First detected in Switzerland in 2011, the invasive Drosophila suzukii, spotted wing drosophila, has caused recurring costs for growers of berries and fruit. Recommended management approaches rely on a set of methods, tailored to suit crop requirements under the prevailing local conditions. Control of D. suzukii represents a substantial economic burden for growers, in terms of material, equipment, new infrastructure and extra labour. However, those growers who invest wisely to deliver unblemished produce are rewarded with high payoffs. We present insights from a growers’ survey conducted in 2015 and 2016 to gauge the impact of the introduction and establishment of D. suzukii on Swiss sweet cherry production. The surveyed growers (111 in 2015 and 298 in 2016) observed the recommended surveillance, sanitation and control measures. The use of insecticides (78% and 79% of respondents in 2015 and 2016, respectively) and the harvest of all fruits (93% and 59% of respondents in 2015 and 2016, respectively) were the most widespread methods used to reduce damage. Nearly one-third of the respondents set up enclosure nets. Our economic evaluation of different scenarios provides a quantitative indication of the potentially incurred costs. We argue for enhanced stakeholder involvement to raise the acceptance of integrated pest management practices, and to inform research and outreach by providing insights into the motivations and barriers to adoption.

We analyze the factors affecting farmers’ choice accounting for farm, farmer, and household characteristics as well as elicited risk perception and risk preferences. We consider three alternative hypothetical methods for assessing risk preferences to test the stability and behavioral validity of them. Our case study focuses on livestock farmers in the German region North Rhine-Westphalia. We find that risk preferences are context depending, i.e. differ across different fields of farm-level decision-making. Furthermore, our analysis shows that risk-averse farmers are more likely to prioritize on-farm risk management strategies over off-farm strategies. Moreover, higher risk perception, age, subjective numeracy, farm succession, farm size, and the proportion of rented land show a significant impact on farmers’ risk behavior

Farm diversification is of growing importance for European farmers to stabilize and/or increase their income, especially in urban and peri-urban areas. This article identifies determinants of the uptake and intensity of on-farm non-agricultural diversification such as agritourism activities, on-farm processing and direct sales activities in the Ruhr metropolitan region in Germany based on a survey among 156 farms. We find a high share of diversified farms in the Ruhr metropolitan region, often with more than one diversification activity. Environmental, farm and farmer characteristics have distinctive effects on the farmers’ diversification decision and the farmers’ decision on the diversification intensity. Younger, risk seeking farmers with secured farm succession and less productive soils are more likely to choose on-farm non-agricultural diversification. Additionally, we find that diversification intensity is positively influenced by proximity to urban areas, as well as a specialization on high value crops and the usage of extension service consultancy.

On-farm non-agricultural diversification is increasingly important for many farmers to stabilize their income and to fulfill societal demand for integrated farming. Especially in peri-urban areas, where the potential for farms' expansion in terms of farm size is limited and public interest in regional products and other services provided at farms is high, the uptake of on-farm non-agricultural diversification is thus an attractive strategy for many farmers. The here presented dataset is based on an online survey conducted in March 2016 with 156 farms located in the Ruhr Metropolitan region (North Rhine-Westphalia, Germany). The survey was used to collect information on farms' and farmers' characteristics. More specifically, the dataset includes detailed information on farms' diversification decisions, location, size and general information on farms' production type and structure. Moreover, the dataset contains information on farmers' risk perception and risk preferences (collected using three different elicitation methods) and general information on farmers and households. In addition, farmers' personal assessments on agricultural production in peri-urban settings and information on the use of extension services were collected.