Distribution of galling insects and their parasitoids on Caryocar brasiliense tree crowns

Caryocar brasiliense Camb. (Malpighiales: Caryocaraceae) is widely distributed in the Brazilian savanna and its fruits are used by humans for food, production of cosmetics, lubricants, and in the pharmaceutical industry. This plant is damaged by galling insects. Number of these galling insects and their parasitoids was recorded, in the field (galls) and in the laboratory (adults emerged from the galls), from three C. brasiliense crown heights, during three years. Numbers of adults of Eurytoma sp. (Hymenoptera: Eurytomidae), galling insect (younger attack) and Sycophila sp. (Hymenoptera: Eurytomidae) (a parasitoid of Eurytoma sp.), were greater on the apical parts of C. brasiliense tree crowns. Numbers and groups of Eurytoma sp. globoid galls (older attack) were higher in the median and basal crown parts. The numbers of Eurytoma sp. galls were higher on apical part of C. brasiliense tree crown and also of their parasitoids.

Role of different types of protective forest stands in fodder productivity of rangeland in the Western Caspian region

Forest pastures with different tree crown cover were studied. The seasonal dynamics of the yield and nutritional value of grass and twig-leaf fodder of forest pasture was studied. The unsystematic use of pastures in arid territories has increased the process of degradation and has become one of the factors of depletion of their natural vegetation. The use of forest reclamation in the 70s and 90s on sandy lands and pastures in the Western Caspian region made it possible to create significant areas of forest pasture land with strip and massive stands of Ulmus pumila L. and Robinia pseudoacacia L. Different types of tree stands created on pastures not only improve the microclimate and form a comfortable environment for grazing animals, but also are an additional source of valuable twig feed. The aim of the research was to study forage productivity of forest pastures with different types of plantings on the sands of the Western Caspian region. The objects of the research were forest pastures with different species composition and tree crown cover. Studies on the forage productivity of forest-reclaimed pastures were conducted in 2018-2020 on the basis of the North Caucasus branch of Federal Scientific Centre of Agroecology, Complex Melioration and Protective Af-forestation of the Russian Academy of Sciences. The research was based on field experiments and laboratory analyses. According to the results of the study, forest-reclaimed pastures with unsystematic grazing had 1.52-fold increase in productivity compared to natural ones. The largest amount of twig-leaf feed was formed in broad-band Robinia stands in summer-autumn period. The total gross stock of natural pastures of the Western Caspian region did not exceed 0.30.4 t/ha of dry weight, the consumed stock was 0.20.3 t/ha. With the help of strip and massive plantings on degraded pasture lands, it is possible to increase significantly their productivity and quality with the achievement of 7 MJ of exchange energy and 0.260.29 feed units in 1 kg of elm and robinia twig-leaf feed during droughts.

Influence of scattered trees in grazing areas on soil properties in the Piedmont region of the Colombian Amazon

Trees dispersed in grazing areas are contribute to the sustainability of livestock systems. The interactions between trees and soil are ecological processes that allow the modification of the biology, fertility, and physics of the soil. This study was aimed to assess the influence of dispersed trees in pastures on soil properties in grazing areas for dual-purpose cattle systems in the Piedmont region of the Colombian Amazon. The work was done in grazing areas with scattered trees at the Centro de Investigaciones Amazónicas CIMAZ–Macagual in Florencia—Caquetá—Colombia. We evaluated the effect of five tree species, Andira inermis, Bellucia pentámera, Guarea Guidonia, Psidium guajava and Zygia longifolia, on soil properties (up to 30 cm soil depth) under and outside the influence of the crown. Under the tree crown, three points were systematically taken in different cardinal positions. This was done at a distance corresponding to half the radius of the tree crown. The sampling points in the open pasture area (out of crown) were made in the same way, but at 15 m from the crown border. The ANOVA showed significant interaction (P < 0.0001) between tree species and location for macrofauna abundance up to 30 cm soil depth. For this reason, we performed the comparison between locations for each tree species. Chemical soil variables up to 10 cm soil depth only showed interaction of tree species-location for exchangeable potassium (P = 0.0004). Soil physical soil characteristics up to 30 cm soil depth only showed interaction of tree species-location at 20 cm soil depth (P = 0.0003). The principal component analysis for soil properties explained 61.1% of the total variability of the data with the two first axes. Using Monte Carlo test, we found crown effect for all species. Trees help to control exchangeable mineral elements that can affect the soil, potentiate basic cations such as magnesium and potassium, increase the abundance of soil macrofauna; but some trees with high ground level of shade in grazing areas could increase soil compaction due to the greater concentration of cattle in these areas.

Pine wilt disease detection in high-resolution UAV images using object-oriented classification

Pine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

Tree Crowns Segmentation and Classification in Overlapping Orchards Based on Satellite Images and Unsupervised Learning Algorithms

Smart agriculture is a new concept that combines agriculture and new technologies to improve the yield’s quality and quantity as well as facilitate many tasks for farmers in managing orchards. An essential factor in smart agriculture is tree crown segmentation, which helps farmers automatically monitor their orchards and get information about each tree. However, one of the main problems, in this case, is when the trees are close to each other, which means that it would be difficult for the algorithm to delineate the crowns correctly. This paper used satellite images and machine learning algorithms to segment and classify trees in overlapping orchards. The data used are images from the Moroccan Mohammed VI satellite, and the study region is the OUARGHA citrus orchard located in Morocco. Our approach starts by segmenting the rows inside the parcel and finding all the trees there, getting their canopies, and classifying them by size. In general, the model inputs the parcel’s image and other field measurements to classify the trees into three classes: missing/weak, normal, or big. Finally, the results are visualized in a map containing all the trees with their classes. For the results, we obtained a score of 0.93 F-measure in rows segmentation. Additionally, several field comparisons were performed to validate the classification, dozens of trees were compared and the results were very good. This paper aims to help farmers to quickly and automatically classify trees by crown size, even if there are overlapping orchards, in order to easily monitor each tree’s health and understand the tree’s distribution in the field.