Social farming (SF) has emerged as a social innovation practice shaping heterogeneous approaches and results. This study discusses the complexity of SF policy and practices, and it is led by the main hypothesis that the relationship between agricultural and social dimensions might be very heterogeneous, not only in different national contexts but also within the same national and local level. SF policy and practices are investigated testing the hypothesis of three main different modalities of interaction according to how the social and the agricultural perspectives interact.

The quest for innovation lies at the heart of European rural development policy and is integral to the Europe 2020 strategy. While social innovation has become a cornerstone of increased competitiveness and the rural situation legitimizes public intervention to encourage innovation, the challenges of its effective evaluation are compounded by the higher ‘failure’ rate implied by many traditional performance measures.

This paper provides a chronology and overview of events and policy initiatives aimed at addressing irrigation sustainability issues in the San Joaquin River Basin (SJRB) of California. Although the SJRB was selected in this case study, many of the same resource management issues are being played out in arid, agricultural regions around the world.

Agriculture is an essential component of food security, sustainable livelihoods, and economic development in sub-Saharan Africa (SSA). Smallholder farmers, however, are restricted in the number of crops they can grow due to small plot sizes. Agriculture inputs, such as fertilizers, herbicides or pesticides, and improved seed varieties, could prove to be useful resources to improve yield. Despite the potential of these agriculture technologies, input use throughout much of SSA remains low.

Industrial agriculture and its requirement for standardized approaches is driving the world towards a global food system, shrinking the role of farmers and shifting decision-making power. On the contrary, a holistic perspective towards a new food-system design could meet the needs of a larger share of stakeholders. Long-term experiments are crucial in this transition, being the hub of knowledge and the workshop of ‘participation in’ and ‘appropriation of’ the research in agriculture over a long term.

The use of technology in agriculture plays an important role in the production chain cycle, as well as in the improvement of processes and productivity. To develop a model for measuring the technological capacity of family agriculture systems, it is necessary to assess the gaps related to indicators and the technological potentialities of these farmer groups, which are often not considered when they require financial support and do not get enough. Thus, the aim of this study is to identify the indicators used to evaluate the technological capacity of farm systems and agriculture.

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse.

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.

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.

Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization.