Investigating geostatistical methods to model within-field yield variability of cranberries for potential management zones

Investigating geostatistical methods to model within-field yield variability of cranberries for... Cranberry harvesting methods give only one yield value per field making characterization of within-field variation, the usual first step in precision farming, difficult. Time-consuming berry count yield and fruit rot estimations are the best “ground truth” indication of yield variation within fields. Correlations and coincidence of binary classifications based on less expensive methods such as enhanced vegetation index (EVI) from imagery, and area to point (AtoP) kriging of useable, poor quality and trash yields were compared with this “ground truth”. In general AtoP kriged values gave higher correlations and kappa statistic values with berry counts and fruit rot than EVI. Geostatistical disaggregation of per field yield totals using AtoP kriging with EVI as an external drift (AtoPKED) was also investigated. Factorial kriging was used to separate the several scales of variation in “ground truth” and EVI data and determine which ones were most spatially coherent/manageable and which related best to the AtoP kriged data. The spatial trend component of pre-harvest berry counts and AtoP kriging of yields both gave a good initial definition of spatially coherent, relatively permanent management zones. They were related to topography and depth of water table in the soil which are key factors governing cranberry yield. AtoP kriging or AtoPKED are recommended for defining management zones as they are less expensive than berry counts. The value of AtoP kriging to precision farmers for other crops to map soils at the farm scale with some imagery and just one bulked soil sample per field or use nutrient levels associated with each polygon of traditional soil survey maps is discussed in the conclusions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Investigating geostatistical methods to model within-field yield variability of cranberries for potential management zones

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Publisher
Springer US
Copyright
Copyright © 2015 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-015-9408-7
Publisher site
See Article on Publisher Site

Abstract

Cranberry harvesting methods give only one yield value per field making characterization of within-field variation, the usual first step in precision farming, difficult. Time-consuming berry count yield and fruit rot estimations are the best “ground truth” indication of yield variation within fields. Correlations and coincidence of binary classifications based on less expensive methods such as enhanced vegetation index (EVI) from imagery, and area to point (AtoP) kriging of useable, poor quality and trash yields were compared with this “ground truth”. In general AtoP kriged values gave higher correlations and kappa statistic values with berry counts and fruit rot than EVI. Geostatistical disaggregation of per field yield totals using AtoP kriging with EVI as an external drift (AtoPKED) was also investigated. Factorial kriging was used to separate the several scales of variation in “ground truth” and EVI data and determine which ones were most spatially coherent/manageable and which related best to the AtoP kriged data. The spatial trend component of pre-harvest berry counts and AtoP kriging of yields both gave a good initial definition of spatially coherent, relatively permanent management zones. They were related to topography and depth of water table in the soil which are key factors governing cranberry yield. AtoP kriging or AtoPKED are recommended for defining management zones as they are less expensive than berry counts. The value of AtoP kriging to precision farmers for other crops to map soils at the farm scale with some imagery and just one bulked soil sample per field or use nutrient levels associated with each polygon of traditional soil survey maps is discussed in the conclusions.

Journal

Precision AgricultureSpringer Journals

Published: Jul 29, 2015

References

  • The potential of high spatial resolution information to define within-vineyard zones related to vine water status
    Acevedo-Opazo, C; Tisseyre, B; Guillaume, S; Ojeda, H
  • On-the-go soil sensors for precision agriculture
    Adamchuk, VI; Hummel, JW; Morgan, MT; Upadhyaya, SK
  • Assessment of the spatial variability of grape anthocyanins using a fluorescence sensor. Relationships with vine vigour and yield
    Baluja, J; Diago, MP; Goovaerts, P; Tardaguila, J
  • Delineation of vine parcels by segmentation of high resolution remote sensed images
    Costa, JP; Michelet, F; Germain, C; Lavialle, O; Grenier, G

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