Soil moisture plays a key role in hydrological, biogeochemical, and energy budgets of terrestrial ecosystems. Accurate soil moisture measurements in remote ecosystems such as the Amazon are difficult and limited because of logistical constraints. Time domain reflectometry (TDR) sensors are widely used to monitor soil moisture and require calibration to convert the TDR's dielectric permittivity measurement (K a) to volumetric water content (θ v). In this study, our objectives were to develop a field-based calibration of TDR sensors in an old-growth upland forest in the central Amazon, to evaluate the performance of the calibration, and then to apply the calibration to determine the dynamics of soil moisture content within a 14.2-m-deep vertical soil profile. Depth-specific TDR calibration using local soils in a controlled laboratory setting yielded a novel K a-θ v third-degree polynomial calibration. The sensors were later installed to their specific calibration depth in a 14.2-m pit. The widely used K a-θ v relationship (Topp model) underestimated the site-specific θ v by 22-42%, indicating significant error in the model when applied to these well-structured, clay-rich tropical forest soils. The calibrated wet-and dry-season θ v data showed a variety of depth and temporal variations highlighting the importance of soil textural differentiation, root uptake depths, as well as event to seasonal precipitation effects. Data such as these are greatly needed for improving our understanding

The response of second crop corn to nitrogen application was evaluated at different stages of development. The experiment was conducted on the São Carlos farm in Vilhena – Rondônia/Brazil. The factors under study included the splitting of N associated with the application of coated urea with NBPT (45-00-00) in top dressing. The control group (witness) showed the highest productivity among all treatments, with 7962 kg ha-1, followed by V3+V7 (7895 kg ha-1) and V4+V8 (7821 kg ha-1). The lowest productivity was 6630 kg ha-1 in V5+V9, indicating that the later the urea application in defining the productive potential of the crop (V4), the lower the yields achieved.

Dada a importância da soja para a economia brasileira e a consequente necessidade de monitorar as safras agrícolas, com este trabalho objetivou-se comparar os dados de precipitações gerados pelo sistema Tropical Rainfall Measuring Mission (TRMM) frente àqueles observados em superfície de estações meteorológicas convencionais para o estado do Rio Grande do Sul. Para isso, empregaram-se dados de três safras de soja (2006/2007 a 2008/2009) para comparação e geração de um modelo de ajuste e de três safras subsequentes (2009/2010 a 2011/2012) para validação. Os dados de precipitações obtidos do TRMM para as safras de ajuste apresentaram alto coeficiente de correlação (0,83) diante daqueles observados nas estações de superfície, com superestimativa média de 11%. O modelo de ajuste não melhorou a estimativa de precipitação e diminuiu a amplitude dos valores estimados para as safras ajustadas. Ademais, o modelo de ajuste pouco alterou os coeficientes de correlação entre ambas as variáveis para todas as safras (mesmo reduzindo o RMSE para duas das três safras analisadas). Dessa forma, quando os dados de precipitações em superfície não estiverem disponíveis, recomenda-se a utilização dos dados originais do TRMM em modelos de estimativa de produtividade da soja.

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