[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125514-en":3,"doc-seo-125514-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125514,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Climate Change Effects on a Diverse Set of Winter Wheat Evaluated Traditionally and With Machine Learning","Climate change is increasing the frequency and intensity of drought, which limits wheat productivity from meeting growing global food demand. A study assessed 77 winter-wheat lines using image-based early vigor traits and 15 mature traits, applying early and late drought 12 and 65 days after vernalization. Machine-learning-assisted phenotyping measured spike area and linked image traits to yield, revealing key drought drivers such as increased sterility (empty spikes) and identifying genotypes with superior stability and performance.","Food and Energy Security  \nORIGINAL ARTICLE  OPEN ACCESS   \nClimate Change Effects on a Diverse Set of Winter Wheat Evaluated Traditionally and With Machine Learning  \nYuzhou Lan  | Aakash Chawade | Ramune Kuktaite | Eva Johansson   \nDepartment of Plant Breeding, The Swedish University of Agricultural Sciences, Lomma, Sweden Correspondence: Eva Johansson ([eva.johansson@slu.se](eva.johansson@slu.se))  \nReceived: 28 May 2025 | Revised: 4 July 2025 | Accepted: 16 July 2025  \nFunding: This work was supported by Trees and Crops for the Future (TC4F) and SLU Grogrund. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  \nKeywords: drought | early vigor | image-based phenotyping | machine learning model | winter wheat  \nABSTRACT  \nClimate change is increasing the frequency and intensity of drought, which hampers wheat productivity from meeting the growing food demand worldwide. Therefore, improvements in yield under drought are urgently needed. This work evaluated a diverse set of 77 winter-wheat lines for two image-based early vigor traits and 15 mature traits of diverse winter-wheat lines. Early and late drought treatments were applied 12 and 65 days after vernalization, respectively. Further, a machine-learning-assisted phenotyping technique was adopted to measure spike area. Old Swedish cultivars showed the lowest early root vigor (4.92 cm) and large root biomass at maturity (5.25 g). No positive correlation was found between root biomass and yield components under the control condition. A high mean of grain yield was obtained in 1RS (9.8 g/plant), 2RL (9.5 g/plant), and cfAD99 (9.5 g/plant) genotypes under control. When including stability across control and two drought treatments, NGB, 1RS, 2RL, and cfAD99 genotypes showed the best performance. Peduncle length, root biomass, and NDVI positively contributed to the grain yield of 2RL genotypes under early drought, while 1000-grain weight and root biomass accounted for the high grain yield of 1RS genotypes under late drought. The image-based spike area measured by a machine-learning model correlated strongly to the yield component grain number (R2 = 0.70***). Furthermore, combined with yield reduction results, the spike area results suggested increased sterility (empty spikes) as the main cause of drought-induced yield loss instead of changes in spike size. Further integration of traditional measurements, modern phenotyping, and computational image analysis is needed to accelerate evaluations of plant traits under drought conditions. Genes potentially governing drought tolerance can be identified in these diverse lines.  \n1 | Introduction  \nWheat (Triticum aestivum L.), one of the three major crops globally, is serving as the main protein and calorie source in the human daily diet (Shiferaw et al. 2013) . Wheat is divided into spring and winter types, referring to the season when the crop is grown. In the northern hemisphere, the winter type is sown in the autumn and needs vernalization during the winter before it can set flowers and produce seeds (Crofts 1989) . Generally, winter wheat is advantageous to spring wheat in regard to yield performance and tolerance to abiotic and biotic stresses (Afzal  \net al. 2015; Entz and Fowler 1991) . In Sweden, according to the latest statistics (2024), the area of harvested winter wheat (413,830 ha) is almost seven times that of spring wheat (61,190 ha), giving approximately 12 times difference in total production between winter (2,708,400 t) and spring wheat (222,200 t; [https://jordbruksverket.se/statistik](https://jordbruksverket.se/statistik); accessed on 6th May 2025). With the predicted climate change, wheat yield is expected to fluctuate largely due to increased levels of abiotic stress, such as drought and heat (Langridge and Reynolds 2021) . Thus, the development of winter wheat genotypes tolerant to t","cbCaiubLbPUOXPLM","https://ap.wps.com/l/cbCaiubLbPUOXPLM","pdf",5385675,1,12,"English","en",105,"# Introduction\n## Climate and drought impacts on wheat\n## Need for drought-tolerant winter wheat\n## Study focus on early vigor and mature traits\n# Abstract-driven results\n## Image-based spike area and yield relationships\n## Drought-induced yield loss mechanisms\n## Genotypic performance and stability","[{\"question\":\"What drought treatments and growth stages were applied in the evaluation?\",\"answer\":\"Early and late drought treatments were applied 12 and 65 days after vernalization, respectively.\"},{\"question\":\"How was spike area measured and linked to yield components?\",\"answer\":\"A machine-learning-assisted phenotyping technique measured spike area, which correlated strongly with grain number (R2 = 0.70***).\"},{\"question\":\"What was suggested as the main cause of drought-induced yield loss?\",\"answer\":\"The spike area results combined with yield reduction suggested increased sterility (empty spikes) as the main cause rather than changes in spike size.\"}]","Climate Change Effects on a Diverse Set of Winter Wheat Evaluated Traditionally and With Machine Learning | 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drought treatments and growth stages were applied in the evaluation?","Question",{"text":75,"@type":76},"Early and late drought treatments were applied 12 and 65 days after vernalization, respectively.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was spike area measured and linked to yield components?",{"text":80,"@type":76},"A machine-learning-assisted phenotyping technique measured spike area, which correlated strongly with grain number (R2 = 0.70***).",{"name":82,"@type":73,"acceptedAnswer":83},"What was suggested as the main cause of drought-induced yield loss?",{"text":84,"@type":76},"The spike area results combined with yield reduction suggested increased sterility (empty spikes) as the main cause rather than changes in spike 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