The aim of the study was to strengthen the capacities of the farmers in a participatory process to adapt to climate change. It was assumed that an innovation platform could support generation and exchange of knowledge on climate change, exchange and identification and implementation of options for adaptation tailored to local needs by the participating farmers

This methodological framework is based on Life Cycle Assessment (LCA) and multi-criteria assessment methods. It integrates CSA-related issues through the definition of Principles, Criteria and Indicators, and involves farmers in the assessment of the effects of CSA practices. To reflect the complexity of farming systems, the method proposes a dual level of analysis: the farm and the main cash crop/livestock production system. After creating a typology of the farming systems, the initial situation is compared to the situation after the introduction of a CSA practice.

This chapter reports on the different functions fulfilled by existing mechanisms for supporting collective innovation in the agricultural and agrifood sectors in the countries of the Global South in order to identify the potential contributions the research community can make to strengthen them. The authors show that a variety of mechanisms are needed to create enabling conditions for innovation and to provide a step-by-step support to innovation communities, according to their capacities and learning needs.

Agricultural Internet of Things (IoT) has brought new changes to agricultural production. It not only increases agricultural output but can also effectively improve the quality of agricultural products, reduce labor costs, increase farmers' income, and truly realize agricultural modernization and intelligence. This paper systematically summarizes the research status of agricultural IoT. Firstly, the current situation of agricultural IoT is illustrated and its system architecture is summarized. Then, the five key technologies of agricultural IoT are discussed in detail.

Although the benefits of genetically modified (GM) crops have been well documented, how do farmers manage the risk of new technology in the early stages of technology adoption has received less attention. We compare the total factor productivity (TFP) of cotton to other major crops (wheat, rice, and corn) in China between 1990 and 2015, showing that the TFP growth of cotton production is significantly different from all other crops. In particular, the TFP of cotton production increased rapidly in the early 1990s then declined slightly around 2000 and rose again.