scholarly journals Spatial and Temporal Distribution of Ecballium elaterium in Almond Orchards

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 751 ◽  
Author(s):  
Lior Blank ◽  
Nitzan Birger ◽  
Hanan Eizenberg
Keyword(s):  
Spatial Pattern ◽  
Weed Management ◽  
Nearest Neighbor ◽  
Temporal Distribution ◽  
Nearest Neighbors ◽  
Chemical Application ◽  
Site Specific ◽  
Herbicide Treatments ◽  
Suitable Target ◽  
Almond Orchards

The concept of site-specific weed management is based on the assumption that weeds are aggregated in patches. In this study, we surveyed four plots in four commercial almond orchards for three years and mapped the locations of Ecballium elaterium, a troublesome weed in Israeli agriculture, specifically in almond orchards. We analyzed the spatial pattern of the plants’ locations using nearest neighbor analysis and Ripley’s L function. The number of E. elaterium plants increased by more than 70% in the four plots from 2015 to 2016. In addition, the observed mean distance between nearest neighbors increased by more than 10% from 2016 and 2017. We found in all four plots that the spatial pattern of E. elaterium was clustered and that these weed patch locations were consistent over the years although the density within the patches increased. The extent of these clusters ranged between 40 to 70 m and remained similar in size throughout the study. These features make E. elaterium a suitable target for site-specific weed management and for pre-emergence patch spraying. Knowledge of the spatial and temporal pattern of weeds could aid in understanding their ecology and could help target herbicide treatments to specific locations of the field and, thus, reducing the chemical application.

Spatial Pattern of Weeds Based on Multispecies Infestation Maps Created by Imagery

Weed Science ◽  
2016 ◽  
Vol 64 (3) ◽  
pp. 474-485 ◽  
Author(s):  
Louis Longchamps ◽  
Bernard Panneton ◽  
Robin Reich ◽  
Marie-Josée Simard ◽  
Gilles D. Leroux
Keyword(s):  
Spatial Distribution ◽  
Image Segmentation ◽  
Spatial Pattern ◽  
Weed Management ◽  
Herbicide Application ◽  
Weed Species ◽  
Site Specific ◽  
Weed Cover ◽  
Weed Infestation ◽  
Level Of Aggregation

Weeds are often spatially aggregated in maize fields, and the level of aggregation varies across and within fields. Several annual weed species are present in maize fields before postemergence herbicide application, and herbicides applied will control several species at a time. The goal of this study was to assess the spatial distribution of multispecies weed infestation in maize fields. Ground-based imagery was used to map weed infestations in rain-fed maize fields. Image segmentation was used to extract weed cover information from geocoded images, and an expert-based threshold of 0.102% weed cover was used to generate maps of weed presence/absence. From 19 site-years, 13 (68%) demonstrated a random spatial distribution, whereas six site-years demonstrated an aggregated spatial pattern of either monocotyledons, dicotyledons, or both groups. The results of this study indicated that monocotyledonous and dicotyledonous weed groups were not spatially segregated, but discriminating these weed groups slightly increased the chances of detecting an aggregated pattern. It was concluded that weeds were not always spatially aggregated in maize fields. These findings emphasize the need for techniques allowing the assessment of weed aggregation prior to conducting site-specific weed management.

scholarly journals Effect of site-specific weed management in winter crops on yield and weed populations

2014 ◽  
Vol 60 (No. 1) ◽  
pp. 27-35 ◽  
Author(s):  
P. Hamouz ◽  
K. Hamouzová ◽  
L. Tyšer ◽  
J. Holec
Keyword(s):  
Weed Management ◽  
Standard Treatment ◽  
Species Abundance ◽  
Herbicide Application ◽  
Weed Species ◽  
Site Specific ◽  
Weed Population Dynamics ◽  
Herbicide Treatments ◽  
Weed Infestation ◽  
Winter Crops

Site-specific weed management (SSWM) methods allow spatially variable treatment of weed populations according to actual weed abundance, thus offering the opportunity for herbicide savings. However, SSWM&rsquo;s effect on weed population dynamics is not sufficiently understood. In this study, SSWM was conducted based on various application thresholds to analyse the effects on crop yield and weed infestation in the succeeding crop. SSWM was used on a 3.07 ha experimental field in winter wheat (2011) and winter oilseed rape (2012). The whole area was split into application cells of 6 &times; 10 m and abundance of all weed species was evaluated manually in each cell. Four different herbicide treatments were tested. Standard whole-field herbicide application (blanket spraying) was treatment 1.<br /> Treatments 2, 3 and 4 comprised SSWM using different thresholds for post-emergent herbicide application. SSWM resulted in savings of post-emergent herbicides ranging from 71.9% to 100%, depending on the application threshold. Differences in winter rape yield among treatments were generally small and statistically insignificant<br /> (P = 0.989). Although some minor changes in weed abundances were observable, the experiment showed that none of the site-specific herbicide treatments caused a significant (&alpha; = 0.05) increase of weed species abundance compared to the standard treatment.

Observation of Superlattice Dislocations on Cube Planes in Ni3Al

1973 ◽  
Vol 31 ◽  
pp. 174-175 ◽  
Author(s):  
J. M. Oblak ◽  
W. H. Rand
Keyword(s):  
Nearest Neighbor ◽  
Antiphase Boundary ◽  
Nearest Neighbors ◽  
High Energy ◽  
Cross Slip ◽  
Octahedral Plane ◽  
Trapping Mechanism ◽  
Slip Traces

The energy of an a/2 <110> shear antiphase. boundary in the Ll2 expected to be at a minimum on {100} cube planes because here strue ture is there is no violation of nearest-neighbor order. The latter however does involve the disruption of second nearest neighbors. It has been suggested that cross slip of paired a/2 <110> dislocations from octahedral onto cube planes is an important dislocation trapping mechanism in Ni3Al; furthermore, slip traces consistent with cube slip are observed above 920°K.Due to the high energy of the {111} antiphase boundary (> 200 mJ/m2), paired a/2 <110> dislocations are tightly constricted on the octahedral plane and cannot be individually resolved.

Temporal Changes in Habitat Selection and Nest Spacing in a Colony of Ross' and Lesser Snow Geese

The Auk ◽  
1983 ◽  
Vol 100 (2) ◽  
pp. 335-343 ◽  
Author(s):  
M. Robert McLandress
Keyword(s):  
Nearest Neighbor ◽  
Average Distance ◽  
Habitat Preferences ◽  
Nearest Neighbors ◽  
Nest Density ◽  
Snow Goose ◽  
Snow Geese ◽  
Lesser Snow Geese ◽  
Average Size ◽  
Island Habitats

Abstract I studied the nesting colony of Ross' Geese (Chen rossii) and Lesser Snow Geese (C. caerulescens caerulescens) at Karrak Lake in the central Arctic of Canada in the summer of 1976. Related studies indicated that this colony had grown from 18,000 birds in 1966-1968 to 54,500 birds in 1976. In 1976, geese nested on islands that were used in the late 1960's and on an island and mainland sites that were previously unoccupied. Average nest density in 1976 was three-fold greater than in the late 1960's. Consequently, the average distance to nearest neighbors of Ross' Geese in 1976 was half the average distance determined 10 yr earlier. The mean clutch size of Ross' Geese was greater in island habitats where nest densities were high than in less populated island or mainland habitats. The average size of Snow Goose clutches did not differ significantly among island habitats but was larger at island than at mainland sites. Large clutches were most likely attributable to older and/or earlier nesting females. Habitat preferences apparently differed between species. Small clutches presumably indicated that young geese nested in areas where nest densities were low. The establishment of mainland nesting at Karrak Lake probably began with young Snow Geese using peripheral areas of the colony. Young Ross' Geese nested in sparsely populated habitats on islands to a greater extent than did Snow Geese. Ross' Geese also nested on the mainland but in lower densities than Ross' Geese nesting in similar island habitats. Successful nests with the larger clutches had closer conspecific neighbors than did successful nests with smaller clutches. The species composition of nearest neighbors changed significantly with distance from Snow Goose nests but not Ross' Goose nests. Nesting success was not affected by the species of nearest neighbor, however. Because they have complementary antipredator adaptations, Ross' and Snow geese may benefit by nesting together.

scholarly journals Tropical Balls and Its Applications to K Nearest Neighbor over the Space of Phylogenetic Trees

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 779
Author(s):  
Ruriko Yoshida
Keyword(s):  
Supervised Learning ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
High Dimensional ◽  
Learning Method ◽  
Dimensional Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

Temporal and Spatial Pattern of a Privet (Ligustrum vulgare) Invasion

2013 ◽  
Vol 6 (2) ◽  
pp. 310-319 ◽  
Author(s):  
Wanying Zhao ◽  
Charles Goebel ◽  
John Cardina
Keyword(s):  
Spatial Pattern ◽  
Spatial Data ◽  
Nearest Neighbor ◽  
Management Strategies ◽  
Age Distribution ◽  
Lag Phase ◽  
Suitable Habitat ◽  
Second Phase ◽  
Invasion History ◽  
Cumulative Number

AbstractPrivet has escaped from cultivation and is invading natural areas throughout eastern North America. Understanding the pattern of invasion over time could help us develop more efficient management strategies. We studied the invasion history and spatial distribution pattern of privet by mapping age and spatial data for established patches in a 132-ha (326 ac) forested natural area in northeast Ohio. We determined the age of 331 geo-referenced patches by counting annual rings, and mapped them with corresponding land habitat. Age distribution and cumulative number of privet patches over about 40 yr showed three phases of invasion. The initial 19-yr lag phase was characterized as a dispersed spatial pattern (based on nearest neighbor analysis), with patches located mostly at edges of different habitats and open places. In a second phase of about 15 yr, an average of 19 patches were initiated yearly, in a pattern that trended towards clustered. The final phase began around 2007, as the rate of new patch establishment declined, possibly because of saturation of the suitable habitat. Establishment of new patches was not associated with specific habitats. Aggregation of patches with similar ages increased after 1998 and became significantly clustered. Mapping of clusters of old and young patches identified invasion hot spots and barriers. Results affirmed that the best time for invasive control is during the lag phase. By monitoring edge habitats associated with early establishment, managers might detect and control early invaders and delay the onset of the expansion phase.

Greenness identification using visible spectral colour indices for site specific weed management

2021 ◽  
Author(s):  
K. Upendar ◽  
K. N. Agrawal ◽  
N. S. Chandel ◽  
K. Singh
Keyword(s):  
Weed Management ◽  
Site Specific ◽  
Colour Indices

scholarly journals Chi-Squared Distance Metric Learning for Histogram Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Yang ◽  
Luhui Xu ◽  
Xiaopan Chen ◽  
Fengbin Zheng ◽  
Yang Liu
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Metric Learning ◽  
Nearest Neighbors ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Projected Gradient Method ◽  
Proper Distance ◽  
Chi Squared ◽  
Real World Datasets

Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. The chi-squared distance is a nonlinear metric and is widely used to compare histograms. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. With the iterative projected gradient method for optimization, we naturally introduce thel2,1norm regularization into the proposed method for sparse metric learning. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method.

The Influence of Weed Management in Wheat (Triticum aestivum) Stubble on Weed Control in Corn (Zea mays)

1998 ◽  
Vol 12 (3) ◽  
pp. 522-526 ◽  
Author(s):  
Theodore M. Webster ◽  
John Cardina ◽  
Mark M. Loux
Keyword(s):  
Weed Control ◽  
Weed Management ◽  
Management Practices ◽  
Field Studies ◽  
Giant Foxtail ◽  
Giant Ragweed ◽  
Herbicide Treatments ◽  
Postharvest Treatment ◽  
Full Rate ◽  
Wheat Stubble

The objectives of this study were to determine how the timing of weed management treatments in winter wheat stubble affects weed control the following season and to determine if spring herbicide rates in corn can be reduced with appropriately timed stubble management practices. Field studies were conducted at two sites in Ohio between 1993 and 1995. Wheat stubble treatments consisted of glyphosate (0.84 kg ae/ha) plus 2,4-D (0.48 kg ae/ha) applied in July, August, or September, or at all three timings, and a nontreated control. In the following season, spring herbicide treatments consisted of a full rate of atrazine (1.7 kg ai/ha) plus alachlor (2.8 kg ai/ha) preemergence, a half rate of these herbicides, or no spring herbicide treatment. Across all locations, a postharvest treatment of glyphosate plus 2,4-D followed by alachlor plus atrazine at half or full rates in the spring controlled all broadleaf weeds, except giant ragweed, at least 88%. Giant foxtail control at three locations was at least 83% when a postharvest glyphosate plus 2,4-D treatment was followed by spring applications of alachlor plus atrazine at half or full rates. Weed control in treatments without alachlor plus atrazine was variable, although broadleaf control from July and August glyphosate plus 2,4-D applications was greater than from September applications. Where alachlor and atrazine were not applied, August was generally the best timing of herbicide applications to wheat stubble for reducing weed populations the following season.

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