CSM-CERES-Rice model to determine management strategies for lowland rice production

Abstract

The cropping system model, namely, the crop environment resource synthesis-rice (CSM-CERES-Rice) model, is a decision supporting tool for the design of crop management. This study aimed to determine management practices for increasing rice (Oryza sativa L.) production in Laos by using the CSM-CERES-Rice model. The model was evaluated with data sets from the TDK8 and TDK11 cultivars in farmers' fields in the Vientiane plain in 2012. Anthesis and harvesting dates, growth and yield for various management scenario combinations (eight transplanting dates × two levels of plant densities × three rates of nitrogen (N) fertilizer application) for both cultivars were simulated by the model from 1980 to 2012. The model evaluation results showed strong agreement between simulated and observed data for days to harvest with a difference within four days. The model provided acceptable accuracy for grain yields with normalized root mean square error values ranging between 1 and 16 %. The results from the model application indicated that TDK8 and TDK11 produced similar yields. Transplanting TDK8 with two plant densities produced similar yields. The highest yield for both cultivars was achieved on the transplanting date of 15 Jan. N-fertilizer application at 60 and 120 kg N ha −1 was able to increase yield for TDK8 by 50 and 87 %, respectively, and for TDK11 by 54 and 70 %, respectively. Rice transplanted on 15 Jan with 5 seedlings hill −1 and N-fertilizer at 120 kg N ha −1 had the highest average yield for both cultivars with 6,460 and 6,351 kg ha −1 for TDK8 and TDK11, respectively. The CSM-CERES-Rice model is an alternative tool in determining crop management practices for rice production.

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

“…Drought stress for photosynthesis and growth expansion for GY (Fig 3) and biomass (Fig 4) indicated that 100 % FC treatment had no water stress during growing period but under 70 % and 50 % FC, crop had stress and increase in drought stress caused decline in GY and biomass (Fig 3, 4). Simulation results for GY and biomass were supported by the findings of Vilayvong et al (2015) who reported that CSM-CERESRice was accurately predicting yield. Soler et al (2013) found that CSM-CROPGRO indicated that simulated drought stress had negative correlation with peanut yield which favored the results of negative effect of simulated drought stress indices on Dawk Pa-yawm performance.…”

“…Days to flowering were under simulated at 50 % FC while maturity was over simulated under all irrigation treatments (Fig 2). CSM-CERES-Rice well predicted the phenology and results were supported by the outcomes of Vilayvong et al (2015). …”

A Rice Model System for Determining Suitable Sowing and Transplanting Dates

“…Thus, a CTC count 63 of ≥ 5 cells per 7.5 ml blood detected by the CellSearch ® system is an independent predictive factor of 64 worse progression-free survival (PFS) and overall survival (OS). First evidences in this regard were 65 shown in 2004 [8] and since then, many studies have confirmed these findings [9][10][11][12][13]. The highest Table 5.…”

“…Li et al [8] demonstrated that ORYZA is a validated and calibrated crop model system that extrapolates the result of field experiments and accurately examines the effects of crop management practices. The crop model system, namely, the crop environment resource synthesis-rice (CSM-CERES-Rice) model can simulate fertilizer application, plant density, and irrigation in rice [9]. Agricultural Production Systems Simulator (APSIM) is a crop model system that performs well in simulating grain yield and analyzing farmer management practices under different seasons [10].…”

Validação do modelo Ceres-Rice para a cultivar IRGA 424 no Rio Grande do Sul

“…As diferenças entre observado e simulado são atribuídas a diversos fatores que o modelo não considera. Vilayvong et al (2015) obteve resultados abaixo de 10% entre os valores simulados e observados. O autor atribuiu o erro a fatores como pestes doenças e outros fatores que o modelo não considera.…”