Spatial Analysis of Digital Imagery of Weeds in a Maize Crop
Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m<sup>2</sup>) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm–18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favored some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches.
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Multidisciplinary Digital Publishing Institute
2018-02-10
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dig-ica-es-10261-1614332021-07-12T06:50:00Z Spatial Analysis of Digital Imagery of Weeds in a Maize Crop San Martín, Carolina Milne, Alice E. Webster, Richard Storkey, Jonathan Andújar, Dionisio Fernández-Quintanilla, César Dorado, José Ministerio de Economía y Competitividad (España) Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m<sup>2</sup>) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm–18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favored some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches. This research was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) under Project AGL2014-52465-C4-1-R. We thank the Ministry and also David Campos and José Manuel Martín for the substantial task of processing the images. The contributions of Alice E. Milne and Jonathan Storkey form part the Soil to Nutrition (S2N) strategic programme (BBS/E/C/000I0330) funded by the Biological Sciences Research Council (BBSRC) and NE/N018125/1 LTS-M ASSIST - Achieving Sustainable Agricultural Systems, funded by NERC and BBSRC (BBS/E/C/000I0140) of the United Kingdom. 2018-02-27T14:26:06Z 2018-02-27T14:26:06Z 2018-02-10 2018-02-27T14:26:06Z artículo http://purl.org/coar/resource_type/c_6501 ISPRS International Journal of Geo-Information 7 (2): 61 (2018) http://hdl.handle.net/10261/161433 10.3390/ijgi7020061 http://dx.doi.org/10.13039/501100003329 #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2014-52465-C4-1-R. Publisher's versión https://doi.org/10.3390/ijgi7020061 Sí open Multidisciplinary Digital Publishing Institute |
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Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m<sup>2</sup>) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm–18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favored some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches. |
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Ministerio de Economía y Competitividad (España) |
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Ministerio de Economía y Competitividad (España) San Martín, Carolina Milne, Alice E. Webster, Richard Storkey, Jonathan Andújar, Dionisio Fernández-Quintanilla, César Dorado, José |
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artículo |
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San Martín, Carolina Milne, Alice E. Webster, Richard Storkey, Jonathan Andújar, Dionisio Fernández-Quintanilla, César Dorado, José |
spellingShingle |
San Martín, Carolina Milne, Alice E. Webster, Richard Storkey, Jonathan Andújar, Dionisio Fernández-Quintanilla, César Dorado, José Spatial Analysis of Digital Imagery of Weeds in a Maize Crop |
author_sort |
San Martín, Carolina |
title |
Spatial Analysis of Digital Imagery of Weeds in a Maize Crop |
title_short |
Spatial Analysis of Digital Imagery of Weeds in a Maize Crop |
title_full |
Spatial Analysis of Digital Imagery of Weeds in a Maize Crop |
title_fullStr |
Spatial Analysis of Digital Imagery of Weeds in a Maize Crop |
title_full_unstemmed |
Spatial Analysis of Digital Imagery of Weeds in a Maize Crop |
title_sort |
spatial analysis of digital imagery of weeds in a maize crop |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018-02-10 |
url |
http://hdl.handle.net/10261/161433 http://dx.doi.org/10.13039/501100003329 |
work_keys_str_mv |
AT sanmartincarolina spatialanalysisofdigitalimageryofweedsinamaizecrop AT milnealicee spatialanalysisofdigitalimageryofweedsinamaizecrop AT websterrichard spatialanalysisofdigitalimageryofweedsinamaizecrop AT storkeyjonathan spatialanalysisofdigitalimageryofweedsinamaizecrop AT andujardionisio spatialanalysisofdigitalimageryofweedsinamaizecrop AT fernandezquintanillacesar spatialanalysisofdigitalimageryofweedsinamaizecrop AT doradojose spatialanalysisofdigitalimageryofweedsinamaizecrop |
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1777663660625231872 |