Soil Quality Index for an Arid Peruvian Irrigation Scheme with 930 Samples

930 composite soil samples from the Peruvian coastal desert were analyzed to create a spatially explicit weighted Soil Quality Index. The index, developed by Russell Poma-Chamana, revealed that 46.7% of the interpolated area was categorized as 'Poor' quality, primarily due to high salinity. The data suggests drip irrigation, without specific management, was associated with higher salinity and nutrient levels than surface irrigation.

Use Cases

• Model spatial patterns of soil salinity based on electrical conductivity measurements.
• Compare soil fertility indicators like available potassium and phosphorus under different irrigation systems.
• Assess the risk of secondary salinization in arid agricultural systems.
• Validate soil quality indices using principal component analysis and kriging methods.

Strengths

• Includes 930 composite soil samples, each composed of 10 subsamples.
• The derived Soil Quality Index explains 76.8% of the total variance in the data.
• Spatial predictions were validated with 10-fold cross-validation (ME ≈ 0; RMSE = 0.100).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• The coefficient of determination for spatial predictions was limited (R² = 0.246).
• Row count for the underlying data table is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Soil samples collected and analyzed for texture, pH, electrical conductivity, organic matter, and nutrients.

Freshness

Last updated 2026-06-04 05:18:28; freshness should be verified.

Geography

Peruvian coastal desert.