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Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Livestock farming is central to global food security and to the sustainability of rural communities throughout Europe. Animal health management has a major impact on farming efficiency. Although animal health research has provided effective prevention strategies for the major endemic diseases of livestock, these strategies typically provide solutions for single infectious diseases and they are often not adequately implemented due to farm-specific constraints. We propose a concept termed "ECONOHEALTH" which aims at including the economic and social context in our understanding of the factors that drive animal health. The concept is elaborated on using the example of the major helminthic diseases of cattle in temperate climate regions (gastrointestinal nematodes, liver fluke and lungworm). By considering major diseases simultaneously and placing disease-complexes in an economic and a social context, we believe that insights will be generated upon which more integrated, situation-adapted and thus more effective prevention strategies can be devised., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Recently, biogas plant managers in Flanders face increased financial uncertainty. Between 2011 and 2012, 20% of the Flemish biogas plants went bankrupt. Difficulties in obtaining feedstock at stable and affordable prices is one reason why the biogas sector struggles. In literature, contracting is often proposed as a way to decrease the volatility of the feedstock costs. However, these studies generally do not consider the context in which the biogas plant manager needs to buy the feedstock. Yet, this context could be of specific importance when biogas plant managers are in competition with other users of the same biomass type. Silage maize is an example of such a feedstock, as it is both used by dairy farmers and biogas plant managers. Using a combination of qualitative research and agent-based modelling, we investigated the effect of specific characteristics of the silage maize market on the acquisition of local silage maize by biogas plant managers. This paper details the institutional arrangements of the silage maize market in Flanders and the results of a scenario analysis, simulating three different scenarios. As shown by the results, the time of entry into the market, as well as the different institutional arrangements used by the biogas plant managers as opposed to dairy farmers could explain the difficulties in obtaining a stable supply of local silage maize by biogas plants. Our findings can help to develop mitigation strategies addressing these difficulties.

The impact of gastrointestinal (GI) nematode infections in dairy farming has traditionally been assessed using partial productivity indicators. But such approaches ignore the impact of infection on the performance of the whole farm. In this study, efficiency analysis was used to study the association of the GI nematode Ostertagia ostertagi on the technical efficiency of dairy farms. Five years of accountancy data were linked to GI nematode infection data gained from a longitudinal parasitic monitoring campaign. The level of exposure to GI nematodes was based on bulk-tank milk ELISA tests, which measure the antibodies to O. ostertagi and was expressed as an optical density ratio (ODR). Two unbalanced data panels were created for the period 2006 to 2010. The first data panel contained 198 observations from the Belgian Farm Accountancy Data Network (Brussels, Belgium) and the second contained 622 observations from the Boerenbond Flemish farmers' union (Leuven, Belgium) accountancy system (Tiber Farm Accounting System). We used the stochastic frontier analysis approach and defined inefficiency effect models specified with the Cobb-Douglas and transcendental logarithmic (Translog) functional form. To assess the efficiency scores, milk production was considered as the main output variable. Six input variables were used: concentrates, roughage, pasture, number of dairy cows, animal health costs, and labor. The ODR of each individual farm served as an explanatory variable of inefficiency. An increase in the level of exposure to GI nematodes was associated with a decrease in technical efficiency. Exposure to GI nematodes constrains the productivity of pasture, health, and labor but does not cause inefficiency in the use of concentrates, roughage, and dairy cows. Lowering the level of infection in the interquartile range (0.271 ODR) was associated with an average milk production increase of 27, 19, and 9L/cow per year for Farm Accountancy Data Network farms and 63, 49, and 23L/cow per year for Tiber Farm Accounting System farms in the low- (0-90), medium- (90-95), and high- (95-99) efficiency score groups, respectively. The potential milk increase associated with reducing the level of infection was higher for highly efficient farms (6.7% of the total possible milk increase when becoming fully technically efficient) than for less efficient farms (3.8% of the total possible milk increase when becoming fully technically efficient)., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Helminth infections are considered to be an important constraint on livestock productivity worldwide. The economic impact of these infections or their control strategies has traditionally been assessed by their effect on animal performance indicators or traditional economic calculation methods (e.g. budgeting and cost-benefit analysis). Because the impact of helminth infections has become more subtle and is farm-specific, one needs more refined economic evaluations of actions meant to increase or maintain the health of livestock on individual farms. This paper proposes an interdisciplinary framework that combines the developments in the veterinary control of helminth infections with economic performance measurements to identify farm-specific and profitable anthelmintic management decisions. Our framework positions individual farms' performance against performance benchmarks and is based on the farms' efficiency in transforming input(s) into output(s). We show how this positioning makes it possible to establish a linkage between input and output transformation, helminth infection levels and effects of control strategies. Furthermore, the framework allows for the identification of improvement paths that are not necessarily related to the helminth infection, but which may lead to other management improvements. We discuss the epidemiological information required and which complementary methods (e.g. efficiency analysis and budgeting techniques) can be used to make the framework operational., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Reducing nitrogen emission from livestock production is usually perceived as costly. Nevertheless, production process related measures addressing the transformation of input(s) into output(s) may result in a cost-saving reduction of nitrogen emission. This paper explores the separate and combined use of traditional key performance indicators and an environmentally adjusted Data Envelopment Analysis (DEA) to reveal firm-specific cost-saving mechanisms for pig-finishing farms. Traditional key performance indicators are easy to communicate but have shortcomings when assessing benchmarks for comparative farm analysis. The environmentally adjusted DEA decomposes both cost and environmental efficiencies into technical and allocative efficiency components, separating the physical production analysis from price and nutrient content information. DEA makes efficiency benchmarks and improvement paths explicit, but the practical value of such efficiency scores is low. This paper proposes to take advantage of both methods: concrete benchmarks and eligible improvement paths are assessed using frontier methods, while the decision variables that steer farmers towards changes are described in terms of traditional key performance indicators. This leads to an improved diagnosis that is easy to communicate to farmers and may therefore facilitate decision support. Our pig-finishing case study shows that farms can achieve economic-environmental win-win situations through improving technical, cost allocative and cost efficiencies, which are mainly driven by feed conversion. Less technical efficient farms are found to require a lower delivery weight in order to minimize costs, which shows the farm-specificity of economic-environmental improvement advice., (Copyright 2010 Elsevier Ltd. All rights reserved.)