Soil pH Response to Lime Amendments with 857 Observations

Data Sheet 1_Exploring the impact of lime amendments: a machine learning analysis of influential factors.pdf is a dataset compiled by Floyd Muyembe Chipatela, last updated on 2026-04-22. It contains 857 observations used to analyze factors influencing soil pH change after lime application via a Gradient Boosting Model. The study achieved an R² of 89.7% and identified key factors like particle size, reaction time, and application rate.

Use Cases

• Predict soil pH change after lime application based on factors like particle size and application rate.
• Rank the relative importance of variables such as neutralizing value and limestone type for lime efficacy.
• Model the complex interactions between factors like reaction time and initial soil pH.
• Evaluate the limitations of traditional indices like Effective Calcium Carbonate Equivalence (ECCE).

Strengths

• Dataset contains 857 observations, providing a substantial sample for analysis.
• Model performance is quantified with a coefficient of determination (R²) of 89.7%.
• Analysis identifies specific optimal ranges, such as a lime application rate of 10 to 21 t ha⁻¹.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic or source bias inherent to the compilation method.

Provenance

Source

Floyd Muyembe Chipatela

Collection Method

Compiled from previous studies, analyzed within a supervised ML framework using a Gradient Boosting Model.

Time Range

Observations likely span the reaction periods studied, with optimal pH response noted between 90 and 400 days.

Freshness

Last updated 2026-04-22 05:34:53; freshness should be verified.

Geography

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