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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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Van Oijen, M., & Höglind, M. (2016). Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design. Euphytica, 207(3), 627–643.
Abstract: Process-based grassland models (PBMs) simulate growth and development of vegetation over time. The models tend to have a large number of parameters that represent properties of the plants. To simulate different cultivars of the same species, different parameter values are required. Parameter differences may be interpreted as genetic variation for plant traits. Despite this natural connection between PBMs and plant genetics, there are only few examples of successful use of PBMs in plant breeding. Here we present a new procedure by which PBMs can help design ideotypes, i.e. virtual cultivars that optimally combine properties of existing cultivars. Ideotypes constitute selection targets for breeding. The procedure consists of four steps: (1) Bayesian calibration of model parameters using data from cultivar trials, (2) Estimating genetic variation for parameters from the combination of cultivar-specific calibrated parameter distributions, (3) Identifying parameter combinations that meet breeding objectives, (4) Translating model results to practice, i.e. interpreting parameters in terms of practical selection criteria. We show an application of the procedure to timothy (Phleum pratense L.) as grown in different regions of Norway.
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