Terrain forms and spatial variability of soil properties in an area cultivatedwith citrus

A técnica de agricultura de precisão e a relação solo-paisagem permitem delimitar áreas para o manejo localizado, o que permite a aplicação localizada de insumos agrícolas e, consequentemente, pode contribuir para a preservação de recursos naturais. Portanto, o objetivo deste trabalho foi caracterizar a variabilidade espacial das propriedades químicas e do teor de argila, no contexto da relação solo-paisagem, em um Latossolo sob cultivo de citros. Amostras de solo foram coletadas na profundidade de 0,0-0,2 m, em uma área de 83,5 ha cultivada com citros, na forma de malha, com intervalos regulares de 50 m, com 129 pontos na forma de relevo côncava e 206 pontos na forma plana, totalizando 335 pontos. Os valores obtidos para as variáveis que expressam as propriedades químicas e para o teor de argila do solo foram submetidos à análise estatística descritiva e geoestatística com a modelagem de semivariogramas para a confecção de mapas de krigagem. Os valores de alcance e mapas de krigagem indicaram maiores variabilidades na forma de relevo côncava (segmento topo), quando comparada com a forma plana (segmentos meia encosta e encosta inferior). A identificação de diferentes formas de relevo mostrou-se eficiente no entendimento da variabilidade espacial das propriedades químicas e do teor de argila do solo sob cultivo de citros.

Challenges of modeling depth-integrated marine primary productivity over multiple decades: A case study at BATS and HOT

The performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth-integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ (14)C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll-alpha was derived from high-performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll-alpha time series were available.

PRODUCTIVITY OF CANE SUGAR: LINEAR AND SPATIAL VARIABILITY BETWEEN COMPONENTS AND TECHNOLOGICAL PRODUCTION

Nowadays, the culture of the sugarcane plays an important role regarding the Brazilian reality, especially in the aspect related to the alternative energy sources. In 2009, the municipality of Suzanapolis (SP), in the Brazilian Cerrado, an experiment was conducted with the culture of the sugarcane in a Red eutrophic, with the aim of selecting, using Pearson correlation coefficients, modeling, simple, linear and multiple regressions and spatial correlation, and also the best technological and productive components, to explain the variability of the productivity of the sugarcane. The geostatistical grid was installed in order to collect the data, with 120 sampling points, in an area of 14.53 ha. For the simple linear regressions, the plants population is the component of production that presents the best quadratic correlation with the productivity of the sugarcane, given by: PRO = -0.553**xPOP(2)+16.14*xPOP-15.77. However, for multiple linear regressions, the equation PRO = -21.11+4.92xPOP**+0.76xPUR** is the one that best presents in order to estimate that productivity. Spatially, the best correlation with yield of the sugarcane is also determined by the component of the production population of plants.