Supplementation of ruminants on winter pastures

Supplementation of ruminants on winter pastures

Industry Sector: Cattle and Small Stock

Research focus area: Livestock production with global competitiveness

Research Institute: University of Pretoria

Researcher: Prof Willem.A. van Niekerk PhD (Agric) Animal Science

Research Team:

TitleInitialsSurnameHighest Qualification
ProfLourens. J.ErasmusPhD (Agric) Animal Science
DrA.Hassan
PhD (Agric) Animal Science
MrR.J.Coetzer
MSc (Agric) Animal Science
MrHMynhardtMSc (Agric) Animal Science

Final report approved: 2016

Aims of the project

  • To develop a cost-effective supplementation strategy for ruminants under low quality winter forage conditions
  • To maintain body weight during the wineter season by assessing different sources and levels of nutrients that enhances poor quality roughage utilisation
  • To investigate intake, fiber degradation and microbial protein production when various types and levels of nutrients are supplemented to ruminants kept at maintenance under extensive conditions

Executive Summary

A series of studies was conducted to evaluate differential energy and nitrogen (N) sources as supplemental feed to sheep grazing low quality winter grazing in the High veldt. Knowledge on supplementation under local conditions are limiting as the majority of supplementation studies are funded and performed in the more temperate areas. Results indicated that higher N and energy inclusion levels are necessary to optimize ruminant production under local conditions compared to temperate areas. In addition, the ratio of fermentable energy to available protein is an important parameter in optimizing supplementation programs. It is concluded that the supplementary recommendations from the current feeding tables does not describe the requirements and nutrient quality of the tropical grasses satisfactorily and as such, cannot be used to predict supplementation responses by the tropical forage fed ruminant.  del can be used for further sensitivity analyses and “what if” scenarios as well as a database to answer specific questions.

Popular

SUPPLEMENTATION OF SHEEP GRAZING LOW QUALITY GRASSES WITH UREA AND STARCH

BY:  *H. MYNHARDT, W. A. VAN NIEKERK AND L. J. ERASMUS, UNIVERSITY OF PRETORIA

Every year sheep might lose up to 30% of their summer body weight gain during the dry winter periods in the high veldt.  While these weight losses have an economic impact on its own, it also is associated with an increased susceptibility for diseases and parasitic infestations and decreased reproductive performances. It generally is considered that protein or non-protein nitrogen (NPN) supplementation is necessary to limit these weight losses during these periods. However, due to the type of grass found in the High veldt area of Southern Africa, data is limiting on the effects of supplementation of ruminants grazing these types of grasses (See box: Differences between C4 and C3 grasses). As such, supplementations recommendations derived from current feeding tables seldom satisfy the needs of the grazing ruminant in Southern Africa. Therefore, a series of studies was conducted at the University of Pretoria to determine and quantify the requirements of the ruminant grazing low quality Eragrostis curvula hay commonly found in the Southern Africa High veldt.

* References and correspondence can be obtained from the author: hermanmynhardt@yahoo.com


Box 1: Differences between C4 and C3 Grasses

The acronyms C3 and C4 refer to the first product of the photosynthetic processes in the respective grasses with the first product of photosynthesis in the C3 grass being phosphoglycerate (a 3 carbon structure) while for the C4 plant, the corresponding molecule is a 4 carbon molecule (oxaloacetate). C3 grasses are temperate grasses and are adapted to the temperate regions of the world where rainfall is more constant with maximum temperatures seldom topping 22 OC. In contrast, C4 grasses are more adapted to the subtropical and tropical climates with temperatures frequently topping 25oC during the growth period. These areas also are associated with seasonal droughts and the occasional frost. Due to these extremes in temperatures and seasonal droughts, C4 grasses contain more bundle sheath cells and less available nutrients compared to C3 grasses during all maturity stages. Ruminant production therefore in general is significantly lower in ruminants grazing C4 grasses compared to temperate C3 grasses, especially during the dormant stage of the grass where lignification of the C4 grasses reduces the availability of the nutrients even further. As such, supplementation requirements and responses differ between ruminants grazing these grasses. However, the majority of supplementation studies in the past have been conducted on C3 grasses as it is found more in the European countries where research funding is more available. As such, as more studies conducted on low quality C3 grasses are incorporated in the current feeding tables, supplementation requirements derived from these tables to the low quality tropical forage fed ruminant are not always accurate. As such, the need was established to conduct research through the financial support of the **RMRD-SA on the nutritional requirements of the low quality tropical forage fed ruminant in order to improve ruminant production in Southern Africa.


*RMRD -SA – Red Meat and Research Development, South Africa



Results and Discussion

Forage intake and digestibility was not influenced by either the level of urea or starch supplementation to the wethers. However, CP-balance, measured as CP intake – CP excretion in the faeces and urine, increased from 12.5 g CP/day in the LU wethers up to 70 g CP/day in the EHU wethers. Based on these observations, only the EHU treatment supplied sufficient protein to potentially satisfy the needs of the 50 kg wethers as they require 65 – 70 g CP for maintenance. These recommendations are significantly higher than the recommendations set in the current feeding standards, however, it is in alignment with the observations and recommendations set out by **Leng (1995) studying ruminants grazing tropical grasses in Australia.

Forage intake and digestibility was not influenced by either the level of urea or starch supplementation to the wethers. However, CP-balance, measured as CP intake – CP excretion in the faeces and urine, increased from 12.5 g CP/day in the LU wethers up to 70 g CP/day in the EHU wethers. Based on these observations, only the EHU treatment supplied sufficient protein to potentially satisfy the needs of the 50 kg wethers as they require 65 – 70 g CP for maintenance. These recommendations are significantly higher than the recommendations set in the current feeding standards, however, it is in alignment with the observations and recommendations set out by **Leng (1995) studying ruminants grazing tropical grasses in Australia.

HIGHER LEVELS OF PROTEIN AND ENERGY SUPPLEMENTATION IS NECCESARY TO OPTIMISE THE GRAZING RUMINANT IN THE S.A. HIGH VELDT DURING THE DRY WINTER MONTHS

An important parameter in ruminant nutrition is microbial protein synthesis (MPS) as it gives an indication of the efficiency of the rumen microbes. During the dry winter months, MPS generally decreases due to the lack of available nutrients in the roughages (Leng, 1990, 1995) which decreases the productivity of the animal which is experienced as weight loss by the farmer.  In this study, MPS increased almost 50% from 78 g MPS to 106 g MPS as the level of starch supplemented was increased from 200 (LS) to 280 (HS) g starch/day. This observation is in agreement with suggestions made by Leng, (1990; 1995) that energy is an important nutrient driving MPS in the tropical forage fed ruminant, provided that the protein requirements of the ruminant have been met. Interestingly, energy supplementation for the temperate forage fed ruminant is not always advocated as these grasses contain higher concentrations of water soluble carbohydrates compared to the tropical grass.

Based on the above results, higher levels of both protein and energy supplementation is necessary to optimise ruminant production during the dry winter months in the High Veldt. The question now was asked whether there was an “ideal” quantity of protein and energy to be supplemented to ruminants grazing low quality “tropical” forages.

Graph 1 is a schematic representation of MPS per unit CP intake (MNS: N intake) while Graph 2 represents the mean rumen ammonia nitrogen (RAN) concentration as influenced by the ratio of starch supplemented to available protein intake.

Graph 1

Urea supplementation across all three starch treatments affected the MPS: CP ratio similarly with the ratio decreasing from almost 3 to below 1 where the wethers were supplemented with the higher urea treatments (HU and EHU). It is important to note that alleviated MPS: CP levels (above 1) could be indicative of CP deficiency as more microbial protein was synthesized in the rumen compared to dietary CP intake. The additional CP required to produce the microbial protein under these circumstances is derived from body protein catabolism which in itself, is an inefficient process, resulting in an excessive body weight loss. As such, in this trial, it is suggested that the protein intake of the wethers supplemented with at least 26.4 g urea/day (HU) was sufficient to meet the requirements of the wethers.

Graph 2

An inverse relationship was observed between RAN and the ratio of starch: digestible protein intake (Graph 2) with RAN decreasing and plateau between 5 and 10 mg RAN/ dL rumen fluid as the ratio increased. An inflexion point was observed where RAN increased exponentially to levels as high as 25 and even 30 mg RAN/dL rumen fluid as the ratio decreased below 2: 1. This graph highlights the importance of supplementation of both rumen available energy sources (starch in this instance) as the supplementation of only RDP sources to the ruminant could lead to an increased risk of ammonia toxicity under these circumstances.

Conclusion

The results from this study suggest that the supplementation requirements of 50 kg wethers grazing low quality tropical forages (2.7% CP) differs to the current feeding standards as:

  • Higher levels of protein (urea supplementation up to 26.4 g urea per day per wether or 3% urea of the total DM intake) is necessary to optimise CP balance in the tropical forage ruminant.
  • Starch supplementation (up to 280 g/wether/day or almost 20% of the total DM intake) in addition to urea supplementation is necessary as tropical grasses not only are deficient in protein, but also in easy available energy.
  • For wethers grazing low quality tropical grasses, the ideal ratio of starch supplemented to digestible protein intake lies between 2 and 3: 1.
  • Additional research is necessary to study the effects of other energy sources and protein sources on rumen environment and the production parameters of the tropical forage fed ruminant as these sources might have different availabilities compared to urea and pure starch within the rumen.

The authors wish to thank the Red Meat Industry and Research Development (RMRD) for their financial support of this study.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project –
Willem van Niekerk on willem.vanniekerk@up.ac.za

Genomic markers in beef tenderness

The effectiveness of genomic markers in predicting the meat tenderness in pure beef genotypes under South African production and slaughter conditions

Industry Sector: Cattle and Small Stock

Research focus area: Livestock production with global competitiveness: Breeding, physiology and management

Research Institute: Agricultural Research Council – Animal Production Institute

Researcher: Dr L Frylinck PhD

Title Initials Surname Highest Qualificaion
Prof PE Strydom PhD Animal Science
Ms A Basson MSc

Year of completion : 2018

Aims of the project

  • To determine the expression of genomic markers in five South African purebred genotypes – Bos indicus (Brahman), Sanga type (Nguni), British Bos taurus (Angus), European Bos taurus (Charolais) and the composite (Bonsmara) for genes associated with beef tenderness in meat.
  • To determine the relationship between the actual physiological tenderness characteristics under South African production and slaughter conditions of the meat from these five main South African genotypes and the known DNA-marker information.
  • To assess the phenotypic variation in meat tenderness within South African selected pure beef genotypes under the same environmental conditions and to build a tenderness prediction model.

Executive Summary

Purebred South African bulls of 5 breeds (n=166) were finished on a grain diet at the Animal Production Institute of the Agricultural Research Council (API-ARC), Irene. Breeds included Angus (n=27; representative of British Bos taurus), Brahman (n=35; Zebu type Bos indicus), Bonsmara (n=35; South African composite breed with large Sanga contribution), Charolais (n=34; European Bos taurus) and Nguni (n=35; Sanga type Bos taurus africanus). Animals were sampled over 3 slaughter periods in 2011 (50 animals), 2012 (50 animals) and 2014/2015 (66 animals). Bulls were sourced from breeders that are registered with the appropriate breeders’ associations and were progeny of registered pure breed bulls and cows. Bulls were ≃9 months old when entering the feedlot and reared under feedlot conditions for ≃120 days to ≃12 months old. Bullas were slaughtered to yield A2/3 carcasses (zero permanent incisors, lean to medium fatness). Bulls were penned overnight with access to water before slaughter following captive bolt immobilization at the abattoir of the API-ARC. All treatments and procedures were approved by the Ethics Committee of the Agricultural Research Council (ARC AEC-I 2010 001).

To determine whether the effects of genotype were additive to electrical stimulation, the right half of the carcass was electrical stimulated for 15 seconds at 500V peak, using 5 ms pulses at 15 pulses per second and directly chilled at 4 °C. The left half of the carcass was not electrically stimulated (served as a control), while chilling was delayed for 6 hours (at 10 °C) to allow for the full development of metabolic processes within muscle fibers before chilling at 4 °C.

Animal measurements included weights, recorded during the feedlot growth period to determine body weight gain (total gain and average daily gain) and liver body weight (BW) measured on the day before slaughter as a final weight. Carcass measurements included hot carcass weight (HCW; used to calculate dressing percentage), cold carcass weight (used to determine carcass mass loss), EMA (in the thoracic region at T9/10), pH and temperature (measured at the lumbar end of the LTL). Beef quality estimates measured from samples collected directly from the carcass or from LTL excised from the lumbar region (L6) up to the thoracic region (T9/10) included myofibrillar fragment length (MFL), Warner-Bratzler shear force (WBSF), calpain enzyme system activities, sarcomere length (SL), colour measurements, energy metabolites, collagen (content and solubility) and water-holding capacity (WHC). Colour was determined using the CIE L*A*b* colour convention with measurements of L*, a*, b*, C* and hab over the ageing period. Energy metabolites included the concentrations of glycogen, glucose 6-phosphate, glucose, lactate, creatine phosphate and ATP determined at 1 h, 3 h, 6 h and 20 h post‑mortem.

The genes that are most likely to affect beef quality, specifically tenderness, as those of the calpain enzyme system. Calpain-1, calpain-2, calpain-3 and calpastatin are all found in the sarcoplasm and are known to determine post‑mortem proteolysis. The genes for these proteins can therefore be identified as causative to proteolysis at least, but potentially also for beef tenderness. We therefore used the 114 SNPs located in these causative genes (capn1capn2capn3 and cast respectively) to determine their genotypic distribution, as well as the association of these genotypes with beef quality traits in order to determine the importance of these genes in determining the quality (tenderness) phenotype. These data were used to identify possible markers for genomic selection (GS), once they were validated for tenderness in South African beef breeds.

  • The capn1 gene (on BTA29) was validated for beef tenderness, with a large number of strong associations (relatively high correlations) with estimates of beef tenderness, found in both the ES and the NS treatment groups. It correlated especially with MFL as a measure of physical tenderness (r2= 0.07 to 0.15), with fewer SNPs explaining the phenotypic variation in WBSF (r2 = 0.09 to 0.10). Almost no associations occurred with calpain-1 enzyme activity itself, but the effects of the SNPs in capn1 was rather a change in the responsiveness of the enzyme to calpastatin inhibition, as shown by several relatively strong correlations (r2 = 0.07 – 0.12) to the relative calpastatin inhibition per calpain(-s).
  • The capn2 gene (on BTA16) was validated for beef tenderness, explaining the phenotypic variation in, especially, the activities of calpain-1 and calpain-2 (r2 = 0.07 – 0.11). Although effects on enzyme activities were evident, these changes only resulted in a few significant associations of the genotypes with physical tenderness MFL (r2 = 0.07 – 0.09).
  • The capn3 gene (on BTA10) exhibited very few associations with beef quality. The protein coded by this gene is responsible for background proteolysis and does not cause variation in tenderness. The lack of an effect of these SNPs on tenderness is therefore unsurprising.
  • The cast gene (on BTA7) is quite large (136,434 bp) and contained a large number of SNPs (63), of which only 4 exhibited extensive effects on tenderness. Many of the correlations with MFL ranged between 0.07 – 0.11, although a few SNPs exhibited strong phenotypic correlations with MFL (r2 = 0.12 – 0.16), while associations with WBSF were less common and less pronounced (r2 = 0.07 – 0.11). These differences in physical tenderness were only in part explained by differences in the total and /or relative inhibition of calpastatin of protease enzyme activities (r2 = 0.07 – 0.12).

Using SNPs of the Illumina Bovine HD SNP BeadChip the capn1capn2 and cast genes were verified for tenderness in SA purebred beef cattle. The amount of phenotypic variation in tenderness estimates explained by some of these SNPs were large, making them useful targets for genomic selection in these breeds. Both Nguni and Bonsmara exhibited high allelic frequencies for alleles that were favorable for tenderness, giving them the genetic potential to produce tender beef.

Popular Article

Inheemse rasse soos die Nguni en Bonsmara het die genetiese potensiaal om sagte vleis te produseer

Basson, A

Inleiding

Hierdie proef is onderneem om vleisbeesgenetika in Suid-Afrikaanse (SA) rasse te ondersoek. As deel van die proef is daar getoets of die rasse wat algemeen vir kruisteling in SA gebruik word, verskil in die verspreiding van voordelige gene vir sagtheid (en ander vleiseienskappe), met spesifieke fokus op die inheemse Bonsmara en Nguni. Die karkasse is gehalveer om die een helfte elektries te stimuleer en dadelik te verkoel, terwyl die ander helfte as kontrole gedien het. Hier is verkoeling vir 6 ure uitgestel om die normale perimortem prosesse soos energieverskaffing in metabolisme, genoeg tyd te gee om te ontwikkel, voordat hierdie nie-gestimuleerde karkas-helftes verkoel is.

Daar is verskeie vrae waarvoor ons antwoorde soek met hierdie navorsing. Ons weet dat die Nguni oor die genetiese en biochemiese potensiaal beskik om sagte vleis te produseer (Frylinck et al., 2009), maar hoe vergelyk dit met Bonsmara, Angus, Charolais en Brahman? Kan die Nguni onder die regte slagtoestande, sagte vleis produseer? Kan ons deur middel van genomiese seleksie (GS) die kwaliteit van beesvleis verbeter in die industrie, waar elektriese stimulering dalk die invloed van voordelige gene sou uitkanselleer, of is verbeterde genetika se positiewe invloed op kwaliteit steeds waargeneem na stimulering?

Die Proef

Vyf vleisbeesrasse is in die proef ingesluit; Angus en Charolais as Bos taurus rasse, Brahman as Zebu-tipe Bos indicus, Bonsmara as ‘n inheemse kruisbeesras met ‘n groot Sanga-tipe bydra en Nguni as inheemse Sanga-tipe Bos taurus africanus. Die stoetbulle is afgerond in die voerkraal tot naastenby 12 maande oud voor slagting, of ‘n karkasklassifisering van A2/3. ‘n Groot aantal monsters is versamel van die Longissimus lumborum et thoracis spier (lende) om die toestande rondom slagting te bepaal, asook lendeskywe wat vakuum-verseël is en verouder is vir 3, 9, 14 en 20 dae, om die invloed van veroudering op vleiskwaliteit te bepaal (met of sonder elektriese stimulering).

Vleis se Kwaliteitseienskappe

Vir kwantitatiewe eienskappe is daar ‘n baie groot aantal gene wat ‘n eienskap bepaal en elkeen van hierdie gene dra slegs ‘n klein proporsie by tot die uiteindelike resultaat, byvoorbeeld sagte vleis. Elkeen van hierdie gene kan honderde (selfs duidende) variasies toon op ‘n molekulêre vlak. Enkel-nukleotied polimorfismes (single nucleotide polymorphisms = SNPs) wat die verskil in een enkele DNA molekule is, kan soms ‘n relatiewe groot invloed op die fenotipe hê. Hierdie SNPs (uitgespreek “snips”) is wat ons geïdentifiseer en getoets het binne-in gene wat sagtheid behoort te beïnvloed.

Genetika en Fisiologie

Spier in die lewendige dier het ‘n baie rigiede proteïenstruktuur wat hoogs ge-orden is, terwyl die omskakelings na vleis in die karkas ‘n ontwrigting van hierdie orde behels – hoe meer die speirstrukture ontwrig word, hoe sagter is die vleis. Die kalpaïen ensiem-sisteem (spesifieke proteases) dra grootliks by tot die ontwikkeling van die finale sagtheid van vleis. Alhoewel kalpaïen‑1 en kalpastatien (die inhibeerder van kalpaïen) die grootste bydra lewer tot die degradering van die proteïene in vleis om dit sagter te maak, kan kalpaïen‑2 en kalpaïen‑3 dalk ook hiertoe bydra. Ons het dus diere met die Bovine-HD SNP BeadChip van Illumina genotipeer vir die gene van die ensieme kalpaïen‑1 (capn1 in chromosoom 29), kalpaïen‑2 (capn2 in chromosoom 16), en kalpaïen‑3 (capn3 in chromosoom 10), asook die ensiem-inhibeerder, kalpastatien (cast in chromosoom 7). Ons bepaal dus eerstens watter gene fisiologies belangrik is en analiseer dan al die geen-variante (of SNPs) om die korrelasie tussen hierdie variante en vleiskwaliteit van die diere te bepaal. ‘n Groot voordeel van hierdie navorsing, wat dit onderskei van ander werk, is dat ons ‘n baie gedetaileerde prentjie van die fisiologie van die vleis het, deur meting van verskeie eienskappe (met of sonder behandeling), gekoppel aan redelik indiepte inligting omtrent die genotipes van hierdie funksionele gene.

Resultate

Brahman bulle (rooi in die grafiek) het deurgaans die hoogste vlakke van kalpastatien per kalpaïene getoon, wat bygedra het tot meer intakte spierveselstrukture (langer miofibril fragment lengtes – MFL) asook verhoogde taaiheid (hoë Warner-Bratzler snyweerstande of WBSW gemeet in kg). In teenstelling het die Nguni (turquois in die grafiek) heelwat laer inhibering van ensiemwerking deur kalpastatien getoon, wat in sommige gevalle die laagste van al die rasse was, met ander woorde die Nguni was die ras met die mees voordelige biochemie. In die Bonsmara was die patroon vir biochemiese en strukturele veranderinge baie soortgelyk aan dié van Nguni’s en die sagtheid van die lendeskywe (verlaging in snyweerstande) het vinnig verbeter tussen dag 3 en 9 van veroudering. Teen 14 dae se veroudering het die snyweerstande gestabiliseer en Bonsmara bulle het nie dieselfde sagtheid as die Nguni bereik nie, inteendeel, hulle snyweerstande was soortgelyk aan Brahman en Charolais.

Kalpaïen-1 is die belangrikste protease wat sagtheid bepaal en die kalpaïen‑1 geen (capn1) behoort dus by te dra tot vleiskwaliteit. Die grootste invloed van capn1 was om die proteïenstruktuur te ontwrig, deur middel van laer relatiewe kalpastatien inhibisie per kalpaïen aktiwiteit. Ons het sterk korrelasies vir verskeie SNPs in hierdie geen geïdentifiseer waar veral MFL (maar ook party van die snyweerstande), sowat 15-20% laer was in die “voordelige” genotipe (voordelig vir sagtheid).

Die kalpaïen-2 ensiem is verantwoordelik vir die ontwikkeling van agtergrond-sagtheid en die geen (capn2) was ge-assosieer met sowat 12 – 15% hoër protease ensiemaktiwiteit, wat in sommige SNPs met soveel as 38% hoër ensiem aktiwiteit geassosieer was. Dit was egter tot ‘n kleiner mate met die bevordering van sagtheid en die ontwrigting van vesels geassosieer.

Kalpastatien aksie kan ‘n groot invloed op sagtheid hê. In die lewendige dier funksioneer dit om die kalpaïen protease ensiemaktiwiteit, wat sellulêre proteïene groot skade sou kon aanrig, in beheer te hou. In die prosesse wat spier omskakel na vleis toe, verhoed dit ook die afbraak van spierproteïene, maar in dié geval sal dit dan die ontwikkeling van sagtheid benadeel. In die kalpastatien geen (cast) was daar ‘n relatief klein aantal SNPs wat ‘n redelike groot invloed op die ontwrigting van spiervesel proteïene gehad het. Die MFL was nagenoeg 10 – 15% laer, terwyl sommige van die SNPs se “voordelige” genotipes tot  meer as ‘n 20% verbetering in die MFL gelei het (i.e. korter lengtes). Dit was gedeeltelik verduidelik deur ‘n verlaging in die totale eenhede kalpastatien werking, met soveel as 20% laer inhibisie vanaf kalpastatien, gekoppel aan ‘n redelike verbetering in die sagtheid van die vleis, veral in die vroeë tot intermediêre stadiums van veroudering.

Bespreking

Uit die 4 gene wat hier getoets is, is die kalpaïen‑1 en kalpastatien gene veral geskik vir genomiese seleksie in Suid-Afrikaanse vleisbeesrasse, terwyl ‘n paar van die SNPs in die kalpaïen-2 geen ook potensiaal toon. Rasverskille in sagtheidseienskappe (fisiese en biochemies) word gereflekteer in verskille in die verspreiding van genotipes tussen die verskillende rasse (sien tabel hier onder)..

Totale Aantal Voordelige Allele*
cast capn1 capn2
Angus (n=27) 189 146 220
Bonsmara (n=35) 270 209 174
Brahman (n=35) 237 39 141
Charolais (n=34) 217 147 215
Nguni (n=35) 256 233 241

* Die groen blok dui die ras met die grootste aantal voordelige allele vir sagtheid aan

Nguni’s hét die genetiese potensiaal om sagte vleis te produseer, maar die noemenswaardige ligter karkasse is geneig om te vinnig te verkoel wat beteken die vleis raak te koud vir metaboliese ensieme om energie optimaal te benut, terwyl die struktuur binne miofibrille ook sub-optimaal word vir die proteases se ensiemwerking. In hierdie proef het Nguni’s die “beste genetika” gehad en die allele wat voordelige is vir sagtheid in die gene wat hier getoets is, was volop in Nguni’s.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project – Lorinda Frylinck on lorinda@arc.agric.za

Discovery of single nucleotide polymorphisms

Genome-wide genetic marker discovery in South African indigenous cattle breeds using next generation sequencing

Industry Sector: Cattle and Small Stock

Research Focus Area: Livestock production with global competitiveness: Animal growth, nutrition and management

Research Institute: Agriculture Research Institute – Animal Production Institute

Researcher: Dr. Avhashoni Zwane

Title Initials Surname Highest Qualification
Prof. Azwihangwisi Maiwashe PhD
Prof Este Van Marle-Koster PhD
Prof Jerry Taylor PhD
Prof Mahlako Makgahlela PhD
Dr Ananyo Choudhury PhD
Dr Farai Muchadeyi PhD

Year of completion : 2018

Aims Of The Project

  • To conduct a genome wide search for new SNPs in local cattle breeds
  • To validate newly identified SNPs using Run 5 data from the 1000 Bull Genomes Project and perform functional annotation and enrichment analysis
  • To identify selective sweeps and a panel of SNP markers to discriminate between the three indigenous breeds

Executive Summary

South African (SA) livestock has played an important role in food security country’s sustainability. Due to the important role of indigenous cattle breeds in SA, it is crucial for these breeds to be included in the generation of genotypic and sequence data. Genomic data provide opportunity for various genetic investigations including identification of breed-informative markers, selective sweeps and genome-wide association studies (GWAS). In this study sequence data were generated and used in combination with genotypic data to conduct a SNP discovery in the three indigenous SA breeds (Afrikaner, Drakensberger, and Nguni) and study potential selective sweeps and identify panel of breed-specific markers. Commercial bovine SNP assays, (BovineSNP50 and GGP-80K) were used for identifying the breed-informative markers, while an approach of breed pooled samples were used for sequencing. Sequencing of the three breeds generated approximately 1.8 billion (184 Giga-bp) of high quality paired-end reads which 99 % reads mapped to the bovine reference genome (UMD 3.1), with an average coverage of 21.1-fold. A total of 17.6 million variants were identified across the three breeds with the highest number of variants identified in NGI (12,514,597) than in AFR (11,165,172) and the DRA (7,049,802). In total 89 % of variants were SNPs and 11 % were Indels. On average, 85 % of the total SNPs identified were also shared among the breeds from 1000 Bull Genomes Project data and the remaining 15 % of SNPs were unique to SA indigenous breeds. Novel SNPs were further annotated to identify genes enriched in novel SNPs. In total, 461, 478 and 542 genomic regions identified from the top (5%) windows were enriched for novel variants (p < 0.001). A total of 174 putative breed-specific SNPs were identified across the breeds and showed the overall 100% breed allocation using PCA and GeneClass 2. This study provides the first analysis of sequence data to discover SNPs in indigenous SA cattle breeds and the results provide insight into the genetic composition of the breeds and offer the potential for further applications in their genetic improvement.

POPULAR ARTICLE

IDENTIFICATION OF SELECTIVE SWEEPS IN AFRIKANER, DRAKENSBERGER AND NGUNI CATTLE USING GENOME-WIDE SEQUENCE DATA

A.A. Zwane1,2, A. Choudhury, M.L. Makgahlela1, E. van Marle-Köster2, A. Maiwashe1,5 and J.F. Taylor4
1Department of Animal Breeding and Genetics, ARC-API, P/Bag X2, Irene, 0062, 2Department of Animal and Wildlife Sciences, University of Pretoria, P/Bag X20, Hatfield, Pretoria, 0028, 3Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, P/Bag 3, Wits, Gauteng, 2050, 4Division of Animal Sciences, University of Missouri, 920 East Campus Drive, Columbia, MO 65211-5300, USA, 5Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein 9300, South Africa
#Corresponding author: zwanea@arc.agric.za

Background: Whole-genome sequencing now provides a suitable platform to examine the entire genome for the identification of selective sweeps. Indigenous South African (SA) breeds including Afrikaner (AFR), Drakensberger (DRA), and Nguni (NGI) are important genetic resources for SA cattle production. These breeds were subjected to strong selection leading to changes in their morphology, physiology and behaviour.

Aim: The aim of this study was to identify selective sweeps that shaped phenotypic diversity among indigenous SA breeds.
Methodologies: Whole genome sequencing of pools of DNA from AFR, DRA, and NGI was performed using an Illumina HiSeq 2000 and 17.6 million variants were discovered across the breeds. To identify the selective sweep regions, SNPs were used to calculate Z-transformations of the pooled heterozygosity (ZHp) in each of the three breeds using a 150 kb sliding window to compute the ZHp Z-scores in each breed. The results were used to plot the distribution of SNP counts within the windows. The regions of selective sweeps were represented by the lower ZHp Z-scores with the minimum threshold of -4. Animal QTL database was used to determine the gene ontology of the genes identified in selective sweep regions.

Results: In total 688 candidate selective sweeps, with the ZHp Z-score ≤ −4 were identified across the three breeds with 223 putative selective sweeps (ZHp Z-score ≤ -5). About 93 regions had extremely low ZHp Z-scores (ZHp scores ≤ −6). These are the regions subjected to selection segninatures. Using animal QTLdb, several genes were identified, e.g., ESM1, CNOT6, ASIC5, KIT and MITF, associated with phenotypic variation in livestock species (Zielak-Steciwko et al., 2014; Fallahsharoudi et al., 2016).

Discussion: The ability to detect selective sweep regions provided useful genomic information for these breeds, whereas functional analysis of these regions revealed the presence of genes of biological and economic importance.
Conclusions and recommendations: This study provides a broad insight into the events that happened during recent selection events and artificial selection processes that have shaped the livestock genome. More work is needed to characterise genomic regions and genes identified in this study.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project –  vhashoni Zwane  on Zwanea@arc.agric.za

Crossbreeding Afrikaner, Bonsmara and Nguni cows

Crossbreeding effects with specialized sire lines in Afrikaner, Bonsmara and Nguni beef cattle herds

Industry Sector: Cattle and Small Stock

Research Focus Area: Livestock production with global competitiveness: Breeding, physiology and management

Research Institute: Agriculture Research Institute – Animal Production Institute

Researcher: Dr. M Scholtz

Title Initials Surname Highest Qualification
Mrs. A. Theunissen MSc
Prof F W C Neser Ph.D.
Mr. L De Lange Nat. Dipl.
Mr. T Jonker M.Sc.
Mr. F J Jordaan M.Sc. (Agric)
Dr M D MacNeil Ph.D.
Mr. O Ntwaeagae B.Tech
Mr. W Pieterson Nat. Dipl.
Ms. M C Mokolobate M.Sc. (Agric)
Ms. G M Pyoos B.Sc. (Agric. Sci.)
Ms. M Mokgadi M.Tech

Year of completion : 2018

Aims Of The Project

  • 1. To estimate the genetic and phenotypic trends in the dam lines
  • 2. To evaluate crossbreeding systems and quantify the phenotypic progress made in economically important traits in crossbred cattle for beef production
  • 3. To characterize the additive and non-additive genetic effects for production and health traits in progeny of terminal sires and dam line breeding cows
  • 4. To validate an existing simulation model for the development of breeding objectives for specialized sire lines on Landrace breed cows for use in small scale and commercial farming that better meet commercial feedlot requirements
  • 5. To make recommendations with regard to future selection and management of beef herds in warm arid areas
  • 6. To evaluate alternative production systems in anticipation of global warming

Executive Summary

Climate has been changing and these changes are predicted to be highly dynamic. Increasing frequencies of heat stress, drought and flooding events are likely, and these will have adverse effects livestock production. It is therefore important that production systems utilizing local landrace and adapted breeds that are better adapted to warmer climates, be investigated.

In South Africa extensive cattle farming dominate primary cattle production systems, while more than 80% of all beef cattle slaughtered in the formal sector in South Africa originate from commercial feedlots. A total of 67% of feedlot animals are crossbreds, indicating that crossbreeding is playing a significant role in the commercial industry in South Africa. Well-structured crossbreeding systems allows producers to capture benefits from complementarity and heterosis.

The study is being conducted at Vaalharts Research Station. The aim is to use the Afrikaner, Bonsmara and Nguni as dam lines in crosses with specialized sire lines from British (represented by Angus) and European (represented by Simmentaler) breeds. In addition these dam lines were also mated with Afrikaner, Bonsmara and Nguni bulls in all combinations. This is producing 15 different genotypes.

It is anticipated that the information from five breeding seasons will be needed for the a more comprehensive study. Currently the information from three seasons are available and have been summarized. A protocol for Phase 2 of the study has been submitted.

The phenotypic trends in production traits of the three breeds over 25 years revealed an increase in cow productivity in all the breeds varying from 10% in the Bonsmara to 18.3% in the Afrikaner, where cow productivity was defined as kg calf weaned per Large Stock Unit mated. This also resulted in a decrease in the carbon footprint of up to 12%. The bottom line is that cow productivity can be improved if the weaning weight of the calf relative to the weight of the cow can be increased; and the inter-calving period reduced. Well-structured crossbreeding should have a much bigger effect on this and therefore the environmental impact, will be included in the final analyses of this study.

The simulation study indicated that breed, weaner and carcass price have an influence in the gross income from weaner and ox production systems. The simulation model in question can be used to quantify the benefits from the different crosses on completion of the study on condition that it is based on sound assumptions regarding weaner and carcass prices.

The information on 550 weaner calves and 125 feedlot bulls are currently available. The heaviest weaning weights are from Simmentaler sires on Afrikaner (220 kg) and Bonsmara (213 kg) dams, as well as Angus sires on Bonsmara (252 kg) dams. The lightest weaner calves were produced from purebred Ngunis (171 kg) and Angus sires on Nguni dams (173 kg). The severe draught and extreme heat of the 2015/2016 summer season had a big effect on the Angus and Simmentaler sired calves. The Sanga sired calves and Angus/Simmentaler sired calves had the same weaning weight (171 kg) in this season. In contrast, the 2016/2017 summer season was cooler and wetter, resulting in the weaning weight of the Angus/Simmentaler sired calves being 27 kg heavier than the Sanga sired calves (210 kg versus 183 kg). This demonstrates the importance of including the effect of climate on the pre- and post-weaning performance in Phase 2 of the experiment.

At the completion of the study all the information will be updated and this baseline information used to evaluate how effective the current crossbreeding systems in South Africa are and to quantify the direct and maternal heterotic effects, the possible/promising advantages of structured crossbreeding, as well as the effect of climate.

The very dry and hot 2015/2016 season also had an effect on the post weaning feed intake and growth. For example, the ADG of the Angus and Simmentaler types decreased by 17%, whereas that of the Sanga and Sanga derived types (Afrikaner, Bonsmara, Nguni) decreased by 9%, as a result of the heat waves experienced.

It is foreseen that indigenous and adapted beef breeds may become more important in South Africa as a consequence of climate change that will result in more challenging environments. The use of specialized sire and dam lines offer an opportunity to increase output by taking advantage of heterosis and complementarity. The effects of weather patterns on beef production in South Africa should also be estimated and thereafter, mitigation strategies developed in the era of climate change to ensure optimal production efficiency.

With the information collect from the GrowSafe system, it will be possible to study feed and water intake patterns as well as behavior of individual animals and different genotypes. This may give valuable information on the effect of climate on animal performance and behavior.

This study produced one M.Sc. thesis, 8 peer reviewed scientific articles, chapters in books and conference proceedings, as well as 8 popular articles.

Popular Article

The principles behind climate smart beef cow efficiency through utilization of structured crossbreeding

Theunissen1, M.C. Mokolobate2 & M.M. Scholtz2,3

1Northern Cape Department of Agricultural, Land Reform and Ruswral Development, Private Bag X9, Jan Kempdorp 8550, South Africa

2ARC-Animal Production Institute, Private Bag X2, Irene, 0062, South Africa

3University of the Free State, Bloemfontein, 9300, South Africa; South Africa

atheunissen@ncpg.gov.za (Corresponding author)

 Background and deliberations

With the ever swelling costs of production, beef cattle producers in South Africa have a sure challenge for sustainability. This is aggravated by the vagaries of climate change. The country’s most recent vulnerability was displayed during the 2015 drought, which was the warmest year ever recorded and was accompanied by extreme heat. The beef industry is one of the agricultural sectors that need to focus on both adaptation and mitigation strategies in response to  greenhouse gas (GHG) emissions and global warming.

The utilization of more hardy breed resources in a changing production environment is one of the alternative strategies to be considered. The most fundamental factor in this strategy will be the emphasis on a high reproductive rate of the selected breeds in the particular environment to increase the overall efficiency of the beef cattle enterprise.

Another alternate is the use of sustainable crossbreeding systems that pool indigenous and exotic breeds, but with retention of the genetic resources, which have shown to be an effective means to reduces GHG, as it has been shown to increase reproduction and production levels in overseas and in local studies. In this regard, a newly developed more sophisticated Large Stock Unit (LSU) calculator by Neser (2012) and Mokolobate (2015) and the measurement of cow efficiency (to calculate kg calf weaned/kg LSU of the dam); initiated an evaluation tool for “cross-bred” selection and breeding to improve cow efficiency; as long as the nutritional needs of animals are fully met.

This expression of cow efficiency is an improved replacement for the biological definition of kg calf weaned/kg mature cow weight that not only has two variables of which anyone or both in the ratio can change to have the same answer, but does not express beef production in terms of an assigned nutrient intake. The advantage of the new biological expression of cow efficiency is that the method increases output and reduces input, which will then support and facilitate the implementation of climate smart production, adaptation and mitigation measures.

Initially Meissner et al. (1983) defined a LSU on the basis of the nutrient requirement of a unit.  However, with differences in frame sizes there are differences in the voluntary feed intake between such animals although they have the same body weight. The LSU equivalents for beef cattle of different frame sizes also vary according to physiological phases, eg. heifers (over 12 months of age) and lactating cows. Table 1 shows examples of the refined estimations of LSU equivalents according to frame sizes of cows that was derived with the calculator.

Table 1: LSU equivalents for beef cattle of different frame sizes and physiological phases

Weight

Kg

Small Frame Medium Frame Large Frame
Heifer (>12 months) Cow &

Calf

Heifer (>12 months) Cow &

Calf

Heifer (>12 months) Cow &

Calf

150 0.37 X X X X X
175 0.42 X X X X X
200 0.47 X 0.50 X X X
225 0.52 0.83 0.56 X X X
250 0.57 0.89 0.61 X 0.67 X
275 0.61 0.95 0.66 X 0.72 X
300 0.66 1.00 0.70 1.05 0.77 X
325 0.70 1.06 0.75 1.11 0.82 X
350 0.73 1.11 0.80 1.17 0.88 X
375 0.77 1.16 0.84 1.23 0.93 1.48
400 0.80 1.22 0.89 1.29 0.98 1.55
425 0.83 1.27 0.93 1.34 1.03 1.61
450 0.85 1.32 0.97 1.40 1.08 1.66
475 X 1.37 1.01 1.45 1.13 1.72
500 X 1.42 1.05 1.50 1.18 1.78
525 X 1.47 1.08 1.55 1.23 1.83
550 X 1.52 1.12 1.60 1.28 1.88
575 X 1.57 X 1.65 1.33 1.93
600 X 1.61 X 1.69 1.38 1.98
625 X X X 1.74 1.43 2.02
650 X X X 1.78 X 2.07
675 X X X X X 2.11
700 X X X X X 2.15

Crossbreeding has proved to increase cow efficiency when it is measured and calculated with the LSU caculator. Table 2 demonstrates the results of a study that was done at Vaalharts Research Station that used mature cows of different breeds. The cow efficiency, estimated by kg calf weaned / cow LSU (KgC/LSU), for the Afrikaner (A), Brahman (B), Charolais (C), Hereford (H) and Simmentaler (S) breed types were calculated according to their different frame sizes and expressed as percentage deviation from the Afrikaner breed in brackets.

Table 2 The estimated cow efficiency (KgC/LSU) for the 29 different breed types and percentage deviation from the Afrikaner breed in brackets

  Sire Breed
 

Dam breed

Afrikaner  (A) Brahman  (B) Charoloais  (C) Hereford  (H) Simmentaler  (S)
A 142.6

(0.0)

144.2

(1.1%)

145.7

(2.1%)

151.2

(6.0%)

143.7

(0.7%)

B 142.0

(-0.4%)

C 124.9

(-12.4%)

H 149.3

(4.6%)

S 139.3

(-2.3%)

BA 148.9

(4.4%)

147.1

(3.1%)

155.6

(9.1%)

162.0

(13.6%)

160.1

(12.3%)

CA 152.3

(6.7%)

155.5

(9.0%)

154.5

(8.3%)

157.1

(10.1%)

158.4

(11.0%)

HA 155.7

(9.2%)

170.1

(19.2%)

175.1

(22.7%)

161.2

(13.0%)

176.8

(23.9%)

SA 155.9

(9.3%)

156.6

(9.8%)

161.1

(12.9%)

163.8

(14.8%)

162.1

(13.6%)

Table 2 shows that with the exception of the Hereford, purebred dams were less efficient than purebred Afrikaner dams under the particular environmental conditions. The purebred Charolais (C) dam was the least efficient dam out of all the genotypes. Crossbreeding the Afrikaner (A) dam line with Brahman (B), Charolais (C), Hereford (H) and Simmentaler (S) as sire lines indicated small effects (between +0.7 to +6.0%) on KgC/LSU above that of the purebred Afrikaner (A). However, the efficiency in the F1 cow increased relative to that of the purebred exotic cows. For example, the cow efficiency of the CA cow, compared to pure C cow increased with +14.5% (from -12.4% to +2.1%).

In the case of FI cows the HA was unsurpassed and increased cow efficiency on average by +17.6%, while the BA, CA and SA dam lines increased cow efficiency by +8.5, +9.0 and +12.1% respectively. Continental and Zebu sire lines mated to the most productive HA crossbred dam line in a three-breed system (S x HA, C x HA and B x HA) increased KgC/LSU on average by +22.7, +23.9 and +19.2% respectively, against that of the A x HA backcross with +9.2%.

The improvement demonstrated in the study concurs with that of Schoeman (2010), which indicated that crossbreeding improves calf/cow efficiency when measured as energy requirements or input costs per kg of equivalent steer weight. Although the effect of heterosis on individual traits is normally relatively small, the cumulative effect on composite traits, such as weight of calf weaned per cow exposed are immense which explains the superiority in kgC/LSU as a composite trait. Conversely, researchers cautioned on the attempt to extrapolate research results to all environments other than those similar to where the studies were conducted because of the presence of genotype x environment interactions.

While KgC/LSU as trait on its own can be used to rank productive cows in a contemporary group, it cannot be used to plan breeding strategies. Fertility, or the number of calves weaned in a cow group should certainly also be considered as a complementary factor that influences cow efficiency. In this study the net effect on weaning rate (WR) was that crossbred dams outperformed their purebred contemporaries by 8%.

Cow efficiency can then be estimated as follows: Y = WR x KgC/LSU

where Y = cow efficiency.

Since weaning rate has a low heritability and largely depends on the climatic and managerial (environmental) factors of a particular farm, this trait can contribute to large deviations in the estimated cow efficiencies that were obtained in Table 2. When weaning rate is included in the metioned Vaalharts study, it showed that when compared to the A, only purebred H and S cows have increased cow efficiency potential (+11.4 and 5.3% respectively). Two-breed progeny of the A dam line increased cow efficiency on average by +16.5%. All these increases are ascribed to the increased WR of the breeds compared to that of the A, B and C pure-breeds.

While A sire line backcrosses increased cow efficiency on average by +20.3%, three-breed progeny from B, C, H and S sire lines had average increases of +21.6, +24.4, +30.2 and 34.8% respectively. The S x HA showed the notable increase of 49.7%. Similarly, the BA, CA, HA and SA dam lines respectively had average increases of +24.1, +18.9. +36.6 and +25.2% on cow efficiency. All crossbred dam genotypes increased cow efficiency, the only exceptions being a trivial increase of +0.6% of the B x CA genotype. In this study the Pearson correlation between kgC/LSU (cow efficiency without WR included) and WR x kgC/LSU (cow efficiency with WR included) is 0.88%.

In the current Vaalharts crossbreeding project, the Bonsmara and Nguni are added to the Afrikaner as dam lines. These dam lines are mated to Angus and Simmentaler as specialized sire lines. In addition, the dam lines are also inter-mated in all possible combinations. The result is 15 different genotypes. The data will be analysed similar to that of the previous crossbreeding project.

Conclusions

A sophisticated Large Stock Unit (LSU) calculator can be used for the measurement of cow efficiency (to calculate kg calf weaned/kg LSU of the dam) of different frame sizes and without additional inputs. Cross-breeding has shown to increase cow efficiency; as long as cow frame sizes do not increase up to a point where the nutritional needs of animals are not fully being met. Increases in cow efficiency (weaning rate x kg calf/large stock unit) in two-breed and three-breed cattle was mainly derived from differences in frame size, fitness and relationships between calf weight and cow Large Stock Units.

The fact that there are large differences in cow efficiency in reproductive cows point to genetic differences and holds the potential for cow ranking and improvement through selection in contemporary groups. Optimum crossbreeding strategies may increase cow efficiency up to a notable 49.7%. This will support climate smart beef production, since it will reduce resource use and reduce the carbon footprint per unit of product produced.

Acknowledgement

This work is based on research supported in part by Red Meat Research and Development South Africa and the National Research Foundation of South Africa (NRF), under grants UID 75122, 75123 and 90097. The Grant-holder acknowledges that opinions, findings and conclusions or recommendations expressed in any publication generated by the NRF-supported research are that of the authors and that the NRF accepts no liability whatsoever in this regard.

References

Meissner, H.H., Hofmeyr, H.S., Van Rensburg, W.J.J. & Pienaar, J.P., 1983. Classification of livestock for realistic prediction of substitution values in terms of a biologically defined Large Stock Unit. Tech. Comm. No. 175. Department of Agriculture, Pretoria.

Mokolobate, M.C., 2015.Novelty traits to improve cow-calf efficiency in climate smart beef production systems. MSc. Dissertation. University of the Free State, Bloemfontein, South Africa.

Neser, F.W.C., 2012. http://www.rpo.co.za/documents/pptrpo/proffrikkieneser.pdf

Schoeman, S.J., 2010. Crossbreeding in beef cattle. In: Beef Breeding in South Africa. 2nd Edition. Agricultural Research Council, Pretoria. ISBN-13 978-1-86849-391-3 pp 21-32.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project – Michiel Scholtz on gscholtz@arc.agric.za

Landscape genomics in South Africa

Genomic technologies for the improvement of South African beef cattle

Industry Sector: Cattle and Small Stock

Research Focus Area: Livestock production with global competitiveness: Breeding, physiology and management

Research Institute: Agriculture Research Institute – OVI

Researcher: Dr. Pranisha Omduth Soma

Title Initials Surname Highest Qualification
Prof. A.N. Maiwashe PhD
Dr F.C. Muchadeyi PhD
Prof. E. van-Marle Koster PhD
Prof. M.M. Makgahlela PhD
Dr M. MacNeil PhD
Dr S.O. Makina PhD

Year of completion : 2018

Aims Of The Project

  • To estimate linkage disequilibrium within South African beef cattle
  • To perform a genome wide scan for signatures of selection in beef cattle
  • To sequence genomic regions targeted by selection in order to identify possible polymorphisms

Executive Summary

South African indigenous and locally developed cattle breeds possess adaptive traits that are usually associated with tolerance to various diseases, extreme temperatures and humidity, and to change in feed availability. These breeds are also adapted to low-input management systems and have shown the ability to survive, produce and reproduce under harsh environments. Thus, these breeds hold potential in the changing South African production environments. However, little is known about the nature or extent of the genetic variation underlying these breeds.

The aim of this study was to conduct a genome wide scan for signatures of selection among Afrikaner, Nguni, Drakensberger, Bonsmara, Angus and Holstein cattle breeds of South Africa using data generated from the Bovine SNP50k BeadChip. The Angus and Holstein breeds were included as reference breeds since they have been extensively characterized using similar tools.

Therefore, in this project, the Bovine SNP50 BeadChip was used to characterize the genetic diversity and population structure of SA cattle breeds, determine the linkage disequilibrium and conduct a genome wide scan for signatures of selection among the Afrikaner (n=44), Nguni (n=54), Drakensberger (n=47) and Bonsmara (n=46)., using the Angus (n=31) and Holstein (n=29) cattle reference groups.

The first experiment performed included the evaluation of the Bovine SNP50 BeadChip to determine its utility for genome wide studies of South African cattle. Results of this experiment revealed that over 50 % of the SNPs were polymorphic (eg. Nguni = 35 843), indicating that the Bovine SNP50 assay would be useful for genome wide studies among South African cattle breeds.

Information about genetic diversity and population structure among cattle breeds is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of cattle breeds. Genetic diversity within the cattle breeds was analyzed using three measures of genetic diversity namely allelic richness, expected heterozygosity and inbreeding coefficient. The genetic diversity and population structure analyses indicated that the Afrikaner cattle had the lowest level of genetic diversity (He=0.24) while the Drakensberger cattle (He=0.30) had the highest among indigenous and locally-developed breeds. As expected, the average genetic distance was the greatest between indigenous breeds and Bos Taurus breeds but the lowest among indigenous and locally-developed breeds. Model-based clustering revealed some level of admixture among indigenous and locally-developed breeds and supported the clustering of the breeds according to their history of origin. Clear genetic divergence between South African (indigenous and locally-developed cattle breeds) and Bos Taurus cattle breeds was observed which suggested distinct genetic resources in South African cattle breeds which should be conserved in order to cope with unpredictable environments.

The extent of linkage disequilibrium (LD) is important for determining the minimum distance between markers for effective genome coverage for genome wide association studies. It can also provide insight into the evolutionary history of a population. The analyses of the extent of linkage disequilibrium (LD) showed that Afrikaner, Angus and Holstein had higher LD compared to Nguni, Drakensberger and Bonsmara cattle at all tested genomic distances. The higher LD within the Afrikaner cattle suggested that this breed has experienced considerable selection forces in contrast to what is expected of indigenous breeds and would require lower marker (50 000) density relative to what will be required for the Nguni, Drakensberger (150 000) and Bonsmara (75 000) cattle for genome wide studies. New breeding strategies may be required for the Afrikaner cattle breed to ensure future fitness of the breed. The effective population size for the Nguni, Drakensberger and Bonsmara were above the FAO recommended level.

The detection of selection signatures among cattle breeds may assist in locating regions of the genome that are, or have been, functionally important and targeted by selection. In this study, two approaches were employed. The first was based on the detection of genomic regions for which haplotypes have been driven towards complete

Fixation within breeds. The second approach identified regions of the genome exhibiting elevated population differentiation (Fst). A total of 47 genomic regions were identified as harboring potential signatures of selection using both methods. Thirty three of these regions were successfully annotated to identify candidate genes. Among these, were keratin genes (KRT222, KRT24, KRT25, KRT26 and KRT27) and one heat shock protein (HSPB9) on chromosome 19 (BTA) at 41,447,971-41,926,734 bp in the Nguni that have been previously associated with adaptation to tropical environments in Zebu cattle.

Furthermore, a number of genes associated with nervous system (WNT5B, FMOD, PRELP, ATP2B), immune response (CYM, CDC6, CDK10), production (MTPN, IGFBP4, TGFBI, AJAPI) and reproductive (ADIPOR2, OVOS2, RBBP8) performances were detected to be under selection in this study.

Target probes for enrichment were designed from exome and 5’ and 3’ untranslated regions of the cattle genome. Many SNP’s were identified in regulatory regions, leading to conformational changes in factor-binding sites. Gene ontology enrichment and clustering, resulted in the enrichment of gene ontology terms involved in fertility-related categories. Taking advantage of the availability of the fully sequenced bovine genome, the South African beef breeds were sequenced to detect genetic variants, in particular, large-scale SNP’s, which may contribute to the beef cattle genomics in South Africa.

The results presented in this study, forms the basis for effective management of South African cattle breeds and provides a useful foundation for the detection of mutations underlying genetic variation in traits of economic importance in South African cattle breeds.

This study produced one PhD thesis, 12 peer reviewed scientific articles and one popular article.

Popular Article

Genomic technology for South African Beef Cattle

Makina¹, F.C. Muchadeyi², E. van-Marle Koster³, A. Maiwashe¹ and P. Soma¹
ARC-Animal Production Institute, Private Bag X2, Irene, South Africa; ²ARC-Biotechnology Platform, Onderstepoort, ³University of Pretoria, Department of Animal and Wildlife Sciences, Private Bag X20, Hatfield, Pretoria, South Africa.

Corresponding author, E-mail: Pranisha@arc.agric.za, Tel: +27 (0)12 672 9218

South African (SA) indigenous and locally developed cattle breeds possess adaptive traits that are usually associated with tolerance to various diseases, extreme temperatures and humidity and to change in the availability to feed. These breeds are also adapted to low-input management systems and have shown the ability to survive, produce and reproduce under harsh environments. Thus, these breeds hold potential in the changing South African production environments. Despite their large numbers and not endangered status, their adaptive traits are of importance and there is a worldwide drive for the effective management of indigenous genetic resources, as they could be most valuable in selection and breeding programs in times of biological stress such as famine, drought or disease epidemics.

The recent development in molecular genetics and bioinformatics has enabled the development of genome wide SNP DNA arrays for livestock species including cattle. These chips present opportunities to study South African cattle breeds in order to unravel population structure as well as the genetic potential of these breeds.

The Bovine SNP50 BeadChip was used to genetically characterize these breeds. The study populations comprised the Afrikaner, Nguni, Drakensberger and Bonsmara cattle breeds with the Angus and Holstein cattle as reference groups. Results of this study demonstrated that the genomic information generated from the BovineSNP50 has potential for application in South African cattle populations and allow for the unravelling of their genetic potential with regard to production, reproduction, disease resistance and adaptation.

There was a clear genetic divergence between South African (indigenous and locally-developed cattle breeds) and <em>Bos taurus</em> cattle breeds which suggested distinct genetic resources in South African cattle breeds that should be properly utilized in order to cope with unpredictable future environments. The level of inbreeding was relatively low across the study populations although the assessment of the inbreeding level should be done every five years to determine any unfavourable change in inbreeding levels, so that appropriate steps can be taken. The population structure analysis in the study revealed some signals of admixture and genetic relationship between Afrikaner, Nguni, Drakensberger and Bonsmara. Nguni cattle shared some genetic links with the Afrikaner cattle, with about 8% of its genome derived from the Afrikaner cattle.   This result may reflect co-ancestry regarding the origin of these breeds as both these came from the same migration route into Southern Africa (Scholtz, 2011).

On the other hand, the Bonsmara cattle shared limited genetic links (0.5%) with Afrikaner cattle, which was unexpected. This low relationship may be attributed to genetic drift or a small sample size. Information generated from this study forms the basis for future management of these cattle breeds. The effective population size appeared to have decreased in all the study breeds in recent generations. The lower effective population sizes for the Afrikaner, Angus and Holstein breeds compared to those of Nguni, Bonsmara and Drakensberger at more recent generations, could be due to intense selection, inbreeding and probably wide spread use of artificial insemination in South Africa and the use of relatively few elite sires after 1970 (Hayes et al., 1990). In order to maximise the net response in genetic gain, Food and Agricultural Organisation (FAO) (FAO 1998) recommended an effective population size of 50 per generation. The Afrikaner, Angus and Holstein were below the FAO recommended number.

This suggested that these breeds are endangered and close to critical stage therefore pointing out the need for implementation of appropriate conservation programs as well as new selection and breeding strategies to ensure long-term fitness of these breeds. These could include increasing the number of animals contributing offspring to each generation by increasing the cow populations. It is critical for food security and rural development because it allows farmers to select stock or develop new breeds in response to changing conditions, including climate change, new or resurgent disease threats, new knowledge of human nutritional requirements, and changing market conditions or societal needs (FAO, 2010).

A total of 47 genomic regions were identified including genes associated with immune response, reproductive performances, coat colour, tropical adaptation and nervous system were identified. For example, the keratin family and one heat shock protein in the Nguni cattle were associated with tropical adaptation. In addition to the role that the keratin genes play during epidermis development, they also play a role in the formation of the hair shaft (Wu et al., 2008). Skin colour and the thickness of hair directly influence the thermos-resistance of cattle living in the tropics. Nguni cattle have smoother and shinier hair coats compared to European cattle breeds. These characteristics provide Nguni cattle with a greater ability to regulate body temperature and to more efficiently maintain cellular function during heat as well as the ability to resist tick infestation (Marufu et al., 2009).

Several candidate genes directly or indirectly involved in reproductive pathways including oestrus process, ovulation rate, testis development and prostaglandin were found. The fact that the Afrikaner, Nguni, Drakensberger and Bonsmara cattle have the ability to produce and reproduce under harsh environment conditions and are considered excellent dam lines for crossbreeding (Scholtz, 2010), supports the strong selection on reproductive loci that is likely to have occurred in their adaptation to South African conditions. Genes involved in muscle organ development and skeleton development were also identified as being under selection in the Bonsmara and Afrikaner cattle populations. The results presented in the study forms the basis for effective management of South African cattle breeds. Furthermore, a genomic understanding of how and where natural selection has shaped the pattern of genetic variation among cattle breeds in SA was unveiled by identifying loci that are important to the development of SA cattle breeds.

Future studies should focus on expanding the breed level analysis through the inclusion of all major African cattle breeds (Gautier et al., 2009) together with cattle breeds of the world. This could further provide insight with regard to the genetic relationship shared among South African cattle breeds and cattle breeds of the world and shed more light on the genomic requirement for survival in African environments.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project – Pranisha Soma on pranisha@arc.agric.za

Genomics for the South African Beef industry

Establishing genomic selection for the South African beef industry

Industry Sector: Cattle and Small Stock

Focus Area: Livestock production with global competitiveness (2)

Research Institute: Department of Animal & Wildlife Sciences, University of Pretoria (UP) and SA Studbook Association

Researcher: Prof Este van Marle-Köster PhD

Research team:

Title Initials Surname Highest Qualification
Dr Japie van der Westhuizen PhD

Final report approved: 2016

Aims of the Project

  • To use high throughput SNP technology to establish reference populations for the SA beef industry.

Executive Summary

The project titled “Establishing genomic selection for the South African beef industry” was conducted at the University of Pretoria, Department of Animal & Wildlife Sciences in collaboration with South African Stud Book and Animal Improvement Association.

The overall aim was to use high throughput SNP technology to establish reference populations for the SA beef industry. To attain this goal, a process for the identification of high impact animals was established, guidelines for sample collection were compiled and genotyping were performed with the available funding using both 80K SNP and 150K SNP GeneSeek (GeneSeek GGP HD) bead chips at GeneSeek (USA. Population structure analyses were performed and parameters for genetic diversity and inbreeding were calculated.

Results has shown that breeds such as the Bonsmara and SA Hereford formed separate clusters while the other indigenous breeds such as the Tuli, Afrikaner and Drakensberger showed a closer relationship. These results are important for future across-breed analyses. The commercial chips also tended to be less informative for the indigenous breeds with regard to the number of SNPs available for analyses, but still had sufficient numbers for application in genomic selection. A substantial number of genotypes have been generated for Bonsmara cattle that have also been phenotypically recorded for the traits of interest.

In this project, the Bonsmara genotypes are currently being applied as a training set for estimation of Genomic Breeding Values (GEBV’s). After GEBVs have been estimated for these animals, validation will commence followed by the roll-out of routine GEBV estimation. This genomic information will provide breeders with an additional, accurate tool for selection of superior stock.

Popular Article

Genomic selection: a new tool for genetic improvement of SA Livestock

Este van Marle-Köster (PhD Pr.Anim. Sci.) & Carina Visser (PhD Pr.Anim. Sci)

Department of Animal & Wildlife Sciences, University of Pretoria

Introduction

Over the past two decades major discoveries and technological developments in the field of molecular genetics opened up new opportunities for genetic improvement in farm animals that were previously beyond the reach of animal breeders. The bovine genome has been mapped and sequenced and several DNA-marker types were discovered. The discovery of Single Nucleotide Polymorphism (SNP) markers and the concurrent development of appropriate high through-put technology gave rise to commercial SNP chips available for generating genomic information.

SNP markers and genomic selection

The DNA markers used for generating genomic information for genomic selection are single nucleotide polymorphisms (SNP) commonly referred to as “SNIPS”. Each of these markers has two alleles and they occur very frequently across the genome. The Bos Taurus genome has an estimated total of nearly 4 million SNPs. It is currently estimated that a trait of economic importance (i.e. weaning weight, carcass weight, feed conversion ratio etc.) is governed by between 100 and 300 genes. Each of these genes contributes only a small amount to the phenotype that is expressed. In the past, individual genes and markers associated with genes were identified and thus only a very small portion of the phenotypic variation could be explained. By using a very dense SNP panel to genotype animals with, it is assumed that each individual SNP will be associated with at least one gene contributing to the trait of interest. When all the SNP effects are added, they should thus explain the total phenotypic variance that is expressed.

If a number of animals of a specific breed have been genotyped, and their phenotypic records are available, it is possible to draw a correlation between a SNP combination and the level of performance associated with it. This correlation is commonly referred to as the “prediction equation” or “SNP key”. Genomic selection is therefore based upon the basic principle of using the information of many DNA markers and accurate and complete phenotypic records. In practical breeding, the genotypic information (Direct Genomic Value) will be included in the Estimated Breeding Value (EBV) of an animal. In this way, genomic data will be included as an additional source of information together with pedigree and performance records used in routine quantitative analyses. This process is referred to as blending and will result in providing a genomic estimated breeding value (GEBV) for each individual.

Prerequisites for implementation

The implementation of genomic selection is not a simple process and each phase requires careful planning to ensure that the end result will be accurate, useful and cost effective. The first step is the selection of the bulls to form the reference or training population. These bulls should represent the specific breed and include bulls with low, medium and high breeding values with accuracies above 60%. It is important that the traits recorded on these bulls will be the traits that breeders would like to include in selection programs and which form part of the breeding objectives for the breed. A biological sample of these animals should be available and this could be a hair, blood, semen or tissue sample for extraction of DNA.

Once the DNA is available the samples will be analyzed using an appropriate high density commercial chip. In this step the SNP effects based on high density chips and their correlation with the animals’ known performance (EBV values) are established, in order to calculate the prediction equation.

Application of SNP technology

There is no doubt that genomic selection has significant advantages for improvement of farm animal genetics. Dairy cattle thus far have led the way and have experienced some of the advantages of having an added source of information. GEBVs can be obtained in a relative short period after birth compared to a 6-7 year period of progeny testing before a progeny-based EBV becomes available. GEBVs have distinct advantages for dairy cattle with regard to reducing costs on progeny testing and decreasing the generation interval. Genomic technology has been well received by dairy cattle breeders in the USA and Canada and indications are that genomic evaluations will replace traditional evaluations in these countries. The use of genomic selection in selection programs holds the most potential for sex-limited traits, traits that are expressed late in life and traits with low heritability.

In South Africa we are fortunate to have a long history of animal recording for a large number of cattle breeds. This data has routinely been used for EBV calculations and are widely used by South African stud breeders. The current challenge is to ensure the banking of biological samples of animals with desired traits and phenotypes, in order to obtain both molecular and phenotypic records from individuals. Application of GS globally in both the dairy and beef industries has become inevitable and smaller countries with fewer resources, like South Africa, will have to collaborate and carefully plan genetic programs to remain part of the international arena.

If you have any queries, please contact the researcher Prof Este van Marle-Köster PhD on Este.vanMarle-Koster@up.ac.za

Agri Benchmark beef and sheep

Agri Benchmark beef and sheep local and international network

Industry Sector: Cattle and Small Stock

Research focus area: Livestock production with global competitiveness

Research Institute: University of the Free State, Department of Agricultural Economics, Thünen Institute of Farm Economics (Braunschweig, Germany)

Researcher: Mr H.N. van Niekerk MSc

Research Team:

Title Initials Surname Highest Qualification
Mr JF Henning Mcom
Mnr FA Maré MSc
Dr DB Strydom PhD

Final report approved: 2017

Aims of the project

  • To uphold and maintain the Agri Benchmark international and local networks. We established a fully functional local Agri Benchmark network for sheep and beef in South Africa. We are now able to simulate “what if” scenarios and compare beef and sheep farms locally. This is however an ongoing project and will continue to grow as time proceeds and industries develops.
  • To include small scale farmers in the local network and to develop the Agri Benchmark model to be more inclusive. Over time the inclusion of small-scale farmers became unsustainable. This is mainly due to government subsidies differ too much between parties assisting small-scale farmers. The inclusion of 2 beef and 2 sheep farms per year will help the network to grow stronger over time to be more inclusive and representative of South Africa

Executive Summary

The Agri Benchmark initiative refers to international comparisons and benchmarking of productivity, costs and income pertaining to livestock production, especially beef and sheep. This concept can also be applied to South African conditions by establishing a local network for local comparisons and to feed the international model with up to date data for international comparisons.

The local comparisons can also be used to benchmark local farms with each other. The farms are selected according to specific regions and then calculated as typical farms. The modelling and analyses have various features that can be used by beef and sheep producers. The results within the model can be used for further sensitivity analyses and “what if” scenarios as well as a database to answer specific questions.

The Agri Benchmark and UFS project comprises of beef and sheep networks where the aim was to establish a local beef and sheep network. This in turn could feed the international model for comparisons. In 2014, we started data collection for our local beef and sheep network. A total of 17 typical usable farms were included into the local network for comparisons for beef and sheep. In 2015, we had a number of strategic sessions with various roll player to discusses the way forward and how Agri Benchmark can contribute more within the agricultural sector.

It was concluded that there is a need for a local network for beef and sheep. In 2015, we will start with the local beef network to be reprehensive in South Africa. The local network plans to grow each year by 2 to 3 farms, so in due time a well-established network will be in place in South Africa. Benchmarking can play a tremendous role in developing farmers. Once the benchmarking process is started we can benchmark farmers on various productivity levels, this will show where the shortcomings or problems occur within the farming scenario. For example; If we are benchmarking two or more commercial and one of the farms returns in lower then, the other the investigation can start quick and easy. Is it in terms of cost structures, management etc., by this meaning; if there is a low lambing percentage what is wrong? It can be above normal losses; more must be spent on animal medicine or problems with heard composition.

The inclusion of the “what if” scenarios contributed tremendously to the livestock industry, these scenarios include increases in farm level labour costs, increases in production cost and risks in livestock farming (drought, predation, changes in input and output prices). Agri Benchmark helped the livestock industry in obtaining reliable and relevant information and answers to burning questions within the livestock sector. All repots can be seen in the appendixes of this report.

We would like to thank the RMRDT for helping us in achieving our goals and making a difference in South African agriculture. Agri benchmark still remains a project in the Unit of livestock Economics at the University of the Free State. We hope to submit future projects to the RMRDT as the networks grow over time.

To conclude, Agri benchmark is a divers project with a large variety of outcomes as illustrated in the appendixes. Agriculture in South Africa will in the future become more challenging and Agri benchmark can help illuminate champagnes by comparing farming bossiness and running “what if” scenarios to improve agriculture in South Africa.

Popular Article

Article to follow later

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project –
HN van Niekerk on vnierkhn@ufs.ac.za

Greenhouse gas emissions from livestock

Characterization of breed-specific additive and heterosis effects on beef sensory and leather quality traits

Industry Sector: Cattle and Small Stock

Research focus area: Sustainable natural resource utilization; Livestock production with global competitiveness

Research Institute: Tshwane University of Technology, University of Pretoria

Researcher: Mr CJL du Toit

Research Team:

Title Initials Surname Highest Qualification
Prof WA van Niekerk PhD
Dr HH Meissner PhD
Dr L Otter PhD

Final report approved: 2014

Aims of the project

  • To calculate on a regional basis the enteric methane emissions from all relevant livestock sectors
  • To calculate on a regional basis the methane emissions from livestock manure
  • To calculate on a regional basis the nitrous oxide emissions from livestock manure

Executive Summary

There are increasing concerns about the impact of agriculture and livestock production on the environment. The objective of the study was to estimate methane and nitrous oxide emissions of South African livestock industries during 2010 on a provincial and national basis. The study focused on direct methane (CH4) and nitrous oxide (N2O) emissions originating from enteric fermentation and livestock manure management systems. Both methane and nitrous oxide are potent greenhouse gasses with 25 and 310 times the global warming potential of carbon dioxide. The Intergovernmental Panel on Climate Change (IPCC) Tier 2 methodology adapted for tropical production systems was used to calculate emissions.

The Tier 2 methodology defines animals, animal productivity, diet quality and management circumstances to support a more accurate estimate of feed intake for use in estimating methane production. Livestock, including privately owned game, emitted and estimated 1330.6 Gg of CH4 and 3.28 Gg of N20 during 2010. In South Africa, the principle species comprise of cattle, game and sheep producing collectively an estimated 95% of the total livestock emissions. Commercial beef cattle were the largest contributors of methane followed by emerging and subsistence cattle, sheep, game, dairy cattle, goats and feedlot cattle with 527 Gg, 276 Gg, 167 Gg, 131 Gg, 130.5 Gg, 40.7 Gg and 30 Gg of methane respectively. The poultry industry emitted the highest amount of N2O producing an estimated 2.61 Gg followed by dairy cattle, horses and pigs with 0.54 Gg, 0.09 Gg and 0.04 Gg of N2O respectively. The Eastern Cape, Kwa-Zulu Natal and the Free State were the provinces with the highest GHG emission profiles, incorporating all species, producing 24.3%, 15.3% and 14.9% of the total national emissions.

Please contact the Primary Researcher if you need a copy of the comprehensive report of this project – Linde du Toit on linde.dutoit@up.ac.za