A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of crop performance trials

Anothai, Jakarat; Patanothai, A.; Jogloy, Sanun; Pannangpetch, K.; Boote, Kenneth J.; Hoogenboom, Gerrit

doi:10.1016/j.fcr.2008.04.012

Cited by 52 publications

(32 citation statements)

References 31 publications

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“…Recent studies have shown that the CSM‐CROPGRO‐Peanut model, which is also included in the DSSAT programme, can be used as a breeding tool in Thailand to help understand the Genotype × Environment interaction and related issues associated with efficient breeding programmes (Banterng et al. 2003, 2006, Anothai et al. 2008, Phakamas et al.…”

Section: Introductionmentioning

confidence: 99%

Application of the Cropping System Model (CSM)‐CROPGRO‐Soybean for Determining Optimum Management Strategies for Soybean in Tropical Environments

Banterng

Hoogenboom

Patanothai

et al. 2010

J Agronomy Crop Science

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The determination of optimum crop management practices for increasing soybean production can provide valuable information for strategic planning in the tropics. However, this process is time consuming and expensive. The use of a dynamic crop simulation model can be an alternative option to help estimate yield levels under various growing conditions. The objectives of this study were to evaluate the performance of the Cropping System Model (CSM)‐CROPGRO‐Soybean and to determine optimum management practices for soybean for growing conditions in the Phu Pha Man district, Thailand. Data from two soybean experiments that were conducted in 1991 at Chiang Mai University and in 2003 at Khon Kaen University were used to determine the cultivar coefficients for the cultivars CM 60 and SJ 5. The CSM‐CROPGRO‐Soybean model was evaluated with data from two experiments that were conducted at Chiang Mai University. The observed data sets from farmers’ fields located in the Phu Pha Man district were also used for model evaluation. Simulations for different management scenarios were conducted with soil property information for seven different soil series and historical weather data for the period 1972–2003 to predict the optimum crop management practices for soybean production in the Phu Pha Man district. The results of this study indicated that the cultivar coefficients of the two soybean cultivars resulted in simulated growth and development parameters that were in good agreement with almost all observed parameters. Model evaluation showed a good agreement between simulated and observed data for phenology and growth of soybean, and demonstrated the potential of the CSM‐CROPGRO‐Soybean model to simulate growth and yield for local environments, including farmers’ fields, in Thailand. The CSM‐CROPGRO‐Soybean simulations indicated that the optimum planting dates from June 15 to July 15 produced maximum soybean yield in a rainfed environment. However, the planting date December 15 produced the highest yield under quality irrigation. Soybean yield was slightly improved by applying nitrogen at a rate of 30 kg N ha−1 at planting. Soybean yield also improved when the plant density was increased from 20 to 40 plants m−2. The results from this study suggest that the CSM‐CROPGRO‐Soybean model can be a valuable tool in assisting with determining optimum management practices for soybean cropping systems in the Phu Pha Man district and might be applicable to other agricultural production areas in Thailand and southeast Asia.

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Section: Introductionmentioning

confidence: 99%

Application of the Cropping System Model (CSM)‐CROPGRO‐Soybean for Determining Optimum Management Strategies for Soybean in Tropical Environments

Banterng

Hoogenboom

Patanothai

et al. 2010

J Agronomy Crop Science

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“…These experiments, however, are time and resource intensive, and are not suited to the estimation of the phenological parameters of the many new cultivars that are released each year. However, there have been some attempts to utilize existing data available from conventional variety trials for this purpose (e.g., Mavromatis et al, 2001;Anothai et al, 2008;Mackay et al, 2011). Because variety trials are commonly used to evaluate new cultivars, the utilization of the resulting data has great potential to improve the efficiency of parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

A methodology for estimating phenological parameters of rice cultivars utilizing data from common variety trials

Fukui

Ishigooka

Kuwagata

et al. 2015

J. Agric. Meteorol.

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Crop phenology models play a pivotal role in predicting yields under climate change. Cultivar-specific model parameters are essential for accurate prediction, but their estimation generally requires elaborate and laborious experiments, and such parameter sets have therefore been available only for a small number of cultivars. We propose methodology for estimating phenological parameters, combining a stochastic parameter estimation method (genetic algorithm) with the use of a database comprising 30 years of records from variety trials conducted at experimental stations across Japan. Optimal parameter sets were selected based on the results of cross-validation tests. This methodology allowed us to estimate phenological parameters objectively. We estimated phenological parameters for the 10 leading cultivars currently planted in Japan, and showed that these parameters reflect the cultivars' sensitivity to temperature and/or photoperiod. The proposed methodology can be used to provide quantitative evaluations of the environmental responses of rice cultivars, without relying on elaborate and laborious experiments, and substantially improves the efficiency of phenological trait phenotyping.

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“…The approach used for the optimization procedure (Fig 1) was similar to that used by Anothai et al [13]. This approach has not been reported for maize, especially in Sub-Saharan Africa.…”

Section: Methodsmentioning

confidence: 99%

“…Several approaches for estimating GSPs have been documented. The genetic coefficient calculator (GENCALC) was used by Anothai et al [13] to determine variety coefficients for new peanut lines in Thailand from standard varietal trials. From their experiments, they were able to successfully calibrate groundnut GSPs using a set of field experiments and yield evaluation experiments using the GENCALC software.…”

Section: Introductionmentioning

confidence: 99%

Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

Adnan

Diels

Jibrin

et al. 2018

Preprint

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28Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide 29 the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect 30 of most modelling exercises because it requires expensive and time-consuming field experiments. 31GSPs could also be estimated using multi-year and multi locational data from breeder evaluation 32 experiments. This research was set up with the following objectives: i) to determine GSPs of 10 33 newly released maize varieties for the Nigerian Savannas using data from both calibration 34 experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the 35 accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-36 Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different 37 experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. 38 Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were 39 evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 40 120kg N ha -1 ). For the model calibration using experimental data, calculated model efficiency (EF) 41 values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. 42 Calibration of time-series data produced nRMSE below 7% while all prediction deviations were 43 below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges 44 were lower. Prediction deviations were below 17% of the means for all measured variables. Model 45 evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high 46 EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen 47 contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from 48 detailed experiments. If unavailable, data from breeder experimental trials collected from many 49 locations and planting dates can be used with lower but acceptable accuracy.

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A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of crop performance trials

Cited by 52 publications

References 31 publications

Application of the Cropping System Model (CSM)‐CROPGRO‐Soybean for Determining Optimum Management Strategies for Soybean in Tropical Environments

Application of the Cropping System Model (CSM)‐CROPGRO‐Soybean for Determining Optimum Management Strategies for Soybean in Tropical Environments

A methodology for estimating phenological parameters of rice cultivars utilizing data from common variety trials

Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

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