized and intensive farms were more likely to have perceived negative impacts. From a societal perspective, this suggests that highly specialized, intensive farms face higher vulnerability to shocks that affect regional to global supply chains. Supporting farmers in the diversification of their production systems while decreasing dependence on service suppliers and supply chain actors may increase their robustness to future disruptions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13593-022-00820-5.

Representing large-scale land acquisitions in land use change scenarios for the Lao PDR.

Agricultural large-scale land acquisition (LSLA) is a process that is currently not captured by land change models. We present a novel land change modeling approach that includes processes governing LSLAs and simulates their interactions with other land systems. LSLAs differ from other land change processes in two ways: (1) their changes affect hundreds to thousands of contiguous hectares at a time, far surpassing other land change processes, e.g., smallholder agriculture, and (2) as policy makers value LSLA as desirable or undesirable, their agency significantly affects LSLA occurrence. To represent these characteristics in a land change model, we allocate LSLAs as multi-cell patches to represent them at scale while preserving detail in the representation of other dynamics. Moreover, LSLA land systems are characterized to respond to an explicit political demand for LSLA effects, in addition to a demand for various agricultural commodities. The model is applied to simulate land change in Laos until 2030, using three contrasting scenarios: (1) a target to quadruple the area of LSLA, (2) a moratorium for new LSLA, and (3) no target for LSLA. Scenarios yield drastically different land change trajectories despite having similar demands for agricultural commodities. A high level of LSLA impedes smallholders' engagement with rubber or cash crops, while a moratorium on LSLA results in increased smallholder involvement in cash cropping and rubber production. This model goes beyond existing land change models by capturing the heterogeneity of scales of land change processes and the competition between different land users instigated by LSLA.