Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models esti-mating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems

Intra-genotypic competition of Eucalyptus clones generated by environmental heterogeneity can optimize productivity in forest stands

The growth structure of Eucalyptus plantations is the result of site environment, genetic material, and different types of interaction between neighboring plants. It is well known that sites that are more homogeneous result in greater forest productivity. However, additional factors inherent in the micro- environment or the quality of cuttings can lead to heterogeneous clonal biomass at the end of the rotation cycle. This study of the growth patterns in commercial stands of Eucalyptus clones had two aims: (i) to determine whether environmental heterogeneity causes competition among genetically identical individuals and (ii) to validate the occurrence of intra-genotypic competition, revealing the potential relationship with forest productivity. The present study was developed based on two linear mixed models: a non-genetic model, which accounts for spatial autocorrelation and is used to estimate the effects of competition between neighboring trees into the single clone plots; and a genetic model to infer the nature of the clonal competition. Three hundred and six square plots containing one hundred plants from eight experiments using a randomized block design, with three replications, were evaluated. The experiments were positioned in different environmental conditions by combining two different plant spacings and two altitude elevations. Using the path analysis procedure, we verified that there were significant direct effects of competition according to the proximity of the trees in the plot. In addition, trees that were more distant caused indirect effects of competition through nearby trees. Stands with uniform growth conditions (measured by residual autocorrelation parameters) actually caused higher productivity. The results from the genetic correlations of intra-genotypic competition and productivity showed that the less competitive clones were always less productive, regardless of the experimental condition. The more competitively aggressive clones could optimize their productivity when planted in sites with high residual levels, reaching productivities similar to those of homogeneous stands. This suggests that the implementation of certain silviculture techniques, seeking to increase site uniformity, is less relevant to these clones. The selection and use of these clones might be useful for large companies, because they offer the opportunity to achieve high productivity, and for smaller producers who do not have access to the silvicultural quality used by large companies

Intercropping of coffee with the palm tree, macauba, can mitigate climate change effects

Global climate changes can affect coffee production in Brazil, and in other coffee producing countries. We examined the potential for an agroforestry system with the native species, macauba (Acrocomia aculeata), to mitigate impacts on coffee production by reducing maximal air temperature and photosynthetic active radiation. The objective of this study was to investigate the influence of an agroforestry system with macauba on productivity, microclimatic characteristics and soil physical quality on a coffee plantation in the Atlantic Rainforest biome, in Southern Brazil. We measured soil attributes (moisture, temperature, and physical properties), microclimate conditions (air temperature, photosynthetic active radiation) and coffee production parameters (productivity and yield). Macauba palm trees were planted at different planting densities on the rows and distances from the coffee rows. Planting density of macauba and their distance from the coffee rows affected soil thermal-water regime. Compared with the traditional unshaded sole coffee planting, the intercropped cultivation provided more coffee yield on both macauba density planting and distance evaluated. On the other hand, coffee productivity was increased by agroforestry systems just for 4.2 m distance between palm trees and coffee rows. Planting density of macaubas did not affect coffee yield and productivity. Best coffee harvest in agroforestry systems with macauba was related to higher soil moisture at the depth of 20–40 cm, higher photosynthetic active radiation, and maximum air temperatures lower than 30 °C. Agroforestry with coffee and macauba trees can be an adaptation strategy under future climatic variability and change related to high temperatures and low rainfall

High-performance prediction of macauba fruit biomass for agricultural and industrial purposes using artificial neural networks

Biomass estimation plays of crucial role in agriculture and agro-based industries. The macauba, Acrocomia aculeata (Jacq.) Lood., ex Mart., is a palm species that has been a focal point for research and development of an alternative biomass-bioenergy crop for the tropics. The macauba fruit components (exocarp, mesocarp, endocarp and seed/kernel) present different constitutional characteristics and their biomass determination, by traditional methods, is labor-consuming. Therefore, the validation of procedures that can streamline this process is relevant, since it can reduce costs and time for both breeding programs and industries. This study tested the efficacy of Artificial Neural Networks (ANN) on biomass prediction of the macauba fruit components by comparing it to the multiple linear regression method. The data used came from fruits collected in 18 localities, distributed throughout the state of Minas Gerais, Brazil. According to their provenance, the matrices were clustered into two groups with the k-means method for posterior ANN cross-validation. Each group was interchangeably used for both training and validation purposes. The ANN was more efficient than multivariate linear model in the predictions of dry weight of the fruit́s four components and oil content of the mesocarp and seed. As for variables related to dry weight, ANN reached 98% predictive accuracy (i.e., 98% accuracy of the value predicted by the network), and for variables related to oil contents, accuracy was around 90%. Additionally, non-invasive measurements of the fruit (i.e., low-cost and low-time measurement variables) were adequate enough to predict most of the variables of interest. These results show the ANN's prediction potential, saving time and efforts for the consolidation of macauba as a crop