^{by Embrapa Digital Agriculture}

To feed a growing global population without expanding into new areas of native vegetation, it is essential to improve the performance of established agricultural systems. In pasture-based livestock farming, a promising solution is to reduce yield gaps, which, according to specialists, is the difference between what a rural property produces currently and what it could produce under ideal conditions.

An international study involving Embrapa researchers that was recently published in the scientific journal Agricultural Systems analyzed the main tools to measure these gaps. It also proposed more precise ways to estimate the productive potential of livestock systems. The goal is to guide farmers, technicians, and public policy makers to invest more strategically, increasing efficiency and reducing environmental impacts.

Scientists warn that current models limit the precision of analyses because they often ignore variables like grazing strategies, pasture composition, and selective grazing by animals. Integrating these aspects, however, could contribute to more efficient resource use and sustainable production intensification, without expanding into new land.

Pastures comprise 70% of the world agricultural land

Covering about 70% of the world’s agricultural land, pastures are crucial for food security and provide vital ecosystem services. As global demand for meat and milk grows annually, the study reinforces the importance of improving existing production systems instead of converting new land for livestock farming.

To advance the sustainable intensification of pasture-based livestock farming, researchers suggest future investigations consider not only technical factors, but also socioeconomic and political contexts. This comprehensive approach can be supported by yield gap analysis, which will enable producers and policymakers to make more efficient and sustainable investments.

Framework

Various methods were discussed, including benchmarking, climate clustering, frontier analysis, and production system models. Each approach differs in its scale of analysis, data requirements, and specific applications. According to Patrícia Menezes Santos, a researcher at Embrapa Southeastern Livestock, some methodologies are suitable for larger scales, like global or national studies, while others apply more locally, such as for farms.

Furthermore, some methods consider socioeconomic factors, while others focus on biophysical aspects. “These tools identify the highest potential areas for increasing productivity,” Santos points out. “In other words, they highlight the most promising areas to strategically direct efforts and investments.”

The researcher emphasizes the importance of these methodologies to enable public managers to better direct their efforts, such as technical and infrastructure assistance. She believes that transformation does not depend solely on the producer; it requires a coordinated work to build a truly favorable environment.

Incentives in Brazil remain low

The study emphasizes benchmarking, a widely adopted method that involves comparing the performance of top-producing farms or regions with others. It is a simple method with commercial applicability; however, it does not account for inputs or economic variables.

The frontier analysis method, applicable across various production scales, examines technical and economic efficiency using statistical and econometric models.

Geraldo Martha, a researcher at Embrapa Digital Agriculture, notes that analyses from the Organisation for Economic Co-operation and Development (OECD) indicate that to truly understand the transformations occurring in the real world of Brazilian agriculture, particularly its livestock sector — which operates with low incentive levels — this perspective, bringing together biophysical and economic dimensions, is highly important.

According to Martha, this is because a farmer’s decisions involve opportunity costs and risks that are unique to each producer-property combination. This singularity arises because the quantity and quality of resources (land, labor, physical and human capital), inputs, and relative prices differ in each specific instance.

The climate clustering method prioritizes the technical potential for increasing productivity, focusing on factors like climate and type of production system, without considering management, nutrition, or grazing strategies. This method is suitable for broader-scale application and evaluates the adoption of successful management practices across regions with similar climates. Although it relies on large-scale databases, the method is flexible and can incorporate regional characteristics or other variables to provide insight into regions with high potential for production system intensification.

Production system models encompass two main approaches: those that consider pasture carrying capacity, analyzing primary pasture productivity and grazing efficiency, and models based on production ecology concepts, which account for the biophysical processes of animals and pastures over time.