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Wallach, D. (2015). Developing skills: how to train adaptive modelers. Advances in Animal Biosciences, 6(01), 52–53.
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Del Prado, A., Crosson, P., Olesen, J. E., & Rotz, C. A. (2013). Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems. Animal, 7 Suppl 2, 373–385.
Abstract: The farm level is the most appropriate scale for evaluating options for mitigating greenhouse gas (GHG) emissions, because the farm represents the unit at which management decisions in livestock production are made. To date, a number of whole farm modelling approaches have been developed to quantify GHG emissions and explore climate change mitigation strategies for livestock systems. This paper analyses the limitations and strengths of the different existing approaches for modelling GHG mitigation by considering basic model structures, approaches for simulating GHG emissions from various farm components and the sensitivity of GHG outputs and mitigation measures to different approaches. Potential challenges for linking existing models with the simulation of impacts and adaptation measures under climate change are explored along with a brief discussion of the effects on other ecosystem services.
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Özkan, Ş., Hill, J., & Cullen, B. (2014). Effect of climate variability on pasture-based dairy feeding systems in south-east Australia. Animal Production Science, 55(9), 1106–1116.
Abstract: The Australian dairy industry relies primarily on pasture for its feed supply. However, the variability in climate affects plant growth, leading to uncertainty in dryland pasture supply. This paper models the impact of climate variability on pasture production and examines the potential of two pasture-based dairy feeding systems: (1) to experience winter deficits; (2) to carry forward the conserved pasture surpluses as silage for future use; and (3) to conserve pasture surpluses as hay. The two dairy feeding systems examined were a traditional perennial ryegrass-based feeding system (ryegrass max. – RM) and a system that incorporated double cropping into the perennial ryegrass pasture base (complementary forage – CF). The conditional probability of the RM and CF systems to generate pasture deficits in winter were 94% and 96%, respectively. Both systems could carry forward the surplus silage into the following lactation almost once in every 4-5 years with the RM system performing slightly better than the CF system. The proportions of the grain-based concentrates fed in the two systems were 25% and 27% for the RM and CF systems, respectively. This study suggests that double-cropping systems have the potential to provide high-quality feed to support the feed gaps when pasture is not available due to increased variability in climatic conditions.
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Özkan, Ş., & Hill, J. (2015). Implementing innovative farm management practices on dairy farms:a review of feeding systems. Turkish Journal of Veterinary and Animal Sciences, 39, 1–9.
Abstract: The Australian dairy industry relies primarily on pasture for its feed supply. However, the variability in rainfall negatively affects plant growth, leading to uncertainty in dryland feed supply, especially during periods of high milk price. New feeding (complementary) systems combining perennial ryegrass with another crop and/or pasture species may have the potential to mitigate this seasonal risk and improve productivity and profitability by providing off-season feed. To date, the majority of research studying the integration of alternative crops into pasture-based systems has focused on substitution and utilization of alternative feed sources. There has been little emphasis on the impacts of integration of forage crops into pasture-based systems. This review focuses on pasture-based feeding systems in southeastern Australia and how transitioning of systems contributes to improved productivity leading to improved profitability for dairy farmers.
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Sándor, R., Ehrhardt, F., Basso, B., Bellocchi, G., Bhatia, A., Brilli, L., et al. (2016). C and N models Intercomparison – benchmark and ensemble model estimates for grassland production. Advances in Animal Biosciences, 7(03), 245–247.
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