Determinants of Spatial Distribution of Organic Farming in Germany

The share of organically managed land is spread unevenly throughout Germany and shows pronounced regional concentrations. The spatial distribution of organic farming is assumed to be influenced by several factors. Location factors of farms are regionally different and thus may influence the spatial distribution of organic farming. Agglomeration effects and therefore spatial dependence are also considered important in determining spatial distribution. These factors with a potential influence on the spatial distribution of organic farming can be divided into four categories: natural factors, farm-structure factors, socio-economic factors and political factors. Their possible influence on the spatial distribution of organic farming is analysed by several statistical methods: ordinary least square regression model, spatial autoregressive models, analysis of variance and Spearman correlation. Of the analysed factors, spatial contiguity has the strongest influence on the spatial distribution of organic farming (indicating relevant agglomeration effects)

Modeling space-time correlations of velocity fluctuations in wind farms

An analytical model for the streamwise velocity space-time correlations in turbulent flows is derived and applied to the special case of velocity fluctuations in large wind farms. The model is based on the Kraichnan-Tennekes random sweeping hypothesis, capturing the decorrelation in time while including a mean wind velocity in the streamwise direction. In the resulting model, the streamwise velocity space-time correlation is expressed as a convolution of the pure space correlation with an analytical temporal decorrelation kernel. Hence, the spatio-temporal structure of velocity fluctuations in wind farms can be derived from the spatial correlations only. We then explore the applicability of the model to predict spatio-temporal correlations in turbulent flows in wind farms. Comparisons of the model with data from a large eddy simulation of flow in a large, spatially periodic wind farm are performed, where needed model parameters such as spatial and temporal integral scales and spatial correlations are determined from the large eddy simulation. Good agreement is obtained between the model and large eddy simulation data showing that spatial data may be used to model the full temporal structure of fluctuations in wind farms.Comment: Submitted to Wind Energ

Analysis of Swine Movements in a Province in Northern Vietnam and Application in the Design of Surveillance Strategies for Infectious Diseases

While swine production is rapidly growing in South-East Asia, the structure of the swine industry and the dynamic of pig movements have not been well-studied. However, this knowledge is a prerequisite for understanding the dynamic of disease transmission in swine populations and designing cost-effective surveillance strategies for infectious diseases. In this study, we assessed the farming and trading practices in the Vietnamese swine familial farming sector, which accounts for most pigs in Vietnam, and for which disease surveillance is a major challenge. Farmers from two communes of a Red River Delta Province (northern Vietnam) were interviewed, along with traders involved in pig transactions. Major differences in the trade structure were observed between the two communes. One commune had mainly transversal trades, that is between farms of equivalent sizes, whereas the other had pyramidal trades, that is from larger to smaller farms. Companies and large familial farrow-to-finish farms were likely to act as major sources of disease spread through pig sales, demonstrating their importance for disease control. Familial fattening farms with high pig purchases were at greater risk of disease introduction and should be targeted for disease detection as part of a risk-based surveillance. In contrast, many other familial farms were isolated or weakly connected to the swine trade network limiting their relevance for surveillance activities. However, some of these farms used boar hiring for breeding, increasing the risk of disease spread. Most familial farms were slaughtering pigs at the farm or in small local slaughterhouses, making the surveillance at the slaughterhouse inefficient. In terms of spatial distribution of the trades, the results suggested that northern provinces were highly connected and showed some connection with central and southern provinces. These results are useful to develop risk-based surveillance protocols for disease detection in the swine familial sector and to make recommendations for disease control. (Résumé d'auteur

ADAPTATION OF STATISTICAL MATCHING IN MICRO-REGIONAL ANALYSIS OF AGRICULTURAL PRODUCTION

Agricultural production and agricultural policy has many special dimensions. The production structure, income positions, and labour input have large regional disparities, the production intensity is not homogenous in space, and farms have different risk factors and market possibilities in the different regions of Hungary. Land use and production technology also varies largely, in many regions farming is competitive, highly specialized with big corporate farms, while other regions have small individual farms with mixed production structure and less concentration in land use. There are no direct data for spatial analysis less aggregated than NUTS 3 level. Only the data of agricultural census and administrative database for direct payments are available at settlement and microregional level, but these databases do not provide information of farm income. The income statistics either cannot be disaggregated to micro-regional level (agricultural accounts) or are not representative at this level (FADN). The administrative database of the Paying Agency contains the land use data and limited livestock numbers for all farms receiving direct payments. The FADN database contains a large accountancy dataset for a low number of farms. Statistical matching combines these two databases and provides a possibility for detailed regional analysis using estimated data.micro-regional, statistical matching, administrative data, FADN, Agricultural and Food Policy, Community/Rural/Urban Development,

Organic farms as refuges for small mammal biodiversity in agro ecosystems

Habitat fragmentation, the process by which relatively continuous habitats is broken into smaller pieces, occurs in natural systems but is to a high degree also human-induced through landscape use. Fragmentation of the landscape produces a series of habitat patches surrounded by a matrix of different habitats and land use regimes. The major landscape consequences of fragmentation are loss of habitat, reduction in habitat patch size, and increasing isolation of habitat patches. In general, population performance declines in response to habitat loss but size of remaining area and isolation effects is known also to influence the population trend. Small mammals are well suited for examination of population responses to habitat fragmentation as they have modest spatial requirements and short generation times. In theory, organic farms could play an important role in the agricultural landscape as refuges for some small mammal species, as the lack of pesticide and fertiliser treatment, less weed control, more diversified crop structure and a general environmental friendly attitude, form a basis for habitats that provide cover and food for small mammals, and thus for larger predators of these species. Furthermore, density and area of small biotopes could be expected to be higher in the organic farms, thus leading to a decreased distance between optimal habitats. This study compares species diversity and abundance of small mammals in conventional farms and intensively and extensively grown organic farms. In a wide range of different fields in conventional and organic farms, the diversity and density of small mammals were investigated by live-trapping sessions, comprising trap lines with 15 meters between each trap. We studied the responses of populations (belonging to 11 species of small mammals) to habitat patches of different size and different surrounding management strategies (ecological and conventional farming). We found a general correlation between the number of small mammal individuals and small biotope size. This correlation applies in autumn as well as in spring. There is only a weak tendency for more small mammals in small biotopes within organic farms compared within conventional farms. The number of small mammal species stabilises at small biotope sizes around 1000 square meters. The value of organic farms in respect to small mammal biodiversity depends mainly upon the number and area of small biotopes, and only to a minor degree upon the treatments of the fields

Spatially explicit farming system modelling for an efficient agri-environmental policy design

A mathematical programming model is developed and associated to a spatial pattern index (Ripley L-function) to analyse the optimal reserve design and implementation for the Little Bustard conservation in Plaine de Niort. The model structure corresponds to three spatial levels, fields, farm and landscape. Simple in terms of area representation it is detailed in term of farm behaviour and spatially explicit. The model is applied in a normative and in a positive way. The major findings of the normative approach relate to the trade-offs between the reserve pattern and its cost. It was found that the environmentally optimal reserve, which is randomly dispersed across the zone, is the most costly one. Within the positive approach, it is illustrated that the various reserve patterns generated within the normative approach can be obtained through relatively simple uniform contract structures. The most effective contract structure is a degressive set of two payments enabling all the farms to enrol at least a small share of their land.Biodiversity, spatial optimization, mathematical programming, agri-environmental policies, Tetrax tetrax, Agricultural and Food Policy, Environmental Economics and Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy,

ORGANIC FARMS AS REFUGES FOR SMALL MAMMAL BIODIVERSITY

Habitat fragmentation, the process by which relatively continuous habitats is broken into smaller pieces, occurs in natural systems but is to a high degree also human- induced through landscape use. Fragmentation of the landscape produces a series of habitat patches surrounded by a matrix of different habitats and/or land use regimes. The major landscape consequences of fragmentation are loss of habitat, reduction in habitat patch size, and increasing isolation of habitat patches. In general, population performance declines in response to habitat loss but size of remaining area and isolation effects is known also to influence the population trend. Small mammals are well suited for examination of population responses to habitat fragmentation as they have modest spatial requirements and short generation times. In theory, organic farms could play an important role in the agricultural landscape as refuges for some small mammal species, as the lack of pesticide and fertiliser treatment, less weed control, more diversified crop structure and a general environmentalfriendly attitude, form a basis for habitats that provide cover and food for small mammals, and thus for larger predators of these species. Furthermore, density and area of small biotopes could be expected to be higher in the organic farms, thus leading to a decreased distance between optimal habitats

A model for prediction of spatial farm structure

Spatial micro structure and its change over time is recorded for Norwegian farm firms. Relative strong correlations between geographically close neighbors are expected, either because growing farms swallow the smaller ones, or because they are affected by some spatially related unobserved factors. Strong correlations over time are also expected because of prevalent family farming. The paper proposes a state-of-the-art Markov chain model in order to predict the spatial and temporal micro structure taking account of both non-stationarity and spatio/temporal correlations by means of techniques from non-linear state space modeling and Gaussian Markov random fields. The model and the complete data set is then a device with which one can investigate the consequences of ignoring spatial and/or temporal correlations, both with complete data and with more sparsely sampled data, like FADN panels or USDA's repeated cross-sections (ARMS).Farm Management,

Modelling and simulating change in reforesting mountain landscapes using a social-ecological framework

Natural reforestation of European mountain landscapes raises major environmental and societal issues. With local stakeholders in the Pyrenees National Park area (France), we studied agricultural landscape colonisation by ash (Fraxinus excelsior) to enlighten its impacts on biodiversity and other landscape functions of importance for the valley socio-economics. The study comprised an integrated assessment of land-use and land-cover change (LUCC) since the 1950s, and a scenario analysis of alternative future policy. We combined knowledge and methods from landscape ecology, land change and agricultural sciences, and a set of coordinated field studies to capture interactions and feedback in the local landscape/land-use system. Our results elicited the hierarchically-nested relationships between social and ecological processes. Agricultural change played a preeminent role in the spatial and temporal patterns of LUCC. Landscape colonisation by ash at the parcel level of organisation was merely controlled by grassland management, and in fact depended on the farmer's land management at the whole-farm level. LUCC patterns at the landscape level depended to a great extent on interactions between farm household behaviours and the spatial arrangement of landholdings within the landscape mosaic. Our results stressed the need to represent the local SES function at a fine scale to adequately capture scenarios of change in landscape functions. These findings orientated our modelling choices in the building an agent-based model for LUCC simulation (SMASH - Spatialized Multi-Agent System of landscape colonization by ASH). We discuss our method and results with reference to topical issues in interdisciplinary research into the sustainability of multifunctional landscapes