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This paper investigates how scientific information and expertise was provided to decision-makers for consideration in situations involving risk and uncertainty. Seven case studies from the earth sciences were used as a medium for this exposition: (1) the 2010–2011 Canterbury earthquake sequence in New Zealand, (2) agricultural farming-system development in North West Queensland, (3) operational flood models, (4) natural disaster risk assessment for Tasmania, (5) deep sea mining in New Zealand, (6) 3-D modelling of geological resource deposits, and (7) land-based pollutant loads to Australia's Great Barrier Reef. Case studies are lead-authored by a diverse range of scientists, based either in universities, industry, or government science agencies, with diverse roles, experiences, and perspectives on the events discussed. The context and mechanisms by which scientific information was obtained, presented to decision-makers, and utilised in decision-making is presented. Sources of scientific uncertainties and how they were communicated to and considered in decision-making processes are discussed. Decisions enacted in each case study are considered in terms of whether they were scientifically informed, aligned with prevailing scientific evidence, considered scientific uncertainty, were informed by models, and were (or were not) precautionary in nature. The roles of other relevant inputs (e.g. political, socioeconomic considerations) in decision-making are also described. Here we demonstrate that scientific evidence may enter decision-making processes through diverse pathways, ranging from direct solicitations by decision-makers to independent requests from stakeholders following media coverage of relevant research. If immediately relevant scientific data cannot be provided with sufficient expediency to meet the demands of decision-makers, decision-makers may (i) seek expert scientific advice and judgement (to assist with decision-making under conditions of high epistemic uncertainty), (ii) delay decision-making (until sufficient evidence is obtained), and/or (iii) provide opportunities for adjustment of decisions as additional information becomes available. If the likelihood of occurrence of potentially adverse future risks is perceived by decision-makers to exceed acceptable thresholds and/or be highly uncertain, precautionary decisions with adaptive capacity may be favoured, even if some scientific evidence suggests lower levels of risk. The efficacy with which relevant scientific data, models, and uncertainties contribute to decision-making may relate to factors including the expediency with which this information can be obtained, the perceived strength and relevance of the information presented, the extent to which relevant experts have participated and collaborated in scientific communications to decision-makers and stakeholders, and the perceived risks to decision-makers of favouring earth science information above other, potentially conflicting, scientific and non-scientific inputs. This paper provides detailed Australian and New Zealand case studies showcasing how science actions and provision pathways contribute to decision-making processes. We outline key learnings from these case studies and encourage more empirical evidence through documented examples to help guide decision-making practices in the future. [ABSTRACT FROM AUTHOR]

Forage brassicas have historically been used in high rainfall/irrigated temperate livestock systems, but there is increasing interest in diverse forage brassicas in drier mixed crop-livestock farming systems. Computer-based modelling is an important decision support tool used in agriculture to explore the adaptability of crops to different climates and agronomic management practices, but existing modelling tools for forage brassicas are limited to temperate environments. We parameterised the APSIM (Agricultural Production Systems Simulator) model for four forage brassica genotypes, including three diverse forage rape cultivars and a raphanobrassica. The model was calibrated using two experiments with repeated measures of biomass components, nutritive value, and leaf and canopy development. We then tested the model extensively using data from a diverse set of environments within Australian and New Zealand (23 sites across four agro-climatic zones). Model predictions of biomass were good for all the genotypes (NSE > 0.60, Nash-Sutcliffe efficiency; RMSE ~1.5 t DM/ha, root mean square error). Predictions of metabolisable energy yield were satisfactory for all genotypes (NSE 0.43–0.73; RMSE ~17.8 GJ ME/ha) but forage dry matter digestibility (DMD) were poorly predicted due to the small variation in observed data. Our robust and widely tested model can be confidently used to predict forage productivity of common and new forage brassicas across a wide range of production environments and agronomic management practices. This model will enable future work to develop a better understanding of the potential value of these important forage crops for livestock production systems. • A forage brassica model, including three diverse forage rape cultivars and a raphanobrassica were parameterised in APSIM. • Forage brassica productivity during vegetative growth can be modelled in different environments and agronomic practices. • These new capabilities enable the value of forage brassicas to farming systems to be explored in different environments. [ABSTRACT FROM AUTHOR]