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Enhancing Management Strategies for ...

van Versendaal Pirez, Emmanuela,

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Enhancing Management Strategies for Crop Yield Improvement via Advanced Data-Informed Decisions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhancing Management Strategies for Crop Yield Improvement via Advanced Data-Informed Decisions // Emmanuela van Versendaal Pirez.
Author:	van Versendaal Pirez, Emmanuela,
Description:	1 electronic resource (134 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Agronomy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31937010
ISBN:	9798315777397

Enhancing Management Strategies for Crop Yield Improvement via Advanced Data-Informed Decisions /
van Versendaal Pirez, Emmanuela,

Enhancing Management Strategies for Crop Yield Improvement via Advanced Data-Informed Decisions /Emmanuela van Versendaal Pirez. - 1 electronic resource (134 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

Generating multi-scale information to understand crop variability and optimize crop management is crucial for increasing yields and improving input efficiency to meet global food demand. This dissertation uses available technological tools to generate actionable insights and improve our understanding of crop behavior at different scales.This dissertation is divided into five chapters. The first chapter serves as an introduction to crop yield improvement strategies and describes technologies that facilitate the generation of knowledge. The remaining chapters present case studies focusing on yield improvement, testing different crop management practices and using data approaches. Chapter 2 uses crop modelling to explore different combinations of soybean planting dates and maturity groups at the regional level in Kansas, USA, under both current and future weather scenarios. Chapter 3 uses the Canopeo tool, crop modelling and Bayesian analysis to assess the effects of equidistant and non-equidistant soybean planting patterns at different site densities in three US states. Chapter 4 uses multiple years of geo-referenced yield monitor data from GPS-enabled combines and spatio-temporal analysis to establish a data pipeline that quantifies within-field yield variation in response to precipitation. Finally, Chapter 5 summarizes the key findings and outlines directions for future research to further improve crop management information and yield outcomes, a critical step in meeting the world's growing food demand.

English

ISBN: 9798315777397Subjects--Topical Terms:

2122783
Agronomy.
Subjects--Index Terms:

Crop management

Enhancing Management Strategies for Crop Yield Improvement via Advanced Data-Informed Decisions /
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245 1 0 $a Enhancing Management Strategies for Crop Yield Improvement via Advanced Data-Informed Decisions / $c Emmanuela van Versendaal Pirez.
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500 $a Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
500 $a Advisors: Ciampitti, Ignacio A.
502 $b Ph.D. $c Kansas State University $d 2025.
520 $a Generating multi-scale information to understand crop variability and optimize crop management is crucial for increasing yields and improving input efficiency to meet global food demand. This dissertation uses available technological tools to generate actionable insights and improve our understanding of crop behavior at different scales.This dissertation is divided into five chapters. The first chapter serves as an introduction to crop yield improvement strategies and describes technologies that facilitate the generation of knowledge. The remaining chapters present case studies focusing on yield improvement, testing different crop management practices and using data approaches. Chapter 2 uses crop modelling to explore different combinations of soybean planting dates and maturity groups at the regional level in Kansas, USA, under both current and future weather scenarios. Chapter 3 uses the Canopeo tool, crop modelling and Bayesian analysis to assess the effects of equidistant and non-equidistant soybean planting patterns at different site densities in three US states. Chapter 4 uses multiple years of geo-referenced yield monitor data from GPS-enabled combines and spatio-temporal analysis to establish a data pipeline that quantifies within-field yield variation in response to precipitation. Finally, Chapter 5 summarizes the key findings and outlines directions for future research to further improve crop management information and yield outcomes, a critical step in meeting the world's growing food demand.
546 $a English
590 $a School code: 0100
650 4 $a Agronomy. $3 2122783
650 4 $a Plant sciences. $3 3173832
650 4 $a Agricultural engineering. $3 3168406
653 $a Crop management
653 $a Crop modeling
653 $a Soybean
653 $a Spatio-temporal analysis
653 $a Summer crop
690 $a 0285
690 $a 0479
690 $a 0539
710 2 $a Kansas State University. $b Department of Agronomy. $e degree granting institution. $3 3815823
720 1 $a Ciampitti, Ignacio A. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-11B.
790 $a 0100
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31937010