This paper comparatively analyzes the structure of agricultural policy development networks that connect organizations working on agricultural development, climate change and food security in fourteen smallholder farming communities across East Africa, West Africa and South Asia.
The aim of this paper is to propose an innovative operational framework that couples life cycle assessment (LCA) and a participatory approach to overcome these issues. The first step was to conduct a progressive participatory diagnosis of the socio-ecological structure of the rural territory and to characterise the main cropping systems. The results of the diagnosis and other data were progressively triangulated, validated and consolidated with the stakeholders at the territorial level. The paper discusses the quality and validity of data obtained using a participatory approach.
The framework is designed to assess resilience to specific challenges (specified resilience) as well as a farming system's capacity to deal with the unknown, uncertainty and surprise (general resilience). The framework provides a heuristic to analyze system properties, challenges (shocks, long-term stresses), indicators to measure the performance of system functions, resilience capacities and resilience-enhancing attributes. Capacities and attributes refer to adaptive cycle processes of agricultural practices, farm demographics, governance and risk management.
Fall Armyworm (Spodoptera frugiperda), or FAW, is an insect native to tropical and subtropical regions of the Americas. In the absence of natural controls or good management, it can cause significant damage to crops. It prefers maize, although it can feed on more than 80 additional species of crops including rice, sorghum, millet, sugarcane, vegetable crops and cotton.
Invasive species such as Ambrosia (an annual weed) pose a biosecurity risk whose management depends on the knowledge, attitudes and practices of many stakeholders. It can therefore be considered a complex policy and risk governance problem. Complex policy problems are characterised by high uncertainty, multiple dimensions, interactions across different spatial and policy levels, and the involvement of a multitude of actors and organisations. This paper provides a conceptual framework for analysing the multi-level and multi-actor dimensions of Ambrosia management.
The European small ruminants (i.e. sheep and goats) farming sector (ESRS) provides economic, social and environmental benefits to society, but is also one of the most vulnerable livestock sectors in Europe. This sector has diverse livestock species, breeds, production systems and products, which makes difficult to have a clear vision of its challenges through using conventional analyses. A multi-stakeholder and multi-step approach, including 90 surveys, was used to identify and assess the main challenges for the sustainability of the ESRS to prioritize actions.
Local extension agents can benefit from the simple procedures in developing irrigation calendars for other irrigated crops. This study gives important lesson for local and regional decision makers, on their endeavour to increase the productivity of small scale irrigated agriculture. This paper is organized as follows: Section 2 describes the study area, practical irrigation schedule development method, alternative irrigation schedules and data collection and analysis methods. Section 3 presents the results.
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 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.
The purpose of the paper, using a comprehensive innovation systems failure framework, is to assess the performance of agrifood innovation systems of Scotland and the Netherlands, through analysis of the key innovation actors (organisations, networks or influential individuals), and their key functions (research provider, intermediary etc), and those mechanisms that either facilitate or hinder the operation of the IS (known as inducing and blocking mechanisms, respectively).