Model Captures Energy Return On Global Agriculture Investment

Dec 18, 2024

A primary output of agriculture is food, an energy source for the human body. But agriculture also requires energy inputs.

Kajwan Rasul and colleagues calculated the global energy return on investment for agriculture over time from 1995 to 2019. The authors constructed a model using two existing models, one that captures the energy use of agriculture and food processing and another that captures flows of agricultural commodities. The work is published in the journal PNAS Nexus.

The authors find that the return on energy investment for global agriculture has increased from .68 to .91 over the study period. However, the ratio seems to have plateaued since around 2014.

Despite the overall improvement in efficiency, the return on investment is still less than one, indicating that more energy is required to grow food than what food provides in the form of calories. This status reflects the use of fossil fuels, especially in food processing, which accounts for 40% of the total energy use in the global agrifood system.

Animal-based products are particularly energy-hungry, accounting for 60% of the energy used in the global agrifood system but providing just 18% of the calories. However, more than two-thirds of people live in regions where the energy return on investment is higher than one, such as Western, Central, and Eastern Africa or Eastern, Southern, and South-eastern Asia.

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Video: Seeing the Whole Season: How Continuous Crop Modeling Is Changing Breeding

Plant breeding has long been shaped by snapshots. A walk through a plot. A single set of notes. A yield check at the end of the season. But crops do not grow in moments. They change every day.

In this conversation, Gary Nijak of AerialPLOT explains how continuous crop modeling is changing the way breeders see, measure, and select plants by capturing growth, stress, and recovery across the entire season, not just at isolated points in time.

Nijak breaks down why point-in-time observations can miss critical performance signals, how repeated, season-long data collection removes the human bottleneck in breeding, and what becomes possible when every plot is treated as a living data set. He also explores how continuous modeling allows breeding programs to move beyond vague descriptors and toward measurable, repeatable insights that connect directly to on-farm outcomes.

This conversation explores:

• What continuous crop modeling is and how it works

• Why traditional field observations fall short over a full growing season

• How scale and repeated measurement change breeding decisions

• What “digital twins” of plots mean for selection and performance

• Why data, not hardware, is driving the next shift in breeding innovation As data-driven breeding moves from research into real-world programs, this discussion offers a clear look at how seeing the whole season is reshaping value for breeders, seed companies, and farmers, and why this may be only the beginning.