Women play a key role in agriculture and food security, making up around 48 percent of the agricultural labour force in low-income countries. Despite this, their important contribution is hardly visible and largely unrecognized. Gender equality regards human rights but gender-based constraints in the sector cause also major inefficiencies in value chains, and are a key impediment for rural development, food security, and social and environmental sustainability. Moreover, the severe and multidimensional constraints faced by women hamper their productive potential and livelihoods.
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.
This working paper summarizes the findings of a portfolio review conducted to explore the gender and youth responsiveness of climate-smart agriculture technologies tested across climate-smart villages. The innovative and integrative aspect of the Climate-Smart Village (CSV) approach can provide useful insights into how to decrease the gender gap in the context of climate change.
Participatory action research (PAR) has been around for years, and can add significant value to agriculture research for development projects. The use of PAR in climate-smart villages (CSVs) is no different. This review aimed to assess the impact that PAR approaches had on the adoption of CSA practices and technologies, with an emphasis on gender and social inclusion. Through a portfolio review, interviews with regional CSV teams, and surveys sent to local partners, this report demonstrates the benefit of PAR use in the implementation of the CSV approach.
This work examined the determinants of the adoption of improved Irish potato technologies by farmers in three divisions of the Western Region of Cameroon. Data were collected from 170 farmers from 14 villages in our study area using a mixed-method approach—structured questionnaires, focus group discussion, key informant interviews, and participatory observations with individual farmers and farmers belonging to cooperative and common initiative groups. The study employed descriptive statistics and regression analysis to assess the adoption status of farmers and its determinants.
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.
This guide explains how to operationalize inclusion of women, Indigenous Peoples and other under-represented groups in multi-stakeholder forums (MSFs).
This paper assesses the relationships between women’s dietary diversity and various indicators of agricultural biodiversity in farms of the Hauts-Bassins, a cotton-growing region in rural western Burkina Faso. A sample of 579 farms representative of the region was surveyed at three different periods of the year. Using a qualitative 24-h dietary recall, we computed a women’s dietary diversity score (WDDS-10) based on ten food groups.
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.