Estimation of spatial and spectral properties of phytophthora root rot and its effects on cranberry yield

• Published in: Computers and electronics in agriculture 2002-12, Vol.37 (1), p.57-70
• Main Authors: Pozdnyakova, Larisa, Oudemans, Peter V, Hughes, Marilyn G, Giménez, Daniel
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Cranberry yield; Fundamental and applied biological sciences. Psychology; Fungal plant pathogens; Geostatistics; Pathology, epidemiology, host-fungus relationships. Damages, economic importance; Phytopathology. Animal pests. Plant and forest protection; Phytophtora root rot; Remote sensing
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/S0168-1699(02)00119-9

Current agricultural practices are aimed at maximizing productivity while minimizing the area of cultivated land. This is especially important in cranberry production because strict federal guidelines curtail development of new cranberry acreage 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 impact on yields. PRR causes a reduction in root mass, which results in reduced canopy biomass and alters the spectral reflectance characteristics of the canopy. Detection of acute cases of PRR using color-infrared (CIR) aerial photography is straightforward from apparent bare soil on May images; however, the level of detectable chronic infection is unknown. The objectives of this study are to investigate the relationships between soil characteristics, spectral properties of the crop surface, and the severity of Phytophthora effects on cranberries. Soil, pathogen, and crop data were entered in a GIS and the relationships among the factors were studied using geostatistical methods and surface maps of the relevant GIS layers. These maps were then compared and incorporated with the data derived from remotely sensed images (CIR aerial photographs—May, 2001 and July, 2001). The spatial pattern of stressed vegetation was fairly consistent through 5 years and corresponded to spread of PRR chronic injury and low yield. The disease develops in surface depressions with low infiltration rates, which have high soil water content during July–August. The results suggest that early-season (May) CIR images have more predictive power for the yield and vine density, whereas late-season (July) images are more correlated with PRR and soil infiltration rate.