In Uruguay, dairy production is essential for the country and is under scrutiny due to the environmental impacts it can cause. Impacts are primarily the result of inefficient management practices which lead to an excessive use of nutrients -mainly nitrogen (N) and phosphorus (P)- and their surplus flow into the environment, affecting soil, groundwater, and rivers. In this context, this paper explains research that has generated new knowledge for Uruguay's dairy sector to achieve more sustainable production by understanding which farm management practices maximize nutrient efficiency while reducing environmental damage. The research used a multi-methodology approach for the case study of a typical Uruguayan dairy farm by applying the Nutrient Budget Method and developing an Agent-Based Model (ABM) called Nitrogen Phosphorus Management (NPM). Nutrient budgeting is a valuable and straightforward method for analyzing farm data and evaluating all the nutrient inputs and outputs. However, it has some visual and research limitations that can be complemented with suitable agent-based modeling. The ABM modeling approach enables the analysis of the model's agents in space and time, capturing the agroecosystem's heterogeneity. Through their combined application, the research results demonstrate that, along with the use of correct management practices, it is possible to improve nutrient efficiency, and, in this way, dairy production systems can be less dependent on external nutrient inputs. The main findings indicate that nitrogen biological fixation, pastoral diets, cow stocking rates, and phosphorus accumulation in soils are key management variables that affect nutrient efficiency and environmental impacts.

Rising global temperatures are a threat to the current state of the Arctic. In particular, permafrost degradation has been impacting the terrestrial cryosphere in many ways, including effects on carbon cycling and the global climate, regional hydrological connectivity and ecosystem dynamics, as well as human health and infrastructure. However, the ability to simulate permafrost dynamics under future climate projections is limited, and model outputs are often associated with large uncertainties. A model structured on a Bayesian Network is presented to address existing limitations in the representation of physically complex processes and the limited availability of observational data. A strength of Bayesian methods over more traditional modeling methods is the ability to integrate various types of evidence (i.e., observations, model outputs, expert assessments) into a single model by mapping the evidence into probability distributions. Here, we outline PermaBN, a new modeling framework, to simulate permafrost thaw in the continuous permafrost region of the Arctic. Pre-validation and expert assessment validation results show that the model produces estimations of permafrost thaw depth that are consistent with current research, i.e., thaw depth increases during the snow-free season under initial conditions favoring warming temperatures, lowered soil moisture conditions, and low active layer ice content. Using a case study from northwestern Canada to evaluate PermaBN, we show that model performance is enhanced when certainty about the system components increases for known scenarios described by observations directly integrated into the model; in this case, insulation properties from vegetation were integrated to the model. Overall, PermaBN could provide informative predictions about permafrost dynamics without high computational cost and with the ability to integrate multiple types of evidence that traditional physics-based models sometimes do not account for, allowing PermaBN to be applied to carbon modeling studies, infrastructure hazard assessments, and policy decisions aimed at mitigation of, and adaptation to, permafrost degradation.

We have developed an approach which examines ecosystem function and the potential effects of climatic shifts. The Lake McDonald watershed of Glacier National Park was the focus for two linked research activities: acquisition of baseline data on hydrologic, chemical and aquatic organism attributes that characterize this pristine northern rocky mountain watershed, and further developing the Regional Hydro-Ecosystem Simulation System (RHESSys), a collection of integrated models which collectively provide spatially explicit, mechanistically-derived outputs of ecosystem processes, including hydrologic outflow, soil moisture, and snowpack water equivalence. In this unique setting field validation of RHESSys, outputs demonstrated that reasonable estimates of SWE and streamflow are being produced. RHESSys was used to predict annual stream discharge and temperature. The predictions, in conjunction with the field data, indicated that aquatic resources of the park may be significantly affected. Utilizing RHESSys to predict potential climate scenarios and response of other key ecosystem components can provide scientific insights as well as proactive guidelines for national park management.