Time filter

Source Type

Warrimoo, Australia

Banhazi T.M.,University of Southern Queensland | Lehr H.,Syntesa sp f Ltd. | Black J.L.,John L Black Consulting | Crabtree H.,Farmex Ltd | And 3 more authors.
International Journal of Agricultural and Biological Engineering

Abstract: Precision Livestock Farming (PLF) is potentially one of the most powerful developments amongst a number of interesting new and upcoming technologies that have the potential to revolutionise the livestock farming industry. If properly implemented, PLF or Smart Farming could (1) improve or at least objectively document animal welfare on farms; (2) reduce greenhouse gas (GHG) emission and improve environmental performance of farms; (3) facilitate product segmentation and better marketing of livestock products; (4) reduce illegal trading of livestock products; and (5) improve the economic stability of rural areas. However, there are only a few examples of successful commercialisation of PLF technologies introduced by a small number of commercial companies which are actively involved in the PLF commercialisation process. To ensure that the potential of PLF is taken to the industry, we need to: (1) establish a new service industry; (2) verify, demonstrate and publicise the benefits of PLF; (3) better coordinate the efforts of different industry and academic organisations interested in the development and implementation of PLF technologies on farms; and (4) encourage commercial sector to assist with professionally managed product development. Source

Lean I.J.,University of Sydney | Golder H.M.,University of Sydney | Black J.L.,John L Black Consulting | King R.,Dairy Australia | Rabiee A.R.,University of Sydney
Journal of Animal Science

Our objective was to evaluate a near-infrared reflectance spectroscopy (NIRS) used in the feed industry to estimate the potential for grains to increase the risk of ruminal acidosis. The existing NIRS calibration was developed from in sacco and in vitro measures in cattle and grain chemical composition measurements. To evaluate the existing model, 20 cultivars of 5 grain types were fed to 40 Holstein heifers using a grain challenge protocol and changes in rumen VFA, ammonia, lactic acids, and pH that are associated with acidosis were measured. A method development study was performed to determine a grain feeding rate sufficient to induce non-life threatening but substantial ruminal changes during grain challenge. Feeding grain at a rate of 1.2% of BW met these criteria, lowering rumen pH (P = 0.01) and increasing valerate (P < 0.01) and propionate concentrations (P = 0.01). Valerate was the most discriminatory measure indicating ruminal change during challenge. Heifers were assigned using a row by column design in an in vivo study to 1 of 20 grain cultivars and were reassigned after a 9 d period (n = 4 cattle/treatment). The test grains were dry rolled oats (n = 3), wheat (n = 6), barley (n = 4), triticale (n = 4), and sorghum (n = 3) cultivars. Cattle were adapted to the test grain and had ad libitum access to grass silage 11 d before the challenge. Feed was withheld for 14 h before challenge feeding with 0.3 kg DM of silage followed by the respective test grain fed at 1.2% of BW. A rumen sample was taken by stomach tube 5, 65, 110, 155, and 200 min after grain consumption. The rumen is not homogenous and samples of rumen fluid obtained by stomach tube will differ from those gained by other methods. Rumen pH was measured immediately; individual VFA, ammonia, and D-and L-lactate concentrations were analyzed later. Rumen pH (P = 0.002) and all concentrations of fermentation products differed among grains (P = 0.001). A previously defined discriminant score calculated at 200 min after challenge was used to rank grains for acidosis risk. A significant correlation between the discriminant score and the NIRS ranking (r = 0.731, P = 0.003) demonstrated the potential for using NIRS calibrations for predicting acidosis risk of grains in cattle. The overall rankings of grains for acidosis risk were wheat > triticale > barley > oats > sorghum. © 2013 American Society of Animal Science. All rights reserved. Source

Black J.L.,John L Black Consulting
Animal Production Science

Mathematical equations have been used to add quantitative rigour to the description of animal systems for the last 100 years. Initially, simple equations were used to describe the growth of animals or their parts and to predict nutrient requirements for different livestock species. The advent of computers led to development of complex multi-equation, dynamic models of animal metabolism and of the interaction between animals and their environment. An understanding was developed about how animal systems could be integrated in models to obtain the most realistic prediction of observations and allow accurate predictions of as yet unobserved events. Animal models have been used to illustrate how well animal systems are understood and to identify areas requiring further research. Many animal models have been developed with the aim of evaluating alternative management strategies within animal enterprises. Several important gaps in current animal models requiring further development are identified: including a more mechanistic representation of the control of feed intake; inclusion of methyl-donor requirements and simulation of the methionine cycle; plus a more mechanistic representation of disease and the impact of microbial loads under production environments. Reasons are identified why few animal models have been used for day-to-day decision making on farm. In the future, animal simulation models are envisaged to function as real-time control of systems within animal enterprises to optimise animal productivity, carcass quality, health, welfare and to maximise profit. Further development will be required for the integration of models that run real time in enterprise management systems adopting precision livestock farming technologies. © CSIRO 2014. Source

Manning R.,Plant Biosecurity | Speijers J.,Biometrics Unit | Harvey M.,Animal Health Laboratories | Black J.,John L Black Consulting
Australian Journal of Entomology

Little is known about which commercial oils could be used as an ingredient in improving artificial feedstuffs for honey bees. To test whether the oils are palatable or whether honey bees showed a preference, they were added to a low-fat pollen known to be attractive to bees. Oils were added at 2% above the known oil content of the pollen to a level about half that of a range of melliferous plants. Of 27 different plant and fish-based oils, only linseed and coconut oil-enhanced pollen diets were consumed by bees at a significantly greater rate (P < 0.05) than pollen itself. Other oils added to pollen that had higher but not significantly different consumption rates by bees to pollen in palatability tests were (highest to lowest): evening primrose, almond, grape seed, apricot, olive, blended vegetable, orange essential oil (EO), linoleic acid, soyabean, avocado, mustard seed, cod liver, sesame and canola. Other oils found unpalatable and discarded from further testing were: gingelly, castor, peanut, rose EO, oleic acid, fish, sunflower, macadamia, rice bran, clary sage EO and lavender EO. At 2%, two of the four essential oils tested were found to be significantly unpalatable and not preferred by bees. The concentration of lavender and clary sage in a pollen diet was perhaps too high. Using alcohol via the addition of 2% rum to enhance volatiles from the pollen diet effectively increased consumption of pollen diets by honey bees. Using both palatability and preference testing methodologies to determine the attractiveness of diets to bees gave the same result. Either test will give the researcher a way of determining the food value of ingredients in diets or full diets. © 2010 The Authors. Journal compilation © 2010 Australian Entomological Society. Source

Black J.L.,John L Black Consulting | Banhazi T.M.,University of Southern Queensland
Precision Livestock Farming 2013 - Papers Presented at the 6th European Conference on Precision Livestock Farming, ECPLF 2013

Three examples are given demonstrating the economic and social value of recording, analysing and interpreting data from pig enterprises. The examples highlight the need to install within piggeries automated measurement, analysis and control systems that will optimise profitability, reduce labour needs and improve animal welfare. Source

Discover hidden collaborations