A Dynamic Weather-Driven Multi-crop Framework for Crop Water Demand Assessment Using GDD-Based Phenology and Climatic Crop Coefficient Adjustment in Semi-Arid Agriculture

DOI

10.2991/978-94-6239-791-0_21How to use a DOI?

Keywords

Evapotranspiration; FAO-56 Penman–Monteith; Growing Degree Days; Climate-responsive irrigation; Crop coefficient adjustment

Abstract

Accurate evaluation of crop evapotranspiration (ETc) is imperative for climate-based irrigation scheduling management within semi-arid zones. Traditional methods often employ predetermined crop calendars along with general crop coefficient values, thereby not adapting themselves to climate-based variability and dynamic crop growth conditions. In this paper, the application of a dynamic weather-based multi-crop evapotranspiration assessment scheme employing FAO-56 Penman-Monteith reference evapotranspiration (ET₀), GDD-based crop phenology modeling, and locally specific climatically adjusted crop-coefficients will be investigated. The daily climatic data set was collected through the NASA POWER database for the period 2023–2025 to evaluate seasonal behavior of crop water demand for crops such as wheat, rice, onion, tomato, and sugarcane under semi-arid condition. It has been found from the comparative analysis that wheat, rice, onion, and tomato crops had relatively low values of ETc under dynamic climate adaptation; however, sugarcane had high evapotranspiration values due to its long growth period and sensitivity to atmospheric demand. Further investigations on the predictive coherence led to another XGBoost analysis that resulted in an RMSE of 0.217 mm/day and an R² of 0.977, illustrating high correlations between climatic variables, heat sums, and dynamically computed ETc trajectories. Although field-based validations were not within the research agenda, the suggested model provides an adaptive solution for irrigation evaluation and water management in semiarid conditions, thus aligning with SDG 2, 6, and 13.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Dhanali Khandagale
AU  - Maya Kurulekar
AU  - Mahesh Kurulekar
AU  - Karthik Kurup
PY  - 2026
DA  - 2026/10/09
TI  - A Dynamic Weather-Driven Multi-crop Framework for Crop Water Demand Assessment Using GDD-Based Phenology and Climatic Crop Coefficient Adjustment in Semi-Arid Agriculture
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 264
EP  - 274
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_21
DO  - 10.2991/978-94-6239-791-0_21
ID  - Khandagale2026
ER  -