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

The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges

The past decade has witnessed the rapid development of ML and DL methodologies in agricultural systems, showcased by great successes in variety of agricultural applications. However, these conventional ML/DL models have certain limitations: They heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, foundation models have demonstrated remarkable successes in language and vision tasks across various domains. These models are trained on a vast amount of data from multiple domains and modalities. Once trained, they can accomplish versatile tasks with just minor fine-tuning and minimal task-specific labeled data. Despite their proven effectiveness and huge potential, there has been little exploration of applying FMs to agriculture fields. Therefore, this study aims to explore the potential of FMs in the field of smart agriculture. In particular, we present conceptual tools and technical background to facilitate the understanding of the problem space and uncover new research directions in this field. To this end, we first review recent FMs in the general computer science domain and categorize them into four categories: language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs. Subsequently, we outline the process of developing agriculture FMs and discuss their potential applications in smart agriculture. We also discuss the unique challenges associated with developing AFMs, including model training, validation, and deployment. Through this study, we contribute to the advancement of AI in agriculture by introducing AFMs as a promising paradigm that can significantly mitigate the reliance on extensive labeled datasets and enhance the efficiency, effectiveness, and generalization of agricultural AI systems.Comment: 16 pages, 2 figure

Validation of resistome signatures through the application of a machine learning prediction algorithm on metagenomic data

Dissertação de Mestrado Integrado em Medicina Veterinária, área científica de Sanidade AnimalABSTRACT- Metagenomic data has been increasingly used in antimicrobial resistance (AMR) studies, but there is still a need for accurate and reliable methods for predicting the relative attribution of AMR determinants to different animal reservoirs. AMR data availability has increased exponentially over the past few years, as has global awareness of the threat that AMR poses to public health, often known as the silent pandemic. This has led to an upsurge in interest in applying machine learning to AMR data. In this study, shot-gun sequences were used from fecal samples of pigs, broilers, turkeys, and veal calves, previously collected during national cross-sectional studies across Europe. The data used in this study corresponded to these samples and their associated relative abundance of AMR determinants. A random forest (RF) model was developed to investigate the relative attribution of AMR determinants to those different reservoirs. Additionally, a descriptive analysis was made to further investigate the 15 most important variables for the RF model. A principal component analysis (PCA) and all-subsets regression were performed to identify reservoir-specific AMR determinants. Ultimately, the reservoir-specific AMR determinants identified here were compared with the resistome signatures identified in a previous study. The results demonstrated that the RF model successfully classified resistomes into corresponding reservoir classes, with high accuracy and reliability. The RF model had more difficulty differentiating pig from veal and broiler from turkey, indicating the similarity of resistome composition between each of these two species. The analyses validated several AMR determinants as resistome signatures of specific animal reservoirs, such as tet(40) and sul2 of veal, tet(Q), mef(A) and cfxA2 of veal and pig, blaTEM-126 of broiler, and tet(A) of broiler and turkey. This study describes a reliable and accurate method for the relative attribution of AMR determinants to different animal reservoirs using metagenomic data. Such results are essential for effective surveillance and control of AMR in animal and human populationsRESUMO - Validação de resistome-signatures através da aplicação de um algoritmo de previsão de machine learning em dados metagenómicos - Dados metagenómicos têm sido cada vez mais usados em estudos de resistência aos antimicrobianos, mas ainda há uma escassez de métodos precisos e fidedignos para prever a atribuição relativa de genes de resistência a diferentes espécies animais. A disponibilidade de dados de resistência aos antimicrobianos aumentou exponencialmente nos últimos anos, assim como a consciencialização global sobre a ameaça que as resistências representam para a saúde pública, geralmente conhecida como pandemia silenciosa. Isto levou a um aumento no interesse em aplicar métodos de machine learning a esses dados. Neste estudo, sequências shot-gun foram usadas a partir de amostras fecais de porcos, frangos, perús e vitelos, recolhidas anteriormente durante estudos nacionais por toda a Europa. Os dados utilizados neste estudo corresponderam a essas amostras e os seus valores FPKM associados. Um modelo de random forest (RF) foi desenvolvido para prever a atribuição relativa de gene de resistência para essas diferentes espécies. Além disso, uma análise descritiva foi feita para investigar melhor as 15 variáveis mais importantes para o modelo de RF. Uma análise de componentes principais (PCA) e regressão all-subsets foram realizadas para identificar genes de resistência específicos de certas espécies. Por fim, esses genes específicos aqui identificados foram comparados com os resistome-signatures identificados num estudo anterior. Os nossos resultados demonstraram que o modelo classificou com sucesso as amostras em classes de espécies correspondentes, com alta precisão e confiabilidade. O modelo teve mais dificuldade em diferenciar porco de vitela, e frango de perú, indicando uma semelhança da composição do resistoma entre cada uma dessas duas espécies. Esta análise validou vários genes como resistome-signatures de animais específicos, como tet(40) e sul2 de vitelos, tet(Q), mef(A) e cfxA2 de vitelos e porcos, blaTEM-126 de frangos, e tet(A) de frangos e perús. Este estudo descreve um método confiável e preciso para a atribuição relativa de genes de resistência a diferentes reservatórios animais usando dados metagenómicos. Estes resultados são essenciais para a vigilância e controlo das resistências aos antimicrobianos em populações animais e humanasN/

PowerAqua: fishing the semantic web

The Semantic Web (SW) offers an opportunity to develop novel, sophisticated forms of question answering (QA). Specifically, the availability of distributed semantic markup on a large scale opens the way to QA systems which can make use of such semantic information to provide precise, formally derived answers to questions. At the same time the distributed, heterogeneous, large-scale nature of the semantic information introduces significant challenges. In this paper we describe the design of a QA system, PowerAqua, designed to exploit semantic markup on the web to provide answers to questions posed in natural language. PowerAqua does not assume that the user has any prior information about the semantic resources. The system takes as input a natural language query, translates it into a set of logical queries, which are then answered by consulting and aggregating information derived from multiple heterogeneous semantic sources

Feeding a Population: Agricultural Education Priorities in Haitian History

The nation of Haiti has experienced a long history of poverty and of tests to its economic development. Among its priorities has been the establishment of an effective educational system. While educational standards remain high, the area of agricultural education—necessary for Haiti’s economy as well as nutritional subsistence—has met with unique challenges. This paper examines analyses and programming policies of the past in order to illuminate contemporary circumstances

Strategic Research Agenda for organic food and farming

The TP Organics Strategic Research Agenda (SRA) was finalised in December 2009. The purpose of the Strategic Research Agenda (SRA) is to enable research, development and knowledge transfer that will deliver relevant outcomes – results that will contribute to the improvement of the organic sector and other low external input systems. The document has been developed through a dynamic consultative process that ran from 2008 to 2009. It involved a wide range of stakeholders who enthusiastically joined the effort to define organic research priorities. From December 2008 to February; the expert groups elaborated the first draft. The consultative process involved the active participation of many different countries. Consultation involved researchers, advisors, members of inspection/certification bodies, as well as different users/beneficiaries of the research such as farmers, processors, market actors and members of civil society organisations throughout Europe and further afield in order to gather the research needs of the whole organic sector

A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.info:eu-repo/semantics/publishedVersio