Crop and field boundary influences on the activity of a wide range of beneficial invertebrate groups on a split conventional/organic farm in northern England

AbstractActivity of 12 beneficial invertebrate groups was assessed in 2005 and 2006 on a farm in northern England split into conventional and organic management halves, using pitfall and pan traps set in both crops and field boundaries. Management, crop and boundary structure influences on invertebrate activity were assessed, as was the relationship between crop and boundary type. Classification of crop and boundary assemblages produced three and two groups, respectively, in both years. Organic arable crops had well-defined assemblages in both years; and, while grass and grass/clover fields were separated from conventional arable fields in 2005, there was mixing in 2006. One boundary group, in both years, was dominated by conventional arable fields with tall herbaceous boundary vegetation. The other group had more organic arable and grassy fields with shorter boundary vegetation. Redundancy analyses showed that a number of groups (Cantharidae, Coccinellidae, Syrphidae, Ichneumonidae, Braconidae, Proctotrupoidea, Lycosidae) were more active in organic arable fields with more Staphylinidae in conventional arable crops and no obvious trend with Carabidae, Hemiptera, Neuroptera and Linyphiidae. Activity of some groups, especially Coccinellidae, Syrphidae and parasitic wasps, was strongly related to weed cover. Staphylinidae were most active in tall herbaceous boundaries by conventional arable crops with more of a number of groups (Cantharidae, Coccinellidae, parasitic wasps) in short herbaceous boundaries by organic arable crops. Organic management produced most differences in aerially-dispersed invertebrates, and management had a profound effect on activity in field boundaries. Possible management prescriptions to increase invertebrate activity include changing sowing times, weed cover manipulation and field boundary and margin management.

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Associative learning for host-induced fruit volatiles in Psyttalia concolor (Hymenoptera: Braconidae), a koinobiont parasitoid of tephritid flies

Bulletin of Entomological Research ◽

10.1017/s0007485314000625 ◽

2014 ◽

Vol 104 (6) ◽

pp. 774-780 ◽

Cited By ~ 16

Author(s):

A. Canale ◽

S. Geri ◽

G. Benelli

Keyword(s):

Associative Learning ◽

Food Reward ◽

Decanoic Acid ◽

Nonanoic Acid ◽

Parasitic Wasps ◽

Wide Range ◽

Geranyl Acetone ◽

Odour Learning ◽

Psyttalia Concolor ◽

Key Resources

AbstractParasitic wasps are fascinating organisms that rely on a wide range of communication channels to locate their hosts. Associative learning for foraging kairomones has been demonstrated for various parasitic wasps, but little is known for parasitoids of Tephritidae flies. Psyttalia concolor (Hymenoptera: Braconidae) is a koinobiont parasitoid able to attack at least 14 tephritid pests. Females are innately attracted by some host-induced plant volatiles (HIPVs), whereas others of the same bouquet are unattractive. We hypothesize that females may detect unattractive HIPVs in association with key resources, such as food and hosts, learning to respond favourably to these cues in consecutive experiences. We evaluated associative learning for HIPVs in P. concolor females, testing if they are able to associate a food reward with the presence of different dosages of three HIPVs, thus developing a preference for an odour innately unattractive. Results demonstrated that P. concolor responded favourably to the learned cue in consecutive experiences. For all tested HIPVs (nonanoic acid, decanoic acid and geranyl acetone), regardless of dosage, trained females preferred the reward-associated odour, whereas naïve did not. Both HIPV-trained and naïve females did not show consistent differences in latencies when choosing HIPVs over blank. HIPV-trained and naïve wasps did not spend more time on HIPVs over blank. Odour learning is of adaptive importance for this generalist parasitoid, since it enhances host location efficiency by reducing the time wasted on the decision of where to search for hosts. From an applied perspective, these HIPVs could be used to train mass-reared P. concolor in pre-release, to potentially improve its efficacy in the field.

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Management of the Soil Environment in High Tunnels

HortTechnology ◽

10.21273/hortsci.19.1.34 ◽

2009 ◽

Vol 19 (1) ◽

pp. 34-36 ◽

Cited By ~ 1

Author(s):

Adam Montri ◽

J.A. Biernbaum

Keyword(s):

Soil Health ◽

Irrigation Water Quality ◽

Soil Environment ◽

Organic Management ◽

High Tunnel ◽

Fertility Management ◽

Wide Range ◽

High Tunnels ◽

Management Techniques ◽

Certified Organic

High-tunnel soil, water, and fertility management can be achieved using a wide range of approaches from polyethylene film-mulched, drip-fertigated beds to certified organic management based on maintaining high soil organic matter (SOM) content. Soil management techniques are discussed in relation to the high tunnel structure used and the length of the growing season, site preparation, soil health, SOM, irrigation water quality, and fertility, with a focus on certified organic management.

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Integration of Visual Stimuli by the Crayfish Central Nervous System

Journal of Experimental Biology ◽

10.1242/jeb.47.3.409 ◽

1967 ◽

Vol 47 (3) ◽

pp. 409-431

Author(s):

C. A. G. WIERSMA ◽

T. YAMAGUCHI

Keyword(s):

Visual Fields ◽

Directional Sensitivity ◽

Field Boundary ◽

Excitatory Effect ◽

Impulse Frequency ◽

Eye Muscle ◽

Total Inhibition ◽

Wide Range ◽

Space Constant ◽

Antagonistic Muscles

1. For the crayfish, properties of visually reacting interneurone types in the optic nerve are described on the basis of single-unit analysis. 2. Sustaining fibres show: (a) ‘surround’ inhibition over the whole retina, including the excitatory field; (b) ‘on’ and ‘off’ effects at field boundaries; (c) a dark discharge in deteriorated preparations; (d) an increase in adapted firing rate and response to flashes in an ‘excited state’ of the preparation; (e) increased overall impulse frequency to fast-moving shadows at frequencies of 2-10/sec. which elicit short bursts. 3. Dimming fibres, having mainly reversed properties, show: (a) bursts followed by adaptation to a lower ferquency level of spikes on light dimming; (b) total inhibition by illumination for times proportional to light intensity; (c) responsiveness to quickly moving shadows. 4. ‘Jittery’ movement fibres lack directional sensitivity. For them: (a) total illumination and contrast have, within a wide range, no effect on discharges to moving targets; (b) large, dark cards moved at constant speed are seen only near their visual field boundary; (c) the less predictable the movement of a small dark object is, the longer its excitatory effect lasts; (d) responsiveness of field parts exposed is decreased for long durations; (e) no firing is caused by active or passive eye motions. 5. Space-constant fibres show changed location and size of their excitatory fields with eye position. Their potential visual fields below the horizontal plane are unresponsive, due to inhibition caused mainly by statocyst input. 6. Two eye muscle motor fibres, also under statocyst control, are influenced by light on the limited sensory fields of two pairs of identifiable sustaining fibres. The two motor fibres, which innervate antagonistic muscles, are reversely excited and inhibited by these fields.

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Retrieving the configuration of grain boundary structure in polycrystalline materials by extraordinary X-ray reflection analysis

Journal of Applied Crystallography ◽

10.1107/s1600576720007803 ◽

2020 ◽

Vol 53 (4) ◽

pp. 1006-1014

Author(s):

Lorena Aarão-Rodrigues ◽

Augusta Isaac ◽

Roberto B. Figueiredo ◽

Angelo Malachias

Keyword(s):

Boundary Structure ◽

Polycrystalline Materials ◽

Nondestructive Monitoring ◽

X Ray ◽

Performance Space ◽

Grain Boundary Structure ◽

Wide Range ◽

Angular Displacements ◽

Microscopy Techniques ◽

Interface Structures

The development of materials is strongly related to our capability of understanding thermal, mechanical and chemical processing on the nanoscale. Unravelling the interface structure is crucial for opening new regimes in property–performance space. Interface arrangements have been characterized by statistically limited microscopy techniques. In this work, a large-angular-range detector was used for synchrotron diffraction measurements on commercially pure Mg. Long acquisitions allowed the retrieval of preferred interface configurations through the observation of extraordinary diffraction peaks located close to the Mg 102, 200, 204 and 300 fundamental reflections. A kinematical simulation scanning possible interface structures established the correspondence of the non-bulk peaks to the interfacial organization of atoms that may be responsible for their appearance. Simulated interfaces were probed for a wide range of angular displacements with respect to the main cleavage planes. The results indicate configurations that allow the observation of X-ray diffraction, representing a long-range-ordered pattern of atomic distributions in Mg. The introduced methodology allows for nondestructive monitoring of systems that undergo processes that modify grain sizes and grain-interface orientation.

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Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection

Remote Sensing ◽

10.3390/rs13040722 ◽

2021 ◽

Vol 13 (4) ◽

pp. 722

Author(s):

Alireza Taravat ◽

Matthias P. Wagner ◽

Rogerio Bonifacio ◽

David Petit

Keyword(s):

Network Architecture ◽

Spatial Information ◽

Cropping Systems ◽

Boundary Detection ◽

Jaccard Coefficient ◽

Agricultural Field ◽

Field Boundary ◽

Convolutional Network ◽

Fully Convolutional Networks ◽

Field Boundaries

Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.

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Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images

Remote Sensing ◽

10.3390/rs13112197 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2197

Author(s):

François Waldner ◽

Foivos I. Diakogiannis ◽

Kathryn Batchelor ◽

Michael Ciccotosto-Camp ◽

Elizabeth Cooper-Williams ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Satellite Images ◽

Boundary Extraction ◽

Field Boundary ◽

Local Council ◽

Digital Services ◽

Temporal Cues ◽

Large Scale Field ◽

Field Boundaries

Digital agriculture services can greatly assist growers to monitor their fields and optimize their use throughout the growing season. Thus, knowing the exact location of fields and their boundaries is a prerequisite. Unlike property boundaries, which are recorded in local council or title records, field boundaries are not historically recorded. As a result, digital services currently ask their users to manually draw their field, which is time-consuming and creates disincentives. Here, we present a generalized method, hereafter referred to as DECODE (DEtect, COnsolidate, and DElinetate), that automatically extracts accurate field boundary data from satellite imagery using deep learning based on spatial, spectral, and temporal cues. We introduce a new convolutional neural network (FracTAL ResUNet) as well as two uncertainty metrics to characterize the confidence of the field detection and field delineation processes. We finally propose a new methodology to compare and summarize field-based accuracy metrics. To demonstrate the performance and scalability of our method, we extracted fields across the Australian grains zone with a pixel-based accuracy of 0.87 and a field-based accuracy of up to 0.88 depending on the metric. We also trained a model on data from South Africa instead of Australia and found it transferred well to unseen Australian landscapes. We conclude that the accuracy, scalability and transferability of DECODE shows that large-scale field boundary extraction based on deep learning has reached operational maturity. This opens the door to new agricultural services that provide routine, near-real time field-based analytics.

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Crop yield, weed cover and ecosystem multifunctionality are not affected by the duration of organic management

Agriculture Ecosystems & Environment ◽

10.1016/j.agee.2019.106596 ◽

2019 ◽

Vol 284 ◽

pp. 106596

Author(s):

Chantal Herzog ◽

Adrian Honegger ◽

Django Hegglin ◽

Raphaël Wittwer ◽

Anne de Ferron ◽

...

Keyword(s):

Crop Yield ◽

Organic Management ◽

Weed Cover ◽

Ecosystem Multifunctionality

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Microstructures, Grain Boundaries and Superplasticity in Fine Grained Ceramics

MRS Proceedings ◽

10.1557/proc-196-313 ◽

1990 ◽

Vol 196 ◽

Cited By ~ 4

Author(s):

C. Carry

Keyword(s):

Grain Boundary ◽

Grain Boundaries ◽

Boundary Segregation ◽

Boundary Structure ◽

Fine Grained ◽

Chemical Effects ◽

Liquid Phases ◽

Grain Boundary Structure ◽

Wide Range ◽

Points Of View

ABSTRACTDeformation studies in compression and in tension have clearly shown evidence for superplasticity for a wide range of fine grained ceramics from both macroscopic and microscopic points of view. The main purpose of this paper is to focus attention on chemical effects in ceramic grain boundaries which can lead to a great variety of behavior. Grain boundary segregation or precipitation, residual impurities or doping elements, and glassy or liquid phases at grain boundaries can strongly affect the macroscopic flow properties of superplastic fine grained ceramics. Some microstructural and grain boundary features, mainly in two oxide materials (alumina and yttria doped zirconia), are analyzed, compared, and discussed in connection with their observed superplastic behavior. Special attention is devoted to the relation between the overall chemistry of the materials (impurities and doping elements) and to the grain boundary structure and chemistry (segregation, precipitation, intergranular phases). Some consequences and implications on the tailoring of ceramic microstructures for superplasticity are discussed. In addition, some recent hot forming and hot bonding experiments are also reported.

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