Nutrient content in aboveground biomass of Brazilian peanut cultivars in conservation tillage on sugarcane straw and pasture area.

Fields studies were conducted in 2004/2005 in order to evaluate the effects of tillage on nutrient content in aboveground biomass of two peanut cultivars, cultivated in rotation after mechanical harvested sugarcane and pastures. These trials were carried out in two types of soils; Oxisol and Ultisol, respectively in Ribeir?ao Preto and Mirassol, S?ao Paulo State, Brazil. The experimental design was split-plot with four replications. Tillage treatments (conventional, minimum and no-tillage) were main plots while sub-plots were peanut genotypes IAC-Tatu ST (Valencia market-type, erect growth habit, red seed coat, maturity range around 100 days after planting) and IAC-Caiap´o (Runner market-type, prostate growth habit, pink testa, maturity range more than 135 days). From 15 to 90 days after emergence, samples of leaves and stems were harvested, dried, weighted and ground to determine macro and micronutrient concentration. At 75 days after sowing, the cultivar IAC-Caiap´o showed higher contents of N, P, K, Cu, and Zn while IAC-Tatu presented higher concentrations of Ca, Mg, and S. Zn content was higher in conservation tillage than in conventional, mainly in Oxisoil for both of cultivars.

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.

Reducing competition in agroforestry by pruning native trees.

The degree to which pruning helps reestablish balance in agroforestry was assessed in a system established in São Carlos, São Paulo, Brazil, in 2008. Seven native tree species were planted at a density of 600 trees/ha in five strips of three rows each, and annual crops were cultivated in the 17-m crop strips between the tree strips. Competition was established after 35 months, decreasing the aboveground biomass production of corn planted close to the trees. An assessment of black oats in the dry season following tree pruning showed that the proximity of trees caused reductions in plant and panicle density, aboveground biomass production, number of grains per panicle and grain weight. Because pruning was not sufficient to maintain crop yields, tree thinning is recommended in order to minimize competition and restore conditions for adequate crop production.