Author's note (May 2026): I wrote these reflections in my university notes in 2008, when I decided to enroll in Computer Science while finishing my Animal Production degree. I'm sharing them now because many people ask me how one gets from livestock farming to digital transformation consulting.

Studying Two Engineering Degrees: What Animal Production Taught Me About Digital Systems

The context: Venezuela 2008

Venezuela in 2008 was experiencing an apparent oil boom, but the cracks that would follow were visible to those willing to see them. Guanare, the spiritual capital of the country and an agricultural city in the Llanos region, had a slow pace and deep connection with the land.

Studying at UNELLEZ — National Experimental University of the Western Llanos "Ezequiel Zamora" — meant immersing myself in that context. The campus was surrounded by actual agricultural production units: farms, animal husbandry laboratories, irrigation systems. It wasn't an abstract university; it was an institution rooted in the agricultural production of the Venezuelan Llanos.

For those of us with technological interests, access was limited: insufficient computer labs, internet connectivity dependent on internet cafés or basic home connections, hardware that was expensive and hard to obtain. But technological passion doesn't wait for perfect conditions to manifest itself.

Why Animal Production first

When I finished high school, my first vocation was accounting. There were no openings in the degree I wanted. Computer Science fascinated me, but at that time it was only available at private universities we couldn't afford.

Animal Production Engineering at UNELLEZ was a solid alternative: a respected degree, relevant in a livestock state like Portuguesa, with clear career prospects in the most dynamic agricultural sector of the country at that time. I chose it for practical reasons: it was an engineering program that made sense in my geographic and economic context.

What I didn't know then — what I understood years later — is that that choice "by elimination" would become the foundation of the most distinctive part of my professional profile.

First real contact with programming

My relationship with technology began much earlier, in primary school, with Windows 95 and MS-DOS. But real programming — the kind that changes how you think — came out of necessity while studying Animal Production.

I worked as a technical equipment maintenance specialist at a local company ("House PC"). I began to notice that I could automate certain tasks: small scripts, macros, basic automations. I understood that code wasn't an academic exercise — it was a tool for solving concrete problems.

The defining moment came at university when I faced statistical analysis of animal production data. I discovered the R programming language for statistical analysis. And there something connected irreversibly: data was the common language between biology and computation. Code was the way to understand natural systems with quantitative precision.

The decision to pursue two degrees

In 2008, while completing my final years of Animal Production, I made the decision: I would enroll in Computer Science at URBE (Universidad Rafael Belloso Chacín) in Maracaibo.

At home they thought I was taking on too much. They were right by the numbers: two degrees simultaneously, working to pay for studies, and the TALS project taking shape in my mind. Time was the scarcest resource.

But the logic was clear to me: Animal Production gave me understanding of the physical world and natural systems. Computer Science would give me the digital tools to model, analyze, and optimize that world. They were complementary, not competitive.

I organized my days in blocks: mornings in field activities or animal husbandry labs, afternoons and evenings in front of the computer. It was an extreme time-management exercise that forged a discipline habit I still depend on today.

What animal production taught me about programming

This is the part people find hardest to believe when I tell them. But it's the most true.

Complex systems thinking A livestock herd is not a sum of animals — it's a system with inputs (nutrition, genetics, management), processes (metabolism, reproduction, conversion), and outputs (meat, milk, work). Learning to model that taught me to think in terms of systems architecture before I even knew that term existed in computer science. When I later studied software design, I already thought in layers, dependencies, and data flows.

Optimization under real constraints In animal production, resources are always finite and variable: forage dependent on rainfall, water scarce in summer, human labor that costs more than it seems. Learning to optimize under those constraints is exactly the mindset needed to write efficient algorithms. Not the most elegant algorithm theoretically — the one that works best given what you have.

Metrics and data-driven decisions In animal production, if you don't measure, you don't improve: feed conversion, daily weight gain, fertility rate. This culture of metrics — "if you can't measure it, you can't manage it" — is identical to that of good software development teams. KPIs, logs, performance metrics: the name changes, the logic is the same.

The thesis that brought it all together My undergraduate thesis in Animal Production was on fish morphometry, and I conducted it entirely in R, performing multivariate statistical analysis on morphological measurements. It was the perfect intersection: biology + data + code. It was also proof that what seemed like two separate worlds were really the same world viewed from different angles.

The paradox it took years to resolve

In 2008, studying Animal Production and Computer Science simultaneously seemed, at best, academic eccentricity. At worst, a dispersal of energy that would prevent me from specializing in either field.

In 2026, working in Portugal for a metallurgical company, developing monitoring applications for industrial production processes, the paradox is completely resolved.

I'm not a programmer who learned from industry. I'm an engineer who understands the physical processes of production — the "dirt," the "metal," the flow of materials — and also knows how to build the software that optimizes them.

That combination isn't common. Pure programmers don't understand why a plasma machine operator needs to see information a certain way. Pure production engineers don't know how to build the real-time system that delivers that information.

I do both things. And that's a direct consequence of a decision made in 2008 that seemed crazy at the time.

Careers aren't straight lines. They're spirals: you return to the same point but from a different height, and each time you return you see the connections you couldn't see the first time.