Evaluation of DSSAT model (CERES rice) on rice production: A review

Abstract

Rice is the staple food for over half of the world population. Crop models potentially offer a means to readily explore management options to increase yield, and to determine trade-off between yield, resource-use efficiency and environmental outcomes. This paper reviews the performance of CERES-Rice model in different regions of the world in relation to their potential application towards increasing resource use efficiency and yield of rice. In this article, the CERES-Rice model evaluation by using the simulated and observed values on crop phenology (anthesis, physiological maturity) and final grain yield mainly over Asian countries by different authors has been compiled and described. Mainly the model was evaluated based on different statistical measures such as RMSE and D-index. Several datasets for the prediction of grain yield and phenological period across different parts of Asia were examined. This particular model predicted those with high-accuracy (nRMSE1-5% for anthesis and 1-4% for physiological maturity days). For various data sets for grain yield, the nRMSE varied between 0.05-5.00 percent with error percentage of 2-5%. The model sometimes over-estimated or under-estimated the values of grain yield, especially under water stress conditions.

Phasic Development of Rice During the Present and Future Climatic Conditions in Central Zone of Kerala

“…Being a user-friendly model, its goal is to enhance the usage of crop models in research and development while requiring less input [17]. Multiple crop models, including CERES-Rice, are included in the International Benchmark Systems Network for Agrotechnology Transfer (IBSNAT) Decision Support System for Agrotechnology Transfer (DSSAT) [18]. The CERES-Rice model simulates crop growth, development, and yield, accounting for weather, genetics, soil water, carbon, and nitrogen, as well as planting, irrigation, and nitrogen fertilizer management [19].…”

Ứng dụng mô hình DSSAT dự báo năng suất lúa ở đồng bằng sông Hồng

“…Một trong số những nền tảng mô hình ứng dụng trong nông nghiệp được sử dụng rộng rãi hiện nay là hệ thống mô hình DSSAT [10,11]. DSSAT là một tập hợp các chương trình độc lập hoạt động cùng nhau, dữ liệu đầu vào bao gồm các thông tin về khí tượng, đất đai, cây trồng, quản lý cây trồng [12][13][14][15][16][17][18]. Nó giúp người dùng mô phỏng các tùy chọn quản lý cây trồng và so sánh các kết quả mô phỏng với các quan sát thực tế để đưa ra các quyết định trong sản xuất.…”