Research on organic agriculture in the Netherlands : organisation, methodology and results

Chapters: 1. Organic agriculture in the Netherlands; 2. Dutch research on organic agriculture: approaches and characteristics; 3. Dutch knowledge infrastructure for organic agricultur'; 4. Sustainable systems; 5. Good soil: a good start; 6. Robust varieties and vigorous propagation material; 7. Prevention and control of weeds, pests and diseases; 8. Health and welfare of organic livestock; 9. Animal production and feeding; 10. Special branches: organic greenhouse production, bulbs, ornamentals and aquaculture; 11. Healthfulness and quality of products; 12. Economy, market and chain; 13. People and society. A publication of Wageningen UR and Louis Bolk Institut

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Systemization of Pluggable Transports for Censorship Resistance

An increasing number of countries implement Internet censorship at different scales and for a variety of reasons. In particular, the link between the censored client and entry point to the uncensored network is a frequent target of censorship due to the ease with which a nation-state censor can control it. A number of censorship resistance systems have been developed thus far to help circumvent blocking on this link, which we refer to as link circumvention systems (LCs). The variety and profusion of attack vectors available to a censor has led to an arms race, leading to a dramatic speed of evolution of LCs. Despite their inherent complexity and the breadth of work in this area, there is no systematic way to evaluate link circumvention systems and compare them against each other. In this paper, we (i) sketch an attack model to comprehensively explore a censor's capabilities, (ii) present an abstract model of a LC, a system that helps a censored client communicate with a server over the Internet while resisting censorship, (iii) describe an evaluation stack that underscores a layered approach to evaluate LCs, and (iv) systemize and evaluate existing censorship resistance systems that provide link circumvention. We highlight open challenges in the evaluation and development of LCs and discuss possible mitigations.Comment: Content from this paper was published in Proceedings on Privacy Enhancing Technologies (PoPETS), Volume 2016, Issue 4 (July 2016) as "SoK: Making Sense of Censorship Resistance Systems" by Sheharbano Khattak, Tariq Elahi, Laurent Simon, Colleen M. Swanson, Steven J. Murdoch and Ian Goldberg (DOI 10.1515/popets-2016-0028

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Evaluating the Accuracy and Quality of Microgreens Training Materials Available on the Internet: A Content Analysis

Microgreens are the young, edible seedlings of various vegetables, spices, herbs, and considered as the intermediate stage of sprouts and mature greens, suggesting microgreens may share similar food safety risks with both of these produce. Even though there are no known outbreaks due to contaminated microgreens, multiple product recalls have been reported, indicating food safety risks associated with microgreens should not be underemphasized. A recent national survey of the U.S. microgreens industry reported that almost half of growers (48.3% of 176) learned to grow microgreens by viewing websites and videos on the internet.1 However, it is unknown whether the content related to growing microgreens is grounded in scientific evidence and clearly presented. The aim of this research was to conduct a content analysis to determine the accuracy and quality of existing microgreens training materials available on the internet. Microgreens training materials were collected using two popular search engines – Google and YouTube. Three coding manuals were created to evaluate included artifacts. One was used to determine the accuracy of the content and was based on FDA Food Safety Modernization Act – Produce Safety Rule (FSMA PSR). The other two manuals were used to determine the quality of Google and YouTube artifacts. Three trained coders contributed to the coding process. Each artifact was coded independently by two coders for accuracy and quality. A total of 223 artifacts (i.e., 86 Google and 137 YouTube) were selected for the analysis. The accuracy results revealed that both online sources had minimally covered the food safety principles in the FSMA PSR. Several areas were completely unaddressed, such as water testing, worker training, environmental monitoring, and record-keeping. Additionally, several important areas were minimally covered (e.g., the water source, worker health and hygiene, pest control, and risks from animals), or were not sufficiently addressed with accurate details (e.g., the treatment of grow medium and proper environmental/storing conditions), which gave very limited food safety information to the microgreens growers. In addition, some of the reported food safety information gave unclear recommendations, such as the parameters of the sources (i.e., grow medium/water/seeds); or conflicting opinions across artifacts, such as the requirement of washing microgreens, cleaning and sanitization methods, seed treatment methods, and waste management. The Google and YouTube quality scoring systems resulted in a mean quality score of 15.81 and 22 of a maximum score of 28, respectively. The most common deficiency observed across all artifacts was it was unknown if the content developers were subject matter experts. In addition, further quality improvements were noted in Google artifacts, such as using relevant images, meeting accessibility requirements, and providing learner assessments. The quality of YouTube artifacts could be improved by varying visual and auditory components and by using time stamps. Our findings can inform and guide existing and future microgreens training contents

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin