Abstract
The worldwide usage of and increasing citations for ORYZA2000 has established it as a robust and reliable ecophysiological model for predicting the growth and yield of rice in an irrigated lowland ecosystem. Because of its focus on irrigated lowlands, its computation ability is limited to the representation of the effects of the highly dynamic environments of upland, rainfed, and aerobic ecosystems on rice growth and yield. Additional modules and routines to quantify daily variations in soil temperature, carbon, nitrogen, and environmental stresses were then developed and integrated into ORYZA2000 to capture their effects on primary production, assimilate allocation, root growth, and water and nitrogen uptake. The newest version has been renamed “ORYZA version 3 (v3)”. Case studies have shown that the root mean square errors (RMSE) between simulated and measured values for total biomass and yields ranged from 11.2% to 16.6% across experiments in non-drought and drought and/or nitrogen-deficient environments. ORYZA (v3) showed a significant reduction of the RMSE by at least 20%, thereby improving the model’s capability to represent values measured under extreme conditions. It has also been significantly improved in representing the dynamics of soil water and crop leaf nitrogen contents. With an enhanced capability to simulate rice growth and development and predict yield in non-stressed, water-stressed and nitrogen-stressed environments, ORYZA (v3) is a reliable successor of ORYZA2000.
Highlights
Rice is the staple food for more than half of the world’s population
Case studies used for model evaluation covered different water environments for rice production, from fully irrigated and intermediate drought stress conditions in WME and aerobic rice field experiment (ARE) to severe drought stress in MVD
Using the calibration datasets (Tables 2–5), the cultivars and soil parameters in the WME, nitrogen fertilizer management experiment (NFM), ARE and MVD were estimated to ensure that simulated and observed values are within acceptable fitness as illustrated by the corresponding statistical variables namely, ␣, ˇ, r2, P(t), RMSEn, and Meff (Table 6 and season of 2011 (S1))
Summary
Between the early 1960s and the early 1990s, global rice consumption more than doubled, from 150 million tons to 350 million tons, due to a combination of rising per capita consumption and population growth (Mohanty, 2013). In the past decade (2006–2016), total global rice consumption has increased by 57 million tons, an increase of nearly 14%. According to Seck et al (2012), total global consumption is projected to increase by another 116 million tons (milled equivalent) in the 25 years. The currently harvested rice area of 160 million hectares is at an all-time high. Since it is unlikely for the rice area to expand further in the future, productivity will have to increase to meet the growing demand and keep rice affordable for millions of poor people. Drought is the most serious and can cause as much as a 40% loss in annual production and a 58% loss of income in South and Southeast Asia (IRRI, 2009; Pandey and Bhandari, 2007, 2006)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
