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.
Geographic information system (GIS) tools allow the visualization of research data that have a strong spatial component. Currently, several proprietary desktop GIS tools are available that enable researchers to generate maps and perform spatial analysis. However, these packages often require licensing agreements and do not provide specific options that enable rapid and uncomplicated analysis of biological diversity data. As an alternative, publicly available GIS applications that perform basic GIS as well as specialized functions are available. For example, DIVA–GIS was developed specifically to allow analysis of genebank and herbarium databases as well as to assess genetic, ecological, and geographic patterns in the distribution of crops and wild species. It is potentially useful for researchers who do not have the time to learn how to use proprietary GIS software, or who cannot justify purchasing a license to perform very basic GIS operations like creating and modifying maps. This presentation describes the basic features as well as some advanced functionality of DIVA–GIS and other publicly available GIS applications.
Global positioning system (GPS) and geographic information system (GIS) technologies are at the cutting edge of an emerging agricultural revolution called site-specific management. Anticipated benefits are both economic and environmental because in this system, herbicides, fertilizers and other inputs are placed only where needed in the precise amounts required. The opportunities for site-specific management of crops, soils, and pests are innumerable. However, most students of agriculture and land resource sciences have little, if any, experience with the GPS and GIS technologies that provide these new opportunities. Beginning in 1995, efforts were undertaken to integrate GPS/GIS technology into the College of Agriculture curriculum. The process began with GPS/GIS training workshops for local and regional faculty. Key faculty modified curriculum within several departmental options and produced instructional modules for 12 different agriculture science courses. Experiential learning opportunities were developed and in some classes, farmer practitioners of site-specific management participated with students in identifying management problems and solutions. Instructional modules and active learning exercises were formally evaluated as to their effects on enhanced student decisionmaking skills and competency in GPS/GIS applications. Recently the new course LRES 357 “GPS/GIS Applications” was added to the curriculum and work is underway to place this course on-line.
Kristian E. Holmstrom, Marilyn G. Hughes, Wesley L. Kline, Sarah D. Walker, and Joseph Ingerson-Mahar
In 1998, Rutgers Cooperative Extension (RCE) and the Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA) at Rutgers University began a joint program to use global positioning system (GPS) and geographic information systems (GIS) technologies to map the spatial distribution of corn earworm (Helicoverpa zea Boddie (Lepidoptera:Noctuidae)) and European corn borer (Ostrinia nubilalis Hübner (Lepidoptera:Pyralidae)). In 1999 the Rutgers Cooperative Extension Vegetable Integrated Pest Management (IPM) Program operated a network of 81 blacklight insect survey traps in New Jersey. These 15 W blacklight traps were used to monitor adult populations of vegetable crop pests including corn earworm and European corn borer. All blacklight trap sites were mapped using a hand held GPS unit. Average daily corn borer population data were imported into a GIS software package, and then linked to corresponding mapped locations throughout New Jersey. State wide spatial distributions of adult corn earworm and European corn borer population data were imported into a GIS software package, and then linked to corresponding mapped locations throughout New Jersey. State wide spatial distributions of adult corn earworm and European corn borer populations were produced weekly, and distributed via extension newsletters and web sites to augment the current RCE IPM outreach program.
The initial surge of interest in precision agriculture technologies exhibited by innovators and early adopters involved in crop production appears to have crossed over an important threshold and made a significant development. As valuable field experience increases and learning by doing advances, successful applications of management practices are being identified. Access to accurate information pertaining to practical applications of site-specific management would be expected to motivate more producers to incorporate technology uses with crop production. This next group of producers has been watching technology developments as they preferred to avoid risk and wait for identifiable benefits. Waiting for detailed case studies involving high value fruit and vegetables may be the wrong approach to take. Fierce competition and strict confidentiality are expected, especially in the fresh-market industry that places quality attributes high on the list of desired features. Practical applications of technology that pertain to manageable factors will be the impetus to implementation of site-specific management. High resolution remote sensing imagery from digital aerial and satellite sensors has been used to identify plant stress, direct plant tissue and soil sampling efforts to identifiable soil variability, and provide valuable information for analysis and interpretation of crop growth. Examples of remote sensing imagery that has provided valuable in season progress reports will be identified. Imagery can then be used in a geographic information system along with field maps based on soil properties and physical characteristics determined by on-the-go tractors using various sensors. The focus will be on practice, not theory, as seen from an industry perspective.
N.S. Lang, R. Smithyman, L. Mills, R.L. Wample, J. Silbernagel, and E.M. Perry
Blackleaf (a.k.a. chocolate leaf) is of worldwide concern in Vitis due to its negative impact on fruit ripening, yield reduction and overall stress on grapevines. Research suggests blackleaf is induced by high levels of UV radiation and overall light intensity, which induce color changes (purple-brown-black) in exposed leaves, resulting in >50% reduction in photosynthesis. The ability to detect blackleaf symptoms before expression can provide insight into metabolic stresses and the possibility of the use and/or timing of management practices to reduce its impact. Remotely sensed imagery and spatial analysis may elucidate reflectance-related processes and symptoms not apparent to the un-aided eye. In this research we mapped canopy growth (leaves/shoot and shoots/vine), metabolic triggers (photosynthesis, leaf water potential, soil moisture), and percent blackleaf expression within vineyards using global positioning system (GPS), infrared gas analyzer, and digital remotely-sensed images. Each image and data record was stored as an attribute associated with specific vine location within a geographical information system (GIS). Spatial maps were created from the GIS coverages to graphically present the progression of blackleaf across vineyards throughout the season. Analysis included summary statistics such as minimum, maximum, and variation of green reflectance, within a vineyard by image capture date. Additionally, geostatistics were used to model the degree of similarity between blackleaf values as a function of their spatial location. Continuing research will be aimed at identifying spectral characteristics of early season stresses due to UV light, water stress, and reduced photosynthetic capacity. Spatial relationships between early season stress and later blackleaf expression will be assessed using joint spatial dependence measures. Overall, information obtained through digital image and spatial analysis will supplement site level information for growers.
The annual yield variation in a Japanese plum (Prunus salicina Lindl.) germplasm collection [with 32 cultivars (cv)] was used to generate regression models to describe fruit yields in terms of climate. A Geographic Information System (GIS) combined with generated regression models was used for a regional analysis of potential areas for growing plums in Zacatecas, Mexico. Three distinct cv groups were obtained by principal component analysis and were included in the study: a) `Frontier'–`Santa Rosa', b) `Ozark Premier'–`Burbank', and c) `Shiro'. The amount of winter chilling and temperatures during bloom time were the climatic conditions most related to yield. `Frontier'–'Santa Rosa' had relatively low chilling requirements (700 chill units) compared to `Ozark Premier'–`Burbank', which required the most chilling (900 chill units). `Shiro' yields were more consistent than those of the other two groups, suggesting that it has less strict requirements and received sufficient chilling every year. High temperatures at bloom reduced fruit yield in all cultivars; however, the dependence of yield on temperatures during bloom in `Shiro' was modified by summer temperatures the previous year, suggesting that temperatures at the floral induction and formation stages affect flower primordia development. Using GIS, three potential areas for growing plums in the region were defined on maps, and the differences in potential yield between the cultivar groups were determined. `Frontier'–`Santa Rosa' may be good choices as plum cultivars for the region because they were the cultivars with the highest potential yield in the largest area; however, the flexibility of the method used allows the user to get a regional gradient of the expected yields with several plum cultivars. Using experimental information and a GIS can extend the applicability of germplasm collection data to regional planning in the establishment of orchards and new fruit industries.
Susan Lindley and D. L. Creech
Stephen F. Austin State University is known as the ``University Among the Pines.” The campus is located along the banks of LnNana creek in the center of Nacogdocha, the oldest town in Texas. Rich with history, the community and the university are now recognizing that cultural. historical and landscape treasure deserve greater protection and conservation. This project involves: 1) collecting a data set of each tree on campus including quadrant identifier, plant ID #, species, dbh, tree health, location, crow diameter, tree height and tree value, 2) placing all trees on a campus map in ArcCAD®, a Geographic Information System (GIS) developed for the PC, 3) linking map entities (trees, polygons, themes) with specific rows in a database, and 4) developing a query strategy to ask questions of the landscape. Database queries are powerful analytical tools which can generate resultant maps that answer specific landscape questions. These maps can then be queried again for further analysis. Examples of typical queries might include: 1) illustrate only those pines with a dbh greater than 24″, 2) identify all oak trees within thirty feet of a building, or 3) illustrate all trees over sixty feet with poor tree health. ArcCAD® links the easy drafting capabilities of AutoCAD® with much of the functionality of a true GIS workstation. Map files can be linked to a database(s), text, and visual images (TIF files). We have scanned and are currently archiving old photographs of the campus for future linkages. By understanding the history of the university landscape and documenting the current status of campus vegetation, decision-makers can have at strategies that lessen the impact of development.
J. Logan and M.A. Mueller
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.
. These include food safety, geographic information systems, image enhancement, hydroponics, insect scouting, turfgrass management, plant growth regulator calculations, landscape design, and plant and tree identification. It outlines how to find these apps