The integration of male and female smallholders in high-end value chains (e.g. those for tree crops like cocoa, oil palm, avocado, and mango), has been promoted throughout the global South as a strategy for poverty alleviation, economic growth, employment generation, gender equality, and improved wellbeing. More critical literature, however, questions the inclusiveness of farmers’ value chain engagement. Despite rapid mainstreaming of inclusiveness in policy discourse, remarkably little literature sheds light on the operationalization of the concept. This paper addresses this gap.

This study developed a model called the Indonesian Palm Oil Simulation (IPOS). This aims to understand the value chain of the palm oil industry. It provides options for policymakers and decision-makers about possible futures for the Indonesian palm oil industry at the national level.

In this paper, the authors apply an innovative multisectoral diagnostic to examine the entry points for potential interventions in food systems to improve the diets in a rural population in Malawi. The paper is structured as follows: The authors begin by describing the country context and the methods necessary to diagnose and contextualize dietary problems in target populations, prioritizing nutritious foods based on their relative and potential contribution to diets.

This paper begins with a brief review of research on nutrition-sensitive value chains in developing countries. It then presents the Value Chains and Nutrition framework for intervention design that explores food supply and demand conditions across a portfolio of local value chains that are relevant for improving nutrition outcomes. The authors explore the framework in a case study on rural Malawi. Available evidence highlights the dominance of maize in diets, but also the willingness of rural households to consume other nutritious foods (e.g.

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.

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.

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.