Are weed patches stable in location? Application of an explicitly two‐dimensional methodology

Heijting, S.; Werf, Wopke Van der; Stein, Alfred; Kropff, M.J.

doi:10.1111/j.1365-3180.2007.00580.x

Cited by 33 publications

(27 citation statements)

References 41 publications

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“…For descriptive purposes, however, Moran’s I still appears to be useful. Alternatively, Syrjala’s method (Syrjala, 1996) or two‐dimensional correlograms (Heijting et al , 2007) may be applied.…”

Section: Discussionmentioning

confidence: 99%

Testing the spatial significance of weed patterns in arable land using Mead’s test

et al. 2007

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Summary There is a need in weed science for statistical tests for patchiness and spatial pattern. The objective of this study was to investigate the performance of Mead’s test for detecting patterns in synthetic data and in real weed counts made in maize, and making a first assessment of its applicability in ecological studies on weeds. In an extension to Mead’s test, made here for the first time, we merge original quadrat count data into rectangular cells of m by n quadrats. Care was taken to rule out the effect of starting point on the test result. Using the synthetic data, we demonstrate the ability of the test to detect both patchiness and homogeneity as deviations from randomness. The first deviation results in right‐sided significance, and the second in left‐sided significance of the test. Analysis of the real weed patterns demonstrated patchiness at many scales for five of the six investigated species and lack of any deviation from randomness in the sixth: Taraxacum officinale. The latter was the only wind dispersing species in the dataset. No deviation towards homogeneity was found in any of the real weed species at any scale. All patchy patterns showed anisotropy, being elongated in the direction of field traffic. As it turns out, Mead’s test is well suited to detect departures from randomness in observed weed patterns and enhances the suite of diagnostic tools that can be employed by weed ecologists.

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Section: Discussionmentioning

confidence: 99%

Testing the spatial significance of weed patterns in arable land using Mead’s test

et al. 2007

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“…A more complex decision rule was made in the case of sub-objects located below the edge of the buffer zone. In this case, the sub-objects in contact with or very close to other weeds were classified as weeds because aggregation among weed plants, i.e., weed patches, was generally observed [25]. …”

Section: Methodsmentioning

confidence: 99%

Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images

et al. 2013

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The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

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“…Alopecurus myosuroides, like many weed species, grows in patches within fields. These patches can vary in size and shape (Cardina et al, 1997;Dieleman et al, 2000;Walter et al, 2002;Heijting et al, 2007). Nevertheless, these patches can be fairly stable, with core areas of A. myosuroides patches moving only 3-4 m over several years (Lutman et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Defining the habitat niche of Alopecurus myosuroides at the field scale

et al. 2018

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SummaryThe distribution of Alopecurus myosuroides (black‐grass) in fields is patchy. The locations of these patches can be influenced by the environment. This presents an opportunity for precision management through patch spraying. We surveyed five fields on various types of soil using a nested sampling design and recorded both A. myosuroides seedlings in autumn and seed heads in summer. We also measured soil properties at those sampling locations. We found that the patches of seed heads within a field were smaller than the seedling patches, suggesting that techniques for patch spraying based on maps of heads in the previous season could be inherently risky. We also found that the location of A. myosuroides patches within fields can be predicted through their relationship with environmental properties and that these relations are consistent across fields on different soil types. This improved understanding of the relations between soil properties and A. myosuroides seedlings could allow farmers to use pre‐existing or suitably supplemented soil maps already in use for the precision application of fertilisers as a starting point in the creation of herbicide application maps.

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Are weed patches stable in location? Application of an explicitly two‐dimensional methodology

Cited by 33 publications

References 41 publications

Testing the spatial significance of weed patterns in arable land using Mead’s test

Testing the spatial significance of weed patterns in arable land using Mead’s test

Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images

Defining the habitat niche of Alopecurus myosuroides at the field scale

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