Carlos Chavez

Tucson’s holiday season sparkles with many beloved traditions, but few capture the wonder and warmth of this time of year quite like Ballet Tucson’s The Nutcracker, presented in partnership with

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Glyphosate is the most widely used herbicide in the world for weed control; however, due to lixiviation, wind and runoff effects, an important fraction can reach the soil, aquifers and surface waters, affecting environmental and human health. The behavior of glyphosate in two agricultural soils (C1: silty clay texture, and C2: silty loam texture) was analyzed in this study using a laboratory-scale model. Water transfer was modeled with the Richards equation, while glyphosate transport was modeled using the advection–dispersion equation, with both solved using finite difference methods. The glyphosate dispersion coefficient was obtained from laboratory concentration data derived from the soil profile via inverse modeling using a non-linear optimization algorithm. The goals of this study were to (i) quantify glyphosate retention in soils with different physical and chemical properties, (ii) calibrate a numerical model for the estimation of dispersivity and simulation of short- and long-term scenarios, and (iii) assess vulnerability to groundwater contamination. The results showed that C1 retained a greater amount of glyphosate in the soil profile, while C2 was considered more vulnerable as it liberated the contaminant more easily. The model accurately reproduced the measured concentrations, as evidenced by the RMSE and R2 statistics, thus supporting further scenario simulations allowing for prediction of the fate of the herbicide in soils. The approach utilized in this study may be useful as a tool for authorities in environmental fields, enabling better control and monitoring of soil contamination. These findings highlight potential risks of contamination and reinforce the importance of agricultural management strategies.

We review information on salmon farming production, environmental and climatic variables covering all salmon farming areas in the Patagonia marine ecosystems over a 10-year period in Chile. With this information, we develop a semi-quantitative Environmental and Productive Risk Assessment (RA) model as a guide for the design of a Traffic Light System (TLS) for salmon farming in Chile that ensures the minimizing impacts to ecosystems and to farmed salmon themselves. This interdisciplinary study is based on the compilation and integration of historical databases from multiple public and private institutions. These databases contain information on production, fish health, oceanographic and climatic information from 79 Salmon Farming Concessions Groupings (ACS) from 2012 to 2022. The model reveals that the highest Environmental and Productive Risks occur where high cumulative production pressure (Hazard) coincides with high Environmental Sensitivity, primarily in the fjords of the Los Lagos and Aysén regions. The study provides a risk-based approach to prioritize monitoring and promote precautionary management in high-risk areas while there is not enough information on ecosystem-level tipping point indicators. Further development of a robust TLS requires the collaborative validation of ecosystem-scale data and involving all relevant stakeholders in a participatory process to establish agreed-upon risk thresholds and governance mechanisms for its implementation.