Elsevier

Ecological Modelling

Volume 290, 24 October 2014, Pages 146-154
Ecological Modelling

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

https://doi.org/10.1016/j.ecolmodel.2013.11.015Get rights and content

Highlights

  • We used the GECROS model to analyse yield differences among potato cultivars and in an F1 population.

  • We have demonstrated, for the first time, the model's performance in a tuber crop.

  • We identified total N uptake and tuber N concentration as key components for yield.

  • Further research needs were identified to enable the GECROS model being useful for the development of potato ideotypes.

Abstract

The generic ecophysiological model ‘GECROS’ simulates crop growth and development as affected by genetic characteristics and climatic and edaphic environmental variables. We used this model to analyse differences in tuber yield of potato in five cultivars covering a wide range of maturity types and 100 individuals of a diploid F1 population segregating for maturity type. Six field experiments were conducted, in which contrasting nitrogen availabilities were created to represent six environments. Values of five genotype-specific model-input parameters were estimated and calibrated. Variation among the 100 F1 genotypes was as wide as, or slightly wider than, that among the five contrasting cultivars for any of the five parameters values but not for tuber yield. For the 100 F1 genotypes, the model accounted for 86% of genotypic differences in across-environment average tuber yield and 89% of environmental difference in across-genotype average yield. But the percentage in the genotypic differences in yield for a given experiment accounted for by the model ranged from 2% to 65%. Model analysis identified Nmax (i.e. total crop N uptake) and tuber N concentration as key components affecting tuber yield for all six experiments. Genotypes with higher Nmax and lower tuber N concentration exhibited higher tuber dry matter yield. The development of potato ideotypes for any specific environments should prioritize optimising N-related traits.

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)

  • W.T.H. Liu et al.

    Application of CERES-maize to yield prediction of a Brazilian maize hybrid

    Agric. Forest Meteorol.

    (1989)
  • L-F.M. Marcelis et al.

    Modelling biomass production and yield of horticultural crops: a review

    Sci. Hortic.

    (1998)
  • F. Tardieu

    Virtual plants: modelling as a tool for the genomics of tolerance to water deficit

    Trends Plant Sci.

    (2003)
  • X. Yin et al.

    C3 and C4 photosynthesis models: an overview from the perspective of crop modelling

    NJAS-Wagenin. J. Life Sci.

    (2009)
  • X. Yin et al.

    A nonlinear model for crop development as a function of temperature

    Agric. Forest Meteorol.

    (1995)
  • X. Yin et al.

    Role of crop physiology in predicting gene-to-phenotype relationships

    Trends Plant Sci.

    (2004)
  • P.K. Aggarwal et al.

    Simulating genotypic strategies for increasing rice yield potential in irrigated, tropical environments

    Field Crop Res.

    (1997)
  • R.W. Allard et al.

    Implications of genotype–environmental interactions in applied plant breeding

    Crop Sci.

    (1964)
  • S. Asseng et al.

    Modelling genotype × environment × management interactions to improve yield, water use efficiency and grain protein in wheat

  • H. Biemond et al.

    Effects of nitrogen on the development and growth of the potato plant. 2. The partitioning of dry matter, nitrogen and nitrate

    Ann. Bot.

    (1992)
  • S.C. Chapman et al.

    Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments

    Aust. J. Agric. Res.

    (2002)
  • D.A. Charles-Edwards

    Shoot and root activities during steady-state plant growth

    Ann. Bot.

    (1976)
  • C. Cilas et al.

    Definition of architectural ideotypes for good yield capacity in Coffea canephora

    Ann. Bot.

    (2006)
  • M. Cooper et al.

    Gene-to-phenotype models and complex trait genetics

    Aust. J. Agric. Res.

    (2005)
  • D.G.G. De Pury et al.

    Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models

    Plant Cell Environ.

    (1997)
  • P. De Willigen

    Nitrogen turnover in the soil-crop system: comparison of fourteen simulation models

    Fert. Res.

    (1991)
  • E. Heuvelink et al.

    Use of crop growth models to evaluate physiological traits in genotypes of horticultural crops

  • R.J. Hijmans

    Global distribution of the potato crop

    Am. J. Potato Res.

    (2001)
  • D.W. Hilbert

    Optimization of plant root:shoot ratios and internal nitrogen concentration

    Ann. Bot.

    (1990)
  • G. Hoogenboom et al.

    Evaluation of a crop simulation model that incorporates gene action

    Agron. J.

    (1997)
  • M.H. Jeuffroy et al.

    Integrated physiological and agronomic modelling of N capture and use within the plant

    J. Exp. Bot.

    (2002)
  • C.L. Johnson et al.

    Petiole NO3-N sufficiency curves in newly developed potato cultivars

    Proc. Univ. Idaho Winter Commodity Schools

    (1995)
  • M.S. Khan

    Assessing genetic variation in growth and development of potato. PhD Thesis

    (2012)
  • Cited by (16)

    • Potassium fertilization as a driver of sustainable management of nitrogen in potato (Solanum tuberosum L.)

      2020, Field Crops Research
      Citation Excerpt :

      Presumably, the optimum Kc level is a guarantee of sufficient tuber sink strength (White et al., 2009). A Kc range of 11-13 g kg-1 as reported in numerous studies conducted both in temperate, dry, and in tropical regions, is the lowest level, limiting the yield of tubers (Blecharczyk et al., 2008; Khan et al., 2014). This study clearly showed that Kc on average was 20 ± 2.1 g kg-1 DM.

    • A model-based approach to analyse genetic variation in potato using standard cultivars and a segregating population. II. Tuber bulking and resource use efficiency

      2019, Field Crops Research
      Citation Excerpt :

      The calculated values of NUET (Tables 1–6; Table S1; Figs. 4–5) were higher and less variable for very late cultivars than for very early cultivars, showed a very large variation among F1 genotypes and significantly differed among environments. A model analysis by Khan et al. (2014) indicated that genotypes with higher N uptake and lower tuber N concentration yielded more. These effects could be mainly associated with differences in maturity type (Van Kempen et al., 1996; Zebarth et al., 2004; Ospina et al., 2014).

    • The combined effect of elevation and meteorology on potato crop dynamics: A 10-year study in the Gamo Highlands, Ethiopia

      2018, Agricultural and Forest Meteorology
      Citation Excerpt :

      The GECROS model was designed to study the responses of biomass and dry matter production in arable crops to both environmental and genotypic characteristics (Khan, 2012; Yin and van Laar, 2005). The model has been tested and widely used to simulate crop growth (Combe et al., 2015; Gu et al., 2014; Yin and Struik, 2010) and potato in particular (Khan et al., 2014). Since the representation of evaporation is crucial here, we follow the improvements suggested by Combe et al. (2015) to obtain more reliable surface energy budget estimates.

    • Yield levels of potato crops: Recent achievements and future prospects

      2015, Field Crops Research
      Citation 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).

    View all citing articles on Scopus
    1

    Present address: Horticulture Section, Agricultural Research Institute, Dera Ismail Khan, Khyber Pakhtunkhwa, Pakistan.

    View full text