Accurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision.
This study aims to clarify the Japanese characteristics of the spread of smart agriculture utilizing digital technology, which is expected to spread worldwide, and to provide policy implications for further dissemination of the technology. We conducted a questionnaire survey on actual conditions related to smart agriculture on Japanese farms. We have also proposed creation of a Smart Agricultural Kaizen Level (SAKL) technology map by applying the evaluation method used in management technology theory for the manufacturing industry.
Climate smart agriculture (CSA) technologies are innovations meant to reduce the risks in agricultural production among smallholder farmers. Among the factors that influence farmer adoption of agricultural technologies are farmers' risk attitudes and household livelihood diversification. This study, focused on determining how farmers' risk attitudes and household livelihood diversification influenced the adoption of CSA technologies in the Nyando basin. The study utilized primary data from 122 households from two administrative regions of Kisumu and Kericho counties in Kenya.
This study aims to investigate blockchain technology for agricultural supply chains during the COVID-19 pandemic. Benefits and solutions are identified for the smooth conduction of agricultural supply chains during COVID-19 using blockchain. This study uses interviews with agricultural companies operating in Pakistan. The findings discover the seven most commonly shared benefits of applying blockchain technology, four major challenges, and promising solutions.
The potential beneficial and harmful social impacts generated by the introduction of novel technologies, in general, and those concerning nutrient recovery and the improvement of nutrient efficiency in agriculture, in particular, have received little attention, as shown in the literature. This study investigated the current social impacts of agricultural practices in Belgium, Germany and Spain, and the potential social impacts of novel technologies introduced in agriculture to reduce nutrient losses.
China is characterized as ‘a large country with many smallholder farmers’ whose participation in modern agriculture is key to the country’s modern agriculture development. Promoting smallholder farmers’ adoption of modern agricultural production technology is one effective way to improve the capabilities of smallholder farmers. This paper aims to explore the impact of Internet use on the adoption of agricultural production technology by smallholder farmers based on a survey of 1 449 smallholders across 14 provinces in China.
El Proyecto "Desarrollo de capacidades para los sistemas de innovación agrícola: ampliación del marco común de la Plataforma de Agricultura Tropical" (en resumen, Proyecto TAP-AIS) es implementado por la Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO) en países de África (Burkina Faso, Malawi, Eritrea, Ruanda, Senegal), América Latina (Colombia), Asia y el Pacífico (Camboya, Lao PDR, Pakistán), con el objetivo estratégico de contribuir al fortalecimiento de capacidades para fomentar, reconocer y fortalecer la innovación rural en el contexto de la transformaci
The spatial and temporal variability of soil properties (fluid composition, structure, and water content) and hydrogeological properties employed for sustainable precision agriculture can be obtained from geoelectrical resistivity methods. For sustainable precision agricultural practices, site-specific information is paramount, especially during the planting season.
The digital transformation in agriculture introduces new challenges in terms of data, knowledge and technology adoption due to critical interoperability issues, and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.
The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach.