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Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content
A study was conducted with the goal of developing an algorithm for use in sensors to monitor available soil N. For this purpose, three different soils were selected. The soils were studied for electrical conductivity (EC) at four different moisture levels and four levels of N. The selection of moist...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502749/ https://www.ncbi.nlm.nih.gov/pubmed/36146077 http://dx.doi.org/10.3390/s22186728 |
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author | Mirzakhaninafchi, Hasan Mani, Indra Hasan, Murtaza Nafchi, Ali Mirzakhani Parray, Roaf Ahmad Kumar, Dinesh |
author_facet | Mirzakhaninafchi, Hasan Mani, Indra Hasan, Murtaza Nafchi, Ali Mirzakhani Parray, Roaf Ahmad Kumar, Dinesh |
author_sort | Mirzakhaninafchi, Hasan |
collection | PubMed |
description | A study was conducted with the goal of developing an algorithm for use in sensors to monitor available soil N. For this purpose, three different soils were selected. The soils were studied for electrical conductivity (EC) at four different moisture levels and four levels of N. The selection of moisture levels was based on optimum moisture levels between tillage moisture and field capacity. The results revealed a significant relationship between electrical conductivity and moisture level of the soil as well as between electrical conductivity and soil N content. Based on these relations, a polynomial model was developed between the EC of each selected soil sample and moisture content as well as N levels. The regression model for moisture content-based EC determination had coefficients of determination of 0.985, 0.988, and 0.981 for clay loam, sandy loam, and sandy loam soils, respectively. Similarly, the regression model for N content-based EC determination had coefficients of determination of 0.9832, 0.9, and 0.99 for clay loam, sandy loam, and sandy loam soils, respectively. An algorithm developed using a polynomial relationship between the EC of each selected soil sample at all moisture and N levels can be used to develop a sensor for site-specific N application. |
format | Online Article Text |
id | pubmed-9502749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95027492022-09-24 Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content Mirzakhaninafchi, Hasan Mani, Indra Hasan, Murtaza Nafchi, Ali Mirzakhani Parray, Roaf Ahmad Kumar, Dinesh Sensors (Basel) Article A study was conducted with the goal of developing an algorithm for use in sensors to monitor available soil N. For this purpose, three different soils were selected. The soils were studied for electrical conductivity (EC) at four different moisture levels and four levels of N. The selection of moisture levels was based on optimum moisture levels between tillage moisture and field capacity. The results revealed a significant relationship between electrical conductivity and moisture level of the soil as well as between electrical conductivity and soil N content. Based on these relations, a polynomial model was developed between the EC of each selected soil sample and moisture content as well as N levels. The regression model for moisture content-based EC determination had coefficients of determination of 0.985, 0.988, and 0.981 for clay loam, sandy loam, and sandy loam soils, respectively. Similarly, the regression model for N content-based EC determination had coefficients of determination of 0.9832, 0.9, and 0.99 for clay loam, sandy loam, and sandy loam soils, respectively. An algorithm developed using a polynomial relationship between the EC of each selected soil sample at all moisture and N levels can be used to develop a sensor for site-specific N application. MDPI 2022-09-06 /pmc/articles/PMC9502749/ /pubmed/36146077 http://dx.doi.org/10.3390/s22186728 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mirzakhaninafchi, Hasan Mani, Indra Hasan, Murtaza Nafchi, Ali Mirzakhani Parray, Roaf Ahmad Kumar, Dinesh Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content |
title | Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content |
title_full | Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content |
title_fullStr | Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content |
title_full_unstemmed | Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content |
title_short | Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content |
title_sort | development of prediction models for soil nitrogen management based on electrical conductivity and moisture content |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502749/ https://www.ncbi.nlm.nih.gov/pubmed/36146077 http://dx.doi.org/10.3390/s22186728 |
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