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G. Hoogenboom - One of the best experts on this subject based on the ideXlab platform.
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A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of Crop Performance trials
Field Crops Research, 2008Co-Authors: J. Anothai, A. Patanothai, S. Jogloy, K. Pannangpetch, Kenneth J. Boote, G. HoogenboomAbstract:Abstract Coefficients of Crop cultivars, a required input for the application of Crop simulation models, are normally derived from experiments designed specifically for their estimation. This procedure is laborious and time consuming even with a reduced data set. Recent studies have shown that cultivar coefficients for soybean lines can be derived from standard Crop Performance trials. However, this needs to be confirmed in other Crops and be simplified for broader applications. The objective of this study was to determine the feasibility of estimating cultivar coefficients for new peanut lines using data from standard Performance trials. Data from Performance trials of 17 peanut lines that were conducted in farmers’ fields and research stations in the northeastern and northern regions of Thailand during 2002–2004, totaling eight environments, were used in this study. The data that were collected included dates of first flower and harvest maturity, final biomass, pod and seed yield, seed size, pod and seed harvest index, and shelling percentage. These data were used for the calculation of the cultivar coefficients using the Genotype Coefficient Calculator (GENCALC) program, which is part of the Decision Support System for Agrotechnology Transfer (DSSAT). Evaluation of the derived cultivar coefficients was conducted with time series growth data collected in three additional experiments grown during the 2002 rainy, 2003 dry, and 2004 dry seasons. The model calibration with GENCALC resulted in cultivar coefficients that produced simulated values for the development and growth characteristics that were close to their corresponding observed values, with root mean square errors (RMSE) ranging from 1.5 to 4.1 days for development traits and 0.20–1.32 t ha−1 for growth traits and coefficient of determinations (r2) ranging from 0.55 to 0.97 for all traits. The evaluation of the cultivar coefficients that were derived from the Performance trials data with independent data worked well for all development traits and fairly well for the plant growth characteristics, as judged by RMSE, r2, normalized root mean square error (RMSEn) and index of agreement (d). The mean RMSE values for days to first flower and to harvest maturity were 1.6 and 2.4 days; and mean r2 were 0.72 and 0.91, respectively. The mean RMSEn values calculated from time series growth data were 17.9, 24.6 and 11.5% with the mean d values of 0.88, 0.93 and 0.93 for the 2002 rainy, 2003 dry and 2004 dry seasons, respectively. It is concluded that the cultivar coefficients of peanut lines can be estimated from typical data that are collected in standard Performance trials using either GENCALC or similar methodologies.
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A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of Crop Performance trials
Field Crops Research, 2008Co-Authors: J. Anothai, A. Patanothai, S. Jogloy, K. Pannangpetch, Kenneth J. Boote, G. HoogenboomAbstract:Coefficients of Crop cultivars, a required input for the application of Crop simulation models, are normally derived from experiments designed specifically for their estimation. This procedure is laborious and time consuming even with a reduced data set. Recent studies have shown that cultivar coefficients for soybean lines can be derived from standard Crop Performance trials. However, this needs to be confirmed in other Crops and be simplified for broader applications. The objective of this study was to determine the feasibility of estimating cultivar coefficients for new peanut lines using data from standard Performance trials. Data from Performance trials of 17 peanut lines that were conducted in farmers' fields and research stations in the northeastern and northern regions of Thailand during 2002-2004, totaling eight environments, were used in this study. The data that were collected included dates of first flower and harvest maturity, final biomass, pod and seed yield, seed size, pod and seed harvest index, and shelling percentage. These data were used for the calculation of the cultivar coefficients using the Genotype Coefficient Calculator (GENCALC) program, which is part of the Decision Support System for Agrotechnology Transfer (DSSAT). Evaluation of the derived cultivar coefficients was conducted with time series growth data collected in three additional experiments grown during the 2002 rainy, 2003 dry, and 2004 dry seasons. The model calibration with GENCALC resulted in cultivar coefficients that produced simulated values for the development and growth characteristics that were close to their corresponding observed values, with root mean square errors (RMSE) ranging from 1.5 to 4.1 days for development traits and 0.20-1.32 t ha-1for growth traits and coefficient of determinations (r2) ranging from 0.55 to 0.97 for all traits. The evaluation of the cultivar coefficients that were derived from the Performance trials data with independent data worked well for all development traits and fairly well for the plant growth characteristics, as judged by RMSE, r2, normalized root mean square error (RMSEn) and index of agreement (d). The mean RMSE values for days to first flower and to harvest maturity were 1.6 and 2.4 days; and mean r2were 0.72 and 0.91, respectively. The mean RMSEn values calculated from time series growth data were 17.9, 24.6 and 11.5% with the mean d values of 0.88, 0.93 and 0.93 for the 2002 rainy, 2003 dry and 2004 dry seasons, respectively. It is concluded that the cultivar coefficients of peanut lines can be estimated from typical data that are collected in standard Performance trials using either GENCALC or similar methodologies. © 2008 Elsevier B.V. All rights reserved.
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Developing genetic for Crop simulation models with data from Crop Performance trials
Crop Science, 2001Co-Authors: T. Mavromatis, Kenneth J. Boote, James W. Jones, Ayse Irmak, D. Shinde, G. HoogenboomAbstract:Successful uses of Crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as soon as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CropGRO-Soybean model from typical information provided by Crop Performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coefficients and site-specific soil traits for use in Crop models, and (iii) to explore the extent to which our approach allowed the Crop model to reproduce observed genotype x environment (GE) interactions, cultivar ranking, and year-to-year yield variability. Crop Performance tests typically record harvest maturity date, seed yield, seed size, height, and lodging. A stepwise procedure using data on 11 cultivars grown at five sites in Georgia over 4 to 10 yr efficiently decreased the root mean square error (RMSE) between observed and predicted data. For 'Stonewall', a maturity group VII cultivar, the RMSE of 769 kg ha(-1) between the actual and modeled seed yield, estimated initially by means of the existing general maturity group coefficients, was reduced to 404 kg ha(-1). For the same cultivar, the initial RMSE of 5.3 and 9.3 d between the actual and simulated anthesis and harvest maturity dates, respectively, estimated by means of the existing general maturity group coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving useful information on site characteristics and cultivar traits, our approach has enabled CropGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype x environment interactions. Across all environments, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments.
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developing genetic coefficients for Crop simulation models with data from Crop Performance trials
Crop Science, 2001Co-Authors: T. Mavromatis, Kenneth J. Boote, James W. Jones, Ayse Irmak, D. Shinde, G. HoogenboomAbstract:Successful uses of Crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as soon as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CropGRO-Soybean model from typical information provided by Crop Performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coefficients and site-specific soil traits for use in Crop models, and (iii) to explore the extent to which our approach allowed the Crop model to reproduce observed genotype x environment (GE) interactions, cultivar ranking, and year-to-year yield variability. Crop Performance tests typically record harvest maturity date, seed yield, seed size, height, and lodging. A stepwise procedure using data on 11 cultivars grown at five sites in Georgia over 4 to 10 yr efficiently decreased the root mean square error (RMSE) between observed and predicted data. For Stonewall', a maturity group VII cultivar, the RMSE of 769 kg ha -1 between the actual and modeled seed yield, estimated initially by means of the existing general maturity group coefficients, was reduced to 404 kg ha -1 . For the same cultivar, the initial RMSE of 5.3 and 9.3 d between the actual and simulated anthesis and harvest maturity dates, respectively, estimated by means of the existing general maturity group coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving useful information on site characteristics and cultivar traits, our approach has enabled CropGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype x environment interactions. Across all environments, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments.
Janice E. Thies - One of the best experts on this subject based on the ideXlab platform.
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choice of organic amendments in tomato transplants has lasting effects on bacterial rhizosphere communities and Crop Performance in the field
Applied Soil Ecology, 2011Co-Authors: Allison L H Jack, Steven W. Culman, Anusuya Rangarajan, Thanwalee Sooksanguan, Janice E. ThiesAbstract:Vegetable transplant media used in certified organic Crop production must both comply with regulations and meet seedling nutrient demand. Transplant media amendments allowable in organic production often contain complex microbial communities, however little is known about their effects on transplant rhizosphere microorganisms and if these effects carry over to mature plants in the field. To address this, we compared (i) plant-based (sesame meal, alfalfa meal) amendments, (ii) composted manure-based (vermicompost, thermogenic compost, industry standard) amendments and (iii) a non-amended peat and vermiculite base mix for organic tomato (Lycopersicon esculentum) production. Organic transplant media amendments affected germination rates, transplant growth in the greenhouse, Crop growth in the field and final yields. Transplant biomass and early yield were highest for vermicompost and plant-based amendments. Total yield was highest for 20% alfalfa and sesame meal amendments in the first season, however this high rate of amendment negatively impacted germination. No significant differences in yield were found among amended treatments in the second season where plant-based amendment rates were reduced to1 and 2.5% for sesame meal and 5% for alfalfa meal. Amendments also influenced bacterial community structure in both the transplant media and in the rhizosphere of the tomato plants. Terminal restriction fragment length polymorphism (T-RFLP) analysis showed significant differences in bacterial communities between all amendments and these differences persisted for at least one month after seedlings were transplanted to the field. Amendment-associated differences in bacterial communities diminished over the course of the field season. By harvest only vermicompost and the base media had unique T-RFLP profiles. Comparing thermogenic compost and vermicompost made from the same starting material showed that the composting process influenced the bacterial community in the transplant material as well as subsequent communities in the Crop rhizosphere. Overall, our results show that the type and rate of organic transplant media amendment can strongly influence transplant quality and subsequent Crop Performance in the field as well as rhizosphere bacterial communities long after seedlings are transplanted to field soil.
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Choice of organic amendments in tomato transplants has lasting effects on bacterial rhizosphere communities and Crop Performance in the field
Applied Soil Ecology, 2011Co-Authors: Allison L H Jack, Steven W. Culman, Thanwalee Sooksa-nguan, Anusuya Rangarajan, Janice E. ThiesAbstract:Vegetable transplant media used in certified organic Crop production must both comply with regulations and meet seedling nutrient demand. Transplant media amendments allowable in organic production often contain complex microbial communities, however little is known about their effects on transplant rhizosphere microorganisms and if these effects carry over to mature plants in the field. To address this, we compared (i) plant-based (sesame meal, alfalfa meal) amendments, (ii) composted manure-based (vermicompost, thermogenic compost, industry standard) amendments and (iii) a non-amended peat and vermiculite base mix for organic tomato (Lycopersicon esculentum) production. Organic transplant media amendments affected germination rates, transplant growth in the greenhouse, Crop growth in the field and final yields. Transplant biomass and early yield were highest for vermicompost and plant-based amendments. Total yield was highest for 20% alfalfa and sesame meal amendments in the first season, however this high rate of amendment negatively impacted germination. No significant differences in yield were found among amended treatments in the second season where plant-based amendment rates were reduced to1 and 2.5% for sesame meal and 5% for alfalfa meal. Amendments also influenced bacterial community structure in both the transplant media and in the rhizosphere of the tomato plants. Terminal restriction fragment length polymorphism (T-RFLP) analysis showed significant differences in bacterial communities between all amendments and these differences persisted for at least one month after seedlings were transplanted to the field. Amendment-associated differences in bacterial communities diminished over the course of the field season. By harvest only vermicompost and the base media had unique T-RFLP profiles. Comparing thermogenic compost and vermicompost made from the same starting material showed that the composting process influenced the bacterial community in the transplant material as well as subsequent communities in the Crop rhizosphere. Overall, our results show that the type and rate of organic transplant media amendment can strongly influence transplant quality and subsequent Crop Performance in the field as well as rhizosphere bacterial communities long after seedlings are transplanted to field soil. © 2011 Elsevier B.V.
Allison L H Jack - One of the best experts on this subject based on the ideXlab platform.
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choice of organic amendments in tomato transplants has lasting effects on bacterial rhizosphere communities and Crop Performance in the field
Applied Soil Ecology, 2011Co-Authors: Allison L H Jack, Steven W. Culman, Anusuya Rangarajan, Thanwalee Sooksanguan, Janice E. ThiesAbstract:Vegetable transplant media used in certified organic Crop production must both comply with regulations and meet seedling nutrient demand. Transplant media amendments allowable in organic production often contain complex microbial communities, however little is known about their effects on transplant rhizosphere microorganisms and if these effects carry over to mature plants in the field. To address this, we compared (i) plant-based (sesame meal, alfalfa meal) amendments, (ii) composted manure-based (vermicompost, thermogenic compost, industry standard) amendments and (iii) a non-amended peat and vermiculite base mix for organic tomato (Lycopersicon esculentum) production. Organic transplant media amendments affected germination rates, transplant growth in the greenhouse, Crop growth in the field and final yields. Transplant biomass and early yield were highest for vermicompost and plant-based amendments. Total yield was highest for 20% alfalfa and sesame meal amendments in the first season, however this high rate of amendment negatively impacted germination. No significant differences in yield were found among amended treatments in the second season where plant-based amendment rates were reduced to1 and 2.5% for sesame meal and 5% for alfalfa meal. Amendments also influenced bacterial community structure in both the transplant media and in the rhizosphere of the tomato plants. Terminal restriction fragment length polymorphism (T-RFLP) analysis showed significant differences in bacterial communities between all amendments and these differences persisted for at least one month after seedlings were transplanted to the field. Amendment-associated differences in bacterial communities diminished over the course of the field season. By harvest only vermicompost and the base media had unique T-RFLP profiles. Comparing thermogenic compost and vermicompost made from the same starting material showed that the composting process influenced the bacterial community in the transplant material as well as subsequent communities in the Crop rhizosphere. Overall, our results show that the type and rate of organic transplant media amendment can strongly influence transplant quality and subsequent Crop Performance in the field as well as rhizosphere bacterial communities long after seedlings are transplanted to field soil.
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Choice of organic amendments in tomato transplants has lasting effects on bacterial rhizosphere communities and Crop Performance in the field
Applied Soil Ecology, 2011Co-Authors: Allison L H Jack, Steven W. Culman, Thanwalee Sooksa-nguan, Anusuya Rangarajan, Janice E. ThiesAbstract:Vegetable transplant media used in certified organic Crop production must both comply with regulations and meet seedling nutrient demand. Transplant media amendments allowable in organic production often contain complex microbial communities, however little is known about their effects on transplant rhizosphere microorganisms and if these effects carry over to mature plants in the field. To address this, we compared (i) plant-based (sesame meal, alfalfa meal) amendments, (ii) composted manure-based (vermicompost, thermogenic compost, industry standard) amendments and (iii) a non-amended peat and vermiculite base mix for organic tomato (Lycopersicon esculentum) production. Organic transplant media amendments affected germination rates, transplant growth in the greenhouse, Crop growth in the field and final yields. Transplant biomass and early yield were highest for vermicompost and plant-based amendments. Total yield was highest for 20% alfalfa and sesame meal amendments in the first season, however this high rate of amendment negatively impacted germination. No significant differences in yield were found among amended treatments in the second season where plant-based amendment rates were reduced to1 and 2.5% for sesame meal and 5% for alfalfa meal. Amendments also influenced bacterial community structure in both the transplant media and in the rhizosphere of the tomato plants. Terminal restriction fragment length polymorphism (T-RFLP) analysis showed significant differences in bacterial communities between all amendments and these differences persisted for at least one month after seedlings were transplanted to the field. Amendment-associated differences in bacterial communities diminished over the course of the field season. By harvest only vermicompost and the base media had unique T-RFLP profiles. Comparing thermogenic compost and vermicompost made from the same starting material showed that the composting process influenced the bacterial community in the transplant material as well as subsequent communities in the Crop rhizosphere. Overall, our results show that the type and rate of organic transplant media amendment can strongly influence transplant quality and subsequent Crop Performance in the field as well as rhizosphere bacterial communities long after seedlings are transplanted to field soil. © 2011 Elsevier B.V.
J. Anothai - One of the best experts on this subject based on the ideXlab platform.
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A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of Crop Performance trials
Field Crops Research, 2008Co-Authors: J. Anothai, A. Patanothai, S. Jogloy, K. Pannangpetch, Kenneth J. Boote, G. HoogenboomAbstract:Abstract Coefficients of Crop cultivars, a required input for the application of Crop simulation models, are normally derived from experiments designed specifically for their estimation. This procedure is laborious and time consuming even with a reduced data set. Recent studies have shown that cultivar coefficients for soybean lines can be derived from standard Crop Performance trials. However, this needs to be confirmed in other Crops and be simplified for broader applications. The objective of this study was to determine the feasibility of estimating cultivar coefficients for new peanut lines using data from standard Performance trials. Data from Performance trials of 17 peanut lines that were conducted in farmers’ fields and research stations in the northeastern and northern regions of Thailand during 2002–2004, totaling eight environments, were used in this study. The data that were collected included dates of first flower and harvest maturity, final biomass, pod and seed yield, seed size, pod and seed harvest index, and shelling percentage. These data were used for the calculation of the cultivar coefficients using the Genotype Coefficient Calculator (GENCALC) program, which is part of the Decision Support System for Agrotechnology Transfer (DSSAT). Evaluation of the derived cultivar coefficients was conducted with time series growth data collected in three additional experiments grown during the 2002 rainy, 2003 dry, and 2004 dry seasons. The model calibration with GENCALC resulted in cultivar coefficients that produced simulated values for the development and growth characteristics that were close to their corresponding observed values, with root mean square errors (RMSE) ranging from 1.5 to 4.1 days for development traits and 0.20–1.32 t ha−1 for growth traits and coefficient of determinations (r2) ranging from 0.55 to 0.97 for all traits. The evaluation of the cultivar coefficients that were derived from the Performance trials data with independent data worked well for all development traits and fairly well for the plant growth characteristics, as judged by RMSE, r2, normalized root mean square error (RMSEn) and index of agreement (d). The mean RMSE values for days to first flower and to harvest maturity were 1.6 and 2.4 days; and mean r2 were 0.72 and 0.91, respectively. The mean RMSEn values calculated from time series growth data were 17.9, 24.6 and 11.5% with the mean d values of 0.88, 0.93 and 0.93 for the 2002 rainy, 2003 dry and 2004 dry seasons, respectively. It is concluded that the cultivar coefficients of peanut lines can be estimated from typical data that are collected in standard Performance trials using either GENCALC or similar methodologies.
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A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of Crop Performance trials
Field Crops Research, 2008Co-Authors: J. Anothai, A. Patanothai, S. Jogloy, K. Pannangpetch, Kenneth J. Boote, G. HoogenboomAbstract:Coefficients of Crop cultivars, a required input for the application of Crop simulation models, are normally derived from experiments designed specifically for their estimation. This procedure is laborious and time consuming even with a reduced data set. Recent studies have shown that cultivar coefficients for soybean lines can be derived from standard Crop Performance trials. However, this needs to be confirmed in other Crops and be simplified for broader applications. The objective of this study was to determine the feasibility of estimating cultivar coefficients for new peanut lines using data from standard Performance trials. Data from Performance trials of 17 peanut lines that were conducted in farmers' fields and research stations in the northeastern and northern regions of Thailand during 2002-2004, totaling eight environments, were used in this study. The data that were collected included dates of first flower and harvest maturity, final biomass, pod and seed yield, seed size, pod and seed harvest index, and shelling percentage. These data were used for the calculation of the cultivar coefficients using the Genotype Coefficient Calculator (GENCALC) program, which is part of the Decision Support System for Agrotechnology Transfer (DSSAT). Evaluation of the derived cultivar coefficients was conducted with time series growth data collected in three additional experiments grown during the 2002 rainy, 2003 dry, and 2004 dry seasons. The model calibration with GENCALC resulted in cultivar coefficients that produced simulated values for the development and growth characteristics that were close to their corresponding observed values, with root mean square errors (RMSE) ranging from 1.5 to 4.1 days for development traits and 0.20-1.32 t ha-1for growth traits and coefficient of determinations (r2) ranging from 0.55 to 0.97 for all traits. The evaluation of the cultivar coefficients that were derived from the Performance trials data with independent data worked well for all development traits and fairly well for the plant growth characteristics, as judged by RMSE, r2, normalized root mean square error (RMSEn) and index of agreement (d). The mean RMSE values for days to first flower and to harvest maturity were 1.6 and 2.4 days; and mean r2were 0.72 and 0.91, respectively. The mean RMSEn values calculated from time series growth data were 17.9, 24.6 and 11.5% with the mean d values of 0.88, 0.93 and 0.93 for the 2002 rainy, 2003 dry and 2004 dry seasons, respectively. It is concluded that the cultivar coefficients of peanut lines can be estimated from typical data that are collected in standard Performance trials using either GENCALC or similar methodologies. © 2008 Elsevier B.V. All rights reserved.
Kenneth J. Boote - One of the best experts on this subject based on the ideXlab platform.
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A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of Crop Performance trials
Field Crops Research, 2008Co-Authors: J. Anothai, A. Patanothai, S. Jogloy, K. Pannangpetch, Kenneth J. Boote, G. HoogenboomAbstract:Abstract Coefficients of Crop cultivars, a required input for the application of Crop simulation models, are normally derived from experiments designed specifically for their estimation. This procedure is laborious and time consuming even with a reduced data set. Recent studies have shown that cultivar coefficients for soybean lines can be derived from standard Crop Performance trials. However, this needs to be confirmed in other Crops and be simplified for broader applications. The objective of this study was to determine the feasibility of estimating cultivar coefficients for new peanut lines using data from standard Performance trials. Data from Performance trials of 17 peanut lines that were conducted in farmers’ fields and research stations in the northeastern and northern regions of Thailand during 2002–2004, totaling eight environments, were used in this study. The data that were collected included dates of first flower and harvest maturity, final biomass, pod and seed yield, seed size, pod and seed harvest index, and shelling percentage. These data were used for the calculation of the cultivar coefficients using the Genotype Coefficient Calculator (GENCALC) program, which is part of the Decision Support System for Agrotechnology Transfer (DSSAT). Evaluation of the derived cultivar coefficients was conducted with time series growth data collected in three additional experiments grown during the 2002 rainy, 2003 dry, and 2004 dry seasons. The model calibration with GENCALC resulted in cultivar coefficients that produced simulated values for the development and growth characteristics that were close to their corresponding observed values, with root mean square errors (RMSE) ranging from 1.5 to 4.1 days for development traits and 0.20–1.32 t ha−1 for growth traits and coefficient of determinations (r2) ranging from 0.55 to 0.97 for all traits. The evaluation of the cultivar coefficients that were derived from the Performance trials data with independent data worked well for all development traits and fairly well for the plant growth characteristics, as judged by RMSE, r2, normalized root mean square error (RMSEn) and index of agreement (d). The mean RMSE values for days to first flower and to harvest maturity were 1.6 and 2.4 days; and mean r2 were 0.72 and 0.91, respectively. The mean RMSEn values calculated from time series growth data were 17.9, 24.6 and 11.5% with the mean d values of 0.88, 0.93 and 0.93 for the 2002 rainy, 2003 dry and 2004 dry seasons, respectively. It is concluded that the cultivar coefficients of peanut lines can be estimated from typical data that are collected in standard Performance trials using either GENCALC or similar methodologies.
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A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of Crop Performance trials
Field Crops Research, 2008Co-Authors: J. Anothai, A. Patanothai, S. Jogloy, K. Pannangpetch, Kenneth J. Boote, G. HoogenboomAbstract:Coefficients of Crop cultivars, a required input for the application of Crop simulation models, are normally derived from experiments designed specifically for their estimation. This procedure is laborious and time consuming even with a reduced data set. Recent studies have shown that cultivar coefficients for soybean lines can be derived from standard Crop Performance trials. However, this needs to be confirmed in other Crops and be simplified for broader applications. The objective of this study was to determine the feasibility of estimating cultivar coefficients for new peanut lines using data from standard Performance trials. Data from Performance trials of 17 peanut lines that were conducted in farmers' fields and research stations in the northeastern and northern regions of Thailand during 2002-2004, totaling eight environments, were used in this study. The data that were collected included dates of first flower and harvest maturity, final biomass, pod and seed yield, seed size, pod and seed harvest index, and shelling percentage. These data were used for the calculation of the cultivar coefficients using the Genotype Coefficient Calculator (GENCALC) program, which is part of the Decision Support System for Agrotechnology Transfer (DSSAT). Evaluation of the derived cultivar coefficients was conducted with time series growth data collected in three additional experiments grown during the 2002 rainy, 2003 dry, and 2004 dry seasons. The model calibration with GENCALC resulted in cultivar coefficients that produced simulated values for the development and growth characteristics that were close to their corresponding observed values, with root mean square errors (RMSE) ranging from 1.5 to 4.1 days for development traits and 0.20-1.32 t ha-1for growth traits and coefficient of determinations (r2) ranging from 0.55 to 0.97 for all traits. The evaluation of the cultivar coefficients that were derived from the Performance trials data with independent data worked well for all development traits and fairly well for the plant growth characteristics, as judged by RMSE, r2, normalized root mean square error (RMSEn) and index of agreement (d). The mean RMSE values for days to first flower and to harvest maturity were 1.6 and 2.4 days; and mean r2were 0.72 and 0.91, respectively. The mean RMSEn values calculated from time series growth data were 17.9, 24.6 and 11.5% with the mean d values of 0.88, 0.93 and 0.93 for the 2002 rainy, 2003 dry and 2004 dry seasons, respectively. It is concluded that the cultivar coefficients of peanut lines can be estimated from typical data that are collected in standard Performance trials using either GENCALC or similar methodologies. © 2008 Elsevier B.V. All rights reserved.
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Developing genetic for Crop simulation models with data from Crop Performance trials
Crop Science, 2001Co-Authors: T. Mavromatis, Kenneth J. Boote, James W. Jones, Ayse Irmak, D. Shinde, G. HoogenboomAbstract:Successful uses of Crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as soon as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CropGRO-Soybean model from typical information provided by Crop Performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coefficients and site-specific soil traits for use in Crop models, and (iii) to explore the extent to which our approach allowed the Crop model to reproduce observed genotype x environment (GE) interactions, cultivar ranking, and year-to-year yield variability. Crop Performance tests typically record harvest maturity date, seed yield, seed size, height, and lodging. A stepwise procedure using data on 11 cultivars grown at five sites in Georgia over 4 to 10 yr efficiently decreased the root mean square error (RMSE) between observed and predicted data. For 'Stonewall', a maturity group VII cultivar, the RMSE of 769 kg ha(-1) between the actual and modeled seed yield, estimated initially by means of the existing general maturity group coefficients, was reduced to 404 kg ha(-1). For the same cultivar, the initial RMSE of 5.3 and 9.3 d between the actual and simulated anthesis and harvest maturity dates, respectively, estimated by means of the existing general maturity group coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving useful information on site characteristics and cultivar traits, our approach has enabled CropGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype x environment interactions. Across all environments, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments.
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developing genetic coefficients for Crop simulation models with data from Crop Performance trials
Crop Science, 2001Co-Authors: T. Mavromatis, Kenneth J. Boote, James W. Jones, Ayse Irmak, D. Shinde, G. HoogenboomAbstract:Successful uses of Crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as soon as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CropGRO-Soybean model from typical information provided by Crop Performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coefficients and site-specific soil traits for use in Crop models, and (iii) to explore the extent to which our approach allowed the Crop model to reproduce observed genotype x environment (GE) interactions, cultivar ranking, and year-to-year yield variability. Crop Performance tests typically record harvest maturity date, seed yield, seed size, height, and lodging. A stepwise procedure using data on 11 cultivars grown at five sites in Georgia over 4 to 10 yr efficiently decreased the root mean square error (RMSE) between observed and predicted data. For Stonewall', a maturity group VII cultivar, the RMSE of 769 kg ha -1 between the actual and modeled seed yield, estimated initially by means of the existing general maturity group coefficients, was reduced to 404 kg ha -1 . For the same cultivar, the initial RMSE of 5.3 and 9.3 d between the actual and simulated anthesis and harvest maturity dates, respectively, estimated by means of the existing general maturity group coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving useful information on site characteristics and cultivar traits, our approach has enabled CropGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype x environment interactions. Across all environments, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments.