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Feed conversion ratio (FCR) in grow-finishing pigs is one of the most important determinants of pig farm profitability and production efficiency. In its simplest form, FCR represents the amount of feed used per unit weight gain of the pig. Yet, this approach entails various limitations hampering its practical applicability such as availability of accurate data and large variation in ways to adapt FCR values for different starting and end weight as well as mortality rates. Various stakeholders are using their own formulas to determine FCR creating a 'definition nonconformity' when comparing FCRs among farms. This study aimed to optimize the calculation of FCR through the use of participatory qualitative research. A multidisciplinary research group of 9 persons (animal scientists, veterinarians and agricultural economists) and a consulting group of 31 stakeholders (representing the Flemish primary sector, feed industry, pharma, genetic companies, large retailers, academia and policy institutions) were involved. The decision problem analysis started with a literature review, followed by 25 in-depth interviews and their analyses (NVivo 11™). This led to an additional literature review and the formation of focus (expert) groups that helped to formulate preliminary FCR formulas. Revision rounds between the research team and the stakeholders further fine-tuned the formulas with the final result being two distinct complimentary formulas that are fit for purpose. Both refer to carcass gain per kg feed intake (plain (CGF) and standardized (CGF standardized)). The first formula (CGF), namely n u m b e r d e l i v e r e d p i g s × a v e r a g e w a r m c a r c a s s w e i g h t − n u m b e r s t o c k e d p i g l e t s × a v e r a g e p i g l e t w e i g h t × p i g l e t c a r c a s s y i e l d f e e d c o n s u m p t i o n is an objective representation of the animals' performance. The second formula (CGF standardized) was developed for farm benchmarking, incorporating a seven-step standardization process that corrects for mortality and 'standardizes' for a fixed (yet fictive) live weight trajectory of 25 to 115 kg. This second formula allows to compare farms (or batches of fattening pigs) with different weight trajectories and different mortality rates. A webtool was designed to ease this standardization process (https://varkensloket.be/tools/CGF). [ABSTRACT FROM AUTHOR]

Economic margins on pig farms are small, and changing slaughter weights may increase farm profitability. However, one can question if the optimal slaughter weight is the same for each sex. On three farms, crossbred pigs (n = 1128) were used to determine the effect of sex and slaughter weight on performance, carcass quality and gross margin per pig place per year. On each farm, an equal number of entire males (EMs), barrows (BAs), immunocastrates (IC) and gilts (GIs) were housed separately in group pens. Pens were randomly divided into three categories of different slaughter weights: 105, 117 and 130 kg BW. In BA, the high average daily feed intake (ADFI) and the lower capacity to gain muscle led to a higher feed conversion ratio (FCR) and lower lean meat percentage in comparison to EM and IC. In all sexes, ADFI and FCR increased with an increasing slaughter weight but the effect of slaughter weight on carcass quality varied between sexes. In BA and GI, slaughter weight had no effect on carcass quality, but in EM and IC, carcass quality improved at higher slaughter weights. Gross margin per pig place per year was calculated as gross margin per pig × barn turnover per year, taking into account fixed costs per round, feed costs and output price per pig. The slaughter weight that gained the highest gross margin per year differed between sexes. Slaughtering BA and GI at 130 kg BW, compared to 105 or 117 kg BW, decreased the gross margin per pig place per year due to the lower margin per pig and barn turnover at higher weights. In IC and EM, no difference in gross margin per pig place per year could be demonstrated between slaughtering at 105, 117 or 130 kg BW. In IC, the increasing gross margin per pig with increasing slaughter weights counteracted with the lower barn turnover. In EM, gross margin per pig did not differ between slaughter weights, but the effect of barn turnover was too small to demonstrate significant differences between slaughter weights on gross margin per pig place per year. In conclusion, slaughter weight has an impact on profitability in BA and GI: they should not be slaughtered at 130 kg BW but at lower weights, but no effect could be demonstrated in EM and IC. [ABSTRACT FROM AUTHOR]

Genetic evaluation of Piétrain sires in Flanders occurs under standardized conditions, on test stations with fixed dam breeds, standardized diets and uniform management practices. As environmental conditions vary on commercial farms and differ from the test stations, this study aimed at understanding to what extent the sire, the dam breed and the interaction between both affects the translation of breeding values to practice. Dams of two commercial breeds were inseminated with semen from one of five different sires selected for contrasting breeding values (daily gain, feed conversion ratio and carcass quality). For each sire by dam breed combination, six pen replicates (with three gilts and three barrows per pen) were evaluated for growth performance from 9 weeks of age (20 kg) to slaughter (110 kg), and for carcass and meat quality. In our experimental setup, both sire and dam breed affected growth, carcass and meat quality traits. No significant sire×dam breed interactions on performance could be detected. Though a tendency for interaction on average daily feed intake between 20 and 110 kg (P =0.087), and on pork colour (lightness) (P =0.093) was present. In general, offspring of all tested sires behaved similarly in both dam breeds, indicating that estimated breeding values for Piétrain sires determined in one dam breed are representative in other dam breeds as well. [ABSTRACT FROM AUTHOR]

The seemingly straightforward question of optimal pig delivery weight is more complex than meets the eye. Despite abundant research insights, the industry continues to request additional applied scientific decision support on the delivery weight problem. The current objective is to investigate whether and how the complex decision of delivery weight can be reshaped (reframed) into a more tangible and comprehensible system of factors that matter for making the right decision. We used a participatory decision problem analysis, which resulted in modelling blueprints that incorporate factors prioritised by stakeholders for determining optimal delivery weights. How to efficiently organise such a 'problem reframing process' is case-specific: it depends on the objective, the initial problem understanding by the stakeholders, and their learning potential. Efficient co-learning is a prerequisite for successful participatory problem analysis. Our study reveals that the first step in such a process of 'problem reframing' should therefore be to answer the question of how to effectively organise co-learning among stakeholders and researchers, instead of starting with a correct and detailed representation of the problem. Useful guidelines for participatory problem reframing processes are (1) providing sufficient participatory learning steps, (2) having few and clearly defined objectives per learning step, (3) providing adapted learning tools per step, (4) establishing a common language and (5) deliberately choosing stakeholders based on prior knowledge of the problem or its context, potential motivation or incentives to be part of the participatory process step and potential role in up-scaling the co-learning process to a larger group of beneficiaries. [ABSTRACT FROM AUTHOR]

Grazing management (GM) interventions, such as reducing the grazing time or mowing pasture before grazing, have been proposed to limit the exposure to gastrointestinal (GI) nematode infections in grazed livestock. However, the farm-level economic effects of these interventions have not yet been assessed. In this paper, the economic effects of three GM interventions in adult dairy cattle were modelled for a set of Flemish farms: later turnout on pasture (GM1), earlier housing near the end of the grazing season (GM2), and reducing the daily grazing time (GM3). Farm accountancy data were linked to Ostertagia ostertagi bulk tank milk ELISA results and GM data for 137 farms. The economic effects of the GM interventions were investigated through a combination of efficiency analysis and a whole-farm simulation model. Modelling of GM1, GM2 and GM3 resulted in a marginal economic effect of € 8.36, € −9.05 and € −53.37 per cow per year, respectively. The results suggest that the dairy farms can improve their economic performance by postponing the turnout date, but that advancing the housing date or reducing daily grazing time mostly leads to a lower net economic farm performance. Overall, the GM interventions resulted in a higher technical efficiency and milk production but these benefits were offset by increased feed costs as a result of higher maintenance and cultivation costs. Because the results differed highly between farms, GM interventions need to be evaluated at the individual level for appropriate decision support. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the effect of GnRH vaccination in boars (BO), barrows (BA), and gilts (GI) slaughtered at an average BW of 133 kg. Forty BO, 40 BA, and 40 GI were housed individually, fed ad libitum, and divided into 2 groups of 20 pigs per sex, a control group (CON) and a GnRH vaccinated group (IC). The IC group received 2 injections of GnRH vaccine (Improvac) at 70 and 105 kg. In BA, GnRH vaccination had minor effects on performance. Observed effects in BO and GI are therefore probably not caused by the GnRH vaccination itself but by the subsequent withdrawal of gonadal hormones. In BO, the increased feed intake after GnRH vaccination (P < 0.001), accompanied by a fall in estradiol and testosterone levels, led to faster growth (P = 0.014), increased back fat thickness (P = 0.021), and a tendency for a decreased meat percentage (P = 0.052). GnRH vaccination of BO decreased the boar taint-related sensory attribute scores similar to the levels of BA and GI (P < 0.001). In GI, the increased feed intake after GnRH vaccination (P < 0.001), accompanied by low progesterone levels, led to faster growth (P < 0.001), increased back fat thickness (P = 0.018), decreased meat percentage (P = 0.032), and a decreased shear force (P = 0.002) without significant differences in the sensory profile. [ABSTRACT FROM AUTHOR]

Efficiency analysis is used for assessing links between technical efficiency (TE) of livestock farms and animal diseases. However, previous studies often do not make the link with the allocation of inputs and mainly present average effects that ignore the often huge differences among farms. In this paper, we studied the relationship between exposure to gastrointestinal (GI) nematode infections, the TE and the input allocation on dairy farms. Although the traditional cost allocative efficiency (CAE) indicator adequately measures how a given input allocation differs from the cost-minimising input allocation, they do not represent the unique input allocation of farms. Similar CAE scores may be obtained for farms with different input allocations. Therefore, we propose an adjusted allocative efficiency index (AAEI) to measure the unique input allocation of farms. Combining this AAEI with the TE score allows determining the unique input-output position of each farm. The method is illustrated by estimating efficiency scores using data envelopment analysis (DEA) on a sample of 152 dairy farms in Flanders for which both accountancy and parasitic monitoring data were available. Three groups of farms with a different input-output position can be distinguished based on cluster analysis: (1) technically inefficient farms, with a relatively low use of concentrates per 100 l milk and a high exposure to infection, (2) farms with an intermediate TE, relatively high use of concentrates per 100 l milk and a low exposure to infection, (3) farms with the highest TE, relatively low roughage use per 100 l milk and a relatively high exposure to infection. Correlation analysis indicates for each group how the level of exposure to GI nematodes is associated or not with improved economic performance. The results suggest that improving both the economic performance and exposure to infection seems only of interest for highly TE farms. The findings indicate that current farm recommendations regarding GI nematode infections could be improved by also accounting for the allocation of inputs on the farm. [ABSTRACT FROM AUTHOR]

Frequently acknowledged as coming forward to environmental issues by reducing external input use, low input (LI) dairy farming is gaining attention. The absence of a clearly delineated description of LI dairying, however, hampers identification and analysis of these farming systems. This paper aims at empirically examining, EU wide, the farm structure, production intensity and productivity of LI with respect to their high input (HI) conventional counterpart and to organic dairying (ORG). A pragmatic quartiles-based categorization of farms from the Farm Accountancy Data Network of 20 important EU dairy countries, with the value of external input costs per grazing livestock unit (GLU) is used as prior discriminating indicator between LI and HI. LI dairy farms are smaller than HI dairy farms, in particular when farm size is expressed as total farm capital. Other variables that differentiate between LI and HI in most countries are number of dairy cows per GLU and area of forage and grassland on total agricultural area. Partial productivities in HI farms exceed those in LI farms, most apparent is milk production per cow. Differentiation of forage production between LI and HI is less uniform throughout Europe. A pairwise matching of differentiation profiles between countries indicates that differentiation between LI and HI is country specific. A similar diversity in country-specific differentiation between ORG and LI farming is found. [ABSTRACT FROM AUTHOR]