Evaluation of CERES-rice model (V. 4.0) under temperate conditions of Kashmir valley, India

Singh, Hari; Singh, K. N.; Hasan, Badrul

doi:10.1556/crc.35.2007.4.18

Cited by 14 publications

(12 citation statements)

References 7 publications

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“…From the result it was observed that biological yield differed due to combined effect of grain yield and straw yield. Variability among different rice varieties for biological yield was also noticed by Laza et al, (2004); Singh et al, (2007); Lack et al, (2012) and Khalifa et al, (2014).…”

Section: Effect Of Varietiesmentioning

confidence: 70%

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Influence of Zinc and Iron Fortification on Yield and Post-Harvest Studies of Different Rice Cultivars (Oryza sativa L.)

Kadam¹,

Bhale²,

Chorey³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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Section: Effect Of Varietiesmentioning

confidence: 70%

“…Superiority of PBNR-03-02 over other varieties indicated that this variety have efficient partitioning for assimilates of photosynthesis from source to sink (panicle). Laza et al, (2004), Singh et al, (2007) and Lack et al, (2012) also reported varietals differences for harvest index.…”

Section: Effect Of Varietiesmentioning

confidence: 92%

Influence of Zinc and Iron Fortification on Yield and Post-Harvest Studies of Different Rice Cultivars (Oryza sativa L.)

Kadam¹,

Bhale²,

Chorey³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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“…The model has been evaluated in a number of agro-environments and crop management strategies (Cheyglinted et al, 2001;Singh et al, 2007;Vilayvong et al, 2012;Yao et al, 2005). It has also been used to determine better rice management systems under irrigated conditions (Ahmad et al, 2012) and simulate rice yield under certain agronomic management practices and changing climatic scenarios (Lamsal and Amgain, 2010).…”

Section: Introductionmentioning

confidence: 99%

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

Vilayvong

Banterng

Patanothai

et al. 2015

Sci. agric. (Piracicaba, Braz.)

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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.

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“…The CSM-CERES-Rice model of DSSAT Version 4.0.2.0 was calibrated and evaluated using the data from a field experiment carried out during the rainy season of 2004/05 at Shalimar, Srinagar (1587 m asl), India (Singh et al ., 2007). A crop-growth simulation model, CERES-Rice, was applied to 16 locations representing major rice-growing regions of Bangladesh to determine baseline yield estimates for four transplanting dates (Mahmood et al ., 2003).…”

Section: Introductionmentioning

confidence: 99%

Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators

Buddhaboon

Jintrawet

Hoogenboom³

2018

J. Agric. Sci.

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Prior to applying the cropping system model-CERES-Rice model to deep water rice (DWR), it is important to estimate the rice genetic coefficients (GC). The goal of the current study was to compare two methods for estimating GC using a GC calculator (GENCALC) and generalized likelihood uncertainty estimation (GLUE) for three flooded rice (FDR) varieties. Data from a field experiment on the effect of planting date and variety on FDR production was conducted in 2009 on a DWR area in Bang Taen His Majesty's Private Development Project, Prachin Buri, Thailand. The experimental design was split-plot with four main plots (planting dates) and three sub-plots (FDR varieties) with four replications. The simulated values for anthesis date, maturity date and grain weight using GENCALC produced normalized root mean square errors (RMSEn) of 3.97, 3.69 and 3.68, while using GLUE produced RMSEn of 3.67, 2.50 and 3.68, respectively. The simulated grain number and grain yield under GENCALC GC were not significantly different from the observed values but were higher than simulated values for GLUE GC. Simulated values of above-ground biomass for both GENCALC (11 727 kg/ha) and GLUE GC (11 544 kg/ha) were overestimated compared to observed values (8512 kg/ha). In addition, good agreements of leaf N values were found with D-index values of 0.94 and 0.96 using GENACALC and GLUE GC simulations, respectively. Therefore, the GENCALC and GLUE GC estimators of DSSAT can both be used for estimating GC of FDR in the DWR area in Thailand and similar agro-ecosystems in Southeast Asia.

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Evaluation of CERES-rice model (V. 4.0) under temperate conditions of Kashmir valley, India

Cited by 14 publications

References 7 publications

Influence of Zinc and Iron Fortification on Yield and Post-Harvest Studies of Different Rice Cultivars (Oryza sativa L.)

Influence of Zinc and Iron Fortification on Yield and Post-Harvest Studies of Different Rice Cultivars (Oryza sativa L.)

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

Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators

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