Céad Mile Fáilte go T-Stór (Welcome to T- Stór)

T-Stór is Teagasc’s Open Access Repository, maintained by the Teagasc Library Service. Stór is the Gaelic word for Repository or Store or Warehouse, and T-Stór is an online “store” of Teagasc Research outputs and related documents. T-Stór collects preserves and makes freely available scholarly communication, including peer-reviewed articles, working papers and conference papers created by Teagasc researchers. Where material has already been published it is made available subject to the open-access policies of the original publishers. About Teagasc

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Animal & Grassland Research & Innovation Programme [704]
Crops, Environment & Land Use Programme [363]
Food Programme [675]
Rural Economy & Development Programme [184]
Irish Journal of Agricultural & Food Research [247]
Other Teagasc Research [231]
  • Why Dairy Farming And Silvopastoral Agroforestry Could Be The Perfect Match

    Irish Agroforestry Forum; Short, Ian; Department of Agriculture, Food and the Marine (Irish Farm Business, 2020)
    Could we be missing a trick here? Could silvopasture be a design solution to the environmental challenges facing farming? Can it be the ideal mechanism to combine agriculture, forestry and ecology with very positive outcomes for farmers? Well -designed silvopasture can help increase profits and productivity, animal, and soil health, diversify the farm business, buffer against increasingly variable weather, drought and flood risks while benefiting the environment, the water cycle and the carbon cycle.
  • Breed- and trait-specific associations define the genetic architecture of calving performance traits in cattle

    Purfield, Deirdre C; Evans, Ross D; Berry, Donagh; European Union; Science Foundation Ireland; 727213; 14/IA/2576); 16/RC/3835 (Oxford University Press (OUP), 2020-05-04)
    Reducing the incidence of both the degree of assistance required at calving, as well as the extent of perinatal mortality (PM) has both economic and societal benefits. The existence of heritable genetic variability in both traits signifies the presence of underlying genomic variability. The objective of the present study was to locate regions of the genome, and by extension putative genes and mutations, that are likely to be underpinning the genetic variability in direct calving difficulty (DCD), maternal calving difficulty (MCD), and PM. Imputed whole-genome single-nucleotide polymorphism (SNP) data on up to 8,304 Angus (AA), 17,175 Charolais (CH), 16,794 Limousin (LM), and 18,474 Holstein-Friesian (HF) sires representing 5,866,712 calving events from descendants were used. Several putative quantitative trait loci (QTL) regions associated with calving performance both within and across dairy and beef breeds were identified, although the majority were both breed- and trait-specific. QTL surrounding and encompassing the myostatin (MSTN) gene were associated (P < 5 × 10−8) with DCD and PM in both the CH and LM populations. The well-known Q204X mutation was the fifth strongest association with DCD in the CH population and accounted for 5.09% of the genetic variance in DCD. In contrast, none of the 259 segregating variants in MSTN were associated (P > × 10−6) with DCD in the LM population but a genomic region 617 kb downstream of MSTN was associated (P < 5 × 10−8). The genetic architecture for DCD differed in the HF population relative to the CH and LM, where two QTL encompassing ZNF613 on Bos taurus autosome (BTA)18 and PLAG1 on BTA14 were identified in the former. Pleiotropic SNP associated with all three calving performance traits were also identified in the three beef breeds; 5 SNP were pleiotropic in AA, 116 in LM, and 882 in CH but no SNP was associated with more than one trait within the HF population. The majority of these pleiotropic SNP were on BTA2 surrounding MSTN and were associated with both DCD and PM. Multiple previously reported, but also novel QTL, associated with calving performance were detected in this large study. These also included QTL regions harboring SNP with the same direction of allele substitution effect for both DCD and MCD thus contributing to a more effective simultaneous selection for both traits.
  • On-farm net benefit of genotyping candidate female replacement cattle and sheep

    Newton, J.E.; Berry, Donagh; Science Foundation Ireland; Department of Agriculture, Food and the Marine; European Union; 16/RC/3835; 727213 (Elsevier BV, 2020-12-07)
    The net benefit from investing in any technology is a function of the cost of implementation and the expected return in revenue. The objective of the present study was to quantify, using deterministic equations, the net monetary benefit from investing in genotyping of commercial females. Three case studies were presented reflecting dairy cows, beef cows and ewes based on Irish population parameters; sensitivity analyses were also performed. Parameters considered in the sensitivity analyses included the accuracy of genomic evaluations, replacement rate, proportion of female selection candidates retained as replacements, the cost of genotyping, the sire parentage error rate and the age of the female when it first gave birth. Results were presented as an annualised monetary net benefit over the lifetime of an individual, after discounting for the timing of expressions. In the base scenarios, the net benefit was greatest for dairy, followed by beef and then sheep. The net benefit improved as the reliability of the genomic evaluations improved and, in fact, a negative net benefit of genotyping was less frequent when the reliability of the genomic evaluations was high. The impact of a 10% point increase in genomic reliability was, however, greatest in sheep, followed by beef and then dairy. The net benefit of genotyping female selection candidates reduced as replacement rate increased. As genotyping costs increased, the net benefit reduced irrespective of the percentage of selection candidates kept, the replacement rate or even the population considered. Nonetheless, the association between the genotyping cost and the net benefit of genotyping differed by the percentage of selection candidates kept. Across all replacement rates evaluated, retaining 25% of the selection candidates resulted in the greatest net benefit when genotyping cost was low but the lowest net benefit when genotyping cost was high. Genotyping breakeven cost was non-linearly associated with the percentage of selection candidates retained, reaching a maximum when 50% of selection candidates were retained, irrespective of replacement rate, genomic reliability or the population. The genotyping breakeven cost was also non-linearly associated with replacement rate. The approaches outlined within provide the back-end framework for a decision support tool to quantify the net benefit of genotyping, once parameterised by the relevant population metrics.
  • Improving robustness and accuracy of predicted daily methane emissions of dairy cows using milk mid‐infrared spectra

    Vanlierde, Amélie; Dehareng, Frédéric; Gengler, Nicolas; Froidmont, Eric; McParland, Sinead; Kreuzer, Michael; Bell, Matthew; Lund, Peter; Martin, Cécile; Kuhla, Björn; et al. (Wiley, 2020-11-22)
    BACKGROUND A robust proxy for estimating methane (CH4) emissions of individual dairy cows would be valuable especially for selective breeding. This study aimed to improve the robustness and accuracy of prediction models that estimate daily CH4 emissions from milk Fourier transform mid‐infrared (FT‐MIR) spectra by (i) increasing the reference dataset and (ii) adjusting for routinely recorded phenotypic information. Prediction equations for CH4 were developed using a combined dataset including daily CH4 measurements (n = 1089; g d−1) collected using the SF6 tracer technique (n = 513) and measurements using respiration chambers (RC, n = 576). Furthermore, in addition to the milk FT‐MIR spectra, the variables of milk yield (MY) on the test day, parity (P) and breed (B) of cows were included in the regression analysis as explanatory variables. RESULTS Models developed based on a combined RC and SF6 dataset predicted the expected pattern in CH4 values (in g d−1) during a lactation cycle, namely an increase during the first weeks after calving followed by a gradual decrease until the end of lactation. The model including MY, P and B information provided the best prediction results (cross‐validation statistics: R2 = 0.68 and standard error = 57 g CH4 d−1). CONCLUSIONS The models developed accounted for more of the observed variability in CH4 emissions than previously developed models and thus were considered more robust. This approach is suitable for large‐scale studies (e.g. animal genetic evaluation) where robustness is paramount for accurate predictions across a range of animal conditions. © 2020 Society of Chemical Industry
  • A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra

    Soyeurt, H.; Grelet, C.; McParland, Sinead; Calmels, M.; Coffey, M.; Tedde, A.; Delhez, P.; Dehareng, F.; Gengler, N.; European Union; et al. (American Dairy Science Association, 2020-10-22)
    Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.

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