Event DAS‐59122‐7 is a novel transgenic trait designed to protect the roots and yield potential of maize from the insect pest corn rootworm Diabrotica spp. (Col.: Chrysomelidae). The increased pest status of corn rootworm, exceptional efficacy of this trait, and anticipated increases in farm efficiency and grower and environmental safety will drive adoption of this trait. Strong grower acceptance of this trait highlights the importance of science‐based and practical resistance management strategies. A non‐diapause trait was introgressed into two laboratory colonies of Diabrotica virgifera virgifera collected from geographically distinct locations: Rochelle, IL and York, NE. Both colonies were divided and each reared on maize containing event DAS‐59122‐7 or its near isoline. Selected and unselected colonies were evaluated for phenotypic change in larval development, injury potential and survival to adulthood during 10 and 11 generations. The F1 generation of both selected colonies displayed increased larval development, survivorship and measurable, but economically insignificant increases in injury potential on DAS‐59122‐7 maize. Survival rates of 0.4 and 1.3% in F1 generations of both selected colonies corroborate field estimates of survival on DAS‐59122‐7 maize. Over later generations, total phenotypic variation declined gradually and irregularly. Despite the absence of random mating, the tolerance trait could not be fixed in either population after 10 or 11 generations of selection. An allele conferring major resistance to DAS‐59122‐7 was not identified in either selected colony. The assessment also concluded that major resistance gene(s) are rare in populations of D. v. virgifera in the United States, and that a minor trait(s) conferring a low level of survival on DAS‐59122‐7 maize was present. The tolerance trait identified in this study was considered minor with respect to its impact on DAS‐59122‐7 maize efficacy, and the role this trait may play in total effective refuge for major resistance genes with recessive inheritance is the basis of future work.
LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r=0.56 to r=0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e. height, width and volume) and on canopy structure (i.e. light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.
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