Estimating Soybean Model Genetic Coefficients From Privatesector Variety Performance Trial Data

Welch, S. M.; Wilkerson, Gail G.; Whiting, K.; Sun, Na; Vagts, Todd; Buol, Gregory S.; Mavromatis, T.

doi:10.13031/2013.9925

Cited by 10 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process is quite expensive, time consuming and requires regular sampling of growth, phenology and yield data for each variety following a set of minimum dataset rules [8]. Since the movement of models from research and policy to adoption by farmers and extension, the need for rapid estimation of GSPs for newly released varieties has become more urgent [14]. Several concerns have been raised even in locations where abundant and high-quality data for calibration of GSPs for model uses are available.…”

Section: 0 Introductionmentioning

confidence: 99%

“…The generalized likelihood uncertainty estimation (GLUE) method was used by He et al [19] to successfully estimate maize GSPs in North Carolina. Welch et al [14] used data from private-sector variety performance trials to develop soybean GSPs in the soybean belt of the United States of America. Buddhaboon et al [20] used GENCALC and GLUE to estimate GSPs of deep water rice using CERES-Rice model.…”

Section: 0 Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data were also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4-year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha -1 ). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.88–0.94 and coefficient of determination (d-index) between 0.93–0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.58–0.88) and d-index (0.56–0.86) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. It is concluded that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.

show abstract

Section: 0 Introductionmentioning

confidence: 99%

Section: 0 Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

Resolving the genetic basis of invasiveness and predicting invasions

et al. 2006

View full text Add to dashboard Cite

Considerable effort has been invested in determining traits underlying invasiveness. Yet, identifying a set of traits that commonly confers invasiveness in a range of species has proven elusive, and almost nothing is known about genetic loci affecting invasive success. Incorporating genetic model organisms into ecologically relevant studies is one promising avenue to begin dissecting the genetic underpinnings of invasiveness. Molecular biologists are rapidly characterizing genes mediating developmental responses to diverse environmental cues, i.e., genes for plasticity, as well as to environmental factors likely to impose strong selection on invading species, e.g., resistance to herbivores and competitors, coordination of life-history events with seasonal changes, and physiological tolerance of heat, drought, or cold. Here, we give an overview of molecular genetic tools increasingly used to characterize the genetic basis of adaptation and that may be used to begin identifying genetic mechanisms of invasiveness. Given the divergent traits that affect invasiveness, "invasiveness genes" common to many clades are unlikely, but the combination of developmental genetic advances with further evolutionary studies and modeling may provide a framework for identifying genes that account for invasiveness in related species.

show abstract

Merging genomic control networks and soil-plant-atmosphere-continuum models

Welch

Roe

Das

et al. 2005

Agricultural Systems

View full text Add to dashboard Cite

Estimating Soybean Model Genetic Coefficients From Privatesector Variety Performance Trial Data

Cited by 10 publications

References 0 publications

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

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

Resolving the genetic basis of invasiveness and predicting invasions

Merging genomic control networks and soil-plant-atmosphere-continuum models

Contact Info

Product

Resources

About

Estimating Soybean Model Genetic Coefficients From Privatesector Variety Performance Trial Data

Cited by 10 publications

References 0 publications

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

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

Resolving the genetic basis of invasiveness and predicting invasions

Merging genomic control networks and soil-plant-atmosphere-continuum models

Contact Info

Product

Resources

About

Estimating Soybean Model Genetic Coefficients From Privatesector Variety Performance Trial Data