Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data.

Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.

Predição de ganhos genéticos via modelos mistos em progênies de café Conilon.

Objetivou-se, neste trabalho, avaliar os ganhos genéticos preditos por meio de diferentes índices de seleção pela metodologia REML/BLUP, em cinco caracteres de interesse ao programa de melhoramento do café conilon do Incaper. Foram avaliadas 8 progênies de meios-irmãos, de ciclo de maturação precoce, média de duas safras, com três repetições, o que totalizou 1368 observações, utilizados os índices de seleção clássico, multiplicativo e com base na soma de postos. Avaliaramse, na época de colheita, as características tamanho dos grãos (TG), produtividade (PRO), porte (PT), vigor vegetativo (VIG) e grau de inclinação (GI). A população foi avaliada na Fazenda Experimental de Marilândia, região Noroeste do estado do Espírito Santo. As análises genético-estatísticas foram realizadas pelo programa Selegen - REM/BLUP. Verificou-se, a partir da análise dos parâmetros genéticos, um excelente potencial seletivo entre famílias, para todas as características avaliadas. O índice Mulamba e Mock foi o que mostrou maior eficiência de seleção entre famílias de meios-irmãos de café conilon.

Identification and effects of mixed infection of Potyvirus isolates with Cucumber mosaic virus in cucurbits.

Mixed infections in cucurbits are frequently observed in natural conditions between viruses from the Potyvirus genus and Cucumber mosaic virus (CMV), which significantly decreases productivity. The objectives of the present study was to compare the host range of PRSV-W, WMV, and ZYMV isolates and evaluate the effects of mixed infections with CMV in zucchini plants (Cucurbita pepo L.). Host range studies comprising 23 plant species confirmed some similarities and biological differences among the isolates of PRSV-W, ZYMV, and WMV. RT-PCR confirmed the amplification of DNA fragments of the PRSV-W, WMV, and ZYMV coat protein gene (cp) and cytoplasm inclusion gene (ci). The virus interaction studies in zucchini Caserta plants indicated synergistic interactions, particularly among species from the Potyvirus genus, and some CMV interference with some virus combinations.

Spatial scale effects of sampling on the interpolation of species distribution models in the southwestern Amazon.

Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.