Application of CSM–CERES–Rice in scheduling irrigation and simulating effect of drought stress on upland rice yield

Abstract

Crop models can provide rapid and cost effective means to deal with rice crop management. The objectives of this study included, exploring the ability of CSM-CERES-Rice in scheduling irrigation and to simulate the effect of drought stress on upland rice yield. Irrigation treatments 100, 70 and 50 % of field capacity (FC) were applied from 80 days after planting (DAP) at flowering stage until maturity and CSM-CERES-Rice was used to predict irrigation amount for each water regime for treatment duration. Results showed that, at 70 and 50 % FC, performance of an upland rice genotype, Dawk Payawm was decreased significantly as compared to 100 % FC. Normalized root mean square error (RMSEn) values less than 10 % for each treatment indicated a strong agreement between simulated and observed grain yield (GY) and biomass. d-index approaching to unity and RMSEn less than 10 % indicated a good agreement between simulated and observed soil moisture contents (SMC) for all irrigation treatments. Overall, it was concluded that drought stress had negative correlation with GY and CSM-CERES-Rice was able to predict irrigation amount for all treatments assuring that, model has potential for its use as a tool to schedule irrigation for experiments under water limited conditions.

Rice phenology and growth simulation using DSSAT- CERES-Rice crop model under the different temperatures changing with climatic condition

“…Hence, 28 °C had an adjustment excellent agreement for leaf area index with lower d-stat values among the three temperature stages for the genotypes. The result similar with the findings of Hussain et al (2018).…”

“…Measured mean tillers number = 56.45 (no/m 2 ) with nRMSE= 3.07 indicated that very excellent agreement with all the temperatures schedule and similar trend was followed by both the observed and simulated values for tillers number. This result is supported by Hussain et al (2018) representing tillers number response to grain yield, i.e., important yield contributing environmental factors.…”

“…Whereas, excellent agreement was observed for harvest index, for the temperature stage 28 °C with lower RMSE value was 0.01 and maximum d-index 1 observed, respectively compared to 30 °C and 32 °C with RMSE value 0.02 and 1.02. Measured mean harvest index 269 = 0.52 (grain/top) with nRMSE= 3.22 indicated that poor agreement for simulated and observed values where similar trend was followed by both the observed and simulated values for harvest index and supported by Hussain et al (2018).…”

Synchronizing Nitrogen Fertilization and Planting Date to Improve Resource Use Efficiency, Productivity, and Profitability of Upland Rice

“…Our results indicate the impact of N fertilization and improvement in productivity with N fertilization according to various PDs. We have determined that the N fertilizer recommendations for upland rice production (DRRD, 2016 , 2017 ; Norsuwan et al, 2020 ) and N fertilizer application rate practiced in Thailand (CARSR, 2003 ; Hussain et al, 2018a , b ; Suwanasa et al, 2018 ; Islam et al, 2020 ) are not adequate. Another study conducted under partially controlled conditions in sheds also exhibited comparable results (Hussain et al, 2022 ).…”

“…In northern areas of Thailand, the application of 10–75 kg N ha −1 by farmers in upland rice fields was reported in a survey conducted by Chiang Mai University, Thailand (CARSR, 2003 ). Different NFRs have been observed as N fertilization of 61.25 kg N ha −1 (Suwanasa et al, 2018 ), 61.25 kg N ha −1 (Hussain et al, 2018a , b ), and a basal fertilization of 15 kg N ha −1 (Islam et al, 2020 ) in upland rice farming in southern Thailand. Corresponding to the Division of Rice Research and Development (DRRD) of Thailand (DRRD, 2016 ; Norsuwan et al, 2020 ), 48.75–82.5 kg N ha −1 based on soil N status was recommended to be used as N fertilization management for rice production.…”