Soil Properties Predicted from MIRS Analysis of 366 Samples in Zambia, 2024

Kamaluddin Tijani ALIYU from CIMMYT Harvested Dataverse provides 366 topsoil samples from Zambia in 2024. Selected soil properties were predicted using mid-infrared spectral (MIRS) analysis, with a subset calibrated against wet chemistry via a machine-learning algorithm from ICRAF Kenya. The dataset includes truncated coordinates for privacy and can be linked to future yield data using a unique farm identifier.

Use Cases

• Predicting soil properties for agricultural planning based on spectral analysis data.
• Calibrating and validating spectral prediction models based on the subset with wet chemistry results.
• Analyzing the relationship between soil properties and lime application rates based on the described on-farm trial context.
• Linking soil data to crop yield outcomes based on the unique farm identifier mentioned in the description.

Strengths

• Includes 366 soil samples, providing a substantial basis for analysis.
• Uses a machine-learning algorithm calibrated against a subset of wet chemistry data, suggesting a validated prediction method.
• Contains a unique farm identifier for linking to complementary yield data.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Coordinates are truncated, limiting precise geospatial analysis.

Provenance

Source

CIMMYT Harvested Dataverse, author Kamaluddin Tijani ALIYU.

Collection Method

Soil properties predicted from mid-infrared spectral (MIRS) analysis of collected samples, with calibration using a machine-learning algorithm from ICRAF.

Time Range

2024

Freshness

Last updated 2026-04-20 14:54:00; freshness should be verified.

Geography

Zambia