South-Central Section - 57th Annual Meeting - 2023

Paper No. 17-7
Presentation Time: 3:55 PM

SIEVE ANALYSIS: PETROGRAPIC AND STATISTICAL ANALYSIS ON SOILS FROM POZO DE LUNA WINERY COMPANY


SUNG, Eunjae, Presbyterian Pan American School, 223 N. FM 772, Kingsville, TX 78363 and JIMENEZ, Alicia, Presbyterian Pan American School, Kingsville, TX 78363

Sieve analysis is a technique used for measuring particle size of all granular materials (Carpenter 1950). Acquired soil samples are placed upon a set of sieves and passed through them. A set of sieves is arranged according to mesh diameters in decreasing order (Anderson 2007). Weight of sediment retained on each sieve is measured and converted into a specific percentage of the total sample (Anderson 2007). Soils must be well distributed in size for them to be useful in agronomy. Soils should be balanced in terms of contributions from soil organic matter, air, water, and mineral components, such as sand, silt, and clay (Parikh 2012). My investigation will allow us to see how sieve analysis is crucial for agricultural purposes. I will relate my studies to a company called Pozo De Luna. The company harvests grapes for wine production and is located in San Luis Potosi, Mexico. Because the company deals specifically with soil improvement, it is appropriate to correlate our studies with data provided by Pozo de Luna. What makes the soil from San Luis Potosi so adequate for harvesting grapes? Is there a relationship between climate and elevation that contributes to the soil quality of San Luis Potosi? We will attempt to answer these questions. In the future, we will travel to San Luis Potosi to meet with Pozo de Luna’s lab technicians to acquire sieve analysis data from their fields. We will acquire soil samples from their vineyards and will perform sieve analysis on them as well. We will get mineral composition of the soil and will perform a petrographic analysis using the petrographic microscope. To better understand the dynamics of grain distribution in soils, we will analyze them statistically and will create models of variation using R-Studio software. This is important because knowing what comprises these soils and their grain size distribution can help us improve soil quality, leading to faster harvesting of goods in general.