Tomato Yield and Soil Fertility Data Under Drought with Compost Amendments
by Majda Oueld Lhaj·Updated 2mo ago
461.1 KB1files
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Description
A dataset from a greenhouse experiment measuring soil and plant responses for tomatoes under controlled water stress. The study, authored by Majda Oueld Lhaj and updated in April 2026, includes data on soil fertility index, plant height, leaf area, relative water content, chlorophyll, and fruit yield across sandy loam and silty clay soils amended with compost or fertilizer. Analyses include multivariate statistical approaches and Monte Carlo Simulation to model productivity outcomes.
Use Cases
Modeling the probability of optimal soil fertility based on compost application and irrigation levels using Monte Carlo Simulation.
Analyzing drivers of tomato yield such as soil moisture retention and chlorophyll stability through multivariate statistical methods.
Comparing plant physiological responses like height and leaf area increase under different soil textures and amendment treatments.
Assessing the impact of compost on soil fertility index values under varying drought conditions.
Strengths
Includes specific quantitative results, such as soil fertility index values ranging from 0.06 to 0.92 and yield increases of 45-75%.
Describes a controlled experimental design with defined variables: two soil textures, three irrigation regimes, and multiple amendment types.
Employs advanced analytical methods including Principal Component Analysis, Partial Least Squares Regression, and Monte Carlo Simulation.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment.
The dataset is small (461.1 KB), indicating limited scope.
Provenance
Source
figshare, authored by Majda Oueld Lhaj.
Collection Method
Data originates from a controlled greenhouse experiment.
Freshness
Last updated 2026-04-17 05:20:24; freshness should be verified.
Geography
Study context suggests relevance to arid and semi-arid regions, particularly North Africa.
Primary data is contained within a PDF file, which may require extraction to access structured data.