Predicting and Simulating Rice Growth and Yield: a Comparative Modeling Study on the Impact of Diverse Production Systems
Year: 2024
Type: article
Abstract: Rice (Oryza sativa) is a vital cereal crop, feeding half of the world's population, with Pakistan ranking tenth globally in production. Despite its significance, rice cultivation in Pakistan faces challenges related to water scarcity and labor-intensive practices, highlighting a need for sustainable solutions. This study aims to address these challenges by evaluating the growth, phenology, and yield of fine (Basmati-515) and coarse (KSK-133) rice cultivars under different planting strategies, using the Decision Support System for Agro-technology Transfer (DSSAT). Conducted at the University of Agriculture Faisalabad, the experiment employed a randomized complete block design with a split-plot arrangement. Transplanted rice (TPR) and direct-seeded rice (DSR) were the main plot factors, and rice cultivars served as sub-plot factors. Data on crop growth and yield characteristics were collected following established procedures, with the CERES-Rice model calibrated and validated using soil, weather, and crop management data. Statistical analysis revealed that TPR with KSK-133 achieved the highest grain yield and related parameters, while DSR exhibited benefits like early flowering and reduced resource use but faced challenges in weed management and crop establishment. DSR demonstrated significant potential for water and labor savings, making it suitable for resource-constrained regions. The study underscores the need for sustainable rice production through advanced agronomic practices, selective cultivation of high-yielding varieties, and policy incentives promoting DSR adoption. These findings contribute to improving rice productivity and ensuring food security in Pakistan, addressing environmental challenges and economic resilience. Further research is recommended to optimize DSR implementation.
Authors: SABA SAJJAD, MUNTAHA MUNIR, HAFIZ MUHAMMAD BILAWAL AKRAM, SOHAIL AKRAM, LUBNA ANJUM, MUHAMMAD SAFDAR, SULTAN AHMAD RIZVI
Language: English
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6930582958
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