Extrapolation of a structural equation model for digital soil mapping

In theory, two separate regions with the same soil-forming factors should develop similar soil conditions. This theoretical finding has been used in digital soil mapping (DSM) to extrapolate a model from one area to another, which usually does not work out well. One reason for failure could be that most of these studies used empirical methods. Structural equation modelling (SEM) is a semi-mechanistic technique, which can explicitly include expert knowledge. We therefore hypothesize that SEM models are more suitable for extrapolation than purely empirical models in DSM. The objective of this study was to investigate the extrapolation capability of SEM by comparing different model settings. We applied a SEM model from a previous study in Argentina to a similar soil-landscape in the Great Plains of the United States to predict clay, organic carbon, and cation exchange capacity for three major horizons: A, B, and C. We concluded that system relationships that were well supported by pedological knowledge showed consistent and equal behaviour in both study areas. In addition, a deeper understanding of indicators of soil-forming factors could strengthen conceptual models for extrapolating DSM models. We also found that for model extrapolation, knowledge-based links between system variables are more effective than data-driven links. In particular, model modifications can improve local prediction but harm the predictive power of extrapolation.Instituto de SuelosFil: Angelini, Marcos Esteban. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina. Universidad Nacional de Luján; ArgentinaFil: Kempen, B. ISRIC — World Soil Information; HolandaFil: Heuvelink, G.B.M. Wageningen University. Soil Geography and Landscape Group; Holanda. ISRIC — World Soil Information; HolandaFil: Temme, Arnaud J.A.M. Kansas State University. Geography Department; Estados UnidosFil: Ransom, Michel D. Kansas State University. Department of Agronomy; Estados Unido

Strategies for mitigation of nitrogen environmental impact from swine production Estratégias para minimização do impacto ambiental do azoto em suinicultura

This work presents strategies that can be implemented in order to minimize the environmental impact of swine slurry on soil, water, and air. This reduction can be achieved through decrease in nitrogen excretion and ammonia emissions. The correct feed formulation according to animal requirements, the increase in diet digestibility and improvement in animal performance can reduce nitrogen excretion. The use of additives either in the diet or in the manure as well as some equipment rearrangements can reduce ammonia emission.<br>Neste trabalho são apresentadas estratégias que podem ser implementadas para minimizar o impacto ambiental dos efluentes da produção suína sobre o solo, a água e a atmosfera. Esta redução pode ser obtida com a excreção de azoto e a sua volatilização. A formulação mais ajustada às necessidades dos animais, o aumento da digestibilidade da dieta e a melhoria do desempenho dos animais podem reduzir a excreção de azoto. O uso de aditivos nas dietas ou nos dejectos e alterações nas instalações pode reduzir a volatilização da amónia

Recovery of dialysis patients with COVID-19: health outcomes 3 months after diagnosis in ERACODA

© The Author(s) 2022.Background. Coronavirus disease 2019 (COVID-19)-related short-term mortality is high in dialysis patients, but longer-term outcomes are largely unknown. We therefore assessed patient recovery in a large cohort of dialysis patients 3 months after their COVID-19 diagnosis. Methods. We analyzed data on dialysis patients diagnosed with COVID-19 from 1 February 2020 to 31 March 2021 from the European Renal Association COVID-19 Database (ERACODA). The outcomes studied were patient survival, residence and functional and mental health status (estimated by their treating physician) 3 months after COVID-19 diagnosis. Complete follow-up data were available for 854 surviving patients. Patient characteristics associated with recovery were analyzed using logistic regression. Results. In 2449 hemodialysis patients (mean ± SD age 67.5 ± 14.4 years, 62% male), survival probabilities at 3 months after COVID-19 diagnosis were 90% for nonhospitalized patients (n = 1087), 73% for patients admitted to the hospital but not to an intensive care unit (ICU) (n = 1165) and 40% for those admitted to an ICU (n = 197). Patient survival hardly decreased between 28 days and 3 months after COVID-19 diagnosis. At 3 months, 87% functioned at their pre-existent functional and 94% at their pre-existent mental level. Only few of the surviving patients were still admitted to the hospital (0.8–6.3%) or a nursing home (∼5%). A higher age and frailty score at presentation and ICU admission were associated with worse functional outcome. Conclusions. Mortality between 28 days and 3 months after COVID-19 diagnosis was low and the majority of patients who survived COVID-19 recovered to their pre-existent functional and mental health level at 3 months after diagnosis