Application of integrated production and economic models to estimate the impact of Schmallenberg virus for various sheep production types in the UK and France

The present study aimed to estimate and compare the economic impact of Schmallenberg virus (SBV) in different sheep production holdings using partial budget and gross margin analyses in combination with production models. The sheep production types considered were lowland spring lambing, upland spring lambing and early lambing flocks in the UK, and grass lamb flocks of the Centre and West of France, extensive lambing flocks and dairy sheep flocks in France. Two disease scenarios with distinct input parameters associated with reproductive problems were considered: low and high impact. Sensitivity analyses were performed for the most uncertain input parameters, and the models were run with all of the lowest and highest values to estimate the range of disease impact

Empirical test of an agricultural landscape model: the importance of farmer preference for risk aversion and crop complexity

Developing models to predict the effects of social and economic change on agricultural landscapes is an important challenge. Model development often involves making decisions about which aspects of the system require detailed description and which are reasonably insensitive to the assumptions. However, important components of the system are often left out because parameter estimates are unavailable. In particular, measurements of the relative influence of different objectives, such as risk, environmental management, on farmer decision making, have proven difficult to quantify. We describe a model that can make predictions of land use on the basis of profit alone or with the inclusion of explicit additional objectives. Importantly, our model is specifically designed to use parameter estimates for additional objectives obtained via farmer interviews. By statistically comparing the outputs of this model with a large farm-level land-use data set, we show that cropping patterns in the United Kingdom contain a significant contribution from farmer’s preference for objectives other than profit. In particular, we found that risk aversion had an effect on the accuracy of model predictions, whereas preference for a particular number of crops grown was less important. While nonprofit objectives have frequently been identified as factors in farmers’ decision making, our results take this analysis further by demonstrating the relationship between these preferences and actual cropping patterns

Integrating the economic and environmental performance of agricultural systems: a demonstration using Farm Business Survey data and Farmscoper

There is a continued need to monitor the environmental impacts of agricultural systems while also ensuring sufficient agricultural production. However, it can be difficult to collect relevant environmental data on a large enough number of farms and studies that do so often neglect to consider the financial drivers that ultimately determine many aspects of farm management and performance. This paper outlines a methodology for generating environmental indicators from the Farm Business Survey (FBS), an extensive annual economic survey of representative farms in England and Wales. Data were extracted from the FBS for a sample of East Anglian cereal farms and south western dairy farms and converted where necessary to use as inputs in ‘Farmscoper’; farm-level estimates of nitrate, phosphorus and sediment loadings and ammonia and greenhouse gas emissions were generated using the Farmscoper model. Nitrate losses to water, ammonia and greenhouse gas emissions were positively correlated with food energy production per unit area for both farm types; phosphorus loading was also correlated with food energy on the dairy farms. Environmental efficiency indicators, as measured by either total food energy or financial output per unit of negative environmental effect, were calculated; greenhouse gas emission efficiency (using either measure of agricultural output) and nitrate loading efficiency (using financial output) were positively correlated with profitability on cereal farms. No other environmental efficiency measures were significantly associated with farm profitability and none were significant on the dairy farms. These findings suggest that an improvement in economic performance can also improve environmental efficiency, but that this depends on the farm type and negative environmental externality in question. In a wider context, the augmentation of FBS-type data to generate additional environmental indicators can provide useful insights into ongoing research and policy issues around sustainable agricultural production