born from seropositive dams to paratuberculosis had 6.6 times more likely to be seropositive compared with calves born from seronegative dams (Aly and Thurmond, 2005). Regarding the postnatal transmission, the fecal-oral route, especially at early life stage, is the main way to contract paratuberculosis in dairycattle at the individual level (Sweeney, 1996; Clarke, 1997). Neonatal calves are more susceptibility to MAP infection than other groups of age (Windsor and Whittington, 2010). Neonatal calves acquire MAP by direct ingestion of MAP- contaminated feces, from the manure-contaminated teat and udder of the calf´s dam or indirectly via MAP-fecal-contaminated colostrum, milk, water, pasture, feedstuff or utensils (Sweeney, 1996; Manning and Collins, 2010; Fecteau and Whitlock, 2010). MAP has been identified in colostrum from subclinically infected cows (Streeter et al., 1995). Colostrum has been establish as a risk factor of MAP infection for calves (Nielsen et al., 2008) and the practice of feeding pooled colostrum or waste milk from cows has been considered to help the spread of infection to many calves (Fecteau and Whitlock, 2010). In conclusion, the major sources of MAP infection for an animal are infected animals (Manning and Collins, 2001). The prompt identification of infected animals and the elimination of factors that increase contamination of the environment and infection of new born calves with MAP are some of the most critical issues in a paratuberculosis control program. Although calves are more susceptible to a paratuberculosis infection than older heifers or adults, these can also get infected by the exposure to high and repeated doses of MAP in a contaminated environment, but are less likely than calves to develop clinical signs of Johne‟s disease (Windsor and Whittington, 2010; Fecteau and Whitlock, 2010). Although this risk is considered low, these animals could excrete organisms in their feces, particularly under nutritional, lactational or other stress (Windsor and Whittington, 2010). On the other hand, transmission to herds without previous history of paratuberculosis is mainly due to purchase of subclinical MAP- infected animals or sharing of breeding bulls between herds (Sweeney, 1996; Manning and Collins, 2010; Fecteau and Whitlock, 2010). Utensils or clothes contaminated with MAP could also be potential sources of infection, but these are considered insignificant compared to the introduction of apparent uninfected animals (Fecteau and Whitlock, 2010).
Middlemen – livestock traders – play an important role for buying and selling dairy animals in peri-urban Faisalabad. Between 35% and 40% of the most recently bought or sold cattle and buffaloes of the HH came from or went to one of these businessmen. Other important business partners for the purchase of animals were rural farmers, peri- urban neighbours and vendors at local livestock markets. Whereas many animals were purchased from villages, only a few of them were directly sold back to rural areas. A large proportion of the adult dairy animals sold by the peri-urban dairy producers went to butchers, providing Faisalabad’s population with meat. Thus, many animals with a good genetic potential for milk production bought from rural areas and brought into the city, were slaughtered after one lactation in the city and withdrawn from breeding. In the long run, this might lead to genetic erosion and decline of the yield potential of the good dairycattle and buffalo breeds in the country, and especially in Punjab. The fact that many high potential animals from rural Punjab are transported to cities where eventually most of them and their offspring are slaughtered, has also been described by other authors (Khan et al. 2007; Khan et al. 2008; Klein et al. 2008).
The selection that is done within the pure breeding schemes in the HF, BS and Jersey population is mostly done in Europe and Northern America and breeding objectives represent the special needs for typical production systems and climatic conditions of these areas. These purebred genotypes were and still are developed in non- challenging production environments regarding climatic conditions, feed supply and disease- resistance (Rege et al., 2011). Madalena (1993) give the example that in dairycattle selection by the criteria milk yield using a yield limit disqualifies pasture based dairy production systems in Latin America. Animals that are selected by these criteria don’t fit the needs of farmers in the high Andean region however their offspring is used in crossbreeding schemes in these areas. In Peru the majority of the semen used comes from the USA or Canada (Calderon, 2009). Breeding objectives in the American HF and BS population fit the intensive production systems run in these countries but not the more extensive systems of the Andes. For example under these conditions a smaller type of dairy cow would have an advantage over larger ones because of their lower feed requirements (Philipsson et al., 2011).
Ranking of scenarios according to average inbreeding coefficients generally corresponded to rankings accord- ing to TBV, but differences in average inbreeding coef- ficients among scenarios were quite small (Table 2 ). The variation of inbreeding coefficients among replicates indicated a substantial impact of individual matings on the actual inbreeding level. A general and similar increase in average inbreeding coefficients as the number of generations increased was observed for all scenarios and for both sexes, with higher levels of inbreeding in bulls than in cows. Average inbreeding rates per genera- tion (ΔF) after generation 0 ranged from 0.312 to 0.576 % (see Additional file 5 ). Such increases are consistent with recently reported values for the German Holstein and international Holstein populations in the pre-genomic era, these values ranging from 0.44 [ 45 ] to 0.95 % [ 38 ]. One reason for these slightly lower average inbreeding coefficients in the simulated data could be that the cho- sen population structure had a relatively small number of active cows compared to the number of active sires, which differs from current practical dairycattle breed- ing programs [ 38 ]. Nevertheless, we aimed at producing valid inbreeding comparisons across the various polled breeding scenarios, because all scenarios were based on founder populations with the same parameters.
During the study period, all farms were visited three times by the same observer. A one-day visit at the beginning of the study (winter 2011/12; Year 0) was used for data collection based on the Welfare Quality ® assessment protocol for dairycattle (Welfare Quality ® , 2009). The assessment primarily rests upon animal-based indicators and all results are expressed on herd level. During the on-farm assessment, dairy cows were individually scored for clinical health and cleanliness. Behavior was assessed using an avoidance distance test, observation of spontaneous social and resting behavior as well as qualitative behavior assessment of the herd. Provision of resources and management procedures were assessed using checklists and questionnaire-based interviews with the farmer at the day of the assessment (for details see Welfare Quality ® , 2009 and Tremetsberger et al., 2015). During a second farm visit, which took place 55 ± 26 days (mean ± sd) after the initial visit, animal health and welfare planning was carried out on the farms (see below). Health and welfare states of the dairy cows were reassessed according to the procedures used during the first visit on average 368 ± 11 days after the health and welfare planning visit (Year 1). Data for economic calculations were collected only during the third farm visit by interviewing the farmer following a questionnaire. This included questions on three input factors (average herd size, annual labor, concentrate use; see below) and one output factor (milk yield per cow and year) in the period between the first and third farm visit, respectively (Year 1). Concerning annual labor, annual work hour estimates were specified for each working step, namely milking, feeding, hygiene measures and herd management. Furthermore, it was assessed whether inputs and output had changed during the study period compared to the 12-month period prior the first farm visit in order to calculate values for Year 0.
2 In general, Ethiopia has great potential for dairy development. Favorable conditions for dairying are the country’s large and diverse cattle population, generally adequate rainfall patterns which offer potential for production of high quality feedstuff, the existence of a large labor pool and opportunities for export (Anteneh et al., 2010; SNV, 2008). Particularly the mixed crop–livestock system in the highlands, although resource-limited, offers the best opportunity for dairy development and can support crossbred and pure dairycattle breeds. A prerequisite is the development of well-designed breeding strategies (Effa et al., 2003; Ketema and Tsehay, 1995; Ahmed et al., 2004; MOARD, 2007). Current impediments of livestock development are poorly developed social sector and economic infrastructure as well as environmentally destructive trends (MOARD, 2007). During the last decade cropping area has increased at the expense of grazing land, especially in Ethiopia’s highlands. Decreasing grazing land combined with a rapidly growing livestock population (CSA 2011) is likely to lead to massive overstocking and overgrazing of available pastures and increased land degradation due to soil erosion (Blata, 2010; Tschopp et al., 2010). This stretches pasture capacity beyond its limits; consequently decreasing pasture quality results in low livestock production performance (SNV, 2008).
In the present study, video clips of dairycattle were evaluated using Free Choice Profiling and Fixed Terms, respectively, for Qualitative Behaviour Assess- ment. The results show that Fixed Terms can be used for on-farm assessment of a dairycattle herd, because the Free Choice Profiling and Fixed Terms results are very similar: the observer agreement as well as the dimensions’ characterization exhibit very similar results. Panellists were able to generate exactly the same terms as de- veloped by experts for the Fixed Terms. Although Free Choice Profiling exhibits ad- vantages (e.g.: independence of the panellists) it is possible to use Fixed Terms if using Free Choice Profiling is not possible due to the time and personal effort. Whether twenty Fixed Terms, as suggested in the Welfare Quality protocol, are en- ough for panellists to assess all dimensions of behaviour, or whether there is an in- fluence of the emotional state of the observers on their evaluations, or whether there are gender differences in the assessment are questions that could be subjects of fur- ther research.
During NEB in dairy cows post partum, not only a massive lipid mobilisation, but also a protein catabolism takes place. The deamination and detoxification of proteins result in elevated systemic urea concentrations . Also, the ruminal flora is not adapted to the sudden increase in food rations appropriate for early lactation, resulting in an imbalance between energy and protein in the rumen, which can also lead to increased blood urea levels . Feeding high dietary protein also results in high concentrations of urea in plasma and milk, and both have been associated with impaired fertility in dairycattle [144–146], but the knowledge about the linking mechanisms is limited . High blood urea concentrations can also be detected in the follicular fluid [139,147,148], as the concentration of urea in plasma, follicular and uterine fluid have a very good correlation in high yielding dairy cows in the postpartum period [139,149]. Urea was long suspected to have a negative influence on follicular and oocytal development and maintenance of pregnancy , before a negative influence of urea on oocyte maturation and fertilisation was confirmed . Fahey suggested a negative influence of elevated urea levels on embryo quality in sheep via changes in the follicle or the oviducts . To our knowledge, no studies have previously investigated the influence of urea on bGC function.
carbohydrates, vitamins, minerals. In addition to being a natural source of nutrition for infant mammals, milk and dairy products are major components of the human diet in many parts of the world. Milk production performance can be improved through genetic selection, optimal feeding and management practices. Previous studies have confirmed that a certain proportion of variation in milk production traits in dairycattle such as milk yield (MY), protein yield (PY) and fat yield (FY) is due to genetic variation (19-41%) [1-3] . Hence, genetic selection can be an effective way to improve these traits. PY and FY are the main basis of dairy farmers’ payment in the Netherlands, so traditionally farmers, breeders, AI station and Breeding companies always center on MY, PY and FY by use of phenotypic selection.
The k-means clustering approach (using the first 10 PC) created three clusters including 856 cows (cluster 1), 14,305 cows (cluster 2) and 760 cows (cluster 3). Genetic distances between animals based on the two most impor- tant PC (the first two PC that contribute to genetic vari- ation) are shown in Fig. 1 . Our study included Holstein dairycattle from only two neighbouring German breed- ing organizations. When tracing back to the ancestors of the calves and heifers from the three clusters, we found that animals in clusters 1, 2 and 3 were daughters from 2, 890, and 11 sires, respectively. One specific influential sire (Gunnar) in cluster 1 had 855 daughters, whereas another sire (Raik) in cluster 1 had only one daugh- ter in cluster 1 and one daughter in cluster 2 (i.e. the only black dot that overlaps with the red dot in Fig. 1 ). The 760 calves and heifers in cluster 3 were daughters from 11 different sires. One specific sire (Guarini) had 750 daughters in cluster 3, and the remaining 10 sires only had one daughter each. The maternal grandsire of the nine daughters was Guarini. Sires in cluster 2 origi- nated from various countries, but more than 75% calves and heifers had German and Dutch sires. The remaining 25% females were daughters of sires from 12 other coun- tries. The average number of daughters per sire in clus- ter 2 was quite small (on average only 16.09). In contrast, the calves and heifers allocated to clusters 1 and 2 were mainly daughters from only two German sires. Conse- quently, as expected from the pedigree structure, genetic distances between animals within clusters 1 and 2 were short. Hence, the stratification that was observed in the genotyped calves and heifers was mainly due to the size and structure of the half-sib groups. The effect of breed- ing organization (geographical location) on population Table 2 Genetic parameters for body weight recorded at different ages based on pedigree and genomic relationship matrices
Crossbreeding of tropical and western dairycattle to improve performance on tropical smallholder farms has been widely advocated, criticised and yet applied. Advantages and disadvantages are documented. Only a small number of crossbreeding interventions has been successful. Little is known how successful adopters introduce and develop crossbreeding at farm level. For this study 248 smallholders successfully applying crossbreeding were interviewed using a pre-tested questionnaire in selected regions of Ethiopia, Uganda and India. Qualitative and quantitative data on motivations, crossbreeding introduction, support received, adaptation of breeding strategies and impacts at farm level have been analysed. A first description of local crossbreeding innovation systems has been made. Results show that in all contexts the reason to introduce crossbreeding was to increase profit. External support and other farmers were essential for successful adoption as information sources and suppliers of exotic genetics. Breeding is adapted if possible to increase performance but a lack of understanding of the crossbreeding concept has been identified. Positive and negative impacts led towards a high-input/high-output system. Many context specific challenges had to be overcome by adopters but they perceived crossbreeding as success. The conclusion is that farmers can increase incomes with crossbreeding. The complexity, initial investment and dependence on support and external inputs are probable reasons for slow crossbreeding uptake. Strengthening availability of breeding inputs enabling farmers to reach suitable and sustainable herd performance must be a priority. If investment capital, supply, support and market linkages are accessible, farmers can and will adopt crossbreeding without ignoring inherent challenges. Whether it proves a sustainable strategy for dairy farmers in the study areas has to be proven over time.
Dairy production in Ecuador is concentrated mostly in the Andean highlands, the Sierra. The milk production in this region reaches 73 % of the national total production repres- enting 3,869,000 kg per day (Grijalva et al., 1995). Within this area, dairy farms use diverse animal and pasture man- agement systems in response to their specificities in terms of farm structure, available land, livestock, mechanisation and human resources. In this regard, methods of pasture man- agement can range from mechanical cuts, to grazing using continuous or intensive rotational grazing (Kay et al., 2017). The proper management of pastures is of utmost importance for the stability and profitability of the dairy operations. It has repercussions on both the biomass production, feeding quality and regeneration capacity of the plant community, as well as livestock performance. The way cattle explore a pas- ture depends on several factors that will in the end contrib- ute to the extra energy requirement they specifically need to move on the paddock. Among these factors, some are linked to the quality of the forage resource and the ease by which animals will be able to take their bites (Arnold, 1985), others
The results of the different method tests with SPIN have shown that both algorithms should be applied to receive robust repeatable outcomes. Hence the current method suggests that, it is best to do an initial partition of the data point (SNPs) in the distance matrix with the STS algorithm followed by the iterative ordering process using the Neighborhood algorithm. In this case only D’ distances in the current cattle data set have the necessary properties to create meaningful orders. The initial ordering results have shown that with r² distances especially the endings of the LODE orders could not have been computed correctly, with the applied SPIN method. It was obvious that the nature of r² was responsible, that SNPs with high distances could not be separated from those with low distances (Humphreys 2007) (Figure 18b). A successful application of our LODE procedure can be predicted, if there is a clear separation of the SNPs in different groups, after the STS run such as in BTA1 (Figure 18a).
Farm 2 puts a lot of work into pasture management. With 10 paddocks in total, rotational grazing for the crossbred herd (which alone occupies 9 paddocks) and pasture conservation by making hay this farm stands out among the rest of study farms. The crossbred pasture chosen for analysis was situated on flat area at the valley floor. It was lightly covered by trees and herbaceous vegetation was dense. Situation of the Ankole pasture was quite different. There were a lot of bushes, trees and shrubs growing in the lower part of the pasture. The ground was very wet, almost swampy. Lemongrass was present in high abundance. The upper part of the Ankole pasture was situated on a steep hillside which was also quite overgrown with bushes, shrubs and trees and apparently quite dry. Herbaceous vegetation was not very dense in the upper pasture area. So assumed better quality of the crossbred pasture was particularly obvious on this farm. Taking high management inputs into account this farm seems to be gaining a lot from their way of keeping it´s crossbred herd. This might explain the dedication of a larger area to crossbred cattle than to Ankole cattle. Farm 1 and farm 2 were also study farms with largest combined pasture sizes (161ha and 185ha respectively).
gathering, collective decision-making, and operational and monitoring costs that are influenced by several factors such as external environmental attributes, resources used in crop-livestock integration, and participating actors and their arrangements (Martin et al., 2016; Martin et al., 2018). Different on-farm activities such as proper storage of crop residues and good manure handling enhance nutrient recycling and therefore resource use (Rufino et al., 2006). In the context of (peri-) urban livestock farming, studies focusing on the enhancement of crop-livestock integration are rather scarce. In Ouagadougou, cropping is carried out by the majority of livestock farmers regardless of the animal species kept (Chapter 2; Roessler et al., 2016). In addition single livestock species farms are not frequently encountered and keeping different livestock species can be seen as farmers' strategy to manage risk and uncertainty not only related to livestock production but also to the whole household including family members' health, education and household food security (Awa et al., 2004). It is likely that multi-species keeping still persists for many decades in (peri-) urban livestock production systems in sub-Saharan Africa. It might be interesting to ask oneself what makes a species the most important in such systems so that the farmer invests more resources in that species, to the detriment of others. In Fulani communities usually large animals (cattle, horse) belong to the household head (generally a man) while small ruminants and poultry are often owned by other household members including women (lro, 1994). It is likely that such socio-anthropological considerations also exist in (peri-) urban livestock production systems, especially since most household heads have a long-term experience since a young age. Dairycattle farming is still strongly influenced by ethnicity (Chapter 2) and is mostly carried out by Fulani households that can be considered as traditional farmers with long-term experience as dairy producers (lro, 1994; Awa et al., 2004; Gautier et al., 2016). Yet there emerges a set of new dairy producers made up of non-Fulani ethnic groups; these are often called "new actors" and are often more open to innovations and adoption of new production technologies (Bonfoh et al., 2007). Given the specificities of rearing dairy animals in landless or land constrained (peri-)urban areas, the high urban demand for dairy products and policies towards enhancement of dairy production (Bonfoh et al., 2007; Duncan et al., 2013; Chagunda et al., 2015), this study compared resource use and use efficiency between both groups (Chapter 3).
PhD Dissertation – Solomon Antwi Boison Page 121 across breeds (Hayes et al., 2009; Hozé et al., 2014) and adding genotyped cows to the reference populations (Ding et al., 2013; Thomasen et al., 2014; Wiggans et al., 2011; Lourenco et al., 2014). Addition of cows to reference population is an attractive alternative since cow information is more readily available. However, GEBVs have been reported to be biased when cows are added to reference population in genomic evaluations (Dassonneville et al., 2012). Genomic selection has been well studied in both large and small populations of Bos taurus dairycattle breeds (VanRaden et al., 2009; Luan et al., 2009; Brøndum et al., 2011; Mulder et al., 2009). However, there is dearth of knowledge on the potential of GS in Bos indicus dairycattle populations. Recent studies in an indicine beef cattle population (Nelore; suggested that GS is a feasible alternative to traditional selection approaches. The present study is undertaken on Gyr (Bos indicus), which is an important dairycattle breed of Brazil. The Brazilian Gyr cattle have gained prominence as an efficient milk production breed in the tropics producing relatively high amount of milk (~3,000 kg of milk per 305 days in milk) and having good adaptability to tropical conditions such as resistance to ticks, worms and mastitis (Santana et al., 2014). In Brazil, about 80% of dairy herds are composed of Gyr and their crosses with Holstein. The Gyr breed is known to have about 400,000 registered animals in its national herdbook. However, progeny testing of bulls began only about 3 decades ago and the number of bulls that have been involved in the program is about 450 (Santana et al., 2014). Furthermore, DNA samples from of some progeny-tested bulls and most of the non-progeny tested bulls from the Brazilian population were not stored. Thus, in Gyr, a large reference population for preliminary GS study could be formed with addition of cow data to the limited bull dataset. The overall aim of this study was to undertake a preliminary analysis on the feasibility of genomic prediction in Brazilian Gyr. Accuracy of genomic predictions for age at first calving, milk yield, fat yield and protein yield were studied.
The benefit of using variable selection methods is expected to be higher when the number of markers is much greater than the number of genotyped animals. Neither of the previous GS studies on real data con- tained such large differences between number of animals in the training set and number of genotyped SNPs, thus our study included a scenario for which the use of vari- able selection methods was expected to provide some benefit. Erbe et al.  also confirmed the advantage of a variable selection method (Bayes R) over GBLUP, after analyzing GS in dairycattle using the same type of high-density panel as we used. These authors suggested that variable selection methods must be used to take full advantage of the increased marker density. The larger empirical accuracies that we obtained with BayesC and BLASSO here corroborate this hypothesis.
There are two main strategies for finding trait loci: association tests which use candidate genes and genome scans which are based on linkage mapping with anonymous DNA markers. The candidate gene approach can be very powerful, in cases where the candidate gene is a true causative gene, even in detecting loci with small effects. But this approach is also time-consuming and can fail because of current insufficient knowledge about gene function. Further dangers lie in the presence of linkage disequilibrium between loosely linked loci or even loci on different chromosomes (Farnir et al., 2000) and in the setting of proper statistical thresholds (Schaid, 2004) when testing with this approach. In contrast, a genome scan will always map a trait locus with a major effect if an accurate genetic model is postulated, reasonable sample size is used and the marker set fully covers the genome. However, it will fail to detect a trait locus with smaller effects, because of the stringent significance threshold applied (Andersson, 2001). Since the first genome wide scan experiment by Georges et al. (1995) a number of full or partial genome scans have been published which were able to detect QTL in dairycattle. For the review, see Khatkar et al. (2004).