Possible Neolithic ard marks and field boundaries at Wellhill and Cranberry, Perth and Kinross, and an evaluation of current physical evidence for Neolithic farming in Scotland

Although the Neolithic period is defined by farming, physical evidence for processes associated with farming are rare, with agricultural practices usually indicated by environmental and biomolecular proxies for domesticates such as pollen evidence, ceramic residues and lipids, animal bones, plant remains and stable isotope studies. This paper will, we hope, invigorate discussion on the recognition, interpretation and significance of physical traces of farming in Scotland. The starting point will be the summary of two excavations, Wellhill and Cranberry, both Perth and Kinross, in 2014 and 2016 respectively, part of the Strathearn Environs and Royal Forteviot (SERF) project. These cropmark sites revealed evidence for possible Neolithic farming in the form of possible ard marks and field ditches. There follows a synthesis of physical evidence for Neolithic farming in Scotland, drawing together evidence for ard marks, field boundaries, cultivation ridges, cultivated middens, and soils. Recommendations are made for recognising and interpreting such features on excavations, and the potential benefits of giving a higher profile to the act of farming in our narratives about Neolithic lifeways in Scotland and beyond are briefly explored.

Development of Ball Detection System with YOLOv3 in a Humanoid Soccer Robot

One of the practical researches of humanoid robots is research on the use of humanoid robots to play soccer. Research in this field is also encouraged by the existence of various humanoid robot soccer competitions. In humanoid robots for soccer, one of the important aspects is the robot’s ability to detect the ball, goal, field boundaries and other players, both friend players and opposing players. This study focuses on the ball detection system which is a basic ability that humanoid robots need to have. The ball detection system developed in this study uses the YOLOv3 method. The test results show that the system built and trained with 3000 image samples can detect balls at a distance of 50 to 900 cm. The time it takes to detect the ball is about 0.033 seconds.

Livin' on the edge: precision yield data shows evidence of ecosystem services from field boundaries.

Field boundaries can provide ecosystem services to crops by creating better abiotic conditions for crop growth, and can also act as habitat for beneficial arthropods. This suggests that crop boundaries may create an intermediate hump-shaped increase in crop yield, where negative edge effects are cancelled out by increased ecosystem services from the field boundary. However, there is little large-scale evidence showing this, largely because plot-scale crop yields are costly and time-consuming to measure. Precision yield data from combine yield monitors has huge potential in this respect, as the equipment is widespread and data is frequently recorded by growers. In this study, we used 252 field-years of yield monitor data from three common crops - wheat (Triticum aestivum), canola (Brassica napus), or peas (Pisum sativum) - recorded across Alberta, Canada, and examined how yield varied with distances from common crop boundary types. Average crop yield tended to increase with distance from crop boundaries before plateauing at about 50 m, and yield variation (SD) tended to decrease with distance. There was evidence of an intermediate increase in yield for wheat away from shelterbelts, and a weak increase in canola, but this was not seen for other crop types or boundary types. This study represents one of the first uses of precision yield data to measure ecosystem service provision at large spatial scales.

Dividing the Land: Time and Land Division in the English North Midlands and Yorkshire

Land divisions are ubiquitous features of the British countryside. Field boundaries, enclosures, pit alignments, and other forms of land division have been used to shape and delineate the landscape over thousands of years. While these divisions are critical for understanding economies and subsistence, the organization of tenure and property, social structure and identity, and their histories of use have remained unclear. Here, the authors present the first robust, Bayesian statistical chronology for land division over three millennia within a study region in England. Their innovative approach to investigating long-term change demonstrates the unexpected scale of later ‘prehistoric’ land demarcation, which may correspond to the beginnings of increasing social hierarchy.

Field Heteronomy and Contingent Expertise: The Case of International Tax Justice

The international tax system is targeted by a diverse range of networked civil society actors, from critical professionals mobilizing their expertise to anti-austerity protestors targeting the consequences of tax dodging. The years following the 2008 financial crisis saw an increase in the range of these actors and their cooperation with one another. This paper argues that a transnational field analysis complements existing expertise-oriented approaches, by identifying the overarching objective of the tax justice agenda as increasing heteronomy in the international taxation field relative to political fields. This objective requires the mobilization of diverse resources across different fields, resulting in network relationships crossing field boundaries to contest inter-field relations, rather than any single bounded field struggle. The findings are supported by an analysis of tax justice advocacy after the 2008 financial crisis in the United Kingdom and Australia, including thirty-seven in-depth interviews with different organizations involved in the network.

Distribution of ground beetles of the genus Bembidion (Coleoptera, Carabidae) in the agricultural landscape in Northwestern Russia

We have observed seven species of predator beetles of the genus Bembidion in the agricultural landscape of the Leningrad Region (Northwestern Russia) between the years 2008 and 2018. These species reach their highest abundance in sun-exposed areas, especially mesophilic B. quadrimaculatum Linnaeus, 1761, B. properans (Stephens, 1828), B. lampros (Herbst, 1784), and B. femoratum Sturm, 1825. A few mesohygrophilic B. guttula (Fabricius, 1792) and B. gilvipes (Sturm, 1825) live primarily in areas of dense vegetation. Finally, the hygrophilic B. bruxellense (Wesmael, 1835) was only observed on the most humid soils. Assemblages of Bembidion ground beetles were separated in the fields, field boundaries, and adjacent habitats.

Farm-Forestry, Smallholder Farms and Policy Support – The Way Ahead

Farm forestry, interchangeably used for the term agroforestry, encompasses growing trees and/or shrubs on farms, mainly to support agricultural production and supplement farm income on smallholder farms. This, as a bonus, also provides for ecosystem services viz., protection of soil and water resources, biodiversity enhancement, carbon sequestration, and improvement in landscape values to the farm holding. In Indian context, this encompasses raising trees mainly on bunds or field boundaries on small holdings or sometimes intercropped in an agroforestry type configuration, if holding size is bigger. The techno-economic viability of this system has been extensively assessed and wide adoption, therefore, warrants a conducive policy support at local and community level. Governments have framed enabling policies towards this goal; however, desired outcome is still awaited. This study attempts to map out the present development and suggest the measures required at local and community level to make the government policies more fruitful. Policies framed at macro level need recalibration to suit local and community specific requirements in the changing climatic conditions for wider adoption and sustenance.

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

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.