https://doi.org/10.4081/ija.2022.1998

Decomposing complex traits through crop modelling to support cultivar recommendation. A proof of concept with a focus on phenology and field pea

Vol. 17 No. 1 (2022)
Submitted: 26 October 2021
Accepted: 7 January 2022
Published: 18 January 2022
WOFOST-GT2, Pisum sativum L., E-FAST, sensitivity analysis, ideotype, cultivar recommendation.
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Cultivar recommendation is crucial for achieving high and stable yields, and crop models can successfully support it because of their capability of exploring genotype   environment   management interactions. Different modelling approaches have been developed to this end, primarily relying on dedicated field trials to characterize the germplasm of interest. Here, we show how even data routinely collected in operational contexts can be used for model-based cultivar recommendation, with a case study on phenological traits and field pea (Pisum sativum L.). Eight hundred and four datasets, including days from sowing to plant emergence, first flower, and maturity, were collected in Northern Italy from 2017 to 2020, and they were used to optimise six parameters (base, optimum, and maximum temperature for development, growing degree days to reach emergence, flowering, and maturity) of the crop model WOFOST-GT2 for 13 cultivars. This allowed obtaining the phenotypic profiles for these cultivars at the level of the functional trait, without the need of carrying out dedicated phenotypisations. Sensitivity analysis (SA) techniques (E-FAST) and the statistical distributions of the optimised parameters were used to design pea ideotypes able to maximise yields and yield stability in 24 agro-climatic contexts (three soil conditions   two sowing times   four agro-climatic classes). For each context, the 13 cultivars were ranked according to their similarity to the ideotype based on the weighted Euclidean distance. Results of SA identified growing degree days to reach flowering as the trait mainly affecting crop productivity, although cardinal temperatures also played a role, especially in the case of early sowings. This is reflected in the ideotypes and, therefore, in cultivar ranking, leading to recommend a panel of cultivars characterised by low base temperature and high thermal requirements to reach flowering. Despite the limits of the study, which is focused only on phenological traits, it represents an extension of available approaches for model-aided cultivar recommendation, given that the methodology we propose can take full advantage of the potentialities of crop models without requiring dedicated experiments aimed at profiling the germplasm of interest at the level of functional traits.

Highlights
- Crop models are powerful tools to support cultivar choice by exploring genotype x environment x management interactions.
- Crop models require cultivar-specific phenotyping data at the level of functional traits.
- We propose a methodology that uses data routinely collected in operational contexts to derive functional trait values.
- This study is a proof of concept of how to increase the applicability of model-based approaches for cultivar choice.

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Google Scholar
Europe PMC
Acutis M, Confalonieri R, 2006. Optimization algorithms for calibrating cropping systems simulation models. A case study with simplex-derived methods integrated in the WARM simulation environment. Ital. J. Agrometeorol. 3:26-34.
Al Majou H, Bruand A, Duval O, Le Bas C, Vautier A, 2008. Prediction of soil water retention properties after stratification by combining texture, bulk density and the type of horizon. Soil Use Manag. 24:383-91. DOI: https://doi.org/10.1111/j.1475-2743.2008.00180.x
Allen RG, Pereira LS, Raes D, Smith M, Ab W, 1998. Crop evapotranspiration - Guidelines for computing crop water requirements. Irrigation and drainage paper 56. Food and Agriculture Organization, Rome, Italy.
Annicchiarico P, 2002. Genotype x Environment Interactions - Challenges and Opportunities for Plant Breeding and Cultivar Recommendations, FAO plant production and protection paper - 174. Food and Agriculture Organization, Rome, Italy.
Bellocchi G, Acutis M, Fila G, Donatelli M, 2002. An indicator of solar radiation model performance based on a fuzzy expert system. Agron. J. 94:1222-33. DOI: https://doi.org/10.2134/agronj2002.1222
Boote KJ, Prasad V, Allen LH, Singh P, Jones JW, 2018. Modeling sensitivity of grain yield to elevated temperature in the DSSAT crop models for peanut, soybean, dry bean, chickpea, sorghum, and millet. Eur. J. Agron. 100:99-109. DOI: https://doi.org/10.1016/j.eja.2017.09.002
Bourgeois G, Jenni S, Laurence H, Roy G, Tremblay N, 2000. Improving the prediction of processing pea maturity based on the growing-degree day approach. Hortic. Sci. 35:611-4. DOI: https://doi.org/10.21273/HORTSCI.35.4.611
Carvalho CGP, Cruz CD, Viana JMS, Silva DJH, 2002. Selection based on distances from ideotype. Crop. Breed. Appl. Biotechnol. 2:171-8. DOI: https://doi.org/10.12702/1984-7033.v02n02a02
Casadebaig P, Mestries E, Debaeke P, 2016. A model-based approach to assist variety evaluation in sunflower crop. Eur. J. Agron. 81:92-105. DOI: https://doi.org/10.1016/j.eja.2016.09.001
Chung SW, Gasman PW, Kramer LA, Williams JR, Gu R, 1999. Validation of EPIC for two watersheds in Southwest Iowa. J. Environ. Qual. 28:971-9. DOI: https://doi.org/10.2134/jeq1999.00472425002800030030x
Cola G, Mariani L, Maghradze D, Failla O, 2020. Changes in thermal resources and limitations for Georgian viticulture. Aust. J. Grape Wine Res. 26:29-40. DOI: https://doi.org/10.1111/ajgw.12412
Confalonieri R, Bellocchi G, Bregaglio S, Donatelli M, Acutis M, 2010a. Comparison of sensitivity analysis techniques: A case study with the rice model WARM. Ecol. Model. 221:1897-906. DOI: https://doi.org/10.1016/j.ecolmodel.2010.04.021
Confalonieri R, Bregaglio S, Acutis M, 2010b. A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions. Ecol. Model. 221:960-4. DOI: https://doi.org/10.1016/j.ecolmodel.2009.12.003
Coucheney E, Buis S, Launay M, Constantin J, Mary B, García de Cortázar-Atauri I, Ripoche D, Beaudoin N, Ruget F, Andrianarisoa KS, Le Bas C, Justes E, Léonard J, 2015. Accuracy, robustness and behavior of the STICS soil-crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Environ. Model. Softw. 64:177-90. DOI: https://doi.org/10.1016/j.envsoft.2014.11.024
Criss RE, Winston WE, 2008. Do Nash values have value? Discussion and alternate proposals. Hydrol. Process. 22:2723-5. DOI: https://doi.org/10.1002/hyp.7072
Guilioni L, Wéry J, Lecoeur J, 2003. High temperature and water deficit may reduce seed number in field pea purely by decreasing plant growth rate. Funct. Plant Biol. 30:1151-64. DOI: https://doi.org/10.1071/FP03105
Hammer GL, Chapman S, Van Oosterom E, Podlich DW, 2005. Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Aust. J. Agric. Res. 56:947-60. DOI: https://doi.org/10.1071/AR05157
Hammer GL, Kropff MJ, Sinclair TR, Porter JR, 2002. Future contributions of crop modelling - From heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. Eur. J. Agron. 18:15-31. DOI: https://doi.org/10.1016/S1161-0301(02)00093-X
Jeuffroy MH, Casadebaig P, Debaeke P, Loyce C, Meynard JM, 2014. Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review. Agron. Sustain. Dev. 34:121-37. DOI: https://doi.org/10.1007/s13593-013-0170-9
Jeuffroy MH, Vocanson A, Roger-Estrade J, Meynard JM, 2012. The use of models at field and farm levels for the ex ante assessment of new pea genotypes. Eur. J. Agron. 42:68-78. DOI: https://doi.org/10.1016/j.eja.2012.04.005
Jørgensen SE, Kamp-Nielsen L, Christensen T, Windolf-Nielsen J, Westergaard B, 1986. Validation of a prognosis based upon a eutrophication model. Scope and Limit in the Application of Ecological Models to Environmental Management. Ecol. Model. 32:165-82. DOI: https://doi.org/10.1016/0304-3800(86)90024-4
Lecomte C, Prost L, Cerf M, Meynard JM, 2010. Basis for designing a tool to evaluate new cultivars. Agron. Sustain. Dev. 30:667-77. DOI: https://doi.org/10.1051/agro/2009042
Luquet D, Dingkuhn M, Kim H, Tambour L, Clement-Vidal A, 2006. EcoMeristem, a model of morphogenesis and competition among sinks in rice. 1. Concept, validation and sensitivity analysis. Funct. Plant Biol. 33:309-23. DOI: https://doi.org/10.1071/FP05266
Mariani L, Cola G, Ferrante A, Martinetti L, Bulgari R, 2016. Space and time variability of heating requirements for greenhouse tomato production in the Euro-Mediterranean area. Sci. Total Environ. 562:834-44. DOI: https://doi.org/10.1016/j.scitotenv.2016.04.057
Mariani L, Parisi SG, Cola G, Failla O, 2012. Climate change in Europe and effects on thermal resources for crops. Int. J. Biometeorol. 56:1123-34. DOI: https://doi.org/10.1007/s00484-012-0528-8
Martre P, Quilot-Turion B, Luquet D, Memmah MMOS, Chenu K, Debaeke P, 2015. Model-assisted phenotyping and ideotype design. Crop Physiol. Appl. Genet. Improv. Agron. Second Ed. 349-73. DOI: https://doi.org/10.1016/B978-0-12-417104-6.00014-5
Meier U, 2001. Growth stages of mono- and dicotyledonous plants, Second. Ed. BBCH Monograph. Federal Biological Research Centre for Agriculture and Forestry.
Messina CD, Technow F, Tang T, Gho C, Cooper M, 2018. Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction (WGP). Eur. J. Agron. 100:151-62. DOI: https://doi.org/10.1016/j.eja.2018.01.007
Moriasi D, Arnold J, Van Liew M, Bingner R, Harmel R, Veith T, 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50:885-900. DOI: https://doi.org/10.13031/2013.23153
Nash JE, Sutcliffe JV, 1970. River flow forecasting through conceptual models part I - A discussion of principles. J. Hydrol. 10:282-90. DOI: https://doi.org/10.1016/0022-1694(70)90255-6
NOAA, 2020a. National Oceanic and Atmospheric Administration. Available from: https://data.noaa.gov/dataset/dataset/global-surface-summary-of-the-day-gsod/
NOAA, 2020b. National Oceanic and Atmospheric Administration. Available from: https://tgftp.nws.noaa.gov/data/observations/metar/stations/ Accessed: 9 September 2021.
NOAA, 2020c. National Oceanic and Atmospheric Administration. Available from: https://tgftp.nws.noaa.gov/SL.us008001/DF.an/DC.sflnd/DS.synop/ Accessed: 9 September 2021.
Olivier FC, Annandale JG, 1998. Thermal time requirements for the development of green pea (Pisum sativum L.). Field. Crop. Res. 56:301-7. DOI: https://doi.org/10.1016/S0378-4290(97)00097-X
Onogi A, Watanabe M, Mochizuki T, Hayashi T, Nakagawa H, Hasegawa T, Iwata, H, 2016. Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates. Theor. Appl. Genet. 129:805-17. DOI: https://doi.org/10.1007/s00122-016-2667-5
Paleari L, Vesely FM, Ravasi RA, Movedi E, Tartarini S, Invernizzi M, Confalonieri R, 2020. Analysis of the similarity between in silico ideotypes and phenotypic profiles to support cultivar recommendation - a case study on Phaseolus vulgaris L. Agronomy 10:1733. DOI: https://doi.org/10.3390/agronomy10111733
Ravasi RA, Paleari L, Vesely FM, Movedi E, Thoelke W, Confalonieri R, 2020. Ideotype definition to adapt legumes to climate change: A case study for field pea in Northern Italy. Agric. For. Meteorol. 291:10881. DOI: https://doi.org/10.1016/j.agrformet.2020.108081
Raveneau MP, Coste F, Moreau-Valancogne P, Lejeune-Hénaut I, Durr C, 2011. Pea and bean germination and seedling responses to temperature and water potential. Seed Sci. Res. 21:205-13. DOI: https://doi.org/10.1017/S0960258511000067
Shapiro SS, Wilk MB, 1965. An analysis of variance test for normality (complete samples). Biometrika 52:591. DOI: https://doi.org/10.2307/2333709
Stella T, Frasso N, Negrini G, Bregaglio S, Cappelli G, Acutis M, Confalonieri R, 2014. Model simplification and development via reuse, sensitivity analysis and composition: A case study in crop modelling. Environ. Model. Softw. 59:44-58. DOI: https://doi.org/10.1016/j.envsoft.2014.05.007
Tarantola, S, Becker, W, 2016. SIMLAB software for uncertainty and sensitivity analysis. In: Ghanem, R, Higdon, D, Owhadi, H (Eds.), Handbook of uncertainty quantification. Springer International Publishing, Cham, pp. 1-21. DOI: https://doi.org/10.1007/978-3-319-11259-6_61-1
USGS, 2020. USGS document. Available from: https://webgis.wr.usgs.gov/globalgis/gtopo30/gtopo30.htm Accessed: 9 September 2021.
van Keulen H, Wolf J, 1986. Modelling of agricultural production: weather, soils and crops. In: Simulation Monographs. Pudoc, Wageningen, The Netherlands, pp. 479.
Violle C, Navas ML, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E, 2007. Let the concept of trait be functional! Oikos. 116:882-92. DOI: https://doi.org/10.1111/j.0030-1299.2007.15559.x
Vocanson A, Jeuffroy MH, 2008. Agronomic performance of different pea cultivars under various sowing periods and contrasting soil structures. Agron. J. 100:748-59. DOI: https://doi.org/10.2134/agronj2005.0301

How to Cite

Paleari, L., Movedi, E., Vesely, F. M., Tettamanti, M., Piva, D., & Confalonieri, R. (2022). Decomposing complex traits through crop modelling to support cultivar recommendation. A proof of concept with a focus on phenology and field pea. Italian Journal of Agronomy, 17(1). https://doi.org/10.4081/ija.2022.1998
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