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In an increasingly globalized and interconnected world, where social and environmental change occur ever more rapidly, careful futures-oriented thinking becomes crucial for effective decision making. Foresight activities, including scenario development, quantitative modeling, and scenario-guided design of policies and programs, play a key role in exploring options to address socioeconomic and environmental challenges across many sectors and decision-making levels. We take stock of recent methodological developments in scenario and foresight exercises, seek to provide greater clarity on the many diverse approaches employed, and examine their use by decision makers in different fields and at different geographic, administrative, and temporal scales. Experience shows the importance of clearly formulated questions, structured dialog, carefully designed scenarios, sophisticated biophysical and socioeconomic analysis, and iteration as needed to more effectively link the growing scenarios and foresight community with today's decision makers and to better address the social, economic, and environmental challenges of tomorrow.

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Governments in Australia are purchasing water entitlements to secure water for environmental benefit, but entitlements generate an allocation profile that does not correspond fully to environmental flow requirements. Therefore, how environmental managers will operate to deliver small and medium‐sized inundation environmental flows remains uncertain. To assist environmental managers with the supply of inundation flows at variable times, it has been suggested that allocation trade be incorporated into efforts aimed at securing water. This paper provides some qualitative and quantitative perspective on what influences southern Murray–Darling Basin irrigators to trade allocation water at specific times across and within seasons using a market transaction framework. The results suggest that while irrigators now have access to greater risk‐management options, environmental managers should consider the possible impact of institutional change before intervening in traditional market activity. The findings may help improve the design of intervention strategies to minimise possible market intervention impacts and strategic behaviour.

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The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on agar plates. It contains 18 000 photos of five different microorganisms as single or mixed cultures, taken under diverse lighting conditions with two different cameras. All the images are classified into countable, uncountable, and empty, with the countable class labeled by microbiologists with colony location and species identification (336 442 colonies in total). This study describes the dataset itself and the process of its development. In the second part, the performance of selected deep neural network architectures for object detection, namely Faster R-CNN and Cascade R-CNN, was evaluated on the AGAR dataset. The results confirmed the great potential of deep learning methods to automate the process of microbe localization and classification based on Petri dish photos. Moreover, AGAR is the first publicly available dataset of this kind and size and will facilitate the future development of machine learning models. The data used in these studies can be found at https://agar.neurosys.com/.