Vision of breeding for organic agriculture

This report describes the results of research into the vision of breeding systems within organic agriculture in the Netherlands. The purpose of the research was to arrive at a vision for breeding in organic agriculture by means of interviews and discussions with organic livestock farmers and social organisations. The research was prompted by the fact that, as things stand, organic livestock farmers generally have to rely on the conventional breeding supply. Neither the breeding method not the animal type produced meet the requirements of organic agriculture. Interest in breeding has increased because organic agriculture is expanding, and as yet too little attention has been paid to the development of specific organic breeding programmes and associated legislation. In recent decades conventional agriculture and breeding have tended more and more towards industrialisation and uniformity with breeding becoming a multinational concern. The breeding organisations have no incentive at present to provide special services for organic agriculture as the market is too small and the costs are too high. Taking the current breeding situation as the starting point, a number of scenarios are described for each animal sector which could gradually lead to a system of breeding which is more organic both in its aims and in the chain-based approach. The naturalness of the breeding techniques is an important factor in considering the available options. The different scenarios served as a basis for the interviews and discussions with livestock farmers and social organisations. We have established that the farmers and organisations consider it important for organic agriculture to work on developing a breeding system which follows the principles of organic agriculture. The most important reasons for this are that: (1) consumers expect all production factors in the chain to be of organic origin, (2) most livestock farmers currently use conventional breeding techniques which fall short of the organic requirements on a number of points, such as the use of artificial reproduction techniques and mono-functional breeding for production. For the development of organic breeding, livestock farmers feel that in the first instance the use of artificial reproduction techniques, including cloning and transgenic techniques, should be restricted. Next the livestock farmers and social organisations want breeding to be adapted to or based on the organic environment. There is a suspicion that owing to genotype-environment interaction (G x E) conventionally-bred animals cannot adapt well to the organic environment, and this leads to health and welfare problems. The farmers would like to see this development taking place within 5 to 10 years. It must however proceed one step at a time since the farmers cannot yet form a complete picture of the impact of all the different factors. Most of the people involved see the ideal form of breeding, with natural reproduction and regional or farm-specific selection, as a standard to be achieved in the distant future. At the moment most livestock farmers have neither the knowledge nor the socio-economic means to set up such breeding programmes. The development of breeding and the associated legislation require an international approach, for which suitable contacts must be sought in other countries. The final chapter of this report looks in more detail at the steps to be taken in each sector. Ideally developments should probably be initiated and supervised by a central body, such as an organic breeding foundation, which could be set up to govern the breeding of all farm animals

Animal breeding in organic farming:Discussion paper

It is uncertain whether animals which have been bred for conventional production are capable of optimum performance in organic conditions. In conventional agriculture there is a movement towards maximum control of production conditions in order to optimise animals' yield in intensive production systems. By contrast, organic agriculture is based on natural processes and closed cycles, and takes into account the underlying connections between production factors. Following organic ideology, production capacity should be curtailed by acting in accordance with guiding principles such as naturalness, animal welfare, efficient use of fossil fuels in the farm cycle, and agri-biodiversity (IFOAM, 1994). Organic production should be tied to the land, with farms preferably being self-sufficient mixed farms with closed cycles. An additional point of concern are the reproduction techniques used in conventional breeding. Artificial insemination (AI) and embryo transfer (ET) are commonplace in conventional animal breeding. But these techniques are 'artificial' and they deprive animals of natural mating behaviour and negatively affect the animals' welfare and integrity. By bringing in animals from conventional agriculture, organic farmers are indirectly making use of these techniques. These and other concerns have led to the project 'Organic breeding: a long way to go', which aims to lay down clear visions and an action plan for an organic breeding system

Dairy cattle breeding objectives combining production and non-production traits for pasture based systems in Ireland.

End of Project ReportThe objectives of this study were: 1) to estimate genetic (co) variances among body condition score, body weight, milk production, linear type traits and fertility, and 2) to investigate the presence of genotype by environment interactions for milk production, body condition score, and body weight, in Irish grass based seasonal calving herds. Genetic parameters were estimated from a potential 8928 primiparous and multiparous Holstein-Friesian cows over two years (1999 and 2000). Heritability estimates for body condition score (BCS) and body weight (BW) were found to be moderate to high; estimates ranged from 0.27 to 0.51 for BCS, and from 0.39 to 0.61 for BW. Heritability estimates for BCS change and BW change at different stages of lactation were all less than 0.11. Heritability for the linear type traits varied from 0.11 to 0.43. Phenotypic and genetic correlations between BCS and BW at the same stage of lactation were all close to 0.50 indicating that approximately 25% of the genetic and phenotypic variation in BW may be attributed to differences in BCS. Genetic correlations between BCS and milk yield tended to be negative (-0.14 to –0.51) and genetic correlations between BW and milk yield were close to zero (-0.07 to 0.09). However, the genetic correlations between BW adjusted for differences in BCS were positive (0.15 to 0.39). Genetic correlations between BCS and the fertility traits investigated were all favourable, indicating that cows with a superior genetic merit for BCS are on average likely to be served sooner, receive less services and have higher pregnancy rates. The genetic correlations between linear type traits and milk yield indicate that selection for milk production has resulted in taller, deeper cows that tend to be more angular and have less body condition. Genetically these cows are predisposed to inferior reproductive efficiency. Moderate genetic correlations were found between some of the linear type traits investigated and somatic cell count. A comparison of BCS, as recorded by Teagasc personnel (scale 1-5) and Holstein herd-book classifiers (scale 1-9) indicated consistency between the two sources. Phenotypic and genetic correlations of 0.54 and 0.86, respectively, were observed between the two measurement sources on the same animals. Genotype by environment interactions, were found for milk yield across different silage quality environments, and for BCS across different herd-year milk yield, concentrate, grazing severity and silage quality environments

Accuracy of predicting milk yield from alternative milk recording schemes

peer-reviewedThe effect of reducing the frequency of official milk recording and the number of recorded samples per test-day on the accuracy of predicting daily yield and cumulative 305-day yield was investigated. A control data set consisting of 58 210 primiparous cows with milk test-day records every 4 weeks was used to investigate the influence of reduced milk recording frequencies. The accuracy of prediction of daily yield with one milk sample per test-day was investigated using 41 874 testday records from 683 cows. Results show that five or more test-day records taken at 8-weekly intervals (A8) predicted 305-day yield with a high level of accuracy. Correlations between 305-day yield predicted from 4-weekly recording intervals (A4) and from 8-weekly intervals were 0.99, 0.98 and 0.98 for milk, fat and protein, respectively. The mean error in estimating 305-day yield from the A8 scheme was 6.8 kg (s.d. 191 kg) for milk yield, 0.3 kg (s.d. 10 kg) for fat yield, and −0.3 kg (s.d. 7 kg) for protein yield, compared with the A4 scheme. Milk yield and composition taken during either morning (AM) or evening (PM) milking predicted 24-h yield with a high degree of accuracy. Alternating between AM and PM sampling every 4 weeks predicted 305-day yield with a higher degree of accuracy than either all AM or all PM sampling. Alternate AM-PM recording every 4 weeks and AM + PM recording every 8 weeks produced very similar accuracies in predicting 305-day yield compared with the official AM + PM recording every 4 weeks

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

<p>Background: Whole-genome sequence data is expected to capture genetic variation more completely than common genotyping panels. Our objective was to compare the proportion of variance explained and the accuracy of genomic prediction by using imputed sequence data or preselected SNPs from a genome-wide association study (GWAS) with imputed whole-genome sequence data. Methods: Phenotypes were available for 5503 Holstein-Friesian bulls. Genotypes were imputed up to whole-genome sequence (13,789,029 segregating DNA variants) by using run 4 of the 1000 bull genomes project. The program GCTA was used to perform GWAS for protein yield (PY), somatic cell score (SCS) and interval from first to last insemination (IFL). From the GWAS, subsets of variants were selected and genomic relationship matrices (GRM) were used to estimate the variance explained in 2087 validation animals and to evaluate the genomic prediction ability. Finally, two GRM were fitted together in several models to evaluate the effect of selected variants that were in competition with all the other variants. Results: The GRM based on full sequence data explained only marginally more genetic variation than that based on common SNP panels: for PY, SCS and IFL, genomic heritability improved from 0.81 to 0.83, 0.83 to 0.87 and 0.69 to 0.72, respectively. Sequence data also helped to identify more variants linked to quantitative trait loci and resulted in clearer GWAS peaks across the genome. The proportion of total variance explained by the selected variants combined in a GRM was considerably smaller than that explained by all variants (less than 0.31 for all traits). When selected variants were used, accuracy of genomic predictions decreased and bias increased. Conclusions: Although 35 to 42 variants were detected that together explained 13 to 19% of the total variance (18 to 23% of the genetic variance) when fitted alone, there was no advantage in using dense sequence information for genomic prediction in the Holstein data used in our study. Detection and selection of variants within a single breed are difficult due to long-range linkage disequilibrium. Stringent selection of variants resulted in more biased genomic predictions, although this might be due to the training population being the same dataset from which the selected variants were identified.</p

Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips

Background: The simulated dataset of the 13th QTL-MAS workshop was analysed to i) detect QTL and ii) predict breeding values for animals without phenotypic information. Several parameterisations considering all SNP simultaneously were applied using Gibbs sampling. Results: Fourteen QTL were detected at the different time points. Correlations between estimated breeding values were high between models, except when the model was used that assumed that all SNP effects came from one distribution. The model that used the selected 14 SNP found associated with QTL, gave close to unity correlations with the full parameterisations. Conclusions: Nine out of 18 QTL were detected, however the six QTL for inflection point were missed. Models for genomic selection were indicated to be fairly robust, e.g. with respect to accuracy of estimated breeding values. Still, it is worthwhile to investigate the number QTL underlying the quantitative traits, before choosing the model used for genomic selection

Effects of data structure on the estimation of covariance functions to describe genotype by environment interactions in a reaction norm model

Covariance functions have been proposed to predict breeding values and genetic (co)variances as a function of phenotypic within herd-year averages (environmental parameters) to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires, estimated genetic correlations between the trait expressed in different environments were 0.93, 0.93 and 0.97 while simulated at 0.89 and estimated genetic variances deviated up to 30% from the simulated values. Non random use of sires, poor genetic connectedness and small herd size had a large impact on the estimated covariance functions, expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd, while losing some information on genotype by environment interaction in the data