Spatial detection and quantification of Phytophthora root rot effects on cranberry yield Larisa Pozdnyakova, Peter V. Oudemans, Marilyn G. Hughes, and Daniel Gimenez
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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 effec ts 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 also alters the spectral reflectance characteristics of the canopy. Detection of severe cases of Phytophthora root rot 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 characteristic s and the severity of Phytophthora effects on cranberries. Soil, pathogen, and crop data were ent ered in a GIS and the relationships among the factors were studied. The data were analyzed using geostatistical methods and surface maps of the relevant GIS layers. 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.
Well-managed cranberry fields can yield as high as 500 barrels of ber ries per acre (56 t/ha) providing gross income of about 10,000$/ acre. However, the yield usually varies considerably within a field as influenced by disease, weed, and insect damage. To intensify the cranberry production it is important to access the spatial distribution of the yield and factors influencing yield within cranberry fields. An accurate estimation of spatial distribution of soil and crop properties within the agricultural fields requires dense sampling, which is costly and time consuming. Aerial photography images are used to outline the areas with stressed vegetation within the fields and target the crop management.Images provide detail, however, qualitative information about field surface. Quantification of the spectral properties and development of therelationships between the information derived from images and ground data on cranberry canopy, yield, and soil properties is necessary. Based on a thorough evaluation of CIR photographs of the souther n New Jersey five cranberry fields were chosen for the further study. The cranberries were planted in the fall of 1992 following removal of blueberries. Problem areas evident on CIR from 1997 and 1999 and seem to line around an old ditch, which ca n be traced back to 1951.
History of planting Nadine cranberry beds
BW air photograph of the beds from 27 March 1951 . Shapes of modern beds are shown in blue.
CIR from 13 June 1993. Cranberries were planted in the fall of 1992 following removal of blueberries.
CIR from 23 May 1997 . Problem areas evident from exposed sand.
Digitized Nadine beds and sampling points locations, August 1999.
CIR from 5 July 1999. New areas of die back evident.
Estimated yield for Nadine-3 bed, 1999
Average yield from Nadine beds
200 150
350
Nadine Nadine Nadine Nadine Nadine
1 2 3 4 5
The annual yield data for the beds were provided by the owner. Data indicate that the healthy bed (Nadine 5) yielded a considerably more dense crop after 4-5 years of production.
100 50 0 1995
1996
http:// www.landviser.com
1997
1998
To estimate the spatial distribution of yield and soil properties, 212 GPS referenced locations were set on bed Nadine 3 in August 1999. The berries were counted on each location. The kriged surfaces of yield allowed yield estimates to be made from the diseased and healthy areas of the bed.
Yield, barrels/acre
Yield, barrels/acre
250
300 250 200 150
diseased area healthy area
244 207
average
118
100 50 0 1
The Phytophthora root rot and vine density ratings were estimated visually at each sampling point. The crop data were analyzed together with soil physical properties, such as elevation, water content, bulk density, resistance to penetration, and infiltrati on rates measured in August-September 1999. The geostatistical analyses were conducted for all the crop and soil properties. The data ob ey strong spatial auto-correlations well described with either spherical or exponential variogram models. The auto-correlation extends within the ranges of 90 m for yield, 54 m for disease, 68 m for vine density, 81 m for normalized difference vegetation index (NDVI), 50 m for elevation, 13 m for average soil resistance to penetration , 24 m for depth to resistant layer and for bulk density, 90 and 120 m for water contents at 9/2/99 and 10/12/99, respectively, and 10 m f or infiltration rate. The data was kriged to estimate the properties at non-sampled locations and to develop surface maps.
“Fairy rings” Phytophthora root rot
The vine density and disease data were kriged to the sampled locations and estimated values rather then obser ved in the field “unit- or-zero” values were used for regression analyses. For the vegetation indexes only correlations with NDVI are shown, since the correlations for other indexes (RVI and SAVI) are practically the same. The correlations for spectral data from 21 May 1999 are provided. The correlations for spectral dat a derived from the 5 July 1999 image with yield, disease, or vine density are about 50% less than those for spectral data from May image. Opposite to crop data, correlation between spectral properties and soil infiltration rate is much higher for July image reaching 0.79 for red band and 0.67 for NDVI. Correlation matrix for studied properties. Marked correlations are significant at p
