[科研项目] An evidence review of the proliferation of digital technology and data analytics in agriculture 进入全文
The Bill & Melinda Gates Foundation, USAID, and Cornell University are spearheading an evidence review of how the proliferation of digital technology and data analytics in agriculture is contributing to the lives of farmers and agricultural service providers in developing country economies. The study team will map, synthesize and describe existing literature to assess how digital agricultural services, delivered individually or as a bundle, act as facilitators and barriers towards achieving outcomes including resiliency, economic growth and sustainability, nutrition, and be inclusive of women and vulnerable populations. In order to improve the range and diversity of resources that are included in this study, we aim to include community-sourced documents. We would be grateful for your assistance in sharing materials from your programs, such as monitoring and learning evaluations, impact assessments, project reports or other materials that might be difficult for the project team to discover without your assistance.
Planting a cover crop between the main growing seasons is a measure that offers multiple potential benefits for sustainable food production. These crops play a major role in preventing the nitrates introduced by fertilisation from leaching into the groundwater. To comply with current regulations, Dutch farmers are required to sow a cover crop after cultivating maize by 1 October at the latest. Researchers from the University of Twente’s Faculty of Geo-Information Science and Earth Observation (ITC) have used satellite images to assess the effective use of cover crops in the Dutch province of Overijssel. The study focuses on analysing satellite images from 2017 and 2018 and the results have now been published in the International Journal of Applied Earth Observation and Geoinformation.
In order to feed a growing global population worldwide and in light of new challenges including climate variability, degrading ecosystems, and loss of arable land, we must leverage the insights, agility, and precision made possible by digital tools and technologies in agriculture worldwide. To achieve this in developing economy contexts, much still must be learned about the appropriate technologies to collect, analyze, and act on relevant data for food security. Fortunately, in the last few decades, the data collection process has evolved. Expensive point-to-point networks gave way to distributed wide-area wireless sensor networks, and this set the stage for the Internet of Things (IoT). The IoT is a recent paradigm under which an array of electronic devices (known collectively as “things”) provide and exchange data through a network, very often through the Internet but it can also be a local or wide-area network. IoT approaches are enabling the development of new services spanning the perception-planning-action cycle. There are many different alternatives to abstract an IoT network, and this report highlights some of the most representative architectures developed by technological leaders, open-source communities, and researchers which are briefly summarized in the following paragraphs
Root crops like cassava, carrots and potatoes are notoriously good at hiding disease or deficiencies which might affect their growth. While leaves may look green and healthy, farmers can face nasty surprises when they go to harvest their crops. This also poses problems for plant breeders, who have to wait months or years before knowing how crops respond to drought or temperature changes. Not knowing what nutrients or growing conditions the crop needs early on also hinder crop productivity. New research using machine learning and to help predict root growth and health with aboveground imagery was published June 14 in Plant Methods.
Data & Knowledge Engineering
In this paper we propose an innovative architecture, called Mo.Re.Farming, for handling agricultural data in an integrated fashion and supporting decision making in the precision agriculture domain. This architecture is oriented to data analysis and is inspired by Business Intelligence 2.0 approaches. It is hybrid in that it couples traditional and big data technologies to integrate heterogeneous data, at different levels of detail, from several owned and open data sources; its goal is to demonstrate that such integration is feasible and beneficial in supporting situ-specific and large-scale analyses. The proposed architecture has been developed in the context of the Mo.Re.Farming project, aimed at providing a Decision Support System for agricultural technicians in the Emilia-Romagna region and to enable analyses related to the use of water and chemical resources. The architecture is fully deployed and serves as a hub for agricultural data in Emilia-Romagna; the integrated data are made available in open access mode and can be accessed through web interfaces and through a set of web services. The paper describes the architecture from the technological and functional points of view and discusses the Mo.Re.Farming project outcomes and lessons learnt.