Skip to content
Research Article Open access CC BY 4.0

Assessing the Impact of Climate Change on Agricultural Production Using Crop Simulation Model

Mangshatabam Annie, Raj kumar Pal, Anjusha Sanjay Gawai, Aman Sharma

International Journal of Environment and Climate Change · pp. 538–550 · Published 15 May 2023

10.9734/ijecc/2023/v13i71906

Abstract

Climate change can have significant impacts on agriculture, both in terms of crop production and livestock management. Rising temperatures, changing rainfall patterns, and extreme weather events such as droughts and floods can reduce crop yields. GCMs have been used to predict climate scenarios and impacts in many cases using the downscaling approach. Crop simulation models are crucial in gaining valuable insights into the intricate interactions between crops and their surroundings. To combat against climate change, researchers have discovered that implementing microclimatic modifications and conducting sensitivity analyses of crop simulation models are effective strategies. In short, these approaches can help mitigate the impacts of climate change on agricultural production by simulating diverse scenarios and predicting the impacts of varying environmental conditions, such as temperature, rainfall, and soil conditions. The resulting information enables assisting farmers in making wise choices about the best times to plant, fertilize, irrigate, harvest, and manage their crops, especially in the context of a changing climate. Moreover, crop simulation models that account for climate change factors can quantify the effect of climate change on crop production, and prioritize and evaluate adaptation measures at the farm level. As a result, Crop simulation modeling has the potential to revolutionize agriculture, leading it towards achieving the goals of sustainability.

Crop simulation model climate change crop production adaptation sustainability

Cited by 22

Development of a frost-specific module and its integration into a process-based model for winter wheat frost damage simulation

Yuanda Zhang, Pei-Juan Wang, Yu-Ping Ma · Agricultural and Forest Meteorology · 2026

Revolutionising crop modelling and resource management by integrating deep learning - A review

C. Guruanand, K. Boomiraj, V. Geethalakshmi · Engineering applications of artificial intelligence · 2026

Development of a prototype system for a rice yield prediction using deep learning

Ho-Young Ban, Seo-Young Yang, Ju-Hee Kim · Journal of Crop Science and Biotechnology · 2026

Dynamic and flexible climate-adaptive long-term planning in irrigated-agriculture

A. Kamalamma, M. Babel, Mohanasundaram Shanmugam · Agricultural Water Management · 2025

Advancing Intercropping of Drought-Resistant Oilseed Crops: Mechanized Harvesting

Luca Cozzolino, S. Bergonzoli, Gian Maria Baldi · AgriEngineering · 2025

NOVEL SOLUTIONS FOR MITIGATING DROUGHT IMPACT AND RESTORING SOIL FUNCTIONALITY IN AGRICULTURE

N. Vanghele, N. Vladut, A. Pruteanu · INMATEH Agricultural Engineering · 2025

PREDICTION OF SOME ASPECTS OF CLIMATE CHANGE IMPACT IN THE REPUBLIC OF SERBIA

Ivana Ilić Krstić, Aleksandra Ilić Petković · Facta Universitatis. Series: Working and Living Environmental Protection · 2025

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

22

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.