Response of vegetation to fire disturbance: short-term dynamics in two savanna physiognomies

Fire is a constitutive ecological force in savanna ecosystems, but few studies have monitored its short-term effects on plant community dynamics. This study investigated changes in plant diversity in the South American savanna (Cerrado) after severe disturbance by fire. We monitored 30 permanent plots (10 m × 5 m) distributed in two Cerrado physiognomies (típico: more forested; ralo: grass-dominated), being 10 plots in the area disturbed by fire, and five in a preserved control area (undisturbed). From August 2010 to June 2011, we evaluated changes in species richness, abundance and composition of savanna vegetation. Monitoring started one week after the fire; disturbed plots were surveyed monthly, while control plots were surveyed every two months. We observed rapid reassembling in both physiognomies: plots affected by fire showed rapid increase in species richness and plant density during the first four months after the disturbance. Concerning species composition, disturbed plots in the cerrado típico tended to converge to control plots after one year, but each local assemblage followed particular temporal trajectories. A different pattern characterized cerrado ralo plots, which showed heterogeneous trajectories and lack of convergence between disturbed and control plots; the structure of these assemblages will likely change in next years. In conclusion, our results showed that fire significantly affected plant diversity in the two savanna physiognomies (cerrado típico and ralo), but also indicated that community reassembling is fast, with different dynamics between Cerrado physiognomies

Economic and climatic models for estimating coffee supply

O objetivo deste trabalho foi estimar a oferta cafeeira por meio da calibração de modelos estatísticos, com variáveis econômicas e climáticas, das principais regiões produtoras do Estado de São Paulo. As regiões estudadas foram Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa e Osvaldo Cruz. Foram utilizados dados de oferta cafeeira, variáveis econômicas (crédito rural, crédito rural na agricultura e valor da produção) e variáveis climáticas (temperatura do ar, precipitação pluvial, evapotranspiração potencial, deficiência e excedente hídrico) de cada região, para o período de 2000–2014. Os modelos foram calibrados com uso de técnicas de regressão linear múltipla, e todas as combinações possíveis foram testadas para a seleção das variáveis. A oferta cafeeira foi a variável dependente, e as demais, as independentes. A acurácia e a precisão dos modelos foram analisadas pelo erro percentual médio e pelo coeficiente de determinação ajustado, respectivamente. As variáveis que mais influenciam a oferta cafeeira são o valor de produção e a temperatura do ar. É possível estimar a oferta cafeeira com regressões lineares múltiplas por meio de variáveis econômicas e elementos climáticos. Os modelos mais acurados são os calibrados para estimar a oferta cafeeira das regiões de Cássia dos Coqueiros e Osvaldo Cruz.The objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000–2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz