An ecophysiological model analysis of yield differences within a set of contrasting cultivars and an F1 segregating population of potato (Solanum tuberosum L.) grown under diverse environments
Introduction
Potato (Solanum tuberosum L.) is one of the most important and widely cultivated non-cereal crops in the world (Walker et al., 1999, Hijmans, 2001). There is a need to increase potato yield via genetic improvement and/or altered crop management. In order to efficiently improve the target traits, analysing the phenotypic characteristics of genotypes under various environmental conditions is crucial (Asseng and Turner, 2007).
Most agronomic traits are genetically complex (Lark et al., 1995, Orf et al., 1999, Daniell and Dhingra, 2002, Stuber et al., 2003) and are strongly dependent on environmental changes (Allard and Bradshaw, 1964, Tardieu, 2003, Cooper et al., 2005). There is a need to dissect complex traits like yield into simpler characters (Yin et al., 2002). Ecophysiological crop growth models have the potential to assess a complex trait at a higher organizational level, via integrating the information about processes at the lower level. Their ability to incorporate knowledge of physiological traits to simulate crop growth and yield as influenced by growing environment and agronomic practices suggests the possibility of using models as a crop breeding tool (Aggarwal et al., 1997, Boote et al., 2001, Mavromatis et al., 2001, Hammer et al., 2002, Tardieu, 2003, Banterng et al., 2004, Hoogenboom et al., 2004, Yin et al., 2004, Letort et al., 2007).
One of the main applications of these models is to analyse the differences in yield potential of genotypes between or within a breeding population on the basis of individual physiological parameters. These parameters could be considered as quantitative traits and are amenable to further analysis (Yin et al., 2004, Quilot et al., 2005), e.g. for evaluating and designing ideotypes (Loomis et al., 1979, Yin et al., 2003a, Yin et al., 2003b, Yin et al., 2003c, Yin et al., 2004, Cilas et al., 2006). This is possible because these parameters, often regarded as ‘genetic coefficients’, are specific to each genotype and supposed to be constant under a wide range of environmental conditions (Boote et al., 2001, Tardieu, 2003, Bannayan et al., 2007). This model feature makes it possible to make predictions about the plant processes of a genotype in a wide range of environments (Hoogenboom et al., 1997). Such models can quantify crop genotype–phenotype relationships (Chapman et al., 2002, Banterng et al., 2004, Yin et al., 2004, Suriharn et al., 2007) and could assist with multi-location evaluation of crop breeding lines (Liu et al., 1989, Piper et al., 1998, Banterng et al., 2004, Mayes et al., 2005). For potato, Kooman and Spitters (1995) showed that simulation models can be useful for predicting tuber yield and gaining insight into crop growth processes and can help to explore options for crop improvement.
However, traditionally, crop models have principally been used to study and predict crop performance in response to environmental conditions and management practices, whereas genotypic impacts on crop performance (especially in the context of plant breeding where large numbers of genotypes are involved) have received less attention. This is partly due to the constraints imposed by time, resources, and large number of genotypes that makes it difficult to measure detailed growth dynamics to fully derive the genotype-specific model coefficients (Anothai et al., 2008), and partly due to the restricted capabilities of models to represent genetic differences (White and Hoogenboom, 1996, Hoogenboom et al., 1997).
To be useful, the physiological frameworks used for trait dissection and modelling at whole-crop level must capture the functional basis of the genetic variation for complex traits of interest (Yin et al., 2000). Most existing crop models, which were constructed to deal mostly with agronomic issues, are not well structured in this regard for instance for capture and use of nitrogen (N) (Jeuffroy et al., 2002) and for carbon (C) and N partitioning (Dingkuhn, 1996). They also lack the ability to describe subtle complexities associated with the differences between genotypes (White and Hoogenboom, 1996, Yin et al., 2004). In order to specify the areas of improvement for such applications, current crop models need to be confronted to analyse yield differences within a real genetic or breeding population (Yin et al., 2000). However, such studies are few and have not been reported for potato.
In this study, we use a recent ecophysiological crop growth model ‘GECROS’ to analyse yield differences among 100 genotypes from an F1 segregating population, their parents and a set of standard cultivars of potato. We then attempt to identify the relative importance of individual physiological traits in determining yield differences. These analyses could assist to identify further research needs in using the model-based approach to designing strategies for potato ideotype breeding for specific environments.
Section snippets
The GECROS model
The model GECROS is a generic ecophysiological model that predicts crop growth and development as affected by genetic characteristics and climatic and edaphic environmental variables (Yin and Van Laar, 2005). Here, only the key processes modelled in GECROS (version 2.0 as used by Yin and Struik, 2010) are summarised.
Coupled modelling of CO2 diffusional (stomatal and mesophyll) conductance, leaf photosynthesis and transpiration in dependence of leaf nitrogen content is implemented according to
Model parameters
There were strong differences in values of genotype-specific model parameters among the standard cultivars (Table 2). As expected, values of most parameters except nSO were higher for late-maturing cultivars (Astarte and Karnico) than for mid-late (Seresta) and (mid)-early (Bintje and Première) cultivars. In contrast, values of nSO were higher for early-maturing cultivars like Première followed by mid-late and late cultivars (Table 2). It has been found in previous research that N uptake and
Conclusions
Yield variation in terms of growth and development of the crop is complex, for it involves the effect of external factors on all the physiological processes, the interrelationships between different processes and their dependence on the genetic constituent of the plant. In this study we used the ecophysiological model ‘GECROS’ to analyse differences in tuber yield in a set of cultivars of maturity types and a diploid F1 segregating population. The model gave insights into the underlying
Acknowledgements
The authors gratefully acknowledge funding from the European Community financial participation under the Seventh Framework Programme for Research, Technological Development and Demonstration Activities, for the Integrated Project NUE-CROPS FP7-CP-IP 222645. The views expressed in this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is
References (66)
- et al.
Reduction in data collection for determination of cultivar coefficients for breeding application
Agric. Syst.
(2008) - et al.
Gene-based modelling for rice: an opportunity to enhance the simulation of rice growth and development?
J. Theor. Biol.
(2007) - et al.
Determination of genetic coefficients of pea-nut lines for breeding applications
Eur. J. Agron.
(2004) - et al.
Physiology and modelling of traits in crop plants: implications for genetic improvement
Agric. Syst.
(2001) - et al.
Modelling the components of plant respiration: some guiding principles
Ann. Bot.
(2000) - et al.
Multigene engineering: dawn of an exciting new era in biotechnology
Curr. Opin. Biotechnol.
(2002) Modelling concepts for the phenotypic plasticity of dry matter and nitrogen partitioning in rice
Agric. Syst.
(1996)- et al.
Future contributions of crop modeling – from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement
Eur. J. Agron.
(2002) - et al.
From genome to crop: integration through simulation modeling
Field Crop Res.
(2004) - et al.
Simulation modeling of soil and plant nitrogen use in a potato cropping system in the humid and cool environment
Agric. Ecosyst. Environ.
(2006)
Application of CERES-maize to yield prediction of a Brazilian maize hybrid
Agric. Forest Meteorol.
Modelling biomass production and yield of horticultural crops: a review
Sci. Hortic.
Virtual plants: modelling as a tool for the genomics of tolerance to water deficit
Trends Plant Sci.
C3 and C4 photosynthesis models: an overview from the perspective of crop modelling
NJAS-Wagenin. J. Life Sci.
A nonlinear model for crop development as a function of temperature
Agric. Forest Meteorol.
Role of crop physiology in predicting gene-to-phenotype relationships
Trends Plant Sci.
Simulating genotypic strategies for increasing rice yield potential in irrigated, tropical environments
Field Crop Res.
Implications of genotype–environmental interactions in applied plant breeding
Crop Sci.
Modelling genotype × environment × management interactions to improve yield, water use efficiency and grain protein in wheat
Effects of nitrogen on the development and growth of the potato plant. 2. The partitioning of dry matter, nitrogen and nitrate
Ann. Bot.
Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments
Aust. J. Agric. Res.
Shoot and root activities during steady-state plant growth
Ann. Bot.
Definition of architectural ideotypes for good yield capacity in Coffea canephora
Ann. Bot.
Gene-to-phenotype models and complex trait genetics
Aust. J. Agric. Res.
Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models
Plant Cell Environ.
Nitrogen turnover in the soil-crop system: comparison of fourteen simulation models
Fert. Res.
Use of crop growth models to evaluate physiological traits in genotypes of horticultural crops
Global distribution of the potato crop
Am. J. Potato Res.
Optimization of plant root:shoot ratios and internal nitrogen concentration
Ann. Bot.
Evaluation of a crop simulation model that incorporates gene action
Agron. J.
Integrated physiological and agronomic modelling of N capture and use within the plant
J. Exp. Bot.
Petiole NO3-N sufficiency curves in newly developed potato cultivars
Proc. Univ. Idaho Winter Commodity Schools
Assessing genetic variation in growth and development of potato. PhD Thesis
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2015, Field Crops ResearchCitation Excerpt :In this contribution, the potential yield of potato is defined as the theoretical yield that can be calculated or modelled for a certain cultivar grown in a certain environment without any limiting or reducing factor being present. The general approach is described in great detail by Spitters (1990), Spitters and Schapendonk (1990), Kooman et al. (1996a,b), Haverkort and Kooman (1997), Caldiz et al. (2002), and Khan et al. (2014), for example by using the simple, robust LINTUL-POTATO model (Kooman and Haverkort, 1994) or by the more complicated, but also more versatile model GECROS (Khan et al., 2014). The cultivar in this definition determines the potential duration of the crop cycle, moderated by the environment (Khan et al., 2013).
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2023, Revista Colombiana de Ciencias Horticolas
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Present address: Horticulture Section, Agricultural Research Institute, Dera Ismail Khan, Khyber Pakhtunkhwa, Pakistan.