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United States Department of Agriculture

Agricultural Research Service

Research Project: OBJECT MODELING AND SCALING OF LANDSCAPE PROCESSES AND CONSERVATION EFFECTS IN AGRICULTURAL SYSTEMS

Location: Agricultural Systems Research Unit

Title: Quantifying Phenotypic Plasticity Using Genetic Information for Simulating Plant Height in Winter Wheat

Authors
item Weiss, A - UNIVERSITY OF NEBRASKA
item Baenziger, P - UNIVERSITY OF NEBRASKA
item McMaster, Gregory
item Wilhelm, Wallace
item Al-Ajlouni, Z - UNIVERSITY OF NEBRASKA

Submitted to: Wageningen Journal of Life Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 30, 2009
Publication Date: December 9, 2009
Citation: Weiss, A., Baenziger, P.S., Mcmaster, G.S., Wilhelm, W.W., Al-Ajlouni, Z.I. 2009. Quantifying Phenotypic Plasticity Using Genetic Information for Simulating Plant Height in Winter Wheat. Wageningen Journal of Life Sciences. 57:59-64. Available: http://dx.doi.org/10.1016/j.njas.2009.10.001.

Interpretive Summary: One challenge facing crop simulation modeling is how to incorporate existing and rapidly emerging genomic information into models to develop new and improved algorithms. The problem is further exacerbated in that gene expression is likely influenced by environmental conditions, particularly water deficits in semi-arid production regions. As a case study in learning how to better address this challenge, the objective of this effort was to simulate plant height in winter wheat across a range of environments in Nebraska using genetic information. Although plant height is a complex trait, it is far simpler than many traits, and the major genes influencing plant height are relatively well understood. We used two data sets from Nebraska in this study. We used an existing crop simulation model, CropSyst, and modified it by incorporating a different phenology algorithm and adding a plant height algorithm that we developed. The plant height algorithm was based on the ratio of the mean optimum final height of a height class (tall semidwarf, semidwarf, short semidwarf) to the mean optimum final heights for two tall cultivars, an environmental index based on the fraction of transpirable water, and the development stage. Final plant heights were simulated very well for the different height classes with a root mean square error of 3.4 cm. Although we could predict final plant height very well, simulated plant height during the growing season was predicted consistently too tall when compared to the observed heights. This problem was related to the phenology algorithm, which simulated anthesis too early because of the unusual weather conditions of the season (e.g., above average precipitation and below normal temperatures and solar radiation). While our main goal of predicting final plant height was met, the newly developed plant height algorithm was partially constrained by the phenology algorithm for accurately predicting within-season plant height due to some unique input requirements of the crop simulation model used in this effort. This conclusion would be true for any model, just that the constraints would change with the model. Development of robust crop simulation models incorporating genetic information can also be used to address the inverse problem we examined, namely how does one go from a model to a gene, as well as problems associated with how different cultivars respond to different environments.

Technical Abstract: A challenge for crop simulation modeling is to incorporate existing and rapidly emerging genomic information into models to develop new and improved algorithms. The objective of this effort was to simulate plant height in winter wheat (Triticum aestivum L.) across a range of environments in Nebraska using genetic information. Plant height is influenced by major genes that are discrete and well characterized in terms of physiological mechanisms and effects on phenotypes. Although plant height is a complex trait, it is far simpler than many traits. Two data sets were used in this study, one from a PhD dissertation where final plant heights were measured at five locations across Nebraska over two years (data set 1) and another study where plant height was measured 13 times before anthesis in one year at one location for one genotype (data set 2). The crop simulation model CropSyst was modified by incorporating a different phenology algorithm and adding a plant height algorithm. The plant height algorithm was based on the ratio of the mean optimum final height of a height class (tall semidwarf, semidwarf, short semidwarf) to the mean optimum final heights for two tall cultivars, an environmental index based on the fraction of transpirable water, and the development stage. Final plant heights were simulated reasonably well for the different height classes with a root mean square error of 3.4 cm (data set 1). For data set 2, although the final plant height was well predicted and there was a very good correlation between observed and simulated intermediate heights (r2 = 0.99), the absolute height values did not agree well. This problem was related to the phenology algorithm, which simulated anthesis too early; specifically the use of the optimum development rate in this algorithm. This algorithm also did not respond well to the unusual weather conditions of this season, where above average precipitation occurred accompanied by below normal temperatures and solar radiation. While the initial goal was generally successful, the plant height algorithm was partially constrained by the phenology algorithm and some unique input requirements of the crop simulation model used in this effort. This conclusion would likely apply to other models, although the constraints would change with the model. Development of robust crop simulation models incorporating genetic information can be used to address the inverse problem, how does one go from a model to a gene?, as well as how to interpret interactions of genotypes with environments (G x E).

Last Modified: 7/30/2014
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