Publication Date: 2013.
Bees provide essential pollination services that are potentially affected both by local farm management and the surrounding landscape. To better understand these different factors, we modelled the relative effects of landscape composition (nesting and floral resources within foraging distances), landscape configuration (patch shape, interpatch connectivity and habitat aggregation) and farm management (organic vs. conventional and local-scale field diversity), and their interactions, on wild bee abundance and richness for 39 crop systems globally. Bee abundance and richness were higher in diversified and organic fields and in landscapes comprising more high-quality habitats; bee richness on conventional fields with low diversity benefited most from high-quality surrounding land cover. Landscape configuration effects were weak. Bee responses varied slightly by biome. Our synthesis reveals that pollinator persistence will depend on both the maintenance of high-quality habitats around farms and on local management practices that may offset impacts of intensive monoculture agriculture.
Author affiliation: Chacoff, Natacha Paola. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tucumán; Argentina
Author affiliation: Garibaldi, Lucas Alejandro. Universidad Nacional de Rio Negro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Author affiliation: Morandin, Lora. Universidad Nacional de Rio Negro; Argentina
Author affiliation: Sáez, Agustín. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; Argentina
Keywords: Agri-environment schemes; diversified farming system; ecologically scaled landscape index; ecosystem services; farm management; habitat fragmentation; landscape structure; organic farming; pollinators; Ecología; Ciencias Biológicas; CIENCIAS NATURALES Y EXACTAS; Agricultura; Agricultura, Silvicultura y Pesca; CIENCIAS AGRÍCOLAS; Agronomía, reproducción y protección de plantas; Agricultura, Silvicultura y Pesca; CIENCIAS AGRÍCOLAS.
Repository: CONICET Digital (CONICET). Consejo Nacional de Investigaciones Científicas y Técnicas
Publication Date: 2018.
Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.
Sociedad Argentina de Informática e Investigación Operativa
Repository: SEDICI (UNLP). Universidad Nacional de La Plata