Analysing attempts to support outdoor learning in Scottish schools

The new ‘Curriculum for Excellence’ in Scotland outlines a policy vision of a more integrated and holistic form of education; a commitment which offers considerable prospects for increased levels of outdoor learning in schools (Learning and Teaching Scotland, 2010). With reference to Fullan’s theorizing on achieving educational change, we investigated four main implementation areas, namely: policy aims, partnerships arrangements and associated professionalism and sustainability issues. We collected evidence through a series of sixteen semi-structured interviews with key stakeholders at national, local authority and school level. Despite increased agreement on aims, we found that improving the frequency and quality of outdoor learning in schools was adversely influenced by the patchwork nature of partnership support at national and local authority levels. This has curtailed the prioritizing of outdoor learning in schools and of teachers being supported when trying to make use of their increased curriculum decision-making responsibilities. Thus, we found only limited evidence of policy-related innovation and considerable evidence of policy stasis. As such, building national capacity is proving difficult. We conclude that further research on how some atypical schools have managed to develop their programmes offers the best prospects for understanding the complexities of achieving greater levels of outdoor learning

Towards a New Food System Assessment: AgMIP Coordinated Global and Regional Assessments of Climate Change

Agricultural stakeholders need more credible information on which to base adaptation and mitigation policy decisions. In order to provide this, we must improve the rigor of agricultural modelling. Ensemble approaches can be used to address scale issues and integrated teams can overcome disciplinary silos. The AgMIP Coordinated Global and Regional Assessments of Climate Change and Food Security (CGRA) has the goal to link agricultural systems models using common protocols and scenarios to significantly improve understanding of climate effects on crops, livestock and livelihoods across multiple scales. The AgMIP CGRA assessment brings together experts in climate, crop, livestock, economics, and food security to develop Protocols to guide the process throughout the assessment. Scenarios are designed to consistently combine elements of intertwined storylines of future society including, socioeconomic development, greenhouse gas concentrations, and specific pathways of agricultural sector development. Through these approaches, AgMIP partners around the world are providing an evidence base for their stakeholders as they make decisions and investments

Using individual tracking data to validate the predictions of species distribution models

The authors would like to thank the College of Life Sciences of Aberdeen University and Marine Scotland Science which funded CP's PhD project. Skate tagging experiments were undertaken as part of Scottish Government project SP004. We thank Ian Burrett for help in catching the fish and the other fishermen and anglers who returned tags. We thank José Manuel Gonzalez-Irusta for extracting and making available the environmental layers used as environmental covariates in the environmental suitability modelling procedure. We also thank Jason Matthiopoulos for insightful suggestions on habitat utilization metrics as well as Stephen C.F. Palmer, and three anonymous reviewers for useful suggestions to improve the clarity and quality of the manuscript.Peer reviewedPostprintPostprintPostprintPostprintPostprin

A Conceptual Framework for Guiding the Participatory Development of Agricultural Decision Support Systems

Scientists develop decision support systems (DSSs) to make agricultural science more accessible for farmers and extension officers. Despite the growing use of participatory approaches in agricultural DSS development, reflection on this endeavour is largely focused on the ‘doing’ of participation or the ‘problem of implementation’, with little reference to relevant theoretical approaches within the field of science and technology studies (STS). However, if DSS development is to reach its full potential, a more conceptually informed understanding of how stakeholders collaborate in the participatory development of DSSs is required. To contribute to this gap, we developed a conceptual framework based on three concepts drawn from STS that can add value to understanding agricultural DSSs: interpretative flexibility, technological frames, and boundary objects. A DSS becomes a boundary object when it enables the various parties involved in its development to collaborate and learn together despite diverse perceptions of the DSS or the issues that the DSS is being used to address. When combined, these three concepts highlight the importance of social learning for participatory DSS development, particularly the need to begin by exploring the parties’ different perspectives and facilitating co-learning. Our framework leads to a re-definition of success for participatory DSS development, by identifying social learning as a valuable outcome that can occur when farmers, extension officers and scientists collaborate. A case study of stakeholder collaboration to develop an irrigation scheduling DSS for the Australian sugarcane industry is used to illustrate the analytical strength of this conceptual framework.social learning, interpretative flexibility, technological frames, boundary objects, irrigation, climate variability

Assessing water quality for cropping management practices: A new approach for dissolved inorganic nitrogen discharged to the Great Barrier Reef

Applications of nitrogen (N) fertiliser to agricultural lands impact many marine and aquatic ecosystems, and improved N fertiliser management is needed to reduce these water quality impacts. Government policies need information on water quality and risk associated with improved practices to evaluate the benefits of their adoption. Policies protecting Great Barrier Reef (GBR) ecosystems are an example of this situation. We developed a simple metric for assessing the risk of N discharge from sugarcane cropping, the biggest contributor of dissolved inorganic N to the GBR. The metric, termed NiLRI, is the ratio of N fertiliser applied to crops and the cane yield achieved (i.e. kg N (t cane)−1). We defined seven classes of water quality risk using NiLRI values derived from first principles reasoning. NiLRI values calculated from (1) results of historical field experiments and (2) survey data on the management of 170,177 ha (or 53%) of commercial sugarcane cropping were compared to the classes. The NiLRI values in both the experiments and commercial crops fell into all seven classes, showing that the classes were both biophysically sensible (c.f. the experiments) and relevant to farmers’ experience. We then used machine learning to explore the association between crop management practices recorded in the surveys and associated NiLRI values. Practices that most influenced NiLRI values had little apparent direct impact on N management. They included improving fallow management and reducing tillage and compaction, practices that have been promoted for production rather than N discharge benefits. The study not only provides a metric for the change in N water quality risk resulting from adoption of improved practices, it also gives the first clear empirical evidence of the agronomic practices that could be promoted to reduce water quality risk while maintaining or improving yields of sugarcane crops grown in catchments adjacent to the GBR. Our approach has relevance to assessing the environmental risk of N fertiliser management in other countries and cropping systems

Motivators and barriers to adoption of improved land management practices. A focus on practice change for water quality improvement in Great Barrier Reef catchments

To protect and improve water quality in the Great Barrier Reef, the Queensland Government's Reef 2050 Water Quality Improvement Plan targets that 90% of sugarcane, horticulture, cropping and grazing lands in priority areas be managed using best management practices for sediment, nutrient and pesticides by 2025. Progress towards this target is insufficient and variable across catchments and industries. The motivation to adopt improvements in management practices is heavily influenced by social, economic, cultural and institutional dimensions. In this paper we synthesise the literature on how these human dimensions influence decision making for land management practice and highlight where future investment could be focussed. We highlight that focussing on —1) investigating systems to support landholder decision making under climate uncertainty (risk); 2) generating a better understanding of the extent and drivers of landholder transaction cost; 3) understanding if there are competing ‘right’ ways to farm; and 4) improving understanding of the social processes, trust and power dynamics within GBR industries and what these means for practice change— could improve practice change uptake in the future

Multimodel Ensembles of Wheat Growth: More Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models

Paddock scale modelling to assess effectiveness of agricultural management practice in improving water quality in the Great Barrier Reef Catchments

Agriculture in the catchment areas adjacent to the World Heritage listed Great Barrier Reef (GBR) Marine Park generates pollutants that are a concern for the health of the Reef. Under the Paddock to Reef Integrated Monitoring, Modelling and Reporting program (P2R) of the Reef Plan, the impacts of improved agricultural management practices on water quality entering the GBR are modelled to evaluate the effectiveness of Government water quality improvement policies. The Source Catchments modelling framework estimates loads of pollutants entering the GBR lagoon from rivers. However, Source Catchments does not have the capacity to represent the collection of management practices available to farmers that affect water quality in runoff and drainage at a paddock scale. Therefore, paddock scale agricultural systems models were used to demonstrate the effects of management practice adoption and to provide input to the catchment scale models. Paddock scale models were used because they represent a level of process detail compatible with the management practice investments and implementation on-ground. A fit-for-purpose modelling approach was used, where the paddock model most suited to a given land use and/or water quality pollutant was applied. Three one-dimensional agricultural systems models were employed; HowLeaky in grains, APSIM in sugarcane with HowLeaky post-processing for herbicides and phosphorous and GRASP in grazing lands. These models share similar soil water balance, ground cover and runoff sub-models. However, they vary in the level of detail, particularly in terms of representing crop growth and in the processes considered, such as pesticide degradation and export. In grains and sugarcane cropping, the pollutant time-series (e.g. load per day per unit area) in the Source Catchments models was replaced with an output time-series from HowLeaky or APSIM for each soil-climate spatial combination. Management practices were grouped into systems classed as A, B, C or D. The proportion of each of these management systems contributing to the modelled loads was adjusted to reflect data on the prevalence of adoption of improved management practices in the GBR catchment. In grazing lands, GRASP pasture utilisation and ground cover time-series outputs were interrogated to derive relationships between changes in grazing system management and changes in the USLE C-factors. The USLE is used to predict hillslope erosion in the Source Catchments model. Scaling indices derived from GRASP outputs were used to adjust the USLE C-factors applied in Source Catchments where management practices had changed. The P2R program has demonstrated the effectiveness of linking paddock scale models or emergent models derived from them with catchment scale models. This has enabled detailed management options to be simulated to investigate broad scale water quality impacts of the adoption of improved agricultural practices