Spatial Detection and Quantification of Phytophthora Root Rot Effects on Cranberry Yield

Larisa Pozdnyakova and Peter V. Oudemans
Rutgers Blueberry & Cranberry Research Center,Chatsworth, NJ
Phone (609)-726-1590 x 20
E-mail (

Marilyn G. Hughes
Rutgers Cooperative Extension & Center for Remote Sensing
Phone (732-932-1582)
E-mail (

Daniel Gimenez
Dept. of Env. Sci., New Brunswick, NJ
Phone (732)-932-9477
E-mail (

Image Source: Ocean Spray Cranberries, Inc.
CIR-aerial photography 1:12000, May 21, 1999

aerial photo of cranberry fields


Current agricultural methodology is aimed at maximizing productivity while minimizing the area of cultivated land. This is especially important in cranberry production because strict federal guidelines curtail new cranberry acreage from being developed on wetlands. A major component of this research is focused on the chronic effects of Phytophthora Root Rot (PRR) because of the difficulties in detection and the significant impacts on yields. PRR causes a reduction in root mass, which results in reduced canopy biomass and also alters the spectral reflectance characteristics of the canopy. Detection of severe cases of PRR using color-infrared (CIR) aerial photography is straight forward; however, the level of detectable chronic infection is unknown. The objectives of this study are to investigate the relationships between soil characteristics and the severity of Phytophthora effects on cranberry. Soil, pathogen, and crop data were entered into a GIS and the relationship among the factors were studied. The data were analyzed using geostatistical methods and surface maps of the relevant GIS layers were constructed. These maps were then compared and incorporated with the data derived from the remotely sensed images (CIR aerial photographs). The results of this research are used to quantify the chronic impacts of Phytophthora Root Rot on crop yield; to determine the soil factors, especially drainage characteristics of soil, that enhance chronic infections; and to utilize the CIR imagery for future diagnosis of disease and yield estimation.

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