CSM-CERES-Maize model (DSSAT v4.0) | Calibration, Validation and Simulation of Maize Growth and Yield. The 2nd Edition

Tej Narayan Bhusal

ISBN 10: 6207467744 / ISBN 13: 9786207467747

Published by LAP LAMBERT Academic Publishing, 2025

About this Item

CSM-CERES-Maize model (DSSAT v4.0) | Calibration, Validation and Simulation of Maize Growth and Yield. The 2nd Edition | Tej Narayan Bhusal | Taschenbuch | 132 S. | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786207467747 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 133580387

Bibliographic Details

• Title: CSM-CERES-Maize model (DSSAT v4.0) | Calibration, Validation and Simulation of Maize Growth and Yield. The 2nd Edition
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6207467744
• ISBN 13: 9786207467747
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 215 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

In recent years, the use of crop models has grown exponentially for agro-climatic and site-specific resource management aimed at increasing grain yield. Among these models, CMS-CERES-Maize is one of the most prominent. However, limited academic literature exists on how to best utilize such simulation models to optimize yield in developing countries like Nepal. Calibration and validation using available datasets are prerequisites for effective application. Site-specific management of resources—such as nitrogen (dose, timing, and method) and water (amount and timing)—has been shown to increase maize yield while minimizing losses. Similarly, factors such as annual climate variation, sowing dates, and initial soil moisture content significantly affect yield performance. Findings from this study suggest that changes in the magnitude of climatic parameters can place stress on resource management systems, thereby affecting grain yield. This analysis contributes to a better understanding of this emerging field and may be particularly useful to professionals and others involved in agriculture.