Efeito do grau de moagem, do tipo de frasco e do volume vazio sobre a variabilidade analítica do fósforo extraído pelos métodos Mehlich-1 e Mehlich-3

Na execução de uma análise química de solo são empregados diversos procedimentos que, mesmo seguindo-se o protocolo preconizado pelo método de análise utilizado, estão sujeitos a variações nos resultados analíticos, causadas por manipulação das amostras ou dos materiais utilizados. Objetivou-se neste estudo avaliar a influência do grau de moagem da amostra, do tipo de frasco e do volume vazio no frasco na execução dos métodos Mehlich-1 e Mehlich-3 para determinar o P no solo. Para tanto, foram conduzidos três experimentos. No primeiro experimento, as amostras de solo foram moídas e tamisadas em peneiras com aberturas de 2,000; 1,700; 0,850; 0,600; e 0,300 mm. No segundo, foram utilizados dois modelos de frasco (erlenmeyer e snap-cap), ambos com volume de 50 mL. Já no terceiro, foi alterado o volume vazio no frasco, mantendo-se a relação solo:solução extratora utilizando-se as quantidades dentro do frasco de 1:10; 1,5:15; 2,5:25; 3:30; e 4:40 cm³ cm-3. O grau de moagem das amostras não influenciou a capacidade de extração do Mehlich-1; entretanto, a capacidade extrativa do Mehlich-3 foi influenciada, principalmente em solos argilosos. Tanto para o Mehlich-1 quanto para o Mehlich-3, os teores de P extraído foram significativamente mais elevados com o uso de frasco tipo snap-cap em relação ao erlenmeyer. O volume vazio no frasco alterou os teores de P extraído para o Mehlich-1 e Mehlich-3 em 100 e 64 % das amostras, respectivamente. Deve-se padronizar a intensidade da moagem das amostras de solo para extração do P pela solução de Mehlich-3. Um modelo único de frasco deve ser adotado pelos laboratórios de rotina para análise do P, independentemente do método de extração, mantendo-se sempre constante no frasco o volume da amostra (cm³) para o volume de solução extratora (cm³).

EPI Update, May 25, 2012

Weekly newsletter for Center For Acute Disease Epidemiology of Iowa Department of Public Health.

APPRAISAL OF THE SNAP MODEL FOR PREDICTING NITROGEN MINERALIZATION IN TROPICAL SOILS UNDER EUCALYPTUS

The Soil Nitrogen Availability Predictor (SNAP) model predicts daily and annual rates of net N mineralization (NNM) based on daily weather measurements, daily predictions of soil water and soil temperature, and on temperature and moisture modifiers obtained during aerobic incubation (basal rate). The model was based on in situ measurements of NNM in Australian soils under temperate climate. The purpose of this study was to assess this model for use in tropical soils under eucalyptus plantations in São Paulo State, Brazil. Based on field incubations for one month in three, NNM rates were measured at 11 sites (0-20 cm layer) for 21 months. The basal rate was determined in in situ incubations during moist and warm periods (January to March). Annual rates of 150-350 kg ha-1 yr-1 NNM predicted by the SNAP model were reasonably accurate (R2 = 0.84). In other periods, at lower moisture and temperature, NNM rates were overestimated. Therefore, if used carefully, the model can provide adequate predictions of annual NNM and may be useful in practical applications. For NNM predictions for shorter periods than a year or under suboptimal incubation conditions, the temperature and moisture modifiers need to be recalibrated for tropical conditions.

Friday Facts, May 25, 2012, Vol. 142

Bureau of Nutrition and Health Promotion part of the Iowa Department of Public Health produces of weekly newsletter about the Iowa WIC Program for the State of Iowa citizen.

From Empirical Bayes to Full Bayes: Methods for Analysing Traffice Safety Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this slight, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

Guidance Document for Executive Order Number 17, March 25, 2010

Executive Order 17, signed by Governor Chester J. Culver on September 25, 2009, emphasizes revitalization of historic properties and cultural and entertainment districts and also supports safe and healthy work places, sustainable design and cost effective use of state resources. Specifically, the Executive Order requires that State entities managing or leasing real estate on behalf of the State shall give priority to the needs of public entities and the populations they serve consistent with the cost effective use of state revenues. It also indicates that existing resources and facilities shall be used where adequate, cost competitive and appropriate for efficient and effective current state operations.

Iowa Crop and Weather Report, June 25, 2012, Vol. 12. no. 17

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Iowa Secretary of Agriculture Bill Northey today commented on the Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October.

Iowa Workforce News, August 2012, Issue 25

Iowa Workforce Development's bi-monthly newsletter.

Iowa Crop and Weather Report, August 20, 2012, Vol. 12. no. 25

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. Iowa Secretary of Agriculture Bill Northey today commented on the Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service. The report is released weekly from April through October.

[Collection de cartes postales classées méthodiquement pour l'enseignement de la géographie, Paris, F. Nathan, s.d. (1928), offert par l'éditeur. 353 cartes postales en 15 paquets. 1. Généralités, définitions principales (1-25) 2. Ports de commerce, de pêche, militaires, fluviaux (26-50) 3. Pêches et industries diverses (51-75) 4. Relief du sol (76-95) 5. Communications en montagne (96-119) 6. Histoire d'un fleuve (120-139) 7. Phénomènes dus à l'érosion (140-159) 8. Voies de communication (160-179) 9. Paris (180-204) 10. Algérie (205-228) 11. Tunisie (229-253) 12. Maroc (254-278) 13. A.O.F. et A.E.F. (279-303) 14. Madagascar (304-328) 15. Indochine (329-353)]

Donateur : Nathan, Fernand (1858-1947)

Iowa Agri-News, November 16, 2012, Vol. 25

Crop and livestock summaries for the state of Iowa, produced by the Iowa Department of Agriculture.

Aging Watch, May 25, 2012, Vol. 12, no. 3

The reintroduction of the Iowa Department on Aging legislative and policy update, now known as “Aging Watch.” The Department is providing this update to better inform you about policy affecting older Iowans. In addition to policy updates from the statehouse and the nation’s capitol, you’ll learn about Department programs and changes affecting the landscape. As you’ll learn reading this and future editions, big changes are coming for the Iowa Aging Network. Over the next year the Department will be reducing the number of local Area Agencies on Aging, as required by legislative action. Not surprisingly, this is a major change for everyone.

Aging Watch, May 25, 2012, Vol. 12, no. 3

The reintroduction of the Iowa Department on Aging legislative and policy update, now known as “Aging Watch.” The Department is providing this update to better inform you about policy affecting older Iowans. In addition to policy updates from the statehouse and the nation’s capitol, you’ll learn about Department programs and changes affecting the landscape. As you’ll learn reading this and future editions, big changes are coming for the Iowa Aging Network. Over the next year the Department will be reducing the number of local Area Agencies on Aging, as required by legislative action. Not surprisingly, this is a major change for everyone.