Articles
INDICATOR CROP BIOASSAYS HELP DEFINE PRODUCTIVITY OF FLORIDA’S FLATWOODS SOILS
Article number
824_34
Pages
289 – 296
Language
English
Abstract
A 10-ha citrus grove located near Wauchula in Hardee County, Florida, USA was examined for spatially correlated yield reduction and soil heterogeneity.
Tree canopy volumes were measured and mapped in the grove using an ultrasonic array and DGPS receiver in order to describe spatial productivity.
Fruit yield was mapped from harvester trucks equipped with DGPS receivers and data loggers.
The grove was classified into five productivity zones representing very good, good, medium, poor, and very poor citrus growth areas based on the canopy volume.
Soil samples were collected from 30 locations at four 15-cm depth increments to 60 cm and analyzed for their chemical and physical properties.
Soil properties showed a continuous trend along the productivity gradient and depth.
A greenhouse bioassay with radish as an indicator crop was used to compare the productivity of five zones at four depths.
Overhead photographs of the potted radish plants were captured periodically with a SLR digital camera and leaf area was calculated by image processing.
Shoot dry weights were also measured.
Analysis of variance showed that differences between the productivity zones were pronounced at the lower two depths for leaf area and shoot dry weights.
The growth curves for leaf area indicated the differences between productivity zones started appearing in the later stages of growth.
Regression analysis showed that the amount of citrus canopy volume spatial variation explained by radish growth increased with soil depth.
The results of the bioassay showed that citrus yield in poor areas of the grove were limited by the shallow depth of productive soil.
The less productive portions of the grove that is unsuitable for deep-rooted perennial crops like citrus could be successfully mapped with soil samples taken at multiple depths.
Topsoil sampling alone, as commonly practiced, proved to be unsuitable for defining the variable citrus productivity of these soils, but may be adequate for shallow-rooted annual crops.
Tree canopy volumes were measured and mapped in the grove using an ultrasonic array and DGPS receiver in order to describe spatial productivity.
Fruit yield was mapped from harvester trucks equipped with DGPS receivers and data loggers.
The grove was classified into five productivity zones representing very good, good, medium, poor, and very poor citrus growth areas based on the canopy volume.
Soil samples were collected from 30 locations at four 15-cm depth increments to 60 cm and analyzed for their chemical and physical properties.
Soil properties showed a continuous trend along the productivity gradient and depth.
A greenhouse bioassay with radish as an indicator crop was used to compare the productivity of five zones at four depths.
Overhead photographs of the potted radish plants were captured periodically with a SLR digital camera and leaf area was calculated by image processing.
Shoot dry weights were also measured.
Analysis of variance showed that differences between the productivity zones were pronounced at the lower two depths for leaf area and shoot dry weights.
The growth curves for leaf area indicated the differences between productivity zones started appearing in the later stages of growth.
Regression analysis showed that the amount of citrus canopy volume spatial variation explained by radish growth increased with soil depth.
The results of the bioassay showed that citrus yield in poor areas of the grove were limited by the shallow depth of productive soil.
The less productive portions of the grove that is unsuitable for deep-rooted perennial crops like citrus could be successfully mapped with soil samples taken at multiple depths.
Topsoil sampling alone, as commonly practiced, proved to be unsuitable for defining the variable citrus productivity of these soils, but may be adequate for shallow-rooted annual crops.
Publication
Authors
K.K. Mann, A.W. Schumann, T.A. Obreza
Keywords
citrus, soil heterogeneity, yield variability, root zone depth
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