Acta Fytotechnica et Zootechnica Online (Faculty of Agrobiology and Food Sciences, Slovak University of Agriculture in Nitra)
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Assimilation capacity by non-destructive in situ measurements in longterm experiment of maize (Zea mays L.) under different plant density and nitrigen supply in Chernozem
Get PDFArticle Details: Received: 2019-10-15 | Accepted: 2019-12-31 | Available online: 2020-03-31https://doi.org/10.15414/afz.2020.23.01.7-14We conducted a long term experiment (set up in 1983) to examine the effects of fertilization and plant density on SPAD and LAI values as well as yield in 2017 and 2018, in two maize hybrids (Sushi, Fornad) in chernozem soil. Hybrid yields varied between 10.2– 16.7 t ha-1 in 2017 and between 9.3–15.9 t ha-1 in 2018, depending on fertilization doses and plant density. The hybrids had SPADmax values in early July (55.5–60.4 in 2017 and 54.9–63.9 in 2018), whereas they got LAImax values in early July (3.0–5.3 m2 m-2 in 2017) and early August (3.5–4.4 m2 m-2 in 2018). Research data evaluated by Pearson correlation calculations proved that fertilization was the main factor that had a significant effect on SPAD and LAI values in the different maize phenophases (r = 0.6**–0.8** for SPAD, r = 0.5**–0.8** for LAI). Correlation among plant density, hybrid and SPAD and LAI values showed very weak correlations in both years (r = 0.1–0.3). The yield of the maize hybrids was most significantly affected by fertilizer in 2017 (r = 0.672**) and plant density in 2018 (r = 0.517**).Keywords: long term experiment, LAI, SPAD, yield, Pearson correlationReferencesAhmad, M. et al. (2010). Agro-physiological traits of three maize hybrids as influenced by varying plant density. The Journal of Animal and Plant Sciences, 20(1), 34–39Azeez, J.O. (2009). Effect of nitrogen application and weed interference on performance of some tropical maize genotypes in Nigeria. Pedosphere,19(5), 654–662. https://doi.org/10.1016/ S1002-0160(09)60160-0Bavec, F. and Bavec, M. (2002). Effects of plant population on leaf area index, cob characteristics and grain yield of early maturing maize cultivars (FAO 100–400). European Journal of Agronomy, 16(2), 151–159. https://doi.org/10.1016/S1161-0301(01)00126-5Bencze, G. and Futó, Z. (2017). Examination of the relationship between the relative chlorophyll content, leaf area index and yield of maize in a monoculture long term experiment. Recent social and economic processes, 12(3), 21–28.Berzsenyi, Z. et al. (2011). Long-term effect of crop production factors on the yield and yield stability of maize (Zea mays L.) in different years. Acta Agronomica Hungarica, 59(3), 191–200 (2011). https://doi.org/10.1556/AAgr.59.2011.3.1Berzsenyi, Z. (2010). Use of growth analysis to describe the N fertiliser responses of maize (Zea mays L.) hybrids. Acta agronomica hungarica: a quarterly of the hungarian academy of sciences: an international multidisciplinary journal in agricultural science, 58(1), 95–101.Carlone, M.R. and Russel, W.A. (1987). Response to plant densities and N levels for four maize cultivars from different ears of breeding. Crop Science, 27(3), 465–470. https://doi. org/10.2135/cropsci1987.0011183X002700030008xCarter, G.A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing, 15(3), 697–703. https://doi. org/10.1080/01431169408954109Dóka, L. F. (2015). The impact of different crop years on the water balance of the soil in mono- and biculture maize in different crop densities. Crop production, 64(2), 5–28.D’haene, K. et al. (2007). Nitrogen and phosphorus balances of Hungarian farms. European Journal of Agronomy, 26(3), 224–234. https://doi.org/10.1016/j.eja.2006.10.005Esechie, H. A. (1992). Effect of planting density on growth and yield of irrigated maize (Zea mays) in the Batinah Coast region of Oman. The Journal of Agricultural Science, 119(2), 165– 169. https://doi.org/10.1017/S0021859600014076Fulton, J.M. (1970) Relationship among soil moisture stress, plant populations, row spacing and yield of maize. Canadian Journal of Plant Science, 50(1), 31–38. https://doi. org/10.4141/cjps70-005Haegele, J.W. et al. (2014). Row arrangement, phosphorous fertility, and hybrid contributions to managing increased plant density of maize. Agronomy Journal, 106(5), 1838–1846. https://doi.org/10.2134/cs2014-47-6-11Hawkins, T.S. et al. (2009). Modeling the Relationship between Extractable Chlorophyll and SPAD-502 Readings for Endangered Plant Species Research. Journal for Nature Conservation, 17(2), 123–127. https://doi.org/10.1016/j.jnc.2008.12.007Körschens, M. (2006). The importance of long-term experiments for soil science and environmental research – a review. Plant Soil Environ., 52(special issue), 1–8.Ma, B.L. et al. (2005). Comparison of Crop-Based Indicators with Soil Nitrate Test for Maize Nitrogen Requirement. Agron. Journal, 97(2), 462–471. https://doi.org/10.2134/ agronj2005.0462Martinez, D.E. and Guiamet, J.J. (2004) Distortion of the SPAD 502 chlorophyll meter readings by changes in irradiance and leaf water status. Agronomie, 24(1), 41–46. https://doi. org/10.1051/agro:2003060Micskei, GY. et al. (2012). Relationships between maize yield and growth parameters in a long-term fertilization experiment. Acta Agronomica Hungarica, 60(3), 209–219. https://doi. org/10.1556/AAgr.60.2012.3.4Nagy, J. (2010). The present and the future of cultivation of maize. Crop Production, 59(3), 85–111.Novenyterm.59.2010.3.6 Novoa, R. and Loomis, R.S. (1981). Nitrogen and Plant Production. Plant and Soil, 58(1–3), 177–204. https://doi.org/10.1007/BF02180053Oikeh, S.O. et al. (1998). Nitrogen Fertilizer Management Effects on Maize Grain Quality in the West African Moist Savanna. Crop Science, 38(4), 1056–1061. https://doi.org/10.2135/ cropsci1998.0011183X003800040029xPepó, P. and Murányi, E. (2014) Plant density impact on grain yield of maize (Zea mays L.) hybrids on chernozem soil of the Eastern Hungary. Columella-Journal of Agricultural and Environmental Sciences, 1(2), 95–100. https://doi.org/10.18380/ SZIE.COLUM.2014.1.2.95Pepó, P. and Murányi, E. (2015). Examinations of cultivated area of different maize (Zea mays L.) hybrids. Crop production, 64(2), 1–17.Remison, S.U. and Lucas, E.O. (1982). Effects of planting density on Leaf Area and Productivity of Two Maize Cultivars in Nigeria. Experimental Agriculture, 18(1) 93–100. https://doi. org/10.1017/S0014479700013478Russel, W.A. (1991). Genetic improvement of maize yields. Adv. Agron., 46(1), 245–298. https://doi.org/10.1016/ S0065-2113(08)60582-9Sárvári, M. and Pepó, P. (2014). Effect of Production Factors on Maize Yield and Yield Stability. Cereal Research Communications, 42(4), pp. 710–720. https://doi.org/10.1556/ CRC.2014.0009Su, Y.J. et al. (2012). Effects of planting density on growth and yield of summer maize Xundan 28. Acta Agriculturae Jiangxi, 24(6), 49–50, 53.Széles, A. (2008). The effect of crop year and fertilization on the interaction between the spad value and yield of maize (Zea mays l.) within non-irrigated conditions. Cereal Research Communications, 36(Supll. 5), 1367–1370.Széles, A. et al. (2011) Effect of N fertilisation on the chlorophyll content and grain yield of maize in different crop years. Crop Production, 60(Suppl.), 161–164.Széll, E. et al. (2010). Results of maize fertilization experiments on 4 different soil type. Crop Production, 59(4), 41–61.Tajul, M.I. et al. (2013). Influence of plant population and nitrogen-fertilizer at various levels on growth and growth efficiency of maize. The Scientific World Journal, 2013(1), 1–9. https://doi.org/10.1155/2013/193018Valadabadi, S.A. and Farahani, H.A. (2010). Effects of planting density and pattern on physiological growth indices in maize (Zea mays L.) under nitrogenous fertilizer application. Journal of Agricultural Extension and Rural Development, 2(3), 40–47.Ványiné, Sz. A. et al. (2012). Irrigation and nitrogen effects on the leaf chlorophyll content and grain yield of maize in different crop years. Agricultural water management, 107(1), 133–144.Vári, E. a Pepó, P. (2011). Vplyv agrotechnických vlastností na agronomické vlastnosti v dlhodobom experimente. Crop Production, 60 (4), 115–130 https://doi.org/10.1556/ Novenyterm.60.2011.4.6Yu, H. a kol. (2010). Vyhodnotenie SPAD a Dualex na odhad stavu dusíkového kukurice v sezóne. Acta Agronomica Sinica, 36 (5), 840 - 847. https://doi.org/10.3724/ SP.J.1006.2010.00840
Genetic structure of breeds of goats bred in Slovakia
Get PDFArticle Details: Received: 2019-12-16 | Accepted: 2020-03-24 | Available online: 2020-06-30https://doi.org/10.15414/afz.2020.23.02.49-50The genetic structure of five goat breeds bred in Slovakia was characterized using a visible genetic profile and biochemical polymorphic systems. Tree dairy goat breeds – White Shorthaired, Brown Shorthaired and Alpine goats – and two wool goat breeds – Angora and Cashmere goats – were evaluated. Calculated were the heterozygosity and the effective number of alleles for each locus based on the allele frequencies of eight genes determining type traits and biochemical polymorphic systems, as well as their average values as indicators characterizing the genetic variability of each breed. The genetic differences between breeds were also determined.Keywords: Goats, White Shorthaired, Brown Shorthaired, Alpine, Angora Cashmere, genetics, SlovakiaReferencesORAVCOVÁ, M. (2013). Pedigree analysis in White Shorthaired goat: First results. Archiv Tierzucht, 56, 547–554
Fatty acid profile analysis of grape by-products from Slovakia and Austria
Get PDFArticle Details: Received: 2020-02-05 | Accepted: 2020-03-16 | Available online: 2020-06-30https://doi.org/10.15414/afz.2020.23.02.78-84The objective of the present study was to determine the fatty acid profile of grape pomace, grape stem and grape bunch of three different cultivars of Vitis vinifera sp. (Green Veltliner, Pinot Blanc and Zweigelt) from two countries as a possible sources for animal nutrition. Fatty acid profile analysis was performed using the Agilent 6890 A GC machine. Significant differences (P <0.05) in fatty acid content of analyzed samples were detected between the countries, as well as between the cultivars within countries. Grape pomaces and grape bunches were rich in polyunsaturated fatty acids (70.91–71.86%), represented mainly by linoleic acid (69.79–70.32%), and low in saturated fatty acids (12.42–12.96%). Grape stems were characterized by a high saturated fatty acids content (24.46–30.85%), but on the other hand, these samples had the highest α-linolec acid concentration (9.98–14.52%). Oleic acid (12.24–15.17%) was the most abundant from monounsaturated fatty acids (12.69–15.33%) in all the analyzed samples. These results indicate a strong impact of the grape variety and location on the fatty acid profile of grape by-products and their potential to be evaluated as feed additives with high polyunsaturated fatty acids concentration in animal nutrition.Keywords: grape pomace, grape stalk, fatty acids, PUFA, SFAReferencesBEKHIT, A. et al. (2015). Technological Aspects of By-Product Utilization. Valorization of Wine Making By-Products, 117–198. DOI: https://doi.org/10.1201/b19423-5BENNEMANN, G. D. et al. (2016). Mineral analysis, anthocyanins and phenolic compounds in wine residues flour. In BIO Web of Conferences, 7, p. 04007.BOTELLA, C. et al. (2005). Hydrolytic enzyme production by Aspergillus awamori on grape pomace. Biochemical Engineering Journal, 26(2–3), 100–106. DOI: https://doi.org/10.1016/j.bej.2005.04.020CHAMORRO, S. et al. (2015). Influence of dietary enzyme addition on polyphenol utilization and meat lipid oxidation of chicks fed grape pomace. Food Research International, 73, 197– 203. DOI: https://doi.org/10.1016/j.foodres.2014.11.054CHEDEA, V. et al. (2018). Intestinal Absorption and Antioxidant Activity of Grape Pomace Polyphenols. Nutrients, 10(5), 588. DOI: https://doi.org/10.3390/nu10050588DOMÍNGUEZ, J., MARTÍNEZ-CORDEIRO, H. and LORES, M. (2016). Earthworms and Grape Marc: Simultaneous Production of a High-Quality Biofertilizer and Bioactive-Rich Seeds. Grape and Wine Biotechnology. DOI: https://doi.org/10.5772/64751FERNANDES, L. et al. (2013). Seed oils of ten traditional Portuguese grape varieties with interesting chemical and antioxidant properties. Food Research International, 50(1), 161– 166. DOI: https://doi.org/10.1016/j.foodres.2012.09.039FONTANA, A. R., ANTONIOLLI, A. and BOTTINI, R. (2013). Grape Pomace as a Sustainable Source of Bioactive Compounds: Extraction, Characterization, and Biotechnological Applications of Phenolics. Journal of Agricultural and Food Chemistry, 61(38), 8987–9003. DOI: https://doi.org/10.1021/jf402586fGARCÍA-LOMILLO, J. and GONZÁLEZ-SANJOSÉ, M. L. (2017). Applications of Wine Pomace in the Food Industry: Approaches and Functions. Comprehensive Reviews in Food Science and Food Safety, 16(1), 3–22. DOI: https://doi.org/10.1111/1541-4337.12238GUERRA-RIVAS, C. et al. (2016). Effects of grape pomace in growing lamb diets compared with vitamin E and grape seed extract on meat shelf life. Meat science, 116, 221–229.GÜLCÜ, M. et al. (2019). The investigation of bioactive compounds of wine, grape juice and boiled grape juice wastes. Journal of Food Processing and Preservation, 43(1), e13850. DOI: https://doi.org/10.1111/jfpp.13850GÜL, H. et al. (2013). Antioxidant activity, total phenolics and some chemical properties of Öküzgözü and Narince grape pomace and grape seed flour. Journal of Food, Agriculture & Environment, 11(2), 28–34.HUSSEIN, S. and ABDRABBA, S. (2015). Physico-chemical characteristics, fatty acid, composition of grape seed oil and phenolic compounds of whole seeds, seeds and leaves of red grape in Libya. International Journal of Applied Science and Mathematics, 2(5), 2394–2894.KAFANTARIS, I. et al. (2018). Effects of Dietary Grape Pomace Supplementation on Performance, Carcass Traits and Meat Quality of Lambs. In Vivo, 32(4), 807–812. DOI: https://doi.org/10.21873/invivo.11311KERASIOTI, E. et al. (2017). Tissue specific effects of feeds supplemented with grape pomace or olive oil mill wastewater on detoxification enzymes in sheep. Toxicology Reports, 4, 364–372. DOI: https://doi.org/10.1016/j.toxrep.2017.06.007MAKRIS, D. P. et al. (2007). Characterisation of certain major polyphenolic antioxidants in grape (Vitis vinifera cv. Roditis) stems by liquid chromatography-mass spectrometry. European Food Research and Technology, 226(5), 1075–1079. DOI: https://doi.org/10.1007/s00217-007-0633-9MIRONEASA, S., Codină, G. G. and MIRONEASA, C. (2016). the effects of wheat flour substitution with grape seed flour on the rheological parameters of the dough assessed by mixolab. Journal of Texture Studies, 43(1), 40–48. DOI: https://doi.org/10.1111/j.1745-4603.2011.00315.xELEONORA, N. et al. (2014). Grape pomace in sheep and dairy cows feeding. Journal of Horticulture, Forestry and Biotechnology, 18(2), 146–150.OVCHAROVA, T., ZLATANOV, M. and DIMITROVA, R. (2016). Chemical composition of seeds of four Bulgarian grape varieties. Ciência e Técnica Vitivinícola, 31(1), 31–40. DOI: https://doi.org/10.1051/ctv/20163101031RIBEIRO, L. F. et al. (2015). Profile of bioactive compounds from grape pomace (Vitis vinifera and Vitis labrusca) by spectrophotometric, chromatographic and spectral analyses. Journal of Chromatography B, 1007, 72–80. DOI: https://doi. org/10.1016/j.jchromb.2015.11.005RONDEAU, P. et al. (2013). Compositions and chemical variability of grape pomaces from French vineyard. Industrial Crops and Products, 43, 251–254. DOI: https://doi.org/10.1016/j.indcrop.2012.06.053RUSSO, V. M. et al. (2017). In vitro evaluation of the methane mitigation potential of a range of grape marc products. Animal Production Science, 57(7), 1437. DOI: https://doi.org/10.1071/an16495SOUQUET, J.-M. et al. (2000). Phenolic Composition of Grape Stems. Journal of Agricultural and Food Chemistry, 48(4), 1076–1080. DOI: https://doi.org/10.1021/jf991171uTANGOLAR, S. G. et al. (2009). Evaluation of fatty acid profiles and mineral content of grape seed oil of some grape genotypes. International Journal of Food Sciences and Nutrition, 60(1), 32–39. DOI: https://doi.org/10.1080/09637480701581551TEIXEIRA, A. et al. (2014). Natural bioactive compounds from winery by-products as health promoters: a review. International journal of molecular sciences, 15(9), 15638–15678.TSIPLAKOU, E. and ZERVAS, G. (2008). The effect of dietary inclusion of olive tree leaves and grape marc on the content of conjugated linoleic acid and vaccenic acid in the milk of dairy sheep and goats. Journal of Dairy Research, 75(3), 270– 278. DOI: https://doi.org/10.1017/s0022029908003270VIVEROS, A. et al. (2011). Effects of dietary polyphenol-rich grape products on intestinal microflora and gut morphology in broiler chicks. Poultry Science, 90(3), 566–578. DOI: https://doi.org/10.3382/ps.2010-00889YI, C. et al. (2009). Fatty acid composition and phenolic antioxidants of winemaking pomace powder. Food Chemistry, 114(2), 570–576. DOI: https://doi.org/10.1016/j.foodchem.2008.09.103YOUSEFI, M. O. R. V. A. R. I. D., NATEGHI, L. E. I. L. A. and GHOLAMIAN, M. (2013). Physico-chemical properties of two types of shahrodi grape seed oil (Lal and Khalili). European Journal of Experimental Biology, 3(5), 115–118.YU, J. and AHMEDNA, M. (2012). Functional components of grape pomace: their composition, biological properties and potential applications. International Journal of Food Science & Technology, 48(2), 221–237. DOI: https://doi.org/10.1111/j.1365-2621.2012.03197.
Growth models and their application in precision feeding of monogastric farm animals
Get PDFSubmitted 2020-07-20 | Accepted 2020-08-31 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.258-264The dichotomy between developed and developing countries was observed not only in asymmetric human population growth, but also in increasing demand for animal products revolving around poultry and pigs in developing World. Modern livestock industry has adopted innovative technologies to improve the biological efficiency of animal production and feeding, and for this purpose different mathematical models have been applied. In this review, the authors summarize the growth models, briefly introduce the principles of precision feeding and provide evidence that models are key elements of these systems. Modelling is an excellent tool to help in understanding and to predict the animal’s response to different farm conditions. Models comprise of equations set describing nutrient flows and animal response. They are essential elements of precision livestock farming system being the basis of the decision support tool since, from yesterday’s data, they provide today what happens tomorrow. Precision farming adopts real-time monitoring systems collecting serial data about individual or group of animals. However, without a well-defined goal-oriented data process, data by itself are not useful to farmers. Available and newly recorded data can be converted to valuable information for management purposes through models applied in farming systems. Nutritional models are integrated part of those systems; therefore, growth modelling is a key tool to improve the efficiency and sustainability of livestock production systems.Keywords: precision feeding, mathematical models, pig, broiler, production efficiencyReferencesBaldwin, R. L. and Gill, M. (1987). Metabolism of the lactating cow: I. Animal elements of a mechanistic model. Journal of Dairy Research, 54, 77-105. https://doi.org/10.1017/S002202990002522XBanhazi, T. M. et al. (2012). Precision Livestock Farming: Precision feeding technologies and sustainable livestock production. International Journal of Agricultural and Biological Engineering, 5(4), 54-61. http://dx.doi.org/10.3965/j.ijabe.20120504.006Berckmans, D. (2015). Precision livestock farming technologies for welfare management in intensive livestock systems. Revue scientifique et technique (International Office of Epizootics), 33(1),189-196. Retrieved August 15, 2020 from https://tice.agrocampus-ouest.fr/pluginfile.php/55014/mod_resource/content/17/res/u324_Berckmans_RevSciTechOffIntEpiz.PDFBerger, Q. et al. (2019). Using high throughput phenotyping of growth and feed intake to improve adaptation of chickens to sustainable diets. 11th European Symposium on Poultry Genetics, 23–25 October 2019, Prague, Czech Republic. Retrieved August 15, 2020 from https://hal.inrae.fr/hal-02737886Black, J. L. (2014). Brief history and future of animal simulation models for science and application. Animal Production Science, 54, 1883-1895. http://dx.doi.org/10.1071/AN14650Black, J. L. et al. (1986). Simulation of energy and amino acid utilisation in the pig. Research and Development in Agriculture, 3, 121-145. Retrieved August 15, 2020 from https://www.researchgate.net/publication/273524971_Simulation_of_energy_and_amino_acid_utilisation_in_pigs.Coles, L. T. et al. (2013). A model to predict the ATP equivalents of macronutrients absorbed from food. Food & Function, 4, 432-442. https://doi.org/10.1039/C2FO30239Jde Lange, C. F. M. and Schreurs, H. (1995). Principles of model applications. In P. J. Moughan, M. W. A. Verstegen and M. I. Visser-Reyneveld (Eds.), Modelling growth in the pig. Wageningen Pers, Wageningen, (pp. 187-208).de Lange, C. F. M. et al. (2001). Application of pig growth models in commercial pork production. Canadian Journal of Animal Science, 81, 1-8. https://doi.org/10.4141/A00-006Dukhta, G. et al. (2019). Use of a dynamic mechanistic broiler model to reduce environmental footprint. 26th International Conference KRMIVA 2019, 5-7 June 2019, Opatija, Croatia. Retrieved August 15, 2020 from https://www.feed-a-gene.eu/sites/default/files/documents/dukhta_2019_KRMIVA_broiler_model_powerpoint.pdfDumas, A. et al. (2008). Mathematical modelling in animal nutrition: a centenary review. The Journal of Agricultural Science, 146(2), 123-142. https://doi.org/10.1017/S0021859608007703Dusart, L. and Meda, B. (2017). Responsable alimentation des volailles et durabilité des systemes. In L’Alimentation de Precision en Poulet de Chair. 12èmes JRA-JRFG, France, Tours, April 2017.Emmans, G. C. (1981). A model of the growth and feed intake of ad libitum fed animals, particularly poultry. In: Hillyer, G.M. et al. (Eds) Computers in Animal Production. Thames Ditton, UK: Occasional Publication No 5., British Society of Animal Production, pp. 103–110. https://doi.org/10.1017/S0263967X00003761Emmans, G. C. and Fisher, C. (1986). Problems in nutritional theory. In Fisher, C. and Boorman, K.N. (Eds.) Nutrient Requirements of Poultry and Nutritional Research. Oxford, UK: Butterworths, 9-39.Emmans, G. C. (1989). The growth of turkeys. Recent advances in turkey science.France, J. et al. (1987). A model of nutrient utilization and body composition in beef cattle. Animal Production, 44, 371-385. https://doi.org/10.1017/S0003356100012307France, J. and Thornley, J. H. (2007). Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences. Wallingford, UK: CABI.Gerrits, W. J. J. et al. (1997). Description of a model integrating protein and energy metabolism in preruminant calves. Journal of Nutrition, 127, 1229-1242. https://doi.org/10.1093/jn/127.6.1229Gill, M. et al. (1984). Simulation of the metabolism of absorbed energy-yielding nutrients in young sheep. British Journal of Nutrition, 52, 621-649. https://doi.org/10.1093/jn/117.1.105Halas, V. et al. (2004). Modelling of nutrient partitioning in growing pigs to predict their anatomical body composition. 1. Model description. British Journal of Nutrition, 92: 707-723. http://dx.doi.org/10.1079/BJN20041237Halas, V. et al. (2017). Preliminary model to predict P-requirement of growing pigs. In EAAP, scientific committee (Ed) Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science, Wageningen, the Netherlands: Wageningen Academic Publishers, (pp. 320-320). https://doi.org/10.3920/978-90-8686-859-9Halas, V. et al. (2018). Models of feed utilization and growth for monogastric animals. In: P. J. Moughan et al. (Eds.) Feed Evaluation Science, Wageningen, the Netherlands: Wageningen Academic Publishers, (pp. 423-456).Hauschild, L. et al. (2010). Systematic comparison of the empirical and factorial methods used to estimate the nutrient requirements of growing pigs. Animal, 4(5), 714–723. https://doi.org/10.1017/S1751731109991546Johnston, S. A. and Gous, R. (2006). Modelling egg production in laying hens. In Johnston S.A. et al. (Eds.) Mechanistic Modelling in Pig and Poultry Production, Wallingford, UK: CAB International, (pp. 229-259).Kebreab, E. et al. (2009). Development and evaluation of a dynamic model of calcium and phosphorus flows in layers. Poultry Science, 88(3), 680-689. https://doi.org/10.3382/ps.2008-00157NRC. (2012). Nutrient Requirements of Swine. Eleventh Revised Edition. National Academic Press, Washington, D.C. 20418 USA.Meda, B. et al. (2015). INAVI: a practical tool to study the influence of nutritional and environmental factors on broiler performance. In Sakomura, N. K. et al. (Eds.), Nutritional Modelling for Pigs and Poultry, Raleigh, NC, USA: CAB International, (pp. 106-124). https://doi.org/10.1079/9781780644110.0106Morris, T. R. (2006). An introduction to modelling in the animal sciences. Mechanistic Modelling in Pig and Poultry Production, 1-5.Moughan, P. J. et al. (1987). Description and validation of a model simulating growth in the pig (20-90 kg liveweight). New Zealand Journal of Agricultural Research, 30, 481-489. https://doi.org/10.1080/00288233.1987.10417960Lizardo, R. et al. (2002). A nutritional model of fatty acid composition in the growing-finishing pig. Livestock Production Science, 75, 167-182. https://doi.org/10.1016/S0301-6226(01)00312-8OECD-FAO. (2017). "Meat", in OECD-FAO Agricultural Outlook 2017-2026, OECD Publishing, Paris, https://doi.org/10.1787/agr_outlook-2017-10-en.Oviedo-Rondón, E. O. (2015). Model Applications in Poultry Production and Nutrition. In Sakomura, N. K. et al. (Eds.), Nutritional Modelling for Pigs and Poultry, Raleigh, NC 27695, USA, CAB International, (pp. 125-140). https://doi.org/10.1079/9781780644110.0125Pomar, C. et al. (2009). Applying precision feeding techniques in growing-finishing pig operations. Revista Brasileira de Zootecnia, 38, 226-237. https://doi.org/10.1590/S1516-35982009001300023Pomar, C. et al. (2011). Precision feeding can significantly reduce feeding cost and nutrient excretion in growing animals. In D. Sauvant, J. Van Milgen, P. Faverdin and N. Friggens (Eds.), Modelling Nutrient Digestion and Utilisation in Farm Animals. Wageningen Academic Publishers, Wageningen, the Netherlands.Pomar, C. and Remus, A. (2019). Precision pig feeding: a breakthrough toward sustainability. Animal Frontiers, 9(2), 52-59. https://doi.org/10.1093/af/vfz006Pomar, C. et al. (2019). 18: Precision livestock feeding, principle and practice. In W. H. Hendriks, M. W. A. Verstegen and L. Babinszky (Eds.), Poultry and pig nutrition: Challenges of the 21st century. Wageningen Academic Publishers, (pp. 89-95). https://doi.org/10.3920/978-90-8686-884-1_18Sauvant, D. (1994). Modelling homeostatic and homeorhetic regulations in lactating animals. Livestock Production Science, 39(1), 105-113. https://doi.org/10.1016/0301-6226(94)90162-7Sifri, M. 1997. Precision Nutrition for Poultry. The Journal of Applied Poultry Research, 6(4), 461. https://doi.org/10.1093/japr/6.4.461Schulz, A. R. (1978). Simulation of energy metabolism in the simple-stomached animal. British Journal of Nutrition, 39, 235-254. https://doi.org/10.1079/BJN19780034van Milgen, J. (2002). Modeling biochemical aspects of energy metabolism in mammals. Journal of Nutrition, 132, 3195-3202. https://doi.org/10.1093/jn/131.10.3195van Milgen, J. et al. (2008). InraPorc: A model and decision support tool for the nutrition of growing pigs. Animal Feed Science and Technology, 143, 387-405. https://doi.org/10.1016/j.anifeedsci.2007.05.020Whittemore, C. T. (1987). Simulation modelling: the prediction of growth response to nutrient supply. In: C. T. Whittemore (Ed.), Elements of Pig Science. Harlow, England, UK: Longmann Scientific & Technical, pp. 140-175.Whittemore, C. T. and Fawcett, R. H. (1974). Model responses of the growing pig to the dietary intake of energy and protein. Animal Production, 19, 221-231. https://doi.org/10.1017/S0003356100022789Whittemore, C. T. and Fawcett, R. H. (1976). Theoretical aspects of a flexible model to simulate protein and lipid growth in pigs. Animal Production, 22, 87-96. https://doi.org/10.1017/S0003356100035455Zuidhof, M. J. et al. (2018). Precision feeding of broiler breeders. Zootechnika International. Retrieved July 15, 2020 from https://zootecnicainternational.com/featured/precision-feeding-broiler-breeders/Zuidhof, M. J. (2020). Precision livestock feeding: matching nutrient supply with nutrient requirements of individual animals. Journal of Applied Poultry Research, 29(1), 11-14. https://doi.org/10.1016/j.japr.2019.12.009
MicroRNA-based markers as a tool to monitor the barley (Hordeum vulgare L.) response to soil compaction
Get PDFArticle Details: Received: 2020-03-26 | Accepted: 2020-05-19 | Available online: 2020-09-30 https://doi.org/10.15414/afz.2020.23.03.139-146Plants are often exposed to adverse environmental conditions that can significantly interfere with their genomic response. Soil compaction induced by heavy field machinery represents a major problem for crop production mainly due to restricted root growth and penetration into soil and therefore reduced water and nutrient uptake by the plants. Tested hypotheses were to declare whether the plant‘s genome responds to soil compaction and whether the microRNA-based markers are suitable to determine this response. A long term field scale experiment was established in 2009 where different levels of soil compaction are researched from the soil and crop point of view. The analyzed barley (Hordeum vulgare L.) plants were collected during the growing season in 2019. The effect of soil compaction was analysed by four different DNA-based markers corresponding to miRNA sequences of dehydratation stress-responsive barley miRNAs (hvu-miR156, and hvu-miR408) and nutrition-sensitive markers (hvu-miR399 and hvu-miR827), within the leaf, stem and root tissues of barley plants. Our preliminary data support hypotheses that plant genome response was tissue-specific due to significant induction of the biomarkers to dehydratation and nutrition stress. The most affected part of the plant by dehydration, were roots and lack of nutrient supply was most pronounced on leaves.Keywords: abiotic stress, crop yield, miRNAs, root growthReferencesANTILLE, D.L. et al. (2019). Review: Soil compaction and controlled traffic farming in arable and grass cropping systems. Agronomy Research, 17(3), 653–682. https://doi.org/10.15159/AR.19.133ARVIDSSON J. (1999). Nutrient uptake and growth of barley as affected by soil compaction. Plant and Soil, 208, 9–19.AXTELL, M.J. et al. (2011). Vive la difference: biogenesis and evolution of microRNAs in plants and animals. Genome Biology, 12(4), pp. 221. https://doi.org/10.1186/gb-2011-12-4-221BARTEL, B. and CITOVSKY, V. (2012). Focus on ubiquitin in plant biology. Plant Physiology, 160. https://doi.org/10.1104/pp.112.203208BARTEL, D.P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116 (2), 281–297. https://doi.org/10.1016/S0092-8674(04)00045-5BARVKAR, V.T. et al. (2013). Genome-wide identification and characterization of microRNA genes and their targets in flax (Linum usitatissimum): Characterization of flax miRNA genes. Planta, 237(4), 1149–1161. https://doi.org/10.1007/s00425-012-1833-5BATEY, T. (2009). Soil compaction and soil management – a review. Soil Use and Management, 25, 335–345. https://doi.org/10.1111/j.1475-2743.2009.00236.xBEJ, S. and BASAK, J. (2014). MicroRNAs: The potential biomarkers in plant stress response. American Journal of Plant Sciences, 5(5), 748–759. https://doi.org/10.4236/ajps.2014.55089BENGOUGH, A.G. et al. (2011). Root elongation, water stress, and mechanical impedance: a review of limiting stresses and beneficial root tip traits. Journal of Experimental Botany, 62(1), 59–68. https://doi.org/10.1093/jxb/erq350BERISSO, F.E. et al. (2012). Persistent effects of subsoil compaction on pore size distribution and gas transport in a loamy soil. Soil and Tillage Research,122, 42–51.BIELEK P. (2003). Soils in Slovakia: Status quo, trends, and developments. Journal of Soils and Sediments, 3, 254–254.BIELEK, P. et al. (2005). Soil survey and managing of soil data in Slovakia. In Jones, R.J. et al. (eds.) Soil Resources of Europe. (2. ed.). Ispra, Italy: European Soil Bureau (pp. 317–329).CANARACHE, A. et al. (1974). Effect of induced compaction by wheel traffic on soil physical properties and yield of maize in Romania. Soil and Tillage Research, 4(2), 199–213. https://doi.org/10.1016/0167-1987(84)90048-5CHAMEN, T. (2015). Controlled traffic farming: From worldwide research to adoption in Europe and its future prospects. Acta Technologica Agriculturae, 18(3), 64–73. https://doi.org/10.1515/ata-2015-0014CHAMEN, W.C.T. and LONGSTAFF, D.J. (1995). Traffic and tillage effects on soil conditions and crop growth on a swelling clay soil. Soil Use and Management, 11(4), 168–176. https://doi.org/10.1111/j.1475-2743.1995.tb00951.xCHANCELLOR, W. J. and SCHMIDT, R. H. (1962). Soil deformation beneath surface loads. Transactions of ASABE 5, 240–246.COLOMBI, T. and KELLER, T. (2019). Developing strategies to recover crop productivity after soil compaction-A plant ecophysiological perspective. Soil and Tillage Research, 191, 156– 161. https://doi.org/10.1016/j.still.2019.04.008CORREA, J. et al. (2019). Soil compaction and the architectural plasticity of root systems. Journal of Experimental Botany, 70(21), 6019–6034. https://doi.org/10.1093/jxb/erz383COLOMBI, T. and WALTER, A. (2017). Genetic Diversity under Soil Compaction in Wheat: Root Numbers as a Promising Trait for Early Plant Vigor. Frontiers in Plant Science, 8(420). https://doi.org/10.3389/fpls.2017.00420CUPERUS, J.T. et al. (2011). Evolution and functional diversification of MIRNA genes. Plant Cell, 23 (2), 431–442. https://doi.org/10.1105/tpc.110.082784DING, Y. et al. (2013). Emerging roles of microRNAs in the mediation of drought stress response in plants. Journal of Experimental Botany, 64(11), 3077–3086. https://doi.org/10.1093/jxb/ert164EC. (2006). Proposal for a Directive of the European Parliament and of the Council establishing a framework for the protection of soil and amending Directive 2004/35/EC. 232 Final. 2006. Brussels: European Commission.ESDAC (2020). Soil Compaction. Retrieved December 14, 2019 form https://esdac.jrc.ec.europa.eu/themes/soil-compactionFU, D. et al. (2013). MicroRNA-based molecular markers: a novel PCR-based genotyping technique in Brassica species. Plant Breeding, 132 (4), 375–381. https://doi.org/10.1111/pbr.12069FULAJTAR, E. (2000). Assessment and determination of the compacted soils in Slovakia. Advances of Geoecology, 32, 384–387GALAMBOŠOVÁ, J. et al. (2017). Field evaluation of controlled traffic farming in Central Europe using commercially available machinery. Transaction of ASABE, 60 (3), 657–669. https://doi.org/10.13031/trans.11833GANIE, S.A. and MONDAL, T.K. (2015). Genome-wide development of novel miRNA-based microsatellite markers of rice (Oryza sativa) for genotyping applications. Molecular Breeding, 35. https://doi.org/10.1007/s11032-015-0207-7HAJYZADEH, M. et al. (2015). miR408 overexpression causes increased drought tolerance in chickpea. Gene, 555, 186–193. https://doi.org/10.1016/j.gene.2014.11.002HLAVAČKOVÁ, L. and RAŽNÁ, K. (2015). Polymorphism of specific miRNAs in the context of flax (Linum usitatissimum L.) genome adaptability to abiotic stress. MendelNet 2015. Brno: Mendel University. 615 pp. IHOUŠKOVÁ, B. (2002). Assessment of the state of soil compaction in Slovakia. Adv Geoecol, 35, 379–385.HOUŠKOVÁ, B. and MONTANARELLA, L. (2008). The natural susceptibility of European soils to compaction. In Tóth, G. et al. (eds.) Threats to Soil Quality in Europe. Luxembourg: Office for Official Publications of the European Communities.HSIEH, L.C. et al. (2009). Uncovering small RNA-mediated responses to phosphate deficiency in Arabidopsis by deep sequencing. Plant Physiology, 151, 2120–2132. https://doi.org/10.1104/pp.109.147280JONES-RHOADES, M.W. et al. (2006). MicroRNAs and their regulatory roles in plants. Annual Reviews in Plant Biology, 57, 19–53. https://doi.org/10.1146/annurev.arplant.57.032905.105218KANT, S. et al. (2011). Genetic regulation by NLA and microRNA827 for maintaining nitrate-dependent phosphate homeostasis in Arabidopsis. PLoS Genetics, 7, e1002021. https://doi.org/10.1371/journal.pgen.1002021KANTAR, M. et al. (2010). Regulation of barley miRNAs upon dehydration stress correlated with target gene expression. Functional & Integrative Genomics, 493–507. https://doi.org/10.1007/s10142-010-0181-4KEHR, L. (2013). Systemic regulation of mineral homeostasis by micro RNA. Frontiers in Plant Sciences, 4. https://doi.org/10.3389/fpls.2013.00145KELLER, T. et al. (2019). Historical increase in agricultural machinery weights enhanced soil stress levels and adversely affected soil functioning. Soil and Tillage Research, 194. https://doi.org/10.1016/j.still.2019.104293KHANUJA, S.P.S. et al. (1999). Rapid isolation of DNA from dry and fresh samples of plants producing large amounts of secondary metabolites and essential oils. Plant Molecular Biology Reporter, 17, 74–84.KOBZA, J. (2018). Actual status of agricultural soils in Slovakia as the basic atribute of their fertility. In Slovak.KROULÍK, M. et al. (2011). Procedures of soil farming allowing reduction of compaction. Precision Agriculture, 12, 317–333. https://doi.org/10.1007/s11119-010-9206-1KRUSZKA, K. et al. (2014). Transcriptionally and posttranscriptionally regulated microRNAs in heat stress response in barley. Journal of Experimental Botany, 65(20), 6123–6135. https://doi.org/10.1093/jxb/eru353KRUSZKA, K. et al. (2012). Role of microRNAs and other sRNAs of plants in their changing environments. Journal of Plant Physiology, 169, 1664–1672. https://doi.org/10.1016/j.jplph.2012.03.009LHOTSKÝ, J. et al. (1991). Degradation of soil by increasing compression. Soil and Tillage Research, 19(2-3), 287–295. https://doi.org/10.1016/0167-1987(91)90096-GLIANG, G. et al. (2015). Uncovering miRNAs involved in crosstalk between nutrient deficiencies in Arabidopsis. Scientific Reports, 5. https://doi.org/10.1038/srep11813LIN, S.I. et al. (2008). Regulatory Network of MicroRNA399 and PHO2 by Systemic Signaling. Plant Physiology, 147, 732–746. https://doi.org/10.1104/pp.108.116269LIPIEC, J. and HATANO, R. (2003). Quantification of compaction effects on soil physical properties and crop growth. Geoderma, 116(1-2), 107–136. https://doi.org/10.1016/S0016-7061(03)00097-1LIU, H.H. et al. (2008). Microarray-based analysis of stressregulated microRNAs in Arabidopsis thaliana. RNA, 14(5), 836–843. https://doi.org/10.1261/rna.895308MA, Ch. et al. (2015). miR408 is involved in abiotic stress responses in Arabidopsis. Plant Journal, 84(1), 169–187. https:// doi.org/10.1111/tpj.12999MACÁK, M. et al. (2018). Comparison of two sowing systems for CTF using commercially available machinery. Agronomy Research, 16(2), 523–533. https://doi.org/10.15159/AR.18.060MELNIKOVA, N.V. et al. (2016). Identification, expression analysis, and target prediction of flax genotroph microRNAs under normal and nutrient stress conditions. Frontiers in Plant Sciences, 7(399). https://doi.org/10.3389/fpls.2016.00399MELNIKOVA, N.V. et al. (2015). Excess fertilizer responsive miRNAs revealed in Linum usitatissimum L. Biochemie, 109, 36– 41. https://doi.org/10.1016/j.biochi.2014.11.017MONDAL, T.K. and GANIE, S.A. (2014). Identification and characterization of salt responsive miRNA-SSR. Gene, 535(2), 204–209. https.//doi.org/10.1016/j.gene.2013.11.033PANT, B.D. et al. (2008). MicroRNA 399 is a longdistance Signal for the Regulation of plant phosphate homeostasis. Plant Journal, 53(5), 731–738, https://doi.org/10.1111/j.1365-313X.2007.03363.xRADFORD, B.J. et al. (2007). Changes in the properties of a Vertisol and responses of wheat after compaction with harvester traffic. Soil and Tillage Research, 54(3-4), 155–170. https://doi.org/10.1016/S0167-1987(00)00091-XRADFORD, B.J. et al. (2001). Crop responses to applied soil compaction and to compaction repair treatments. Soil and Tillage Research, 61(3-4), 157–166. https://doi.org/10.1016/S0167-1987(01)00194-5RAJWANSHI, R. et al. (2014). Orthologous plant microRNAs: microregulators with great potential for improving stress tolerance in plants. Theoretical and Applied Genetics, 127(12), 2525–2543. https://doi.org/10.1007/s00122-014-2391-yRAŽNÁ, K. and HLAVAČKOVÁ, L. (2017). The applicability of genetic markers based on molecules microRNA in agriculturel research. Open Access Journal of Agricultural Research, 2, 000125. https://doi.org/10.23880/OAJAR-16000125REIS, R.S. et al. (2015). Missing Pieces in the Puzzle of Plant MicroRNAs. Trends in Plant Science, 20(11), 721–728. https://doi.org/10.1016/j.tplants.2015.08.003RIVERA, M. et al. (2019). Soil compaction induced changes in morpho-physiological characteristics of common bean. Journal of Soil Science and Plant Nutrition, 19, 217–227. https://doi.org/10.1007/s42729-019-0007-ySCHÄFER-LANDEFELD, L. et al. (2004). Effects of agricultural machinery with high axle load on soil properties of normally managed fields. Soil and Tillage Research, 75(1), 75–86. https://doi.org/10.1016/S0167-1987(03)00154-5SCHREIBER, A.W. et al. (2011). Discovery of barley miRNAs through deep sequencing of short reads. BMC Genomics, 12, 129. http://www.biomedcentral.com/1471-2164/12/129SINGH, N. et al. (2020). Stress physiology and metabolism in hybrid rice. III. Puddling and soil compaction regulate water status and internal metabolism during drought. Journal Of Crop Improvement. https://doi.org/10.1080/15427528.2020.1723766SOUMITRA, P. et al. (2015). miRNA regulation of nutrient homeostasis in plants. Frontiers in Plant Science, 6, 232. https://doi.org/10.3389/fpls.2015.00232SUN, X. et al. (2019). Regulation mechanism of microRNA in plant response to abiotic stress and breeding. Molecular Biology Reports, 46, 1447–1457. https://doi.org/10.1007/s11033-018-4511-2TRINDADE, I. et al. (2010). miR398 and miR408 are up-regulated in response to water deficit in Medicago truncatula Planta, 231, 705–716. https://doi.org/10.1007/s00425-009-1078-0UNGER, P. W. and KASPAR, T. C. (1994). Soil compaction and root growth: A review. Agronomy Journal, 86, 759–766. https://doi.org/10.2134/agronj1994.00021962008600050004xVAN den AKKER, J.J.H. and CANARACHE, A. (2001). Two European concerted actions on subsoil compaction. Landnutzung und Landentwicklung, 42(1), 15–22.VERO, S.E. et al. (2014). Field evaluation of soil moisture deficit thresholds for limits to trafficability with slurry spreading equipment on grassland. Soil Use and Management, 30(1), 69– 77. https://doi.org/10.1111/sum.12093WU, X. et al. (2014). In silico identification and characterization of conserved plant microRNAs in barley. Central European Journal of Biology, 9, 841–852. https://doi.org/10.2478/s11535-014-0308-zYADAV, C.B.Y. et al. (2014). Development of novel microRNAbased genetic markers in foxtail millet for genotyping applications in related grass species. Molecular Breeding, 34, 2219–2224. https://doi.org/10.1007/s11032-014-0137-9ZHANG, B. (2015). MicroRNA: a new target for improving plant tolerance to abiotic stress. Journal of Experimental Botany, 66, 1749–1761. https://doi.org/10.1093/jxb/erv013ZHOU, L. et al. (2010). Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. Journal of Experimental Botany, 61, 4157–4168. https://doi.org/10.1093/jxb/erq23
Laying hen biodiversity: a study on the effect of age on the yield performance and quality of eggs produced by two Italian purebred hens
No full textSubmitted 2020-07-24 | Accepted 2020-08-10 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.296-304Yield performance and external and internal quality traits of eggs laid by two Italian purebreds, Ermellinata di Rovigo (ER) and Pepoi (PP) were studied at 36 and 50 weeks of age. Daily egg production and egg traits were evaluated by a factorial model (2 x 2) with genotype and age as main effects and their interaction. The daily laying rate was similar between the breeds and the hen-day egg mass was higher (p<0.01) for ER than for PP. The ER eggs differed (p<0.01) from PP eggs with higher egg weight, yolk and albumen weight, and a more coloured eggshell with lower L, higher a* and b*, and lower eggshell thickness. PP showed more (p<0.05) inclusion of albumen than yolk in comparison to ER. With increased hen age, egg weight, eggshell lenght, width, lightness and thickness increased (p<0.01), whereas shape index and a* and b* did not change. Throughout the laying period, the eggs were classified as small (PP) and medium size (ER). With increased hen age, PP eggs were more spherical, ER eggs were more ovoid; yolk and albumen weight increased (p<0.01) in ER, whereas yolk and eggshell weight increased (p<0.01) in PP. The yolk:albumen ratio increased (p<0.01) along with hen‘s age and the Haugh Units and egg inclusions decreased (p<0.01). The ER and PP eggs differed for many egg external traits, which allow to distinguish the eggs by weight, shape, and eggshell colour and for the eggshell thickness and the albumen traits.Keywords: laying hen, local breed, eggshell traitReferencesAltuntaș, E. & Șekeroğlu, A. (2008). Effect of egg shape index on mechanical properties of chicken eggs. Journal of Food Engineering, 85, 606-612.Casiraghi, E. et al. (2005). Influence of weight grade on shell characteristics of marketed hen eggs. In: Proc. European Symposium on the quality of eggs and egg products, WPSA, Doorwerth, The Netherlands, pp. 183-188.Cassandro, M. et al. (2014). Conservazione e caratterizzazione delle razze avicole venete - Programma Bionet. Rete regionale per la conservazione e caratterizzazione della biodiversità di interesse agrario. Gruppo di lavoro Avicoli. Veneto Agricoltura. Legnaro (PD).Commission Regulation (EC) (2008). Commission Regulation No 589/2008 of 23 June 2008 laying down detailed rules for implementing Council Regulation (EC) No 1234/2007 as regards marketing standards for eggs. Official Journal of the European Union of 24.6.2008, L 163.Di Rosa, A. et al. (2020). Egg quality from Siciliana and Livorno Italian autochthnous chicken breeds reared in organic system. Animals, 10, 864.European Commission (2020). Dashboard Eggs. Methods of production communicated according to Commission implementing. Retrieved July 20, 2020 from http://ec.europa.eu/info/sites/info/files/food-farming-fisheries/Giersberg, M.F. et al. (2020). The dual-purpose hen as a chance: avoiding injurious pecking in modern laying hen husbandry. Animals, 10, 16.González Ariza, A. et al. (2019a). Sensory preference and professional profile affinity definition of endangered native breed eggs compared to commercial laying lineages’eggs. Animals, 9, 920.González Ariza, A. et al. (2019b). Non-parametrical canonical analysis of quality-related characteristics of eggs of different varieties of native hens compared to laying lineage. Animals, 9, 153.Hadinia, S. H. et al. (2020). Post-photostimulation energy intake accelerated pubertal development in broiler breeder pullets. Poultry Science, 99, 2215-2229.Hidalgo, A. et. al. (2008). A market study on the quality characteristics of eggs from different housing systems. Food Chemistry, 106, 1031-1038.Hy-Line International (2020). Commercial Layers. Management Guide. Retrieved May 18, 2020 from https://www.hyline.com.Lordelo, M. et al. (2020). A comparison between the quality of eggs frpom indigenous chicken breeds and that from commercial layers. Poultry Science, 99, 1786-1776.Nowaczewski, S. et al. (2013). Egg morphometry and eggshell quality in ring-necked pheasant kept in cages. Annals of Animal Science, 13, 531-541.Odobasi, A.Z., et al. (2007). Changes in brown eggshell color as the hen ages. Poultry Science, 86, 356-363.Philippe, F.X. et al. (2020). Comparison of egg production, quality and composition in three production systems for laying hens. Livestock Science, 232, 103917.Rizzi, C. (2020). Yield performance, laying behaviour traits and egg quality of purebred and hybrid hens reared under outdoor conditions. Animals, 10, 584.Rizzi, C. & Chiericato, G.M. (2005). Organic farming production. Effect of age on the productive yield and egg quality of hens of two commercial hybrid lines and two local breeds. Italian Journal of Animal Science, 4 (suppl.3), 160-162.Rizzi, C. & Chiericato, G.M. (2010). Chemical composition of meat and egg yolk of hybrid and Italian breed hens reared using an organic production system. Poultry Science, 89(), 1239-1251.Rizzi, C. & Marangon, A. (2012). Quality of organic eggs of hybrid and Italian breed hens. Poultry Science, 91, 2330-2340.Zeidler, G. (2002). Shell egg quality and preservation. In Bell, D. & Weaver, W.D. jr. Chicken meat and egg production. (5th ed.). Norwell, Massachusetts, USA: Kluwer Academic Publishers (pp. 1199-1217).
Factors affecting egg consumption in young consumers
Get PDFSubmitted 2020-06-11 | Accepted 2020-07-09 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.1-6The research was carried out on consumers aged 20 to 30 years. The survey was conducted among the young population, on a sample of 200 respondents; male (M, n = 100) and female (F, n = 100). Respondents were asked to answer three sets of questions: a) egg quality indicators; b) which are the benefits of consuming eggs compared to other animal products; and c) which are the disadvantages of consuming eggs. A Likert scale (min = 1, max = 5) was used to evaluate the responses on the factors that influence egg consumption. The respondents (M 4.50 : F 4.11; P 0.05). Interval estimation of the mean values μ in male and female populations was made. The research indicates the attributes that consumers value when choosing and buying products, which can serve as a future guide for egg producers.Keywords: consumption, egg quality, nutritional value, freshnessReferencesBao, P.P., Shu, X.O., Zheng, Y., Cai, N., Ruan, Z.X., Kai, G., Yinghao, S., Yu-Tang, G.,Wei, Z., Wei, L. (2012). Fruit, vegetable and animal intake and breast cancer risk by hormone receptor status. Nutrition Cancer, 64(6), 806-819.Barraj, L., Tran, N., Mink, P. (2009). A Comparison of Egg Consumption with Other Modifiable Coronary Heart Disease Lifestyle Risk Factors: A Relative Risk Apportionment Study. Risk Analysis, 29(3), 401-415.Bejaei, M., Wiseman, K., Cheng, K.M. (2011). Influences of demographic characteristics, attitudes, and preferences of consumers on table egg consumption in British Columbia, Canada. Poultry Science, 90(5), 1088-1095.Bertechini, A.G., Mazzucco, H. (2013). The table egg: A review. Ciência e Agrotecnologia, 37(2), 115-122.Bobetić, B. (2019). The Challenges and Expectations of the EU and Poultry Production in Croatia in the Medium-Term to 2030. Proceedings of 13th Symposium “Poultry Days 2019” with International Participation, May 8 – 11, 2019, (pp. 18-23). In Croatia.Čalić, S., Friganovic, E., Maleš, V., Mustapić, A. (2011). Functional food and consumers. Practical Management, 2(2), 51-57.EBN - Egg Borad Nutritio (2019). Egg composition. Retrived December 22, 2019 from https://www.eggnutritioncenter.org/topics/nutrients-in-eggs/, accessedFernandez, M.L. (2006). Dietary cholesterol provided by eggs and plasma lipoproteins in healthy populations. Current Opinion in Clinical Nutritional and Metabolic Care, 9(1), 8-12.Garza, C., Rasmussen, K.M. (2000). Pregnancy and Lactation. In: Garrow, G.S., James, W.P.T., Ralph, A. (eds.) Human Nutrition and Dietetics (10. Ed.). Churchill Livingstone, London (pp. 437-448).Guter, M.M., Low, E.M. (2008). The British egg marketing board 1957-71-A reassessment. Journal of Agricultural Economics, 22(3), 247-265.Hasler, C.M. (2002). Functional foods: benefits, concerns and challenges - a position paper from the American Council on Science and Health. Journal of Nutrition, 132(12), 3772-3781.Kralik G., Škrtić Z., Kralik Z. (2012). Biometrika u zootehnici. Sveučilište J. J. Strossmayera u Osijeku (pp. 76-81, 91-93).Kralik, I., Kralik Z., Zelić, S. (2014). Consumer preferences of table eggs. Proceedings of 49th Croatian and 9th International Agronomy Symposium. 16-21 February 2014, Faculty of Agriculture University of Josip Juraj Strossmayer in Osijek (pp. 56-160). In Croatia.Kralik, I., Zelić, A., Kralik, G. (2017). Influence of socio-demographic characteristics of examinees on egg quality awareness and consumption. Interdisciplinary management research. 19-21 May 2017, Josip Juraj Strossmayer University of Osijek, Faculty of Economics in Osijek (pp. 1193-1205). In Croatia.Kralik, Z., Rebekic, A. (2018). Consumers preferences on the usefulness In and consumption of enriched products. Krmiva, 60(1), 17-24.Lunven, P., Le Clement de St Marcq, C., Carnovale, E., Fratoni, A. (1973). Amino acid composition of hen`s egg. British Journal of Nutrition, 30(2), 189-194.Missmer, S.A., Smith-Warner, S.A., Spiegelman, D., Yaun, S.S., Adami, H.O., Beeson, W.L. Van den Brandt, P.A., Fraser, G.E., Freudenheim, J.L., Goldbohm, R.A., Graham, S., Kushi, L.H., Miller, A.B., Potter, J.D., Rohan, T.E., Speizer, F.E., Toniolo, P., Willett, W.C., Wolk, A., Zeleniuch-Jacquotte, A., Hunter, D.J. (2002). Meat and diary food consumption in breast cancer: a pooled analysis of cohort studies. International Journal of Epidemiology, 31(1), 78-85.Mutungi, G., Ratliff, J., Puglisi, M., Torres-Gonzalez, M., Vaishnav, U., Leite, J. O., Quann, E., Volek, J. S., Fernandez, M. L. (2008). Dietary cholesterol from eggs increases plasma HDL cholesterol in overweight men consuming a carbohydrate-restricted diet. Journal of Nutrition, 138(2), 272-276.Patil, S.R., Cates, S., Morales, R. (2005). Consumer food safety knowledge, practices and demographic differences: Findings from a meta-analysis. Journal of Food Protection, 68(9), 1884-1894.Official Gazette 115/2006, Ministry of Agriculture, Forestry and Water Management (2006). Rulebook on egg quality. Retrived December 22, 2019 from https://www.poslovni-savjetnik.com/propisi/pravilnik-o-kakvoci-jaja-urednicki-procisceni-tekst-nn-br-1152006-692007-i-762008Ruxton, C.H.S., Derbyshire, E., Gibson, S. (2010). Nutritional properties and health benefits of eggs. Nutrition and Food Science, 40(3), 263-279.Shin, J.Y., Xun, P., Nakamura, Y., He, K. (2013). Egg consumption in relation to cardiovascular disease risk and diabetes: Systematic review and meta-analysis. American Journal of Clinical Nutrition, 98(1), 146-159.Sparks, N.H.C. (2006). The hen`s egg- is its role in human nutrition changing? World`s Poultry Science Journal 62(2), 308-315.Zaheer, K. (2015). An Updated Review on Chicken Eggs: Production, Consumption, Management Aspects and Nutritional Benefits to Human Health. Food and Nutrition Sciences 6(13), 1208-1220.Zelić, A., Kralik, Z., Kralik, I., Mahmutović, H. (2015). Consumer Preferences When Purchasing Table Eggs In The Area Of Tuzla City In Bosnia And Herzegovina. Krmiva, 57(2), 75-79.Zhang, B., X., Pan, M., X., Wang, L., Mo, X.F., Chen, Y.N., Lin, F.Y., Ho, S.C. (2013). Choline and betaine intake is inversely associated with breast cancer risk: a two-stage case-control study in China. Cancer Science, 104(2), 250-258.
Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows
Get PDFSubmitted 2020-07-14 | Accepted 2020-08-18 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.224-232Pregnancy assessment is a very important tool for the reproductive management in efficient and profitable dairy farms. Nowadays, mid-infrared (MIR) spectroscopy is the method of choice in the routine milk recording system for quality control and to determine standard milk components. Since it is well known that there are changes in milk yield and composition during pregnancy, the aim of this study was to develop a discriminant model to predict the pregnancy state from routinely recorded MIR spectral data. The data for this study was from the Austrian milk recording system. Test day records of Fleckvieh, Brown Swiss and Holstein Friesian cows between 3 and 305 days of lactation were included in the study. As predictor variables, the first derivative of 212 selected MIR spectral wavenumbers were used. The data set contained roughly 400,000 records from around 40,000 cows and was randomly split into calibration and validation set by farm. Prediction was done with Partial Least Square Discriminant Analysis. Indicators of model fit were sensitivity, specificity, balanced accuracy and Area Under Receiver Operating Characteristic Curve (AUC). In a first approach, one discriminant model for all cows across the whole lactation and gestation lengths was applied. The sensitivity and specificity of this model in validation were 0.856 and 0.836, respectively. Splitting up the results for different lactation stages showed that the model was not able to predict pregnant cases before the third month of lactation and vice versa not able to predict non-pregnancy after the third month of lactation. Consequently, in the second approach a prediction model for each different (expected) pregnancy stage and lactation stage was developed. Balanced accuracies ranged from 0.523 to 0.918. Whether prediction accuracies from this study are sufficient to provide farmers with an additional tool for fertility management, it needs to be explored in discussions with farmers and breeding organizations.Keywords: MIR spectroscopy, pregnancy prediction, dairy cow, PLSReferencesBalhara, A. K., Gupta, M., Singh, S., Mohanty, A. K., & Singh, I. (2013). Early pregnancy diagnosis in bovines: Current status and future directions. The Scientific World Journal, 2013. hhttps://doi.org/10.1155/2013/958540Bekele, N., Addis, M., Abdela, N., & Ahmed, W. M. (2016). Pregnancy Diagnosis in Cattle for Fertility Management: A Review. Global Veterinaria, 16(4), 355–364. https://doi.org/10.5829/idosi.gv.2016.16.04.103136Benedet, A., Franzoi, M., Penasa, M., Pellattiero, E., & De Marchi, M. (2019). Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows. Journal of Dairy Science, 102(12), 11298–11307. https://doi.org/10.3168/jds.2019-16937Delhez, P., Ho, P. N., Gengler, N., Soyeurt, H., & Pryce, J. E. (2020). Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy? Journal of Dairy Science, 103(4), 3264–3274. https://doi.org/10.3168/jds.2019-17473Egger-Danner, C., Fürst, C., Mayerhofer, M., Rain, C., & Rehling, C. (2018). ZuchtData Jahresbericht 2018. Vienna. [Online]. Available at: https://zar.at/Downloads/Jahresberichte/ZuchtData-Jahresberichte.html. [Accessed: 2020, May 15].Gengler, N., Tijani, A., Wiggans, G. R., & Misztal, I. (1999). Estimation of (Co)variance function coefficients for test day yield with a expectation-maximization restricted maximum likelihood algorithm. Journal of Dairy Science, 82(8), 1849.e1-1849.e23. https://doi.org/10.3168/jds.S0022-0302(99)75417-2Grelet, C., Fernández Pierna, J. A., Dardenne, P., Baeten, V., & Dehareng, F. (2015). Standardization of milk mid-infrared spectra from a European dairy network. Journal of Dairy Science, 98(4), 2150–2160. https://doi.org/10.3168/jds.2014-8764Grelet, C., Bastin, C., Gelé, M., Davière, J. B., Johan, M., Werner, A., Reding, R., Fernandes Pierna, J. A., Colinet, F. G., Dardenne, P., Gendler, N., Soyeurt, H. & Dehareng, F. (2016). Development of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network. Journal of Dairy Science, 99(6), 4816–4825. https://doi.org/10.3168/jds.2015-10477Hirpa, A., Yehualaw, B., Wube, A., Asnake, A., Jemberu, A., Medicine, V., & Box, P. O. (2018). Review on Pregnancy Diagnosis in Dairy Cows, 9(2), 45–55. https://doi.org/10.5829/idosi.jri.2018.45.55Ho, P. N., Bonfatti, V., Luke, T. D. W., & Pryce, J. E. (2019). Classifying the fertility of dairy cows using milk mid-infrared spectroscopy. Journal of Dairy Science. https://doi.org/10.3168/jds.2019-16412Humblot, P. (2001). Monitor Pregnancy and Determine the Timing , Frequencies and Sources of Embryonic Mortality in Ruminants. Theriogenology, 56(01), 1417–1433.Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1–26.Lainé, A., Bel Mabrouk, H., Dale, L. M., Bastin, C., & Gengler, N. (2014). How to use mid-infrared spectral information from milk recording system to detect the pregnancy status of dairy cows. Communications in Agricultural and Applied Biological Sciences, 79(1), 33–38.Lainé, A., Bastin, C., Grelet, C., Hammami, H., Colinet, F. G., Dale, L. M., Gillon, A., Vandenplas, J., Deharend, F. & Gengler, N. (2017). Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra. Journal of Dairy Science, 100(4), 2863–2876.https://doi.org/10.3168/jds.2016-11736Lantz, B. (2015). Machine Learning with R. Machine Learning (Second Edi). Packt Publishing Ltd. https://doi.org/10.1002/9781119642183.ch14Mineur, A., Köck, A., Grelet, C., Gengler, N., Egger-Danner, C., & Sölkner, J. (2017). First Results in the Use of Milk Mid-infrared Spectra in the Detection of Lameness in Austrian Dairy Cows Genomic evaluation View project MACSUR View project. Agriculturae Conspectus Scientifi Cus, Vol. 82(No. 2 (163-166)), (163-166). Retrieved from https://www.researchgate.net/publication/325450513Olori, V. E., Brotherstone, S., Hill, W. G., & McGuirk, B. J. (1997). Effect of gestation stage on milk yield and composition in Holstein Friesian dairy cattle. Livestock Production Science, 52(2), 167–176. https://doi.org/10.1016/S0301-6226(97)00126-7Pohler, K. G., Franco, G. A., Reese, S. T., Dantas, F. G., Ellis, M. D., & Payton, R. R. (2016). Past, present and future of pregnancy detection methods. Applied Reproductive Strategies in Beef Cattle 7-8 September 2016, 251–259.Rienesl, L., Khayatzadeh, N., Köck, A., Dale, L., Werner, A., Grelet, C., Gengler, N., Auer, F-J., Egger-Danner, C., Massart, X. & Sölkner, J. (2019). Mastitis detection from milk mid-infrared (MIR) spectroscopy in dairy cows. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(5), 1221–1226. https://doi.org/10.11118/actaun201967051221Santos, J. E. P., Thatcher, W. W., Chebel, R. C., Cerri, R. L. A., & Galvão, K. N. (2004). The effect of embryonic death rates in cattle on the efficacy of estrus synchronization programs. Animal Reproduction Science, 82–83, 513–535. https://doi.org/10.1016/j.anireprosci.2004.04.015SAS Institute Inc. (2017). SAS software 9.4. SAS Institute Inc., Cary, NC, USA.Soyeurt, H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D. P., Coffey, P. & Dardenne, P. (2011). Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science, 94(4), 1657–1667. https://doi.org/10.3168/jds.2010-3408Soyeurt, H., Bastin, C., Colinet, F. G., Arnould, V. M.-R., Berry, D. P., Wall, E., Dehareng, F., Nguyen, H. N., Pardenne, P., Schefers, J., Vandenplas, J., Weigel, K., Coffey, M., Théron, L., Detilleux, J., Reding, E., Gengler, N. & McParland, S. (2012). Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis. Animal, 6(11), 1830–1838. https://doi.org/10.1017/s1751731112000791Toffanin, V., De Marchi, M., Lopez-Villalobos, N., & Cassandro, M. (2015). Effectiveness of mid-infrared spectroscopy for prediction of the contents of calcium and phosphorus, and titratable acidity of milk and their relationship with milk quality and coagulation properties. International Dairy Journal, 41, 68–73. https://doi.org/10.1016/j.idairyj.2014.10.002Vanlierde, A., Vanrobays, M.-L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., S., Lewis, E., Deighton, M. H., Grandl, F., Kreuzer, M., Gredler, B., Dardenne, P. & Gengler, N. (2015). Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. Journal of Dairy Science, 98(8), 5740–5747. https://doi.org/10.3168/jds.2014-8436Vanlierde, A., Soyeurt, H., Gengler, N., Colinet, F. G., Froidmont, E., Kreuzer, M., Grandl, F., Bell, M., Lund, P., Olijhoek, D. W., Eugéne M., Martin, C., Kuhla, B. & Dehareng, F. (2018). Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers. Journal of Dairy Science, 101(8). https://doi.org/10.3168/jds.2018-14472
NIRS to assess chemical composition of sheep and goat cheese
No full textSubmitted 2020-07-01 | Accepted 2020-09-02 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.97-104The present study aimed to evaluate the performances of Fourier transform near-infrared spectroscopy technique to determine the chemical and the fatty acid composition of different types of cheeses. A total of 95 cheeses from sheep and goat raw milk were produced in small local dairies of Siena province (Tuscany). For each cheese, spectrum was collected in intact slices of the sample and fatty acid profile was determined in ground samples. Outliers were identified and different mathematical pre-processing treatments (SNV, MSC, baseline correction and de-trending) were applied when necessary. Considering traditional chemical analysis and raw cheese spectral data, calibration and cross-validation models were carried out using partial least squares regression (PLS). The best results were evaluated in terms of coefficient of determination in calibration and cross-validation (R2cv), and root mean square error in calibration and cross-validation, and residual prediction deviation (RPD). Moisture, protein and ash showed the best R2cv (0.89, 0.74 and 0.72, respectively) and RPD values (3.0, 2.6 and 2.1, respectively). Saturated, monounsaturated and polyunsaturated fatty acids showed R2cv which ranged from 0.75 to 0.67, and RPD 0.70). Obtained results are promising and additional samples could strongly increase the predictive ability for small dairy farms.Keywords: FT-NIRS, cheese, fatty acid, qualityReferencesAOAC, 2019. Official Methods of Analysis. 21st ed., Association of Official Analytical Chemists, Washington, DC, USA.Birth, G. and Hecht, H. (1987). The Physics of Near-Infrared Reflectance. Near Infrared Technology in the Agricultural and Food Industries. American Association of Cereal Chemists, Inc. St. Paul, Minnesota, USA.Cuibus, L. et al. (2014). Preliminary discrimination of cheese adulteration by FT-IR spectroscopy. Bulletin UASVM Food Science and Technology, 71, 142-146. https://doi.org/10.15835/buasvmcn-fst:10795De Marchi, M. et al. (2018). Invited review: Use of infrared technologies for the assessment of dairy products - Applications and perspectives. Journal of Dairy Science, 101, 10589–10604. https://doi.org/10.3168/jds.2018-15202Esbensen, K. H. et al. (2014). The RPD myth... NIR news, 25(5), 24-28. https://doi.org/10.1255/nirn.1462Faber, N. M. and Rajko, R. (2007). How to avoid over-fitting in multivariate calibration—The conventional validation approach and an alternative.Analytica Chimica Acta, 595(1-2), 98-106. https://doi.org/10.1016/j.aca.2007.05.030Folch, J., Lees, M. and Sloane Stanley, G. H. (1957). A simple method for the isolation and purification of total lipides from animal tissues. Journal of Biological Chemistry, 226, 497-509.Fox, P. F. et al. (1993). Cheese: Chemistry, Physics and Microbiology. London: Chapman & Hall.González-Martín, M. I. et al. (2017). Discrimination between cheeses made from cow’s, ewe’s and goat’s milk from unsaturated fatty acids and use of the canonical biplot method. Journal of Food Composition and Analysis, 56, 34-40. https://doi.org/10.1016/j.jfca.2016.12.005González-Martín, M. I. et al. (2020). The determination of fatty acids in cheeses of variable composition (cow, ewe's, and goat) by means of near infrared spectroscopy. Microchemical Journal, 156, 104854. https://doi.org/10.1016/j.microc.2020.104854Holroyd, S. E. (2013). Review: The use of near infrared spectroscopy on milk and milk products. Journal of Near Infrared Spectroscopy, 21 (5), 311-322. https://doi.org/10.1255/jnirs.1055Karoui, R. and Dufour, E. (2003). Dynamic testing rheology and fluorescence spectroscopy investigations of surface to centre differences in ripened soft cheeses. International Dairy Journal, 13, 973-985. https://doi.org/10.1016/S0958-6946(03)00121-3Karoui, R. et al. (2006). Chemical characterisation of European Emmental cheeses by near infrared spectroscopy using chemometric tools. International Dairy Journal, 16, 1211–1217. https://doi.org/10.1016/j.idairyj.2005.10.002Kraggerud, H. et al. (2014). Prediction of sensory quality of cheese during ripening from chemical and spectroscopy measurements. International Dairy Journal, 34, 6–18. https://doi.org/10.1016/j.idairyj.2013.07.008Lucas, A. et al. (2008a). Prediction of dry matter, fat, pH, vitamins, minerals, carotenoids, total antioxidant capacity, and color in fresh and freeze-dried cheeses by visible-near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry, 56, 6801–6808. https://doi.org/10.1021/jf800615aLucas, A. et al. (2008b). Prediction of fatty acid composition of fresh and freeze-dried cheeses by visible–near-infrared reflectance spectroscopy. International Dairy Journal,18, 595–604. https://doi.org/10.1016/j.idairyj.2007.12.001Manuelian, C. L. et al. (2017). Prediction of minerals, fatty acid composition and cholesterol content of commercial cheeses by near infrared transmittance spectroscopy. International Dairy Journal, 71, 107-113. https://doi.org/10.1016/j.idairyj.2017.03.011Markiewicz-Kęszycka, M. et al. (2013). Fatty acid profile of milk - a review. Bulletin of the Veterinary Institute in Pulawy, 57, 135–139. https://doi.org/10.2478/bvip-2013-0026Mazerolles, G. et al. (2001). Infrared and fluorescence spectroscopy for monitoring protein structure and interaction changes during cheese ripening. Le Lait, 81, 509-527. https://doi.org/10.1051/lait:2001148Morrison, W. R. and Smith, L. M. (1964). Preparation of fatty acid methyl esters and dimethylac-etals from lipids with boron fluorid methanol. Journal of Lipid Research, 5, 600–608.Nudda, A. et al. (2005). Seasonal variation in conjugated linoleic acid and vaccenic acid in milk fat of sheep and its transfer to cheese and ricotta. Journal of Dairy Science, 88, 1311-1319. https://doi.org/10.3168/jds.S0022-0302(05)72797-1Ozen, B. F. and Mauer, L. J. (2002). Detection of hazelnut oil adulteration using FT-IR spectroscopy. Journal of Agricultural and Food Chemistry, 50, 3898–3901. https://doi.org/10.1021/jf0201834Pierce, M. M. and Wehling, R. L. (1994). Comparison of sample handling and data treatment methods for determining moisture and fat in Cheddar cheese by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 42, 2830-2835. https://doi.org/10.1021/jf00048a033Pollard, A. et al. (2003). Textural changes of natural Cheddar cheese during the maturation process. Journal of Food Science, 68, 2011-2016. https://doi.org/10.1111/j.1365-2621.2003.tb07010.xRodriguez-Otero, J. L., Hermida, M. and Cepeda, A. (1995). Determination of fat, protein, and total solids in cheese by near-infrared reflectance spectroscopy. Journal of AOAC International, 78, 802-806. https://doi.org/10.1093/jaoac/78.3.802Salvadori del Prato, O. (2001). Trattato di Tecnologia Casearia. Dairy Technology. Bologna: Edagricole-New Business Media.Stocco, G. et al. (2019). Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and spectrum intervals. Journal of Dairy Science, 102, 9622-9638. https://doi.org/10.3168/jds.2019-16770Strzałkowska, N. et al. (2009). Chemical composition, physical traits and fatty acid profile of goat milk as related to the stage of lactation. Animal Science Papers and Reports, 27, 311–320.Williams, P. (2014). The RPD Statistic: A Tutorial Note. NIR News, 25, 22 - 26. https://doi.org/10.1255/nirn.1419Wehling, R. L and Pierce, M. M. (1988). Determination of moisture in Cheddar cheese by near infrared reflectance spectroscopy. Journal of Association of Official Analytical Chemists, 71, 571-574. https://doi.org/10.1093/jaoac/71.3.571
Effect of season and temperature before and after calving on the future milk production of born heifers
Get PDFArticle Details: Received: 2020-06-30 | Accepted: 2020-10-15 | Available online: 2020-12-31https://doi.org/10.15414/afz.2020.23.04.224-229The aim of the study was to evaluate the effect of birth season, average maximum temperatures 6 weeks before and after birth of heifers on their first lactation milk yield. In chosen herd, the effect of birth weight, weight gain until weaning on first lactation milk yield was also investigated. Additionally, the effect of the average maximum temperatures before birth, effect of birth season on birth weight were evaluated. The data were collected from the herd “A” in Orava region consisting of Slovak spotted breed (127 records), the herd “B” in Lower Nitra (150 records) and herd “C” in Upper Nitra (116 records) both consisting of black Holstein Friesian cows. Birth season tended to influence the heifers first lactation milk yield in the herd “C” (P 0.66, herd “A”; P >0.59, herd “B”; P >0.38, herd “C”). In the herd “B” there was insignificant effect of prenatal temperatures, birth season on birth weight of heifers (P >0.97; P >0.74). However, the heifers with the highest weight gains until weaning had numerically higher first lactation milk yield (P >0.20).Keywords: dairy calves, temperature, season, milk yield, gestation lengthReferencesCALLINAN P.A. and FEINBERG A. P. (2006). The emerging science of epigenomics. Human Molecular Genetics, 15(1), R95-R101. https://doi.org/10.1093/hmg/ddl095COLLIER, R. J. et al. (2006). Use of gene expression microarrays for evaluating environmental stress tolerance at the cellular level in cattle. Journal of Animal Science, 84(13), 1–13. https://doi.org/10.2527/2006.8413_supplE1xDAHL, G. E., TAO, S. and MONTEIRO, A. P. A. (2016). Effects of late-gestation heat stress on immunity and performance of calves. Journal of Dairy Science, 99(4), 3193–3198. DOI: https://doi.org/10.3168/jds.2015-9990DAHL, G. E., TAO, S. and THOMPSON, I. M. (2012). LACTATION BIOLOGY SYMPOSIUM: Effects of photoperiod on mammary gland development and lactation. Journal of Animal Science, 90(3), 755–760. https://doi.org/10.2527/jas.2011-4630HEINRICHS, A. J. and HEINRICHS, B. S. (2011). A prospective study of calf factors affecting first-lactation and lifetime milk production and age of cows when removed from the herd. Journal of Dairy Science, 94(1), 336–341. https://doi.org/10.3168/jds.2010-3170KASARDA, R. et al. (2018). Estimation of heritability for claw traits in Holstein cattle using Bayesian and REML approaches. Journal of Central European Agriculture, 19(4), 784–790. https://doi.org/10.5513/JCEA01/19.4.2338LAPORTA, J. et al. (2017). In utero exposure to heat stress during late gestation has prolonged effects on the activity patterns and growth of dairy calves. Journal of Dairy Science, 100(4), 1–9. https://doi.org/10.3168/jds.2016-11993MIGLIOR, F. et al. (2017). Identification and genetic selection of economically important traits in dairy cattle. Journal of Dairy Science, 100(12), 10251–10271. DOI: https://doi.org/10.3168/jds.2017-12968MOALLEM, U. et al. (2010). Long-term effects of ad libitum whole milk prior to weaning and prepubertal protein supplementation on skeletal growth rate and first-lactation milk production. Journal of Dairy Science, 93(6), 2639–2650. DOI: https://doi.org/10.3168/jds.2009-3007MONTEIRO, A. P. A. et al. (2013). Effect of heat stress in utero on calf performance and health through the first lactation. Journal of Animal Science, 91, 184. https://doi.org/10.3168/jds.2015-9990MONTEIRO, A. P. A. et al. (2014). Effect of heat stress during late gestation on immune function and growth performance of calves: Isolation of altered colostral and calf factors. Journal of Dairy Science, 97(10), 6426–6439. https://doi.org/10.3168/jds.2013-7891MONTEIRO, A. P. A. et al. (2016a). Effect of maternal heat stress during the dry period on growth and metabolism of calves. Journal of Dairy Science, 99(5), 3896–3907. https://doi.org/10.3168/jds.2015-10699MONTEIRO, A. P. A. et al. (2016b). In utero heat stress decreases calf survival and performance through the first lactation. Journal of Dairy Science, 99(10), 8443–8450. https://doi.org/10.3168/jds.2016-11072OSBORNE, V. R. et al. (2007). Effects of photoperiod and glucose-supplemented drinking water on the performance of dairy calves. Journal of Dairy Science, 90(11), 5199–5207. https://doi.org/10.3168/jds.2007-0402RIUS, G. and DAHL, G. E. (2006). Exposure to Long-Day Photoperiod Prepubertally May Increase Milk Yield in FirstLactation Cows. Journal of Dairy Science, 89(6), 2080–2083. https://doi.org/10.3168/jds.S0022-0302(06)72277-9SCHAEFFER, L. R. (2006). Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 123, 218–223. https://doi.org/10.1111/j.1439-0388.2006.00595.xSOBERON, F. et al. (2012). Preweaning milk replacer intake and effects on long-term productivity of dairy calves. Journal of Dairy Science, 95(2), 783–793. https://doi.org/10.3168/jds.2011-4391SOBERON, F. and VAN AMBURGH, M. E. (2013). Lactation Biology Symposium: The effect of nutrient intake from milk or milk replacer of preweaned dairy calves on lactation milk yield as adults: A meta-analysis of current data. Journal of Animal Science, 91(2), 706–712. https://doi.org/10.2527/jas.2012-5834STRAPÁK, P., JUHÁS, P. and BUJKO, J. (2013). The influence of health status in calves with subsequent growth of heifers and milk production in dairy cows. Journal of Central European Agriculture, 14(3), 347–356. https://doi.org/10.5513/JCEA01/14.3.1326TANČIN, V. et al. (1994). Different nutrition of calves in relation to the levels of thyroid-hormones and some biochemical indexes. Živočíšna výroba, 39(11), 961–971.TANČIN, V., MIKLÁŠ, Š. and MAČUHOVÁ, L. (2018). Possible physiological and environmental factors affecting milk production and udder health of dairy cows: A review. Slovak Journal of Animal Science, 51(1), pp. 32–40.TAO, S. et al. (2012). Effect of late gestation maternal heat stress on growth and immune function of dairy calves. Journal of Dairy Science, 95(12), 7128–7136. https://doi.org/10.3168/jds.2012-5697TAO, S. et al. (2018). Symposium review: The influences of heat stress on bovine mammary gland function. Journal of Dairy Science, 101(6), 5642–5654. https://doi.org/10.3168/jds.2017-13727TAO, S. et al. (2019). Effects of heat stress during late gestation on the dam and its calf. Journal of Animal Science, 97(5), 2245–2257. https://doi.org/10.1093/jas/skz061UHRINČAŤ, M. et al. (2007). The effect of growth intensity of heifers till 15 months of age on their milk production during first lactation. Slovak Journal of Animal Science, 40(2), 83–88.VACULIKOVA, M. and CHLADEK, G. (2015). Air temperature impacts on the behaviour of holstein calves in individual outdoor calf hutches according to age of observed calves. In O. Polák, R. Cerkal and N. Březinová-Belcredi (Eds.), The Conference MendelNet 2015 (pp. 169–173). Brno: Mendel University in Brno.VAN EETVELDE, M. et al. (2017). Season of birth is associated with first-lactation milk yield in Holstein Friesian cattle. Animal, 11(12), 2252–2259. https://doi.org/10.1017/S1751731117001021VAN EETVELDE, M. and OPSOMER, G. (2017). Innovative look at dairy heifer rearing: Effect of prenatal and postnatal environment on later performance. Reproduction in Domestic Animals, 52(3), 30–36. https://doi.org/10.1111/rda.13019WIGGANS, G. R. et al. (2017). Genomic Selection in Dairy Cattle: The USDA Experience. Annual Review of Animal Biosciences, 5, 309–327. https://doi.org/10.1146/annurev-animal-021815-111422WU, G. F. et al. (2006). Board-Invited Review: Intrauterine growth retardation: Implications for the animal sciences. Journal of Animal Science, 84(9), 2316–2337. https://doi.org/10.2527/jas.2006-156YATES, D., GREEN, A. and LIMESAND, S. (2011). Catecholamines mediate multiple fetal adaptations during placental insufficiency that contribute to intrauterine growth restriction: Lessons from hyperthermic sheep. Journal of Pregnancy, Article ID 740408, pp. 1–9. https://doi. org/10.1155/2011/74040