This data article contains annotation data characterizing Multi Criteria Assessment (MCA) Methods proposed in the agri-food sector by researchers from INRA, Europe's largest agricultural research institute (INRA, https://institut.inra.fr/en). MCA can be used to assess and compare agricultural and food systems, and support multi-actor decision making and design of innovative systems for crop production, animal production and processing of agricultural products.
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.
The mergers of some of the world's largest agribusinesses have led to speculation about what sort of global citizens the new companies will become and whether vulnerable rural populations, especially smallholder men and women farmers, will be negatively impacted. As innovation leaders in the agriculture industry, these new companies will be expected to play key roles in finding solutions for major agricultural challenges facing the world today.
While livestock constitute a strategic sector to reduce poverty and enhance growth in developing countries, decision makers often lack data reflecting the diversity of livestock functions and systems. The authors therefore mobilised the Livestock Sector Investment Policy Toolkit to assess the economic contributions of livestock in Zambia. Valuing their plural contributions by system, we found that mixed rainfed systems were the main contributors to added value, even if specialised intensive systems provided around 45% of meat and milk production.
This research delves into the implementation of Good Agricultural Practices (GAP) among seven types of independent smallholders in Rokan Hulu regency, Riau province. The research area consisted of a relative established agricultural area on mineral soils and a relative frontier, mostly on peat. Smallholder types ranged from small local farmers to large farmers who usually reside in urban areas far from their plantation and regard oil palm cultivation as an investment opportunity.
Increasing on-farm production diversity and improving markets are recognized as ways to improve the dietary diversity of smallholders. Using instrumental variable methods to account for endogeneity, this paper studies the interplay of production diversity, markets and diets in the context of seasonality in Afghanistan. Accordingly to the authors improved crop diversity over the year is positively associated with dietary diversity in the regular season, but not in the lean season.
Global population growth, an increasing demand for animal products and scarcity of conventional feed ingredients drive the search for alternative protein sources for animal feed. Extensive research indicates that insects provide good opportunities as a sustainable, high quality and low-cost component of animal feed. Here, we discuss how insect farming can promote inclusive business for smallholder farmers in the agribusiness value chain.
How can the transition and transformation towards more sustainable food and agriculture (SFA) materialize at country-level? Who will own, drive and be committed to this process? How can the process be sustainable and reach scale? The practical, "how-to" contribution titled "System-Wide Capacity Development for SFA" attempts to answer these questions.
Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems.
For an intelligent agricultural robot to reliably operate on a large-scale farm, it is crucial to accurately estimate its pose. In large outdoor environments, 3D LiDAR is a preferred sensor. Urban and agricultural scenarios are characteristically different, where the latter contains many poorly defined objects such as grass and trees with leaves that will generate noisy sensor signals. While state-of-the-art methods of state estimation using LiDAR, such as LiDAR odometry and mapping (LOAM), work well in urban scenarios, they will fail in the agricultural domain.