Blog IICA

Evidences

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PAI: focused on specialized public infrastructure

09/10/2025 Go to Evidence

0.4% of the 497 productive articulation initiatives (PAI) (same 20 countries; 2023–2025) focus on specialized public infrastructure development (ECLAC, 2025).

0,4% de las 497 iniciativas de articulación productiva (IAP) (en los 20 países listados; 2023–2025) reporta como foco estratégico el desarrollo de infraestructura pública especializada, según el informe (CEPAL, 2025).

PAI Governance

09/10/2025 Go to Evidence

In “almost all” cases, productive articulation initiatives (PAI) (in the same 20 countries) are managed by a decision-making body averaging 11 people, with composition mainly private sector (72.7%), then civil society (15.0%), public sector (10.2%), and academia (1%) (ECLAC, 2025).

En “casi la totalidad” de los casos, las iniciativas de articulación productiva (IAP) (en los 20 países listados) son gestionadas por un órgano de decisión conformado por un promedio de 11 personas; además, ese órgano se compone mayoritariamente por sector privado (72,7%), seguido de sociedad civil (15,0%), sector público (10,2%) y sector académico (1%), según el informe (CEPAL, 2025).

Decline in RIP agriculture in LAC

09/10/2025 Go to Evidence

In 2023, the relative internal productivity of the Agriculture sector in Latin America and the Caribbean was 44 (with total productivity coefficient = 100), showing its lag compared to sectors with higher productivity, according to the sectoral graph of the report (ECLAC, 2025).

En 2023, la productividad relativa interna del sector Agricultura en América Latina y el Caribe fue 44 (con coeficiente de productividad total = 100), evidenciando su rezago frente a sectores de mayor productividad, según el gráfico sectorial del informe (CEPAL, 2025).

Agricultural employment in LAC vs EU

09/10/2025 Go to Evidence

In 2023, agriculture accounted for 12.1% of employment in Latin America and the Caribbean, versus 3.8% in the European Union, per the report’s employment-by-sector chart (ECLAC, 2025).

En 2023, la agricultura representó 12,1% del empleo en América Latina y el Caribe, frente a 3,8% en la Unión Europea, según el gráfico comparativo de participación del empleo por actividad económica (CEPAL, 2025).

Agricultural policies in Peru 2019-2022

22/08/2025 Go to Evidence

State support for the Peruvian agricultural sector (2019–2022) is analyzed using the OECD methodology (PSE and GSSE), quantifying subsidies, general services, and competitiveness programs, and linking them to the sector's GHG emissions. It assesses its consistency with the NDCs and the National Agricultural Policy 2021–2030, highlighting the need to reorient incentives towards climate-smart and low-carbon agricultural practices.

Banco Interamericano de Desarrollo (BID)

De los Santos, J., Muñoz, G., Tellez-Lara, N., Rondinone, G., & De Salvo, C. P. (2025). Políticas agropecuarias en Perú 2019-2022: análisis y cuantificación de los apoyos y su vinculación con las emisiones de gases de efecto invernadero.

Building towards Sustainable Development Goal

22/07/2025 Go to Evidence

Building towards the goal of zero hunger, the food and agriculture sector must be considered when designing nutritional interventions (Duncan et al., 2022).

Construyendo hacia el objetivo de hambre cero, el sector alimentario y agrícola debe ser considerado al diseñar intervenciones nutricionales (Duncan et al., 2022).

Duncan, E., Ashton, L., Abdulai, A. R., Sawadogo-Lewis, T., King, S. E., Fraser, E. D. G., Vosti, S., Haines, J., Knight, F., & Roberton, T. (2022). Connecting the food and agriculture sector to nutrition interventions for improved health outcomes. Food Security, 14, 657-675. https://doi.org/10.1007/s12571-022-01262-3

Predictive models and food security

16/07/2025 Go to Evidence

The study conducts a comprehensive review of the use of predictive models to strengthen food security in Latin America, with an emphasis on Colombia. It analyzes 65 studies published between 2014 and 2024 that apply algorithms such as Random Forests and Neural Networks to anticipate agricultural yields, manage climate risks, and optimize resources. The results show that agricultural, climatic, and technological variables predominate, while socioeconomic and nutritional dimensions are underrepresented. The article highlights the need to integrate multidimensional data to develop more robust models that strengthen the resilience and sustainability of food systems in the region.

Universidad de Santander: Bucaramanga, Santander, Colombia. Universidad El Bosque: Bogotá, Bogota D.C., Colombia. Universidad Tecnológica de Pereira: Pereira, Colombia.

Trejos-Suárez, J., Pardo, L. V. C., Tabares, J. E., Garzón, S., Bryon, A., & Alarcón, Z. (2025). Modelos predictivos para la seguridad alimentaria en América Latina: Una revisión de alcance. Archivos Latinoamericanos de Nutrición, 75(2), 129-142.