Soil – humble, overlooked and often taken for granted – is one of the most vital resources on Earth. Yet, understanding its complex chemistry remains a scientific challenge, particularly in the vast, varied landscapes of South Africa.

A recent study by Anru-Louis Kock, Prudence Dimakatso Ramphisa-Nghondzweni and George Van Zijl from the North-West University (NWU) has brought this challenge into sharp focus. Their work, published in the European Journal of Soil Science, explores the promise and pitfalls of using mid-infrared (MIR) spectroscopy to assess soil quality in the Western Highveld region – a cornerstone of the country’s agricultural economy.

Spectroscopy, in essence, is the art of making invisible details visible. It measures how light interacts with matter, revealing the chemical composition of a sample without the need for messy, time-consuming lab work. In theory, this should be perfect for soil analysis – quick, cost-effective and eco-friendly. But as the authors of this study discovered, the reality is more complex.

Soil spectroscopy has long been championed as a game-changer for precision agriculture – the practice of tailoring crop inputs to the exact needs of the soil. However, as Anru-Louis and his colleagues reveal, the one-size-fits-all approach to soil analysis favoured by global databases like the Open Soil Spectral Library (OSSL) often falls short when applied to local contexts. "Global models, while impressive in scope, struggle to capture the unique characteristics of South African soils," Anru-Louis explains. "Our study clearly shows that if you want accurate predictions for key soil properties like pH, calcium and magnesium, you need locally calibrated models."

The team’s study used 979 soil samples from the Western Highveld to create locally calibrated MIR models. These models significantly outperformed their global counterparts, achieving prediction accuracy with R² values exceeding 0,76 for critical soil properties like pH, calcium and magnesium – a feat the OSSL models could not match. The problem, as the researchers point out, is that global databases simply lack the depth of local knowledge needed to accurately interpret the soil spectra of this diverse region.

The Western Highveld, known for its nutrient-poor, highly variable soils, presents a unique challenge for soil scientists. It is a region where farmers grow maize, wheat and sunflowers in conditions that range from dry, sandy plains to heavier clay soils, each with its own mineral profile. This variability can significantly affect crop yields and nutrient uptake, making precise soil analysis essential for sustainable farming.

"The OSSL models consistently underpredicted key soil properties," notes Dimakatso. "They lacked the nuanced understanding of our local soils, which meant they couldn’t provide the reliability needed for precision agriculture." This finding, while disappointing

for proponents of global data-driven agriculture, reinforces a critical point – that local context still matters, even in a world increasingly dominated by big data.

The study also highlights the importance of investing in local soil libraries. "Without locally calibrated models," George adds, "farmers could be making decisions based on inaccurate data, potentially leading to poor crop performance and financial losses. Our work is a step towards building a comprehensive South African soil spectral library that can support smarter, more sustainable farming practices."

As global agriculture grapples with the twin challenges of climate change and population growth, local data will remain essential for making accurate, region-specific decisions. The work of Anru-Louis, Dimakatso and George serves as a timely reminder that even in the age of artificial intelligence and machine learning, sometimes the best insights come from the ground up.

From left are Anru-Louis Kock, Prudence Dimakatso Ramphisa-Nghondzweni and George Van Zijl.