Slash and burn management and permanent or rotation agroforestry systems: A comparative study for C sequestration by century model simulation.

Understanding the effects of agroforestry systems (AFs) on soil organic carbon (SOC) requires long-term experiments, but scenarios simulations can anticipate the potential of these systems to sequester or lose carbon (C). This study aimed to simulate the SOC dynamics in slash and burn management (BURN) and AFs using the Century model. Data from a long-term experiment implemented in the Brazilian semiarid region were used to simulate SOC dynamics under BURN and AFs situations, and the natural vegetation (NV) "Caatinga" as a reference. BURN scenarios considered different fallow periods (0, 7, 15, 30, 50 and 100 years) among cultivation of the same area. The two types of AFs (agrosilvopastoral-AGP and silvopastoral-SILV) were simulated in two contrasting conditions: (i) each one of the AFs and also NV area were permanently conducted with no rotation among these areas; and (ii) the two AFs and NV rotated among them every 7 years. The correlation coefficients (r), coefficients of determination (CD) and coefficients of residual mass (CRM) showed adequate performance, meaning that the Century model is able to reproduce the SOC stocks in the slash and burn management and AFs situations. The equilibrium points of NV SOC stocks stabilized around 30.3 Mg ha-1, as similar to the measured average of 28.4 Mg ha-1 at field conditions. The adoption of BURN without a fallow period (0 years) resulted in a reduction of 50% of SOC, approximately 20 Mg ha-1, after the first 10 years. Permanent (p) and rotating (r) AFs management systems recovered (in 10 years) fast to the original SOC stocks, resulting in higher SOC stocks than NV SOC at equilibrium. The fallow period of 50 years is necessary to recovery SOC stocks in the Caatinga biome. The simulation shows that the AFs systems increase more SOC stocks than observed in natural vegetation in long-term.

Physical and hydrological characteristics and modelling of the soil water retention curve in the brazilian semi-arid region

Semiarid regions are characterised by water scarcity, a limiting factor on plant growth and development. The Sertão Canal was built in the semiarid region of Brazil, more specifically in the state of Alagoas, with the aim of making year-round irrigation possible. However, for the best water management, a physical and hydrological knowledge of the soils is necessary. As such, the aim of this study was to determine the physical and hydrological characteristics of three different types of soil (Argisol, Quartzarenic Neossol and Regolithic Neossol) under native vegetation (Caatinga) and agricultural systems in the semiarid region of Alagoas, as well as to adjust the soil water retention characteristic curves. Soil samples were collected at depths of 0-10, 10-20 and 20-30 cm in the municipalities of Inhapi, Delmiro Gouveia and Pariconha, in the state of Alagoas. The points of the moisture characteristic curve were determined by the Richards method, at pressures of 33, 100, 500, 1000 and 1500 kPa. Retention curves were modelled using the exponential decay equation and compared using the van Genuchten equation, modelled with the help of the RETC computer software. Particle size varied according to the textural classification of the different soils, from Sand to a Sandy Clay Loam. The retention curve fluctuated due to the particle size of the soil, with the Red-Yellow Argisol (Inhapi) having a greater capacity for water retention. Extremely sandy soils, such as those in the Delmiro Gouveia region, had a low capacity for retaining water. For each soil sample, the exponential decay equation gave the best fit, with values for R2adjust of greater than 0.93. When the measured soil moisture levels were compared with the levels estimated by the RETC model, some of the treatments were unable to estimate accurately the moisture levels obtained with the soil water retention curves.

Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study.

OBJECTIVE: Estimating the potential number of COVID-19 deaths in Brazil for the coming months. METHODS: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17th to May 15th, 2020. These data were collected from an official Brazilian website of the Ministry of Health. The Boltzmann function was applied to a data simulation for each set of data regarding all states of the country. RESULTS: The model data were well-fitted, with R2 values close to 0.999. Up to May 15th, 14,817 COVID-19 deaths have been confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco are estimated to experience a substantial increase in the rate of cumulative cases until July 15th. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul, and Santa Catarina will show lower rates per 1,000,000 inhabitants. CONCLUSION: We estimate a substantial increase in the rate of cumulative cases in Brazil over the next months. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can assist in the planning of measures to contain COVID-19.

Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study.

The COVID-19 death rate in Northeast Brazil is much higher when compared to the national average, demanding a study into the prognosis of the region for planning control measures and preventing the collapse of the health care system. We estimated the potential total cumulative cases of COVID-19 in the region for the next three months. Our study included all confirmed cases, from March 8 until April 28, 2020, collected from the official website that reports the situation of COVID-19 infections in Brazil. The Boltzmann function was applied to a data simulation for each set of data regarding different states. The model data were well fitted, with R2 values close to 0.999. Up to April 28, 20,665 cases were confirmed in the region. The state of Ceará has the highest rate of accumulated cases per 100,000 inhabitants (75.75), followed by Pernambuco. We estimated that the states of Ceará, Sergipe and Paraíba will experience a dramatic increase in the rate of cumulative cases until July 31. Maranhão, Pernambuco, Rio Grande do Norte and Piauí showed a more discreet increase in the model. For Bahia and Alagoas, a 4.7 and 6.6-fold increase in the rate was estimated, respectively. We estimate a substantial increase in the rate of cumulative cases per 100,000 inhabitants in the region within three months, especially for Ceará, Sergipe and Paraíba. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can help planning the measures to contain COVID-19.

PREDICTION OF CUMULATIVE RATE OF COVID-19 DEATHS IN BRAZIL: A MODELING STUDY / PROJEÇÃO DA TAXA CUMULATIVA DE ÓBITOS POR COVID-19 NO BRASIL: UM ESTUDO DE MODELAGEM

Purpose: We estimated the potential number of COVID-19 deaths for Brazil for the next months. Methods: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17 until May 15, 2020. These data were collected from an official Brazilian website of the Ministry of Health. The Boltzmann function was applied to a data simulation for each set of data regarding to all states of the country. Results: The model data were well-fitted, with R2 values close to 0.999. Up to May 15, 14,817 COVID-19 deaths were confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). We estimated that Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco will experience a substantial increase in the rate of cumulative cases until July 15. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul, and Santa Catarina will show lower rates per 1,000,000 inhabitants. Conclusion: We estimate a substantial increase in the rate of cumulative cases in Brazil over the next months. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can assist in the planning of measures to contain COVID-19.

Objetivo: estimar o número potencial de mortes por COVID-19 no Brasil nos próximos meses. Métodos: O estudo incluiu todos os casos confirmados de óbitos do COVID-19, desde o primeiro óbito confirmado em 17 de março até 15 de maio de 2020. Esses dados foram coletados no site oficial do Ministério da Saúde. A função Boltzmann foi aplicada a uma simulação de dados para cada conjunto de dados referente a todos os estados do país. Resultados: Os dados do modelo foram bem ajustados, com valores de R² próximos a 0,999. Até 15 de maio, 14.817 mortes de COVID-19 foram confirmadas no país. O Amazonas possui a maior taxa de casos acumulados por 1.000.000 habitantes (321,14), seguido por Ceará (161,63). Estimou-se que o Rio de Janeiro, Roraima, Amazonas, Pará e Pernambuco sofrerão um aumento substancial na taxa de casos acumulados até 15 de julho. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul e Santa Catarina apresentarão taxas potenciais mais baixas por 1.000.000 habitantes. Conclusão: Foi estimado um aumento substancial na taxa de casos cumulativos no Brasil nos próximos meses. A função Boltzmann provou ser uma ferramenta simples para previsão epidemiológica que pode auxiliar no planejamento de medidas para conter o COVID-19.

Correlation between weather, population size and COVID-19 pandemic: a study of Brazilian capitals / Correlação entre clima, tamanho da população e pandemia da COVID-19: um estudo das capitais brasileiras

Objective: To analyze the correlation between weather, population size and cases of COVID-19 in the capitals of Brazil. Methods: All confirmed cases of COVID-19 infection, from the first confirmed case from February 26 until May 01, 2020 were included. For weather variables, average temperature (°C), dew point (°C), average humidity (%) and wind speed (m s-1) were extracted from the Instituto de Meteorologia database. The population size of each capital was used as a control variable, with data obtained from Instituto Brasileiro de Geografia e Estatística. Spearman rank correlation tests were utilized to examine the correlation between variables. Results: The analysis showed a significant and strong positive correlation between the total cases of COVID-19 and the population size (p<0,01). There was a significant positive correlation with the average humidity of the air and cumulative cases (p<0,05). There was no significant correlation with other climate variables. Conclusion: Our results confront some expectations commented around the world about a possible seasonality of COVID-19 during periods of low humidity and can assist government and health authorities in decision making to control the pandemic. Studies in other regions are important to strengthen the findings.

Objetivo: Analisar a correlação entre clima, tamanho da população e casos de COVID-19 nas capitais do Brasil. Métodos: Foram incluídos todos os casos confirmados de infecção por COVID-19, do primeiro caso confirmado de 26 de fevereiro a 01 de maio de 2020. Para variáveis meteorológicas, temperatura média (° C), ponto de orvalho (° C), umidade média (%) e velocidade do vento (m s-1) foram extraídos da base de dados do Instituto de Meteorologia. O tamanho da população de cada capital foi utilizado como variável de controle, com dados obtidos no Instituto Brasileiro de Geografia e Estatística. Correlação de Spearman foi utilizado para verificar a correlação entre variáveis. Resultados: A análise mostrou uma correlação positiva significativa e forte entre o total de casos de COVID-19 e o tamanho da população (p <0,01). Houve correlação positiva significativa com a umidade média do ar e os casos acumulados (p <0,05). Não houve correlação significativa com outras variáveis climáticas. Conclusão: Os resultados confrontam algumas expectativas comentadas em todo o mundo sobre uma possível sazonalidade do COVID-19 durante períodos de baixa umidade e podem auxiliar autoridades governamentais e de saúde na tomada de decisões para controlar a pandemia. Estudos em outras regiões são importantes para fortalecer os resultados.

Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study / Projeção da taxa de casos acumulados de COVID-19 no Nordeste brasileiro: um estudo de modelagem com base na função de Boltzmann / Previsión de la tasa de incidencia acumulada por infección de COVID-19 en la región nordeste de Brasil: un estudio de modelado basado en funciones de Boltzmann

Abstract: The COVID-19 death rate in Northeast Brazil is much higher when compared to the national average, demanding a study into the prognosis of the region for planning control measures and preventing the collapse of the health care system. We estimated the potential total cumulative cases of COVID-19 in the region for the next three months. Our study included all confirmed cases, from March 8 until April 28, 2020, collected from the official website that reports the situation of COVID-19 infections in Brazil. The Boltzmann function was applied to a data simulation for each set of data regarding different states. The model data were well fitted, with R2 values close to 0.999. Up to April 28, 20,665 cases were confirmed in the region. The state of Ceará has the highest rate of accumulated cases per 100,000 inhabitants (75.75), followed by Pernambuco. We estimated that the states of Ceará, Sergipe and Paraíba will experience a dramatic increase in the rate of cumulative cases until July 31. Maranhão, Pernambuco, Rio Grande do Norte and Piauí showed a more discreet increase in the model. For Bahia and Alagoas, a 4.7 and 6.6-fold increase in the rate was estimated, respectively. We estimate a substantial increase in the rate of cumulative cases per 100,000 inhabitants in the region within three months, especially for Ceará, Sergipe and Paraíba. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can help planning the measures to contain COVID-19.

Resumo: A Região Nordeste do Brasil tem uma taxa de letalidade muito mais elevada por COVID-19, comparado com a média nacional, o que exige uma investigação do prognóstico da região para o planejamento de medidas de controle e para prevenir o colapso do sistema de saúde. Estimamos o total potencial de casos acumulados de COVID-19 na região nos próximos três meses. O estudo incluiu todos os casos confirmados de COVID-19, desde o primeiro caso confirmado, em 8 de março, até 28 de abril de 2020, coletados no site oficial que relata a situação das infecções por COVID-19 no Brasil. A função de Boltzmann foi aplicada a uma simulação de dados para cada conjunto de dados dos diversos estados do Nordeste. Os dados do modelo mostraram bom ajuste, com valores de R2 próximos a 0,999. Até 28 de abril, haviam sido confirmados 20.665 casos na Região Nordeste. O estado do Ceará apresenta a maior taxa de casos acumulados por 100.000 habitantes (75,75), seguido pelo estado de Pernambuco. Estimamos que Ceará, Sergipe e Paraíba apresentarão um aumento dramático na taxa de casos acumulados até 31 de julho. Maranhão, Pernambuco, Rio Grande do Norte e Piauí mostraram aumentos mais discretos de acordo com o modelo. Para Bahia e Alagoas, foram estimados aumentos de 4,7 e 6,6 vezes nas taxas, respectivamente. Estimamos um aumento substancial na taxa de casos acumulados por 100.000 habitantes na Região Nordeste ao longo dos próximos três meses, especialmente no Ceará, Sergipe e Paraíba. A função de Boltzmann mostrou ser uma ferramenta simples para projeções epidemiológicas, podendo auxiliar no planejamento de medidas para conter a COVID-19.

Resumen: La región del nordeste brasileño cuenta con una tasa de mortalidad mucho más alta debido a la COVID-19, si se compara con la media nacional, por lo que es necesario un estudio en la prognosis de la región para planificar medidas de control y prevenir el colapso del sistema de salud. Estimamos el potencial total acumulativo de casos de COVID-19 en esta región durante los próximos tres meses. El estudio incluyó todos los casos confirmados de COVID-19, desde el primer caso, confirmado el 8 de marzo, hasta el 28 de abril de 2020, recogido del sitio web oficial que informa la situación de las infecciones por COVID-19 en Brasil. La función de Boltzmann se aplicó a la simulación de datos para cada conjunto de datos, referentes a diferentes estados. El modelo de datos estuvo bien ajustado, con valores R2 cercanos a 0,999. Hasta el 28 de abril, se confirmaron 20.665 casos en la región. Ceará contó con la tasa más alta de incidencia acumulada por 100.000 habitantes (75,75), seguida de Pernambuco. Estimamos que Ceará, Sergipe y Paraíba sufrirán un dramático aumento en la tasa de incidencia acumulada de casos hasta el 31 de julio. Maranhão, Pernambuco, Rio Grande do Norte y Piauí mostraron un incremento más discreto en este modelo. En el caso de Bahía y Alagoas, se estimó un incremento de un 4,7 y 6,6, respectivamente. Estimamos un aumento sustancial en la tasa de incidencia acumulada de casos por 100.000 habitantes dentro de esta región, respecto a los tres próximos meses, especialmente en Ceará, Sergipe y Paraíba. La función de Boltzmann probó ser una herramienta simple para la previsión epidemiológica que puede ser de ayuda en la planificación de medidas para contener a la COVID-19.

Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study / Projeção da taxa cumulativa de óbitos por COVID-19 no Brasil: um estudo de modelagem

ABSTRACT: Objective: Estimating the potential number of COVID-19 deaths in Brazil for the coming months. Methods: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17th to May 15th, 2020. These data were collected from an official Brazilian website of the Ministry of Health. The Boltzmann function was applied to a data simulation for each set of data regarding all states of the country. Results: The model data were well-fitted, with R2 values close to 0.999. Up to May 15th, 14,817 COVID-19 deaths have been confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco are estimated to experience a substantial increase in the rate of cumulative cases until July 15th. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul, and Santa Catarina will show lower rates per 1,000,000 inhabitants. Conclusion: We estimate a substantial increase in the rate of cumulative cases in Brazil over the next months. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can assist in the planning of measures to contain COVID-19.

RESUMO: Objetivo: Estimar o número potencial de mortes por COVID-19 no Brasil nos próximos meses. Métodos: O estudo incluiu todos os casos confirmados de óbitos do COVID-19, desde o primeiro óbito confirmado em 17 de março até 15 de maio de 2020. Esses dados foram coletados no site oficial do Ministério da Saúde. A função Boltzmann foi aplicada a uma simulação de dados para cada conjunto de dados referente a todos os estados do país. Resultados: Os dados do modelo foram bem ajustados, com valores de R2 próximos a 0,999. Até 15 de maio, 14.817 mortes de COVID-19 foram confirmadas no país. O Amazonas possui a maior taxa de casos acumulados por 1.000.000 habitantes (321,14), seguido pelo Ceará (161,63). Estimou-se que os estados do Rio de Janeiro, Roraima, Amazonas, Pará e Pernambuco sofrerão um aumento substancial na taxa de casos acumulados até 15 de julho. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul e Santa Catarina apresentarão taxas potenciais mais baixas por 1.000.000 habitantes. Conclusão: Foi estimado um aumento substancial na taxa de casos cumulativos no Brasil nos próximos meses. A função Boltzmann provou ser uma ferramenta simples para previsão epidemiológica que pode auxiliar no planejamento de medidas para conter o COVID-19.