In creating a usable Information System (IS), the quality of information is crucial for making the right decisions. Although, many Information Quality (IQ) features have been identified in a broader context, only certain IQ features would become applicable for each domain from the usability perspective. This study focuses on a theoretical analysis to identify the IQ features which would be significant to produce a usable agricultural information system with respect to the developing countries.

The prevalence of “grass-fed” labeled food products on the market has increased in recent years, often commanding a premium price. To date, the majority of methods used for the authentication of grass-fed source products are driven by auditing and inspection of farm records. As such, the ability to verify grass-fed source claims to ensure consumer confidence will be important in the future. Mid-infrared (MIR) spectroscopy is widely used in the dairy industry as a rapid method for the routine monitoring of individual herd milk composition and quality.

Ensuring food security in developing countries is highly challenging due to low productivity of the agriculture sector, degradation of natural resources, high post farming losses, less or no value addition, and high population growth. Researchers are striving to adopt newer technologies to enhance supply to narrow the food demand gap. Nanotechnology is one of the promising technologies that could improve agricultural productivity via nano fertilizers, use of efficient herbicides and pesticides, soil feature regulation, wastewater management, and pathogen detection.

Decision support systems (DSS) have long been used in research, service provision and extension. Despite the diversity of technological applications in which past agricultural DSS canvass, there has been relatively little information on either the functional aspects of DSS designed for economic decisions in irrigated cropping, or the human and social factors influencing the adoption of knowledge from such DSS.

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.

The overall objective of the technical workshop was to present the guidelines on AIS and EAS assessments, the results at country level and to design and develop a framework of indicators to complement those assessments. Specific objectives were to:

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.

The study assesses the farmers’ use of Global System for Mobile (GSM) for communication among farmers in agricultural extension programs in Taraba State, Nigeria. Specifically, the objectives include: identify key areas in which GSM are used for communication in agricultural extension programs activities, determine the frequency of usage of GSM for information exchange between farmers and extension personnel; and ascertain farmers’ satisfaction in the use of GSM in obtaining information in the various agricultural extension programs.