Spatial interpretation of plant parameters in winter wheat

Spatial interpretation of plant parameters in winter wheat A methodology is described for the spatial interpretation of plant parameters (SIOPP), which was used to diagnose the nutritional status of winter wheat. The data used in this study were collected in 2010 throughout the monitoring of two fields (52 and 38 ha) with uniform and conventional agricultural management, located in the Czech Republic. The survey was carried out at BBCH 30 phenological stage in a regular sampling grid with 150 m of distance between grid points (27 and 18 samples). The plant height and the chlorophyll concentration (Yara N-Tester) were recorded. Plant and soil samples were taken to analyse the nutrient concentrations (N, P, K, Mg, Ca, and S). A crop development index (CDI) was developed combining plant height and N-Tester values to quantify the growth of the plant (biomass and vigour). The relationship between this index and the concentration of nutrients were studied and confirmed by cross-validation and spatial analysis; the aim was to determine the factors that limit plant growth. The method revealed the limiting factors in field #1 were potassium, calcium (pH problems) and nitrogen (in descending order of relevance). In field #2, CDI was only related to the soil moisture. In all cases, it was found that the spatial variability of the indices and the limiting factors followed a pattern result of the combination of the gradients in climate, topography and soils of each field. This allowed the interpolation of the maps for variable-rate application using only 0.5 samples per hectare arranged in regular mesh, which was insufficient for the use of geostatistics. All diagnoses were consistent with the crop yield, the soil sampling and the DRIS diagnoses. The results showed that if leaf analyses are complemented with a few additional measures, instantaneous and with a minimal cost, it is possible to deduce the diagnosis using statistical and spatial analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Spatial interpretation of plant parameters in winter wheat

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Publisher
Springer US
Copyright
Copyright © 2013 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/s11119-013-9340-7
Publisher site
See Article on Publisher Site

Abstract

A methodology is described for the spatial interpretation of plant parameters (SIOPP), which was used to diagnose the nutritional status of winter wheat. The data used in this study were collected in 2010 throughout the monitoring of two fields (52 and 38 ha) with uniform and conventional agricultural management, located in the Czech Republic. The survey was carried out at BBCH 30 phenological stage in a regular sampling grid with 150 m of distance between grid points (27 and 18 samples). The plant height and the chlorophyll concentration (Yara N-Tester) were recorded. Plant and soil samples were taken to analyse the nutrient concentrations (N, P, K, Mg, Ca, and S). A crop development index (CDI) was developed combining plant height and N-Tester values to quantify the growth of the plant (biomass and vigour). The relationship between this index and the concentration of nutrients were studied and confirmed by cross-validation and spatial analysis; the aim was to determine the factors that limit plant growth. The method revealed the limiting factors in field #1 were potassium, calcium (pH problems) and nitrogen (in descending order of relevance). In field #2, CDI was only related to the soil moisture. In all cases, it was found that the spatial variability of the indices and the limiting factors followed a pattern result of the combination of the gradients in climate, topography and soils of each field. This allowed the interpolation of the maps for variable-rate application using only 0.5 samples per hectare arranged in regular mesh, which was insufficient for the use of geostatistics. All diagnoses were consistent with the crop yield, the soil sampling and the DRIS diagnoses. The results showed that if leaf analyses are complemented with a few additional measures, instantaneous and with a minimal cost, it is possible to deduce the diagnosis using statistical and spatial analysis.

Journal

Precision AgricultureSpringer Journals

Published: Dec 10, 2013

References

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