Tennessee is located in an area of diverse topography, ranging in elevation from <100 m to ≈2000 m, with numerous hills and valleys. The physiography makes it very difficult to spatially interpolate weather data related to vegetable production, such as spring and fall freeze dates and growing degree days (GDD). In addition, there is a poor distribution of cooperative weather stations, especially those with 30 years or more of data. There are climate maps available for Tennessee, but they are of such a general format as to be useless for operational applications. This project is designed to use a geographic information system (GIS) and geospatial techniques to spatially interpolate freeze (0 °C) dates and GDD for different base temperatures and make the data available as Internet-based maps. The goal is to develop reasonable climate values for vegetable growing areas <1000 m in elevation at a 100 square km resolution. The geostatistics that we are evaluating include Thiessen polygons, triangulated irregular network (TIN), inverse distance weighting (IDW), spline, kriging, and cokriging. Data from 140 locations in and around Tennessee are used in the analysis. Incomplete data from 100 other locations are used to validate the models. GDD, which have much less year-to-year variability than freeze dates, can be successfully interpolated using inverse distance weighting (IDW) or spline techniques. Even a simple method like Thiessen produces fairly accurate maps. Freeze dates, however, are better off analyzed on an annual basis because the patterns can vary significantly from year to year. The annual maps can then be superimposed to give a better estimate of average spring and fall freeze dates.
J. Logan and M.A. Mueller
Gayle M. Volk
collection methods. This group of standards is referred to as the Darwin Core and has been adopted by hundreds of institutions worldwide. The Geospatial Extensions of the Darwin Core provide standards for georeference data. Among the fields described are
Janet S. Hartin, David W. Fujino, Lorence R. Oki, S. Karrie Reid, Charles A. Ingels, and Darren Haver
geospatial analysis under controlled conditions monitored by a network of time-domain reflectometry soil moisture sensors that will continuously monitor soil water status within and below the root zone. Another large water waster continues to be low sprinkler
Said Ennahli and Sorkel Kadir
Variability due to soil types, topography, and climate within a vineyard influences grapevine physiological parameters and fruit quality. Technical feasibility of using precision Geographic Information System (GIS) as a viticulture tool to improve vineyard management and increase wine quality will be investigated. The study was conducted in an experimental vineyard where rows consist of plots with 24 cultivars and selections randomly planted and managed similarly. Monitored vineyard parameters collected by Global Positioning System (GPS) location include soil characteristics, soil moisture, vine growth, crop load, and fruit characteristics. Geospatial maps are used to differentiate yield between the cultivars and selections as high, medium, or low. Production was determined from each variety/selection within the vineyard. Yield parameters were number of clusters, cluster weight, and weight of 50 berries; fruit composition (such as pH), titratable acidity, soluble solids concentration, and anthocyanins were measured. Maps for each factor will be derived via GIS tools and spatial analysis will be conducted to assess which spatial variability factor has more effect on grapevine physiology, yield, and fruit quality. This type of analysis can be used by grape growers to achieve specific wine characteristics in a large or small vineyard by controlling all sources of variability, leading to the ability to perform precision viticulture in the future, with low cost.
Paul E. Read*, William J. Waltman, and Stephen Gamet
Terroir embodies a defined place, integrating soils, geology, climate, the cultivar, and the role of cultivation, culture, and history in producing wine (Wilson, 1999; White, 2003).The understated topographic changes, thick loess soils, diffuse climatic boundaries (humid to arid), and brief viticultural history contribute to a misconception that “terroir” may not be applicable or that niche microclimates for vineyards may not exist in Nebraska. With many new cultivars and selections now available that are adapted to growing environments once considered marginal vineyard settings and the wealth of geospatial resource databases (soils, climate, and topography) available, we have begun to combine traditional field cultivar evaluation studies with the geophysical data to determine appropriate site/cultivar suitability. Our data have shown that cultivars that were previously considered unlikely to be successful may be suited to viticulture in specific locations, e.g., Riesling, Lemberger, Cynthiana/Norton, Vignoles, and Chambourcin in southeast Nebraska (our “vinifera triangle”). Mean hardiness ratings (scale 1 to 9, where 1 = dead and 9 = no injury) have been obtained for more than 50 cultivars and selections, ranging from 1.86 for Viognier to 8.66 for Frontenac and 8.71 for Saint Croix, for example. Data for most of the cultivars under test will be presented and matched with “terroirs”, providing growers with a vineyard decision support system that can help match genotypes to their specific vineyard sites and help avoid poor cultivar selection.
Kent D. Kobayashi*
How do we enhance students' learning experience and help them be aware of current and emerging technology used in horticulture? An undergraduate course on “Computer Applications, High Technology, and Robotics in Agriculture” was developed to address these needs. Its objectives were to familiarize students with the ways computers, high technology, and robotics are used in agriculture and to teach students how to design, build, and run a robot. The diverse topics included computer models and simulation, biosensors and instrumentation, graphical tracking and computer scheduling, new methods in plant ecology, automation and robotics, Web-based distance diagnostic and recommendation system, GIS and geospatial analysis, and greenhouse environmental control. An individual speaker presented one topic each week with students also visiting some speaker's labs. The students did active, hands on learning through assignments on computer simulations (STELLA simulation language) and graphical tracking (UNH FloraTrack software). They also built, programmed, and ran robots using Lego Mindstorms robotic kits. The course was evaluated using the Univ.'s CAFE system. There were also open-ended questions for student input. On a scale of 1 (strongly disagree) to 5 (strongly agree), mean scores of the 20 CAFE questions ranged from 3.71 to 4.75 with an overall mean of 4.22. When comparisons to other TPSS courses were possible, this course had a higher mean score for four out of seven questions. Course evaluations indicated this special topics course was important and valuable in helping enhance the students' learning experience.
Gayle M. Volk and Christopher M. Richards
to organized, digitized, parsed, queriable, and complete information increases the number of requests for that germplasm ( Day Rubenstein et al., 2006 ). Basic passport, geospatial, habitat, and sampling data fields place ex situ samples in their
M.R. Gorham, T.M. Waliczek, A. Snelgrove, and J.M. Zajicek
sent to a San Antonio, TX, company called GeoSpatial Training Services where the data were geocoded to create a shapefile. Geocoding refers to the process in which an address is given an x/y (latitude/longitude) coordinate. A shapefile is “a set of
red sheet suspended behind it. Porosity was calculated using the digital analyzer program Erdas Imagine, Version 9.1 (Leica Geosystems Geospatial Imaging, Norcross, GA). Flowering. The effect of irrigation on flowering was evaluated by counting
Eileen M. Perry, Ian Goodwin, and David Cornwall
. Mean values for each band were used to compute the reflectance indices. The image processing was performed using Erdas Imagine (Hexagon Geospatial, Madison, AL) and R ( R Core Team, 2017 ). Fig. 1. Example multispectral imagery showing ( A ) reflectance