Internet of things (IoT) results in massive amount of streaming data, often referred to as “big data”, which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review we present an overview of IoT, big data, and artificial intelligence (AI) and their disruptive role in shaping the future of agri-food systems.

The food production and processing value chain is under pressure from all sides—increasing demand driven by a growing and more affluent population; dwindling resources caused by urbanization, land erosion, pollution and competing agriculture such as biofuels; and increasing constraints on production methods driven by consumers and regulators demanding higher quality, reduced chemical use, and most of all environmentally beneficial practices ‘from farm to fork’.

Organic agriculture has experienced remarkable growth in recent decades as societal interest in environmental protection and healthy eating has increased. Research has shown that relative to conventional agriculture, organic farming is more e cient in its use of non-renewable energy, maintains or improves soil quality, and has less of a detrimental e ect on water quality and biodiversity. Studies have had more mixed findings, however, when examining the impact of organic farming on greenhouse gas (GHG) emissions and climate change.

Boll guard I & II were introduced in 2002 and 2006 by Mahyco Monsanto with Genetic Engineering Appraisal Committee, Govt. of India. Indian cotton farmers adopted Bt hybrid cotton between 2002-2013 reaching 92% of the cotton area and 95% of the production, replacing conventional hybrids/ varieties of all the species in both rainfed and irrigated conditions. Bt hybrid cotton was presumed to produce record highest average productivities in India and other major cotton growing countries also in 2007 and 2012 with a decline after 2008 due to excess rains. Farmers invested Rs.

Since development agencies often implement interventions through collective-action groups such as farmer cooperatives and self-help groups, there is a need to understand how participation is affected by group-level and leader attributes. This study collected gender-disaggregated, quantitative and qualitative data on sixty-eight self-help groups in Zambia to understand the participation of men and women farmers in different crop-production activities. Results show that participation rates of men and women are the same across all maize production activities except harvesting.

The adoption of genome editing depends among others, on a clear and navigable regulatory framework that renders consistent decisions. Some countries like the United States decided to deregulate specific transgene-free genome edited products that could be created through traditional breeding and are not considered to be plant pests, while others are still challenged to fit emerging technologies in their regulatory system.