A Method for Upscaling Genetic Parameters of CERES-Rice in Regional Applications

Min, Jiyoung; Jin, Zhiqing

doi:10.1016/s1672-6308(08)60093-1

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Crop simulation models have been developed based on the theory of crop physiological ecology (Graves et al ., 2002). They are dynamic in the mechanism, comprehensiveness and applicability, and can be used to simulate the interaction of weather, soil, genotype and crop management on crop growth and yield (Jones et al ., 2003; Jiang and Jin, 2009). The concept of crop development mainly involves crop phenology, leaf age increment and appearance of various organs such as leaf blades, leaf sheaths, tillers, roots, stem internodes and panicles (Ritchie et al ., 1987; Gao et al ., 1992).…”

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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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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“…Early studies used the coarse-grid scenarios deduced from GCMs (Global Circulation Models) to analyse the impact of climate change on crop production at given sites, and now the fine-grid scenarios generated from Regional Climate Models (RCMs) are used at the regional scale. Xiong et al (2008) and Qin et al (2011) discussed the downscaling technique of climate models, and Jiang & Jin (2009) introduced an upscaling method for rice model parameters. Tsvetsinskaya et al (2003) and Xiong et al (2014) analysed the impacts of different spatial scales on simulated results.…”

Section: Introductionmentioning

confidence: 99%

Response of rice production to climate change based on self-adaptation in Fujian Province

et al. 2017

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SUMMARYClimate change has greatly affected agricultural production, and will lead to further changes in cropping system, varietal type and cultivation techniques for each region. The potential effects of climate change on rice production in Fujian Province, China, were explored in the current study with CERES-Rice model and climate-change scenarios, based on the self-adaptation of rice production. The results indicated that simulated yields of early rice in the double-rice region in south-eastern Fujian under scenarios A2, B2 and A1B increased by 15·9, 18·0 and 19·2%, respectively, and correspondingly those of late rice increased by 9·2, 7·4 and 7·4% when self-adaptation adjustment was considered, compared to scenarios without that consideration. In the double-rice region in north-western Fujian, simulated yields of early rice increased by 21·2, 20·5 and 18·9% and those of late rice by 14·7, 14·8 and 7·2% under scenarios A2, B2 and A1B, respectively, when self-adaptation was considered, compared to without consideration. Similar results were obtained for the single-rice region in the mountain areas of north-western Fujian, correspondingly increasing by 4·9, 5·0 and 2·9% when self-adaptation was considered compared to when it was not. In this single-rice region, double rice might be grown in the future at the Changting site under scenarios A1 and B2. When the self-adaptation adjustment was considered, the simulated overall output of rice crops in Fujian under scenarios A2, B2 and A1B increased by 5·9, 5·2 and 5·1%, respectively. Thus, more optimistic results were obtained when the self-adaptation ability of rice production was considered.

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Improving Agricultural Management in a Large‐Scale Paddy Field by Using Remotely Sensed Data in the Ceres‐Rice Model

Rezaei

Shahnazari

Sarjaz

et al. 2016

Irrigation and Drainage

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Compared to the small‐scale situation, some constraints in large‐scale rice fields have caused crop growth models to fail to reach an acceptable estimate of yield. This study was conducted to investigate the possibility of enhancing the accuracy of the CERES‐Rice model prediction at a large scale through the use of Landsat 5 satellite imagery (termed ‘assimilation’). Firstly, the model was calibrated by data taken from local research. The model accuracy was then evaluated in 110 paddy fields over 26 000 ha (method A). Then the model was recalibrated by paddy yield estimated from Landsat 5 image (method B). The two methods were compared based on their results. The results revealed that RMSEn in simulating grain yield in small‐scale field experiments on the Hashemi cultivar for calibration and validation of the model were 9 and 8%, respectively (R2 = 0.7), which indicated the model's high accuracy in yield prediction. While RMSEn in simulating grain yield in large‐scale (methods A) was 22% (R2 = 0.54), the use of Landsat images in the assimilation method (method B) increased its accuracy dramatically to RMSEn of 12.7% (R2 = 0.72). Copyright © 2016 John Wiley & Sons, Ltd.

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A Method for Upscaling Genetic Parameters of CERES-Rice in Regional Applications

Cited by 4 publications

References 5 publications

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

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

Response of rice production to climate change based on self-adaptation in Fujian Province

Improving Agricultural Management in a Large‐Scale Paddy Field by Using Remotely Sensed Data in the Ceres‐Rice Model

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