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Sustainable Agriculture Through Data...

DiScenza, Peggy Clareece,

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Sustainable Agriculture Through Data-Driven Land and Soil Management for Rural Farming Communities /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sustainable Agriculture Through Data-Driven Land and Soil Management for Rural Farming Communities // Peggy Clareece DiScenza.
Author:	DiScenza, Peggy Clareece,
Description:	1 electronic resource (58 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-12.
Contained By:	Masters Abstracts International86-12.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32044010
ISBN:	9798315796176

Sustainable Agriculture Through Data-Driven Land and Soil Management for Rural Farming Communities /
DiScenza, Peggy Clareece,

Sustainable Agriculture Through Data-Driven Land and Soil Management for Rural Farming Communities /Peggy Clareece DiScenza. - 1 electronic resource (58 pages)

Source: Masters Abstracts International, Volume: 86-12.

This thesis aims to investigate sustainable farming practices by utilizing a database containing soil, weather, and water data. Additionally, it evaluates cropping strategies using a system that incorporates nutrient dynamics and environmental values. The objective is to monitor, develop predictive models, and assess the impact of both controlled and uncontrolled nutrient variables. This is achieved through the development of an algorithm that provides information on soil health based on datasets and land use, with a particular focus on residual values after harvesting for deeper analytical insights and interpretation. This study focuses on key nutrients, including pH levels, water availability, and temperature forecasts. Utilizing the available dataset, machine learning algorithms are applied to evaluate and compare two main scenarios: pure cropping and intercropping. Additionally, the proposed model assesses the selection process for these features using three methodologies, which guide the identification of either a primary crop or a companion crop, depending on the scenario. This approach optimizes rural farming operations by providing farmers with predictive insights into soil health and nutrient availability, thereby promoting the long-term sustainability impact for both farmers and their communities.

English

ISBN: 9798315796176Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Crop strategies

Sustainable Agriculture Through Data-Driven Land and Soil Management for Rural Farming Communities /
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245 1 0 $a Sustainable Agriculture Through Data-Driven Land and Soil Management for Rural Farming Communities / $c Peggy Clareece DiScenza.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
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500 $a Advisors: Rahman, Md. Fashiar; Smith, Eric David Committee members: Tseng, Tzu-Liang Bill; Houghtalen, Lori M.
502 $b M.S. $c The University of Texas at El Paso $d 2025.
520 $a This thesis aims to investigate sustainable farming practices by utilizing a database containing soil, weather, and water data. Additionally, it evaluates cropping strategies using a system that incorporates nutrient dynamics and environmental values. The objective is to monitor, develop predictive models, and assess the impact of both controlled and uncontrolled nutrient variables. This is achieved through the development of an algorithm that provides information on soil health based on datasets and land use, with a particular focus on residual values after harvesting for deeper analytical insights and interpretation. This study focuses on key nutrients, including pH levels, water availability, and temperature forecasts. Utilizing the available dataset, machine learning algorithms are applied to evaluate and compare two main scenarios: pure cropping and intercropping. Additionally, the proposed model assesses the selection process for these features using three methodologies, which guide the identification of either a primary crop or a companion crop, depending on the scenario. This approach optimizes rural farming operations by providing farmers with predictive insights into soil health and nutrient availability, thereby promoting the long-term sustainability impact for both farmers and their communities.
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590 $a School code: 0459
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650 4 $a Agriculture. $3 518588
650 4 $a Agricultural engineering. $3 3168406
650 4 $a Soil sciences. $3 2122699
650 4 $a Land use planning. $3 2122760
653 $a Crop strategies
653 $a Data modeling
653 $a Soil management
653 $a Rural farming
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690 $a 0536
690 $a 0481
710 2 $a The University of Texas at El Paso. $b Engineering. $e degree granting institution. $3 3818687
720 1 $a Rahman, Md. Fashiar $e degree supervisor.
720 1 $a Smith, Eric David $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-12.
790 $a 0459
791 $a M.S.
792 $a 2025
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