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dc.contributor.authorMerkle, Scott A.en
dc.contributor.authorFeret, Peter P.en
dc.contributor.authorBramlett, David L.en
dc.contributor.authorQueijo, Donald L.en
dc.date.accessioned2019-09-11T19:58:48Z
dc.date.available2019-09-11T19:58:48Z
dc.date.issued1982en
dc.identifier.urihttp://hdl.handle.net/10919/93555
dc.description.abstractSouthern pine seed orchards covering more than 10,000 acres currently produce over 160,000 pounds of improved seed having a potential of 1 billion seedlings annually. By the year 2000, annual seed production · is expected to reach 500,000 pounds. Seed orchards not only represent potential for improved growth, wood quality and pest resistance, but they also represent a large capital investment in orchard establishment and equipment and the ~ignificant annual costs of orchard maintenance, protection and harvesting. Since the immediate goal of the seed orchard investment is the annual production of cones and seed, the task of the seed orchard manager could be greatly lightened by a system that will forecast annual cone and seed crops and monitor production efficiency. Such a system has been developed by Bramlett and Godbee (1982). In the Inventory- Moni taring System (IMS) a set of sample trees are chosen from the seed orchard population. Then, based on the survival of cones on tagged sample branches in each sample tree, the expected number of cones and seed from the orchard can be predicted as early as 18 months prior to cone harvest. Bramlett and Godbee (1982) detailed various procedures for the selection of sample trees as well as methods for choosing sample branches and conducting flower, cone let and cone counts. Besides providing guidelines for data collection in the orchard, the authors also defined the variables used in the calculation of predicted cone and seed yields, including cone efficiency, seed potential, seed efficiency, extraction efficiency and germination efficiency. They showed how to compute (or update) the values of these variables and how to apply them in models to calculate predicted bushels of cones, predicted pounds of seed, predicted number of seedlings and other predicted values for the orchard. In addition to these predicted values, Bramlett and Godbee (1982) also demonstrated how the IMS can be used by the orchard manager to evaluate orchard productivity, identify the factors reducing yields, and formulate corrective action, including fertilization and pest management. Because a great deal of record-keeping and repetitive mathematical operations are involved in the IMS, it is ideally suited to computerization. A computerized version of the IMS not only has the advantage of efficient data storage and manipulation, but it also makes possible the application of more sophisticated mathematical models as they become available, and facilitates the utilization of productivity data accumulated from year to year to improve the accuracy of the system.en
dc.description.sponsorshipUSDA Forest Service, Southeastern Forest Experiment Stationen
dc.format.extent89 pagesen
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherVirginia Tech. Division of Forestry and Wildlife Resourcesen
dc.relation.ispartofseriesFWS-2-82en
dc.rightsIn Copyright (InC)en
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s). For other uses you need to obtain permission from the rights holder(s).en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleA Computer Program Package For Use With the Southern Pine Seed Orchard Inventory-Monitoring Systemen
dc.typeReporten
dc.contributor.departmentForest Resources and Environmental Conservationen_US
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen
dc.identifier.sourceurlhttps://frec.vt.edu/content/dam/frec_vt_edu/research/fws/FWS-2-82.pdfen


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