Long-term monitoring of Vermont's forest soils: early trends and efforts to address innate variability.

Long-term monitoring of forest soils is necessary to understand the effects of continued environmental change, including climate change, atmospheric deposition of metals, and, in many regions, recovery from acidic precipitation. A monitoring program was initiated in 2002 at five protected forest sites, primarily Spodosol soils, in Vermont, northeastern USA. Every 5 years, ten soil pits were sampled from random subplots in a 50 × 50-m plot at each site. Samples were taken by genetic horizon and, to reduce variability and improve comparability, from four specific layers: the combined Oi/Oe layer, the combined Oa/A layer, the top 10 cm of the B horizon, and 60-70 cm below the soil surface (usually the C horizon). The samples were archived and a subset analyzed for carbon, nitrogen, and exchangeable cations. After four sampling campaigns, the average coefficients of variation (CVs) at each site had a broad range, 10.7% for carbon in the Oa/A horizon to 84.3% for exchangeable Ca2+ in the B horizon. An investigation of variability within the upper 10 cm of the B horizon across a 90-cm soil pit face showed similar CVs to the entire site, emphasizing the need for consistent and careful sampling. After 15 years, temporal trends were significant in the Oa/A and B horizons at two of the five sites, with one site showing an increase in carbon concentration in both layers along with increases in both exchangeable Ca2+ and Al3+ in the B horizon, perhaps linked to recovery from acidification. The monitoring program plans to continue at 5-year intervals for the next century.

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils.

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental changes. Repeated sampling to monitor these changes in forest soils is a relatively new practice that is not well documented in the literature and has only recently been broadly embraced by the scientific community. The objective of this protocol is therefore to synthesize the latest information on methods of soil resampling in a format that can be used to design and implement a soil monitoring program. Successful monitoring of forest soils requires that a study unit be defined within an area of forested land that can be characterized with replicate sampling locations. A resampling interval of 5 years is recommended, but if monitoring is done to evaluate a specific environmental driver, the rate of change expected in that driver should be taken into consideration. Here, we show that the sampling of the profile can be done by horizon where boundaries can be clearly identified and horizons are sufficiently thick to remove soil without contamination from horizons above or below. Otherwise, sampling can be done by depth interval. Archiving of sample for future reanalysis is a key step in avoiding analytical bias and providing the opportunity for additional analyses as new questions arise.

Estudio piloto para la identificación de indicadores antropométricos asociados a marcadores de riesgo de síndrome metabólico en escolares mexicanos / Pilot study to identify anthropometric indices associated with metabolic syndrome risk markers in school-aged Mexican children

Introducción. La obesidad aumenta el riesgo de presentar hipertensión arterial, hiperinsulinemia, obesidad abdominal e hipertriacilgliceridemia en todos los estratos etarios. Debido a la necesidad de crear herramientas de tamizaje para detectar oportunamente estas comorbilidades, es necesario conocer cuáles son los indicadores antropométricos que se asocian con marcadores de riesgo de desarrollo de síndrome metabólico. Métodos. Estudio piloto en donde se realizó una evaluación antropométrica (índice de masa corporal [IMC], circunferencia de cintura, índice circunferencia de cintura/estatura) y su asociación con marcadores de riesgo de síndrome metabólico en 188 escolares de la Ciudad de México (9-12 años de edad). Resultados y conclusiones. La medición de la circunferencia de cintura demostró ser un mejor indicador de riesgo de hipertensión e hipertriacilgliceridemia con respecto al IMC; el índice circunferencia cintura/estatura fue el mejor predictor de la hipertriacilgliceridemia. En escolares con obesidad, la circunferencia de cintura explica la mitad de la variabilidad de la presión arterial.

Introduction. Obesity increases the risk of hypertension, dyslipidemia, abdominal obesity and high insulin levels. To implement effective screening tools to identify these comorbidities, there is a need to know the anthropometric factors associated with the presence of metabolic syndrome risk markers. Methods. In this pilot study, a complete nutrition evaluation was applied to 188 school-aged children (9-12 years old) from 3 urban public schools in Mexico City. Anthropometric indices (body mass, index, waist circumference and waist-to-height ratio) were associated with metabolic syndrome risk markers. Results and conclusions. Waist circumference is a better predictor of hypertension and hypertriglyceridemia than body mass index. Waist-to height ratio was the most significant predictor for hypertriglyceridemia. Waist circumference accounts for half of blood pressure variation in obese children.

[Validation of resting metabolic rate estimation equations in 9- to 12- year-old Mexican children with and without obesity]. / Validación del uso de fórmulas para estimar el gasto energético en reposo en niños mexicanos de 9 a 12 años de edad con y sin obesidad.

INTRODUCTION: Childhood obesity is a public health problem that has increased in the last years. The measurement of resting metabolic rate (RMR) is an important tool to establish any obesity intervention. Existing equations to estimate RMR have not been evaluated in Mexican children. MATERIAL AND METHODS: We describe the association and the agreement between the WHO, Schofield, Harris-Benedict and the Tverskaya equations to estimate the RMR in 114 students (9-12 years old) with normal weight and obesity, of two public schools in Mexico. RMR was measured by indirect calorimetry, in addition, we did an anthropometric and body composition evaluation (electrical bioimpedance method). RESULTS: The fat free mass was the variable that predicted the most variability in RMR. The WHO equation showed no significant difference in the RMR (calorimetry), while the Schofield, Harris-Benedict and the Tverskaya equations showed underestimation. The equation that presents the greatest agreement with the gold standard is the Tvershaya equation in both children with normal weight and obesity. CONCLUSIONS: The non concordant results in the RMR are explained by the differences in body composition. An equation that considers fat mass and fat free mass, as the one proposed by Tverskaya, seems to be the most appropriate equation to be used in clinical settings.