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Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar–environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.
Ramirez-Villegas J, Watson J, Challinor AJ. Identifying traits for genotypic adaptation using crop models. J Exp Bot. 2015;66(12):3451–3462.
Pauli D, Chapman SC, Bart R, Topp CN, Lawrence-Dill CJ, Poland J, Gore MA. The quest for understanding phenotypic variation via integrated approaches in the field environment. Plant Physiol. 2016;172:662–634.
Tardieu F, Granato ISC, Van Oosterom EJ, Parent B, Hammer GL. Are crop and detailed physiological models equally ‘mechanistic’ for predicting the genetic variability of whole-plant behaviour? The nexus between mechanisms and adaptive strategies. In Silico Plants. 2020;2(1):diaa011.
White JW, Hoogenboom G, Kimball BA, Wall GW. Methodologies for simulating impacts of climate change on crop production. Field Crop Res. 2011;124(3):357–368.
Hammer G, Messina C, Wu A, Cooper M. Biological reality and parsimony in crop models—Why we need both in crop improvement! In Silico Plants. 2019;1(1):diz010.
Nagelmüller S, Kirchgessner N, Yates S, Hiltpold M, Walter A. Leaf length tracker: A novel approach to analyse leaf elongation close to the thermal limit of growth in the field. J Exp Bot. 2016;67(6):1897–1906.
Tschurr F, Kirchgessner N, Hund A, Kronenberg L, Anderegg J, Walter A, Roth L. Frost damage index: The antipode of growing degree days. Plant Phenomics. 2023;5:0104.
Steinberg RA, Garner WW. Response of certain plants to length of day and temperature under controlled conditions. J Agric Res. 1936;52(12):943–960.
Slafer GA. Differences in phasic development rate amongst wheat cultivars independent of responses to photoperiod and vernalization. A viewpoint of the intrinsic earliness hypothesis. J Agric Sci. 1996;126(4):403–419.
Bogard M, Ravel C, Paux E, Bordes J, Balfourier F, Chapman SC, Le Gouis J, Allard V. Predictions of heading date in bread wheat (Triticum Aestivum L.) using QTL-based parameters of an ecophysiological model. J Exp Bot. 2014;65(20):5849–5865.
Ochagavía H, Prieto P, Zikhali M, Griffiths S, Slafer GA. Earliness per se by temperature interaction on wheat development. Sci Rep. 2019;9(1):2584.
Bonhomme R. Bases and limits to using ‘degree.day’ units. Eur J Agron. 2000;13(1):1–10.
Parent B, Tardieu F. Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species. New Phytol. 2012;194:760–774.
Wang E, Martre P, Zhao Z, Ewert F, Maiorano A, Rötter RP, Kimball BA, Ottman MJ, Wall GW, White JW, et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat Plants. 2017;3:17102.
Kronenberg L, Yates S, Boer MP, Kirchgessner N, Walter A, Hund A. Temperature response of wheat affects final height and the timing of stem elongation under field conditions. J Exp Bot. 2021;72(2):700–717.
Grieder C, Hund A, Walter A. Image based phenotyping during winter: A powerful tool to assess wheat genetic variation in growth response to temperature. Funct Plant Biol. 2015;42(2):387–396.
Roth L, Piepho H-P, Hund A. Phenomics data processing: Extracting dose-response curve parameters from high-resolution temperature courses and repeated field-based wheat height measurements. In Silico Plants. 2022;4:1, diac007.
Friedli M, Kirchgessner N, Grieder C, Liebisch F, Mannale M, Walter A. Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions. Plant Methods. 2016;12:9.
Roth L, Fossati D, Krähenbühl P, Walter A, Hund A. Image-based phenomic prediction can provide valuable decision support in wheat breeding. Theor Appl Genet. 2023;136(7):162.
Wallach D, Hwang C, Correll MJ, Jones JW, Boote K, Hoogenboom G, Gezan S, Bhakta M, Vallejos CE. A dynamic model with QTL covariables for predicting flowering time of common bean (Phaseolus vulgaris) Geno types. Eur J Agron. 2018;101:200–209.
Viswanathan M, Scheidegger A, Streck T, Gayler S, Weber TKD. Bayesian multi-level calibration of a process-based maize phenology model. Ecol Modell. 2022;474:110154.
White JW, Markus H, Hunt LA, Payne TS, Hoogenboom G. Simulation-based analysis of effects of Vrn and Ppd loci on flowering in wheat. Crop Sci. 2008;48(2):678–687.
Messina CD, Technow F, Tang T, Totir R, Gho C, Cooper M. Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction (WGP). Eur J Agron. 2018;100:151–162.
Mielewczik M, Friedli M, Kirchgessner N, Walter A. Diel leaf growth of soybean: A novel method to analyze two-dimensional leaf expansion in high temporal resolution based on a marker tracking approach (Martrack leaf). Plant Methods. 2013;9:30.
Kirchgessner N, Liebisch F, Yu K, Pfeifer J, Friedli M, Hund A, Walter A. The ETH field phenotyping platform FIP: A cable-suspended multi- sensor system. Funct Plant Biol. 2016;44(1):154–168.
Millet EJ, Kruijer W, Coupel-Ledru A, Prado SA, Cabrera-Bosquet L, Lacube S, Charcosset A, Welcker C, van Eeuwijk F, Tardieu F. Genomic prediction of maize yield across European environmental conditions. Nat Genet. 2019;51(6):952–956.
Sadras VO, Reynolds MP, de la Vega AJ, Petrie PR, Robinson R. Phenotypic plasticity of yield and phenology in wheat, sunflower and grapevine. Field Crop Res. 2009;110:242–250.
Salazar-Gutierrez MR, Johnson J, Chaves-Cordoba B, Hoogenboom G. Relationship of base temperature to development of winter wheat. Int J Plant Prod. 2013;7(4):741–762.
McMaster GS, Wilhelm WW. Growing degree-days: One equation, two interpretations. Agric For Meteorol. 1997;87(4):291–300.
Pérez-Valencia DM, Rodríguez-Álvarez MX, Boer MP, Kronenberg L, Hund A, Bosquet LC, Millet EJ, van Eeuwijk FA. A two-stage approach for the Spatio-temporal analysis of high-throughput phenotyping data. Sci Rep. 2022;12:3177.
Roth L, Rodríguez-Álvarez MX, van Eeuwijk F, Piepho H-P, Hund A. Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points. Field Crop Res. 2021;274:108314.
Kronenberg L, Yu K, Walter A, Hund A. Monitoring the dynamics of wheat stem elongation: Genotypes differ at critical stages. Euphytica. 2017;213:157.
Roth L, Camenzind M, Aasen H, Kronenberg L, Barendregt C, Camp K-H, Walter A, Kirchgessner N, Hund A. Repeated multiview imaging for estimating seedling tiller counts of wheat genotypes using drones. Plant Phenomics. 2020;2020:3729715.
Roth L, Barendregt C, Bétrix C-A, Hund A, Walter A. High-throughput field phenotyping of soybean: Spotting an ideotype. Remote Sens Environ. 2022;269:112797.
Zenkl R, Timofte R, Kirchgessner N, Roth L, Hund A, Van Gool L, Walter A, Aasen H. Outdoor plant segmentation with deep learning for high-throughput field phenotyping on a diverse wheat dataset. Front Plant Sci. 2022;12:774068.
Fischler MA, Bolles RC. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Graph Image Process. 1981;24(6):381–395.
Pauli V, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, et al. SciPy 1.0: Fundamental algorithms for scientific computing in python. Nat Methods. 2020;17:261–272.
Roth L, Streit B. Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: An applied photogrammetric approach. Precis Agric. 2018;19:93–114.
Meier U. Growth Stages of Mono-and Dicotyledonous Plants: BBCH-Monograph. Germany: Open Agrar Repositorium; 2018.
Anderegg J, Yu K, Aasen H, Walter A, Liebisch F, Hund A. Spectral vegetation indices to track senescence dynamics in diverse wheat germplasm. Front Plant Sci. 2020;10:1749.
Baker CK, Gallagher JN. The development of winter wheat in the field 1. Relation between apical development and plant morphology within and between seasons. J Agric Sci. 1983;101(2):327–335.
Pinheiro JC, Bates DM. Mixed-effects models in S and S-PLUS. New York (NY): Springer-Verlag New York; 2000.
R Core Team. R: A language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing; 2019.
Durbán M, Harezlak J, Wand MP, Carroll RJ. Simple fitting of subject-specific curves for longitudinal data. Stat Med. 2005;24(8):1153–1167.
Piepho HP, Büchse A, Emrich K. A Hitchhiker’s guide to mixed models for randomized experiments. J Agron Crop Sci. 2003;189(5):310–322.
Tschurr F, Feigenwinter I, Fischer AM, Kotlarski S. Climate scenarios and agricultural indices: A case study for Switzerland. Atmosphere. 2020;11(5):535.
Porter JR, Gawith M. Temperatures and the growth and development of wheat a review. Eur J Agron. 1999;10:23–36.
Gallagher JN, Biscoe PV, Wallace JS. Field studies of cereal leaf growth: Ⅳ. Winter wheat leaf extension in relation to temperature and leaf water status. J Exp Bot. 1979;30(117):657–668.
Anderegg J, Aasen H, Perich G, Roth L, Walter A, Hund A. Temporal trends in canopy temperature and greenness are potential indicators of late-season drought avoidance and functional stay-green in wheat. Field Crop Res. 2021;274:108311.
Vicente-Serrano SM, Beguería S, López-Moreno JI. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J Clim. 2010;23(7):1696–1718.
Thronthwaite CW. An approach toward a rational classification of climate. Soil Sci. 1948;38(1):55–94.
Beguería S, Vicente-Serrano SM, Reig F, Latorre B. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int J Climatol. 2014;34(10):3001–3023.
Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28(5):1–26.
Shaykewich CF. An appraisal of cereal crop phenology modelling. Can J Plant Sci. 1995;75(2):329–341.
Wang E, Engel T. Simulation of phenological development of wheat crops. Agric Syst. 1998;58(1):1–24.
Parent B, Millet EJ, Tardieu F. The use of thermal time in plant studies has a sound theoretical basis provided that confounding effects are avoided. J Exp Bot. 2019;70(9):2359–2370.
Jamieson PD, Brooking IR, Porter JR, Wilson DR. Prediction of leaf appearance in wheat: A question of temperature. Field Crop Res. 1995;41(1):35–44.
Kronenberg L, Yates S, Ghiasi S, Roth L, Friedli M, Ruckle ME, Werner RA, Tschurr F, Binggeli M, Buchmann N, et al. Rethinking temperature effects on leaf growth, gene expression and metabolism: Diel variation matters. Plant Cell Environ. 2021;44(1):2262–2276.
Malosetti M, Visser RGF, Celis-Gamboa C, van Eeuwijk FA. QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato. Theor Appl Genet. 2006;113(2):288–300.
Benaouda S, Dadshani S, Koua P, Léon J, Ballvora A. Identification of QTLs for wheat heading time across multiple-environments. Theor Appl Genet. 2022;135(8):2833–2848.
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