The study proposes a knowledge-intensive and qualitative research methodology based on researcher-farmer participatory approach, with the aim to improve the state of knowledge on organic rice, explore the yield potential and variability, and identify the successful agronomic practices. A wide range of cropping systems placed in North Italy were monitored and analysed during three years by a multi-actor network.

One solution that may help farmers face climate challenges is for them to access real-time water-related information by using smart Information and Communication a Technology (ICT).

Despite the positive attributions ascribed to Digital Platforms (DPs), empirical studies that explore the role of DPs in smallholder credit access are lacking, particularly that which takes into account the dynamics of trust in complex actor interactions in the value chain. Consequently, it remains unclear whether, and how DPs influence trust and actor cooperation in value chain financing of maize production in Ghana.

Crowdsourcing, understood as outsourcing tasks or data collection by a large group of non-professionals, is increasingly used in scientific research and operational applications. In this paper, we reviewed crowdsourcing initiatives in agricultural science and farming activities and further discussed the particular characteristics of this approach in the field of agriculture. On-going crowdsourcing initiatives in agriculture were analysed and categorised according to their crowdsourcing component.

While there is a lot of literature from a natural or technical sciences perspective on different forms of digitalization in agriculture (big data, internet of things, augmented reality, robotics, sensors, 3D printing, system integration, ubiquitous connectivity, artificial intelligence, digital twins, and blockchain among others), social science researchers have recently started investigating different aspects of digital agriculture in relation to farm production systems, value chains and food systems. This has led to a burgeoning but scattered social science body of literature.

The impulse towards a larger introduction of Information and Communication Technology (ICT) in the agricultural field is currently experiencing its momentum, as digitisation has large potentialities to provide benefits for both producers and consumers; on the other hand, pushing technological solutions into a rural context encounters several challenges.

The economic globalisation has opened new pathways for commerce and triggered a logistical revolution, which in turn has produced enormous technological innovations. In this context, the role of startups is becoming increasingly crucial since they are positioning themselves as innovation enablers among large and small companies. Between these innovations, IoT, Big Data Analytics and Blockchain can be used in various domains, among which the logistics of the whole wine supply chain.

Agricultural extension in sub-Saharan Africa has often been criticised for its focus on linear knowledge transfer, and limited attention to systemic approaches to service delivery. Currently, the region is experiencing a new-ICT revolution and there are high expectations of new-ICTs to enhance interaction and information exchange in extension service delivery. Using an innovation systems perspective, we distinguish the roles demand-articulation, matching demand and supply, and innovation process management for innovation-intermediaries.

The Women's Empowerment in Agriculture Index (WEAI) is a direct, multi-dimensional measure of women's access to resources and decision-making in various domains of agriculture. However, several challenges characterize its use: adaptation of questionnaires to local agricultural contexts, modifications to index construction once underlying activities and adequacy thresholds are modified, and sensitivity analysis. In this paper, the authors address such challenges based on our experience of adapting and using the WEAI across 3600 households in India.

The Colombian Ministry of Agriculture Colombia, an international research center and a national farmers’ organization developed a data-driven agricultural program that: (i) compiles information from multiple sources; (ii) interprets that data; and (iii) presents the knowledge to farmers through the local advisory services. Data was collected from multiple sources, including small-scale farmers. Machine learning algorithms combined with expert opinion defined how variation in weather, soils and management practices interact and affect maize yield of small-scale farmers.