This paper explores preferences among the general public in Sweden for attributes related to the establishment of wind power farms. The method applied is a choice experiment where people are asked to choose between two hypothetical wind farms characterized by different attributes. Five attributes are included in the experiment: (i) type of landscape, (ii) type of ownership, (iii) the degree of local participation in the planning process, (iv) the choice to transfer revenue to the society in a pre-specified way, and (v) a monetary cost in terms of an additional electricity certificate fee. The data are analyzed with multinomial logit, random parameter logit, and latent class models. The results indicate that consumers in Sweden are more likely to accept the higher renewable electricity certificate fee if: (a) wind power farms in areas used for recreational purposes are substantially avoided, (b) the establishment is anchored by whole or partial ownership in the local community and, (c) the locals are involved in the planning and implementation process. Our policy simulation exercise shows that respondents are willing to pay a higher electricity fee corresponding to about 0.6 Euro cents per kWh to avoid wind farms located in the mountainous area and private ownership.
Numerous sources provide evidence of trends and patterns in average farm size and farmland distribution worldwide, but they often lack documentation, are in some cases out of date, and do not provide comprehensive global and comparative regional estimates. This article uses agricultural census data (provided at the country level in Web Appendix) to show that there are more than 570 million farms worldwide, most of which are small and family-operated. It shows that small farms (less than 2 ha) operate about 12% and family farms about 75% of the world’s agricultural land. It shows that average farm size decreased in most low- and lower-middle-income countries for which data are available from 1960 to 2000, whereas average farm sizes increased from 1960 to 2000 in some upper-middle-income countries and in nearly all high-income countries for which we have information. Such estimates help inform agricultural development strategies, although the estimates are limited by the data available. Continued efforts to enhance the collection and dissemination of up-to date, comprehensive, and more standardized agricultural census data, including at the farm and national level, are essential to having a more representative picture of the number of farms, small farms, and family farms as well as changes in farm size and farmland distribution worldwide.
Generally people are more positive towards offshore wind farms compared to on-land wind farms. However, the attitudes are commonly assumed to be independent of experience with wind farms. Important relations between attitude and experience might therefore be disregarded. The present paper gives a novel contribution to this field. First of all, we give a thorough review of the studies that have analysed the relation between experience with wind turbines and attitude. In addition, we supplement the review by analysing the effect of travel distance to the nearest offshore wind farm and the wind farms attributes on attitude towards offshore wind farms. The results point towards that the travel time and the attributes of the nearest offshore wind farm influence the attitude significantly. Travel time has mixed effects on the attitude, whilst offshore wind farms with many turbines generate more positive attitudes compared to wind farms with fewer turbines.
Farm system and nutrient budget models are increasingly being used to inform and evaluate policy options on the impacts of land use change on regional environmental and economic performance. In this study, the common approach of up-scaling representative farm systems to a regional scale, with a limited input of resource information, was compared with a new approach that links a geospatial land resource information data base (NZLRI, Agribase™) that includes independent estimates of the productive capacity of land parcels, with individual farm-scale simulation (Farmax® Pro and Farmax® Dairy Pro) and nutrient budgeting models (Overseer®). The Southland region of New Zealand, which is currently undergoing enormous land use change, was used as a case study. Model outputs from the new approach showed increased profit of about 75% for the region if the current land area under dairying increases from 16% to 45%, with the shift to dairy constrained to high pasture production classes only. Environmental impacts associated with the change were substantial, with nitrate leaching estimated to increase by 35% and greenhouse gas emissions by 25%. Up-scaling of representative farm systems to the regional scale with limited input of resource information predicted lower potential regional profit and higher N leaching from dairy conversion. The new approach provides a farm scale framework that could easily be extended to include different systems, different levels of farming performance and the use of mitigation technologies.
Background The highly consistent association of growing up on a farm with a reduced asthma risk has so far been attributed to direct farm exposure. In contrast, geographic determinants of the larger environment have never been assessed. In this study, the effects of proximity to farms and environmental variables in relation to the residential address on asthma and atopy were assessed. Methods Addresses of 2265 children of the Bavarian arm of the GABRIELA study were converted into geocodes. Proximity to the nearest cow farm was calculated, and environmental characteristics were derived from satellite data or terrestrial monitoring. Bacterial diversity in mattress dust samples was assessed in 501 children by sequencing of the 16S rRNA amplicons. Logistic regression models were used to calculate associations between outcomes and exposure variables. Results Asthma and atopy were inversely associated with the presence of a farm within a radius of maximum 100 m. The environmental variables greenness, tree cover, soil sealing, altitude, air pollution differed not only between farm and non‐farm children but also between farm children with and without another farm nearby. The latter distinction revealed strong associations with characteristics of traditional farms including a broader diversity of microbial exposure, which mainly contributed to the protective effect on asthma. In non‐farm children, the protective effect of a farm nearby was completely explained by consumption of farm milk. Conclusions Clustering of farms within a neighborhood of 100 m is strongly associated with the protective effect on asthma and may represent a more traditional style of farming with broader microbial exposure.
The survival of family farming in Europe is a crucial issue, as it assures landscape maintenance in marginal areas and provides transmission and accumulation of site-specific knowledge in agricultural activity. Using data from a sample of Italian horticultural farms, we explored the multiple forces driving farm succession in a high value added sector. In addition to the traditional factors examined in the literature (farm, farmer and family features), we treated the farm transfer choice as the complement of the decision to migrate out of the agricultural sector, testing the effects of local labour market conditions (employment, income gap between farm and non-farm sector) and population density around the farm, as a proxy of rural-urban interface relationships. It has been shown that both traditional factors and territorial and labour market conditions influence the probability of farm succession. Interestingly labour market conditions exerted an effect in line with occupational choice theory only in less inhabited areas; in more densely populated regions a rural-urban linkage effect seems to prevail, creating an environment that fosters succession of young horticultural farmers. Peri-urban areas may thus be a favourable location for professional and specialised horticultural farms, as well as multifunctional and de-specialised ones, if their assets are properly protected against farmland subtraction. More generally, these findings confirm the validity of a more comprehensive approach toward farm succession, which takes occupational choice theory and rural-urban farm adaptation strategies into account.
Is there an alternative model to small family farming that could provide sustainable livelihoods to millions of resource-constrained and often non-viable smallholders in developing countries? Could group farming constitute such an alternative, wherein smallholders voluntarily pool land, labour and capital to create larger farms that they manage collectively? In South Asia, for instance, over 85% of farmers are small and increasingly female. Potentially, group farming could provide them economies of scale, a dependable labour force, more investible funds and skills, and greater bargaining power with governments and markets. But can this potential be realised in practice? In particular, can group farms economically outperform small family farms? A rare opportunity to test this is provided by two experiments begun in the 2000s in the Indian states of Kerala and Telangana. Constituted only of women, the groups lease in land to farm collectively, sharing labour, the cost of inputs, and the returns. But the states differ in several respects, including the technical support the groups receive, and their institutional base, composition, land access and cropping patterns. Based on the author's primary sample surveys in both states, this paper compares the productivity and profitability of group farms with that of small individual family farms in the same state. Kerala’s groups perform strikingly better than the predominantly male-managed individual farms, both in their annual value of output per hectare and annual net returns per farm, while in Telangana group farms perform much worse than individual farms in annual output, but are equivalent in net returns. In both states, groups do much better in commercial crops than in traditional foodgrains, where the largely male-managed individual farms, owning good quality land and with longer farm management experience, have an advantage. The factors underlying the differential performances of Kerala and Telangana, and the lessons learnt for possible replication, are also discussed. Overall, the paper demonstrates that group farming provide an effective alternative, subject to specified conditions and adaptation of the model to the local context.
Highly pathogenic avian influenza virus (HPAIV) H5N1 has been reported in Asia, including Indonesia since 2003. Although several risk factors related to the HPAIV outbreaks in poultry in Indonesia have been identified, little is known of the contact structure of farms of different poultry production types (backyard chickens, broilers, layers, and ducks). This study aims to quantify the contact rates associated with the movement of people, and movements of live birds and products and equipment that affect the risk of HPAIV H5N1 transmission between poultry farms in Indonesia. On 124 poultry farms in 6 districts in West Java, logbooks were distributed to record the movements of farmers/staff and visitors and their poultry contacts. Most movements in backyard chicken, commercial native chicken, broiler and duck farms were visits to and from other poultry farms, whilst in layer farms visits to and from poultry companies, visits to egg collection houses and visit from other poultry farms were most frequent. Over 75% of persons visiting backyard chicken and duck farms had previously visited other poultry farms on the same day. Visitors of backyard chicken farms had the highest average contact rate, either direct contact with poultry on other farms before the visits (1.35 contact/day) or contact during their visits in the farms (10.03 contact/day). These results suggest that backyard chicken farms are most at risk for transmission of HPAIV compared to farms of the other poultry production types. Since visits of farm-to-farm were high, backyard farms could also a potential source for HPAIV transmission to commercial poultry farms.
Prior studies have demonstrated an influence of the built environment on the human nasal microbiota. However, very little is known about the influences of working on a pig farm on the human nasal microbiota. We longitudinally collected samples from 30 pig farms (air and nasal swabs from humans and pigs) in Switzerland from 2014 to 2015. As controls, nasal swabs from cow farmers and individuals with no contact with farm animals were included. An analysis of the microbiota for all samples (n = 609) was performed based on 16S rRNA gene sequencing (MiSeq) and included the investigations of source-sink dynamics. The numbers of indoor airborne particles and bacterial loads in pig farms were also investigated and were highest in winter. Similarly, the microbiota analyses revealed that the alpha diversity values of the nares of pig farmers were increased in winter in contrast to those of samples from the nonexposed controls, which displayed low alpha diversity values throughout the seasons. Source-sink analyses revealed that bacteria from the noses of pigs are more commonly coidentified within the pig farmers' microbiota in winter but to a less extent in summer. In addition, in winter, there was a stronger intrasimilarity for samples that originated from the same farm than for samples from different farms, and this farm specificity was partially or completely lost in spring, summer, and fall. In conclusion, in contrast to nonexposed controls, a pig farmer's nasal microbiota is dynamic, as the indoor-air microbiota of pig farms drives the composition of the pig farmer's nasal microbiota in a season-dependent manner. IMPORTANCE The airborne microbiota of pig farms poses a potential health hazard and impacts both livestock and humans working in this environment. Therefore, a more thorough understanding of the microbiota composition and dynamics in this setting is needed. This study was of a prospective design (12 months) and used samples from different sites. This means that the microbiota of air, animals (pigs), and humans was simultaneously investigated. Our findings highlight that the potential health hazard might be particularly high in winter compared to that in summer.