Yield stability evaluation of peanut lines: A comparison of an experimental versus a simulation approach

Banterng, Poramate; Patanothai, A.; Pannangpetch, K.; Jogloy, Sanun; Hoogenboom, Gerrit

doi:10.1016/j.fcr.2005.06.008

Cited by 29 publications

(27 citation statements)

References 24 publications

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“…This study confirms the findings of Banterng et al (2003, 2006) that the CSM‐CROPGRO‐Peanut model can be used to assist yield performance and stability evaluation of peanut breeding lines. The model can be run for as many locations as needed and thus could provide information on environmental responses of the test genotypes over a wide range of environments that could not be obtained from actual field testing.…”

Section: Resultssupporting

confidence: 91%

“…Also, the test environments do not normally cover the entire range of environmental conditions of all major production areas of a crop in a target region. Dynamic crop simulation models are now so advanced that they can be used as a multipurpose tool for application in agricultural research (Hoogenboom et al, 1992; Penning de Vries et al, 1993; Boote et al, 2003; Jones et al, 2003; Banterng et al, 2006; Mercau et al, 2007). Because these models can simulate growth and development of crop cultivars under different environmental conditions and management practices, they also have potential to be used in assisting multienvironment evaluation of crop breeding lines (Aggarwal et al, 1995; White, 1998).…”

mentioning

confidence: 99%

“…The model is physiologically based and can simulate yields of peanut cultivars under various management and environmental conditions (Singh et al, 1994;Boote et al, 1998;Kaur and Hundal, 1999). The applicability of the CSM-CROPGRO-Peanut model in assisting multilocation and yield stability evaluation of peanut breeding lines was investigated by Banterng et al (2003Banterng et al ( , 2006. They found that the model predicted the relative mean pod yields of the test peanut lines across the test environments and their relative yield responses to diff erent environments reasonably well.…”

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confidence: 99%

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Yield Performance and Stability Evaluation of Peanut Breeding Lines with the CSM‐CROPGRO‐Peanut Model

et al. 2008

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Multienvironment trials of crop breeding lines consume many resources. Crop models have a potential to assist in this process. The goal of this study was to evaluate the potential of the CSM‐CROPGRO‐Peanut model in assisting with yield performance and stability evaluation of peanut (Arachis hypogaea L.) breeding lines. Seventeen peanut lines were tested in 11 environments in Thailand during both the rainy and the dry seasons of 2002 to 2004. Yields of these lines were also simulated with the CSM‐CROPGRO‐Peanut model for the same 11 test sites. The cultivar coefficients of the peanut lines used for model simulation were derived from a separate experiment conducted in two seasons and were evaluated against an independent data set from another experiment. Yield performance and stability of the peanut lines were evaluated on the basis of observed and simulated data using the genotype and genotype × environment interaction biplot method. The results showed that the model predicted the relative performances and yield stability for pod yield of the test peanut lines for the 11 actual test sites reasonably well. Out of the nine highest yielding lines, that is, the upper 52%, identified by observed data, six were identified by model simulation. Similarly, out of the nine stable lines identified by observed data, six were common with those identified by simulated data. We conclude that the CSM‐CROPGRO‐Peanut model could be used in assisting yield performance and stability evaluation of peanut breeding lines.

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Section: Resultssupporting

confidence: 91%

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Yield Performance and Stability Evaluation of Peanut Breeding Lines with the CSM‐CROPGRO‐Peanut Model

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“…2003). 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.…”

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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“…This process consumes a lot of time and resources, and the scope of the test environments normally does not cover the entire range of environmental conditions for the different production areas in the country. The potential of such an application as a breeding tool has been demonstrated by our recent studies using the CSM‐CROPGRO‐Peanut model (Banterng et al, 2003, 2006). We found that the model could predict the relative mean performances of 12 advanced large‐seeded peanut lines tested in 12 environments and of 14 advanced small‐seeded peanut lines tested in three environments.…”

Section: Resultsmentioning

confidence: 98%

Determination of Cultivar Coefficients of Peanut Lines for Breeding Applications of the CSM‐CROPGRO‐Peanut Model

et al. 2007

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Breeding applications of a crop simulation model require cultivar coefficients for a large number of breeding lines, making the recommendation to determine these coefficients from field experiments conducted over several environments impractical. The objective of this study was to confirm our earlier findings that experimental data from only two seasons are sufficient to determine the cultivar coefficients of peanut lines for breeding applications of the CSM‐CROPGRO Peanut model. Seventeen peanut lines varying in yield level, seed type and maturity duration were selected for this study and were grown during the 2002 rainy and 2003 dry seasons. Data were collected on plant development and growth characteristics and were then used for model calibration to determine the cultivar coefficients of the individual peanut lines. A separate experiment was conducted in the dry season of 2004 for model evaluation. The calibration resulted in cultivar coefficients that provided simulated values of the various traits that were close to their corresponding observed values. Similar results were obtained for model evaluation with an independent data set. Differences among peanut lines were also expressed in the derived cultivar coefficients. These results confirmed our earlier finding that the cultivar coefficients of peanut lines derived from detailed experimental data for two seasons are sufficiently accurate for breeding applications of the CSM‐CROPGRO‐Peanut model.

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Yield stability evaluation of peanut lines: A comparison of an experimental versus a simulation approach

Cited by 29 publications

References 24 publications

Yield Performance and Stability Evaluation of Peanut Breeding Lines with the CSM‐CROPGRO‐Peanut Model

Yield Performance and Stability Evaluation of Peanut Breeding Lines with the CSM‐CROPGRO‐Peanut Model

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

Determination of Cultivar Coefficients of Peanut Lines for Breeding Applications of the CSM‐CROPGRO‐Peanut Model

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