The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse.
The impact of global warming on crop growth periods and yields has been evaluated by using crop models, which need to provide various kinds of input datasets and estimate numerous parameters before simulation. Direct studies on the changes of climatic factors on the observed crop growth and yield could provide a more simple and intuitive way for assessing the impact of climate change on crop production.
Soil texture is a key soil property influencing many agronomic practices including fertilization and liming. Therefore, an accurate estimation of soil texture is essential for adopting sustainable soil management practices. In this study, we used different machine learning algorithms trained on vis–NIR spectra from existing soil spectral libraries (ICRAF and LUCAS) to predict soil textural fractions (sand–silt–clay %). In addition, we predicted the soil textural groups (G1: Fine, G2: Medium, and G3: Coarse) using routine chemical characteristics as auxiliary.
Recent Society 5.0 efforts by the Government of Japan are aimed at establishing a sustainable human-centered society by combining new technologies such as sensor networks, edge computing, Internet of Things (IoT) ecosystems, artificial intelligence (AI), big data, and robotics. Many research works have been carried out with an increasing emphasis on the fundamentals of wireless sensor networks (WSN) for different applications; namely precision agriculture, environment, medical care, security, and surveillance.
Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field.
Rice is a primary food for more than three billion people worldwide and cultivated on about 12% of the world’s arable land. However, more than 88% production is observed in Asian countries, including Pakistan. Due to higher population growth and recent climate change scenarios, it is crucial to get timely and accurate rice yield estimates and production forecast of the growing season for governments, planners, and decision makers in formulating policies regarding import/export in the event of shortfall and/or surplus.
The Newsletter of the Tropical Agriculture Platform (TAP) provides regular updates on global activities by TAP and its partners, on the CDAIS projects and on upcoming related events. This issue specifically refers to the period from October 2020 to January 2021, including also some activities of February 2021.
The publication is a part of the FAO work to assist the member countries in reforming their national Extension and Advisory Services (EAS). It highlights the main elements and provide concrete guidelines for the policy makers to coordinate pluralism in extension and advisory services (EAS), i.e. ensuring that multiple EAS providers from public, private sector and NGOs/donors, provide quality services that contribute to national agricultural priorities and wellbeing of rural producers, collaborate and exchange information to maximise synergies and minimise gaps and duplications.
This video illustrates the goals and work of the Tropical Agriculture Platform (TAP), a G20 initiative supported by the European Union to improve efficiency and effectiveness of capacity development programmes and of knowledge sharing in order to strengthen agricultural innovation systems in the tropics and sub-tropics. The Secretariat of TAP is hosted by the Research and Extension Unit, Office of Innovation, of the Food and Agriculture Organization (FAO) of the United Nations.
This policy brief presents a methodology for assessing agricultural innovation systems (AIS), developed and pilot tested by the Food and Agriculture Organization of the United Nations (FAO) in the context of the Tropical Agriculture Platform, a G20 initiative to develop capacities for agricultural innovation in the tropics supported by the European Union. Using participatory, multi-stakeholder methods and tools, the assessment of a country’s AIS take stock of enabling and hindering factors in innovation processes, identifies gaps and challenges, and advices on ways to strengthen the AIS.