The herd dynamics model (HDM) component of the Linked Economic and Animal Systems (LEAS) model specifically documented here is developed in the context of the Feed the Future Innovation Lab for Livestock Systems financed by the United States Agency for International Development (USAID) and managed by the University of Florida’s Institute of Food and Animal Sciences. The main objective of this project is to develop a comprehensive analytical approach or systems model capable of assessing (i) how animal herd or flock sizes change over time and in response to on-farm policies; (ii) how alternative national trends and policies affect future development of the livestock system as a whole; (iii) how changes in livestock policies affect people working throughout the livestock system; and (iv) how changes in animal-source food (ASF) production and prices affect the real incomes and consumption patterns of different population groups. The HDM developed provides a highly detailed description of the cattle sector while laying a framework that can be easily adapted to other types of livestock. The model allows one to closely examine the performance of the livestock sector disaggregated by agroecology zones or regions. The HDM is linked both ways with a core economywide model through economic variables such as relative prices of livestock activities, prices and availability of intermediate inputs including feed, and changes in supply of livestock capital in the meat and milk production sectors. Given the complex interplay in the livestock sector such as offtake decisions, death rates, milk and meat yield, and feeding practices (through quality indexed feed demand), the HDM developed under this project is a fully dynamic livestock sector model that provides several avenues for policy analysis on livestock management, the sector’s future trajectory, and its dynamics, given risks and opportunities within the sector and beyond.

Climate and weather shocks pose significant threats to crop-livestock systems, leading to economic losses and humanitarian crises. Utilizing a modeling framework that innovatively integrates the crop and livestock systems, this study examines the interactions and dynamic adjustments within these systems following weather shocks, using Ethiopia as a case study. We also evaluate the effectiveness of various adaptation strategies in sustaining farm incomes, food security, and welfare. Results show unique effects on the crop and livestock sectors resulting from a joint shock on the two systems. While food crops experience a strong and immediate growth effect that fades quickly, the livestock sector faces the full impact of the shock a year later, with the effect persisting to some degree. We also find diverging economic and livestock system adjustment trajectories from the separate shocks to the crop and livestock systems. Further, the intervention options analyzed show contrasting impacts on various outcome indications, with only the resilient crop intervention causing sector-indifferent impacts. Our findings emphasize the importance of proactive measures to enhance the resilience of crop-livestock systems, with implications for policy and practice aimed at safeguarding food security and livelihoods in semi-subsistence economies.

Livestock value chains present a multitude of challenges in the analysis of resilience given their multidimensional role in livelihoods and complexities associated with their biological aspects of production. [Author] This paper discusses these challenges at length and provides some guidance on methodological and operational modalities to tease out these nuances that better inform public policy and the choice of technical and policy interventions. [Author] Specific advice on models, data and applications is provided. [Author]

As Rwanda is expected to return to its rapid growth trajectory following the COVID-19 pandemic, agriculture will continue to play a central role in the structural transformation of the entire economy. To this end, the Government of Rwanda continues to invest in the agricultural sector by building on Strategic Plans for the Transformation of Agriculture (PSTAs) that began in the early 2000s. The challenging question is how to prioritize public expenditures across a broad portfolio of policies and programs. Ambitious plans, whether in the short or long term, require difficult decisions. The prioritization of public investment becomes even more complex as Rwanda’s structural transformation advances and as new investments—beyond the farm—become critically important for the agricultural sector. The structural transformation process itself means that as agriculture becomes more integrated with the rest of the economy, public resource allocations need to address a wider range of issues across the entire food system; these include nutrition-sensitive food production systems, inclusive value chain development, nonfarm rural enterprise development, and climate-resilient sustainable intensification of both crops and livestock. This study provides evidence that is designed to assist the Government of Rwanda in its selection of agricultural policy, investment, and expenditure portfolios that reflect the country’s broad focus on its food system and structural transformation. This process of prioritization will need to incorporate multiple public investments targeting multiple development outcomes and will need to be grounded in the costeffective use of public resources in a largely market-led transformation process. This data-driven and evidence-based approach must critically underpin an informed investment prioritization process that helps achieve ambitious targets in an environment constrained by limited public resources. The study uses the Rural Investment and Policy Analysis (RIAPA) economywide model developed by the International Food Policy Research Institute (IFPRI), with contributions from colleagues at the Ministry of Agriculture and Animal Resources (MINAGRI), the Ministry of Finance and Economic Planning (MINECOFIN) and the National Institute of Statistics of Rwanda (NISR). The study draws on data from multiple sources as well as expert insights to inform the application of RIAPA’s Agricultural Investment for Data Analyzer (AIDA) module as a tool to measure the impacts of alternative public expenditure options on multiple development outcomes. Using this integrated modeling framework, the study links agricultural and rural development spending to four specific outcomes: economic growth, job creation, poverty reduction, and diet quality improvement; at the same time, it considers the synergies and tradeoffs associated with the different investment options in the transformation process. The paper first assesses the contribution of public expenditures to agricultural and rural development under the fourth Strategic Plan for Agriculture Transformation (PSTA 4) that extends between 2018 and 2024. These findings are important, given the fact that since the beginning of PSTA 4, the budget allocated to MINAGRI (measured in constant prices) has stagnated. Our results suggest that increased spending on agriculture is well justified and that such spending is essential if the Government of Rwanda is to achieve its long-term development goals.

Subsistence production: a sign of market failure. Commercialization cannot be left to the market. Household effects of commercialization. Nutrition effects of commercialization. Policy action needed.

Harnessing science, technology and innovation (STI) is key to meeting the aspirations of efficient, inclusive, resilient and sustainable agrifood systems and leveraging emerging opportunities to achieve the Sustainable Development Goals (SDGs). The FAO Strategic Framework 2022–2031 identifies STI as having enormous transformative potential and underlines the potential of emerging technologies. It also recognizes that STI can present substantial risks, such as reinforcing inequality and market concentration, or contributing to the degradation of natural resources. As one of four accelerators identified by the FAO Strategic Framework 2022–2031, technology is expected to “accelerate impact while minimizing trade-offs”. This report examines the technology accelerator trends across publicly available FAO knowledge reports, technical guidance and convening summaries. Leveraging AI-assisted classification of nearly 40 000 documents, this report offers a bird’s-eye perspective of six types of technology – digital technologies, biotechnologies, mechanization, irrigation technologies, renewable energy technologies and food processing technologies – as well as high-level trends for outcomes and social and demographic details about the communities using these technologies.