Cranberry production is typically located in low-lying, sandy soils. In New Jersey there are approximately 3,500 acres in production, primarily within a region of New Jersey called the Pinelands. Despite being a high value crop, these acreages are unlikely to expand significantly due to strict federal regulations prohibiting renovation and cultivation of wetland areas. Thus growers are looking for alternative methods to increase their yields.
In North America, cranberry yields vary from 5,000-70,000 lb/acre. In New Jersey, yields of 18,000 lb/acre are typical. However, variation within a sinlge bed can be as much as 200-fold. Although components of yield (flower number, berry size, etc.) have been well researched, the environmental effects on these factors have not. In particular, high spatial variations in yield within a sinlge bed have been documented, but the environmental components leading to this high level of variation have not. It appears that increases in yield are possible, at least theoretically, but more research is needed to establish these yield constraints in New Jersey cranberry culture.
Dr. Peter Oudemans of the Rutgers Blueberry and Cranberry Research Center in Chatsworth, NJ is leading a project that focuses on the areas of yield prediction, disease monitoring, mapping, and precision agriculture. Ultimately an efficient system for mapping and producing field maps for growers and IPM personnel will be developed to facilitate site-specific application of cultural practices (i.e. drainage tiles) as well as pesticide and fertilizer applications.
The use of Global Positioning Systems (GPS), Geographic Information Systems (GIS), and Remote Sensing (RS), for cranberry, bridges a gap in the relative scale used in precision agriculture today. Since cranberries are grown in small parcels of land (5-10 acres), GPS measurements, sampling methods, and GIS mapping techniques reflect much higher sampling density and require more accurate methods than those currently used in field crops. This research should provide some basic methodologies for other high intensity crops.
Research begun in 1996 utilizing color-infrared aerial photography indicate that a number of features within cranberry beds can be identified through spectral analysis of remote sensing data. These include variations in vegetative cover, irrigation and drainage systems, and areas of beds damaged by insects and fungal disease. Linking ground based data with the remotely sensed imagery enabled us to gain further insight into the spatial variation of factors affecting crop yield and health. The results from this study were presented and published at the 4th annual Conference on Precision Agriculture, July 18-22, 1998 in St. Paul Minnesota and formed the basis for our current and future projects.