Macro nutrients of soil & atmospheric conditions

Published: 17 December 2025| Version 1 | DOI: 10.17632/whcvzvjrtx.1
Contributor:
gajalakshmi pattam

Description

The central hypothesis of this study is that soil macronutrients—Nitrogen (N), Phosphorus (P), and Potassium (K)—are significantly influenced by prevailing atmospheric and environmental conditions such as rainfall, temperature, humidity, and soil pH, and that their combined interaction determines soil fertility and crop productivity. It is assumed that optimal atmospheric conditions enhance nutrient availability, while adverse conditions lead to nutrient loss or reduced uptake by plants. The dataset comprises measured concentrations of soil macronutrients (N, P, and K) collected from agricultural fields, along with corresponding atmospheric and soil parameters including rainfall, air temperature, relative humidity, and soil pH. These variables were recorded over the same spatial and temporal scale to enable meaningful correlation and comparative analysis. Nitrogen levels indicate organic matter decomposition and fertilizer efficiency, phosphorus reflects soil mineral composition and pH sensitivity, while potassium represents soil exchange capacity and moisture dynamics. Analysis of the data shows that nitrogen availability tends to increase with moderate rainfall and optimal temperature, but excessive rainfall is associated with leaching losses. Phosphorus availability is strongly controlled by soil pH, with reduced availability observed in highly acidic or alkaline conditions. Potassium levels show a positive relationship with soil moisture and moderate humidity, indicating improved ion exchange under favorable moisture conditions. Temperature and humidity collectively influence microbial activity, indirectly affecting nutrient cycling. Notable findings suggest that integrated consideration of both soil and atmospheric variables provides a more reliable assessment of soil fertility than nutrient analysis alone. The data can be interpreted to support precision agriculture practices, crop-specific nutrient recommendations, and climate-resilient soil management strategies aimed at improving sustainable agricultural productivity.

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Institutions

  • RV College of Engineering

Categories

Machine Learning, Agricultural Development

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