Capacity development interventions are considered critical entry points for advancing gender equality in agricultural research systems. However, the impacts of capacity development programs are often difficult to track. Academic reviews highlight that insufficient attention is paid to the suitability of gender training programs to increase capacity and limited evidence is available on their longer-term impacts.
There is widespread need for gender-responsive agricultural research, yet the question of how this kind of research can be implemented and its success measured needs further interrogation. This paper presents a framework, developed on the basis of literature and validated by experts, for tracking the gender responsiveness of agricultural research throughout the research cycle, from the research plan to the dissemination of research findings. The framework was tested in Uganda and Rwanda on 14 research projects considered to be gender-responsive.
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.
Bees provide a critical link in the maintenance of ecosystems, pollination. They play a major role in maintaining biodiversity, ensuring the survival of many plants, enhancing forest regeneration, providing sustainability and adaptation to climate change and improving the quality and quantity of agricultural production systems. In fact, close to 75 percent of the world’s crops that produce fruits and seeds for human consumption depend, at least in part, on pollinators for sustained production, yield and quality.
CABI and the Cereal Growers Association (CGA) have been sharing information with farmers in Kenya on how to effectively and safely manage the continuing threat of the invasive fall armyworm (Spodoptera frugiperda). This was achieved thanks to a development communication campaign that combined video sharing through a network of lead farmers and social media.
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.