The Power of Agricultural Productivity: A Pathway to Poverty Reduction 

by Bert Lenaerts and Jean Balié 

Agricultural productivity is a cornerstone of economic development and a critical factor in the fight against poverty. When we discuss improving agricultural output, we aren’t just looking at statistics; we’re aiming to propose a conceptual as well as practical framework that can significantly impact real lives and help lift communities out of poverty.  

Historical Perspectives and Modeling Challenges

Historically, agricultural productivity has been modeled through economic surplus modeling, a method pioneered by researchers such as Alston, Norton, and Pandey. Rooted in partial equilibrium analysis, this approach has provided valuable insights into how agricultural outputs can be optimized to maximize economic benefits. However, this model also highlights several challenges that must be addressed before scaling its effectiveness across diverse agricultural systems.  

Each crop has a unique production system, exhibiting significant variations across different economies and socio-political contexts, ranging from smallholder farmers to large importers or major exporting nations. Moreover, the lack of robust data and the over-reliance on traditional tools like Excel spreadsheets impede our ability to derive actionable insights. Addressing these challenges is crucial for unlocking the full potential of modeling expected impacts from agricultural productivity. 

The Role of Modeling in Agricultural Decision-Making

 Modeling is a critical tool for decision-making and prioritization within the agricultural sector. It enables the analysis, interpretation, and synthesis of large and complex datasets to identify trends, patterns, and insights into effective agricultural practices. Predictive modeling, in particular, supports scenario forecasting, allowing stakeholders to simulate various conditions, such as crop price fluctuations and weather changes, to inform strategic planning and decision-making.   

In resource-constrained agricultural settings, modeling helps identify crops or practices that yield the highest economic returns. Additionally, it is invaluable for risk assessment, enabling the modeling of potential threats like pests and climate change, and the development of contingency plans or mitigation strategies. Moreover, modeling establishes key performance indicators (KPIs) for monitoring the effectiveness of agricultural interventions and aligns program efforts with strategic objectives for poverty reduction and livelihood improvement.  

These capabilities are essential for effective decision-making, particularly in the fight against poverty and for sustainable development. By utilizing modeling, food systems’ stakeholders can maximize impact by focusing on high-benefit interventions, enhance collaboration across various groups, promote sustainable agricultural practices, and drive innovation through the implementation of new technologies. In essence, employing modeling in agricultural decision-making is crucial for crafting effective strategies that can lead to meaningful improvements in the lives of marginalized communities facing poverty and food insecurity. By leveraging data-driven modeling insights, stakeholders can make more informed choices that support sustainable development initiatives. 

CGIAR’s Commitment to Data-Driven Research

At the core of optimizing CGIAR’s agricultural productivity research lies a commitment to data-driven decision-making. The CGIAR’s Poverty, Livelihoods, and Jobs Impact Area Platform is at the forefront of establishing a framework of key performance indicators (KPIs), metrics, and methodologies to support this endeavor. By centralizing data management and developing standardized assessment frameworks, this initiative enhances CGIAR stakeholders’ capacity to make informed decisions aligned with strategic goals for poverty reduction, decent job creation, and livelihood improvement.  

Therefore, the Poverty, Livelihoods, and Jobs Impact Area Platform, in collaboration with the Market Intelligence Initiative, has launched a new tool for analyzing current state and future trends in poverty, employment, and rural demographics. The tool also models changes in poverty across 170 countries and 46 crops. It builds upon established development indicators like the World Bank’s $2.15/day poverty line and the International Labour Organization’s youth engagement metrics. Projected benefits are estimated using a continuous version of the consumer and producer surplus functions. Poverty reductions are estimated by combining different published poverty elasticities. To account for the time lag between agricultural interventions—from research, dissemination, extension, and implementation to scaling of innovations— and impact, we project-modeled benefits towards 2030. In line with CGIAR guidelines, we provide both breadth (the magnitude or number of people lifted out of poverty) and prevalence estimates (per capita, normalized, reduction in poverty) of poverty reduction. All results are freely available on the Global Market Intelligence Platform. 

Evidence on current and future poverty and modeled changes is of interest to diverse stakeholders, including CGIAR and NARES scientists, donors and investors, private seed companies, and NGOs. This evidence aligns with the priorities defined by donors like the Bill & Melinda Gates Foundation, who seek evidence-based prioritization to maximize the returns on breeding investments. The data and models are being institutionalized within the Breeding for Tomorrow and Genebanks Science Programs to ensure their ongoing use and development. Additionally, this evidence can inform the strategies of other Science Programs and Accelerators, such as Policy Innovations and Gender Equality and Inclusion, by guiding research efforts toward areas with high poverty prevalence or large absolute numbers of poverty or significant gender inequality in poverty rates. Lastly, the GloMIP platform has already contributed to strengthening several research proposals by different CGIAR Centers in 2024. 

What’s next?

In conclusion, modeling agricultural productivity is a complex endeavor with the potential to lead to profound and lasting positive changes in poverty reduction and economic development. By adopting innovative modeling techniques at scale, prioritizing data-driven decision-making, and empowering stakeholders, we stand far better chances of creating a sustainable future for agriculture, uplifting marginalized communities, and improving livelihoods worldwide.   

Future research should delve deeper into the multifaceted nature of agricultural productivity, considering factors such as land use, labor dynamics, and the role of purchased and farm-owned inputs. Understanding the intricate interplay of these elements is vital for a comprehensive assessment of productivity and its impact on economic outcomes. Additionally, as consumer demand and preferences evolve, incorporating factors like price premiums for organic or sustainably sourced into our productivity models will be essential. This holistic approach will enable us to prioritize interventions that maximize agricultural productivity, ultimately contributing to poverty reduction.