Earth and Environmental Sciences

Manure solid-liquid separation offers a potential pathway for redistributing nutrients from livestock-intensive, nutrient-surplus regions to cropland with fertilizer demand, but the competitiveness of separated manure products with synthetic phosphorus fertilizer depends on manure quality, acquisition price, and hauling distance. We developed a probabilistic techno-economic framework to evaluate separated dairy manure solids (SDMS) relative to synthetic phosphorus fertilizer under representative market and agronomic conditions and to identify transaction-level thresholds for competitive viability. The analysis combined Sobol global sensitivity analysis, Monte Carlo uncertainty propagation, deterministic scenario surfaces, and leverage analysis. SDMS acquisition price was the dominant driver of variance in net savings, followed by P 2 O 5 concentration and hauling distance. Competitiveness declined sharply with transport distance, with the 50% probability crossover occurring between 161 and 201 km and the most decision-sensitive range concentrated around 121-201 km under the modeled conditions. Scenario analysis showed that lower acquisition price and higher P 2 O 5 concentration substantially expanded the viable hauling radius, while moisture reduction also improved competitiveness but with different marginal returns across the quality surface. The leverage surface showed that the relative benefit of dewatering versus P 2 O 5 enrichment depends on the current product state; across much of the evaluated commercial range, a 1 percentage point reduction in moisture produced a greater gain in breakeven hauling distance than a 0.1 percentage point increase in P 2 O 5 concentration, although the reverse held at low P 2 O 5 concentrations combined with low moisture. The results also identified an acquisition-price ceiling beyond which additional processing no longer preserved competitiveness. These findings provide transaction-level economic criteria for manure redistribution and are implemented in NutrientChoice ( https://www.dairyconservation.org/NutrientChoice ), an open-source decision-support tool.

Livestock has become the dominant large herbivore guild in most rangelands worldwide. Current climate adaptation frameworks emphasize extreme climatic events, although the extent to which these episodic drivers capture the environmental conditions shaping animal behaviour remains unclear. Rising temperatures and more frequent extreme climatic events (ECEs) are expected to influence animal behaviour, but the relative importance of daily climatic conditions versus discrete extreme events remains poorly quantified in managed grazing systems. Movement responses are especially informative as early-warning indicators of behavioural adjustments to environmental stress. We analysed GPS data from 26 sheep herds monitored in southern Spain between 2021 and 2022 (∼12,000 animals; 6464 monitoring days). Daily distance travelled, core area (50% kernel), and home range (95% kernel) were modelled against daily maximum air temperature, precipitation, pasture productivity (NDVI), distance to water sources, and the heat-wave indicator. Herds travelled shorter daily distances and used smaller core areas and home ranges as daily maximum air temperature increased, particularly during mid-summer. Across the annual cycle, all three responses showed nonlinear relationships with temperature, peaking at intermediate values, and were negatively related to NDVI. In contrast, precipitation and the heat-wave indicator were not retained in the best-supported models. Overall, daily maximum air temperature was consistently associated with all three movement and space-use metrics, whereas the heat-wave indicator was not retained in the best-supported models. As warming progresses, these contractions may reduce livestock movement, with potential consequences for vegetation dynamics and biodiversity. Anticipating such behavioural responses may help inform monitoring and management of Mediterranean grazing systems.

Real-time control of stormwater systems improves flood resilience, yet optimization-based methods, such as model predictive control (MPC), remain difficult to implement due to high computational demands and limited operational transparency. As a result, cities continue to rely on rule-based control (RBC), despite the lack of standardized approaches for deriving effective rule sets. This study introduces a framework that formulates interpretable, near-optimal control rules by learning directly from optimal behavior using regression decision trees. MPC is applied offline to generate optimal gate trajectories for 1158 historical rainfall events from 2007 to 2025, which are used to train decision-tree models that extract if-then rules reproducing optimal logic in real time without online optimization. The framework is applied on a basin where the objective is to maintain water levels between 14.8 m and 15.4 m to prevent overflows and preserve recreational activities. Results show that the derived RBC performs better than baseline static control and achieves performance comparable to MPC. In terms of safety, defined by the number of events during which the maximal water level was exceeded, RBC retained 86% of MPC performance, with a total exceedance duration 23% longer, and an increase of 21% in total overflow volume, while reducing gate movement by 57% and computational time by 88%. The extracted rules provided an interpretable representation of optimal control logic, enabling operator understanding, validation, and adjustment. By combining MPC optimization with the transparency and operational simplicity of RBC, this study offers a framework for implementing near-optimal real-time control in stormwater systems.

The Minimum Bisection Problem is a fundamental NP-hard problem with applications in parallel computing, network design, and large-scale data processing. When formulated as a Quadratic Unconstrained Binary Optimization problem for quantum annealing, solution quality depends critically on the choice of the penalty parameter that enforces balanced partitions. However, selecting this parameter is problem-dependent and typically relies on manual tuning or heuristics, limiting practical applicability in engineering settings. This paper proposes a machine learning–based approach for automatic penalty parameter tuning. We employ a Gradient Boosting Regressor to predict suitable penalty values from structural graph features, specifically the number of nodes and graph density. The predicted parameter is then used to construct the model solved by D-Wave’s quantum annealing solvers. The proposed method is evaluated on a large dataset of Erdős–Rényi graphs with up to 4,000 nodes and compared against classical partitioning heuristics, including Metis and Kernighan–Lin algorithms. Experimental results show that the learned parameter selection significantly improves constraint satisfaction and cut minimization, enabling the hybrid quantum annealing solver to consistently outperform classical baselines.

Abstract Other effective area-based conservation measures (OECMs) serve as an important tool to recognise diverse areas for their contributions to conservation, yet implementation remains slow. A key barrier to implementation is the lack of engagement with stakeholders who manage these areas and thus uncertainty about how best to secure their participation. We interviewed stakeholders representing different land uses that could be candidate OECMs in Australia, to capture their perspectives on OECMs and the factors that may motivate or deter their engagement. Stakeholder responses revealed an openness to and interest in OECMs, although many questions that are critical to their participation were raised. Concerns around long-term obligations and conflict between different management objectives were recurrent limitations to engagement. The opportunity for wider recognition of conservation efforts and increased social licence were key motivating factors. Important differences between stakeholder groups indicate that tailored strategies may improve engagement, which is a critical step in progressing OECMs.