https://www.frontiersin.org/journals/agronomy RSS Feed for Frontiers in Agronomy | New and Recent Articles en-us Frontiers Feed Generator,version:1 2026-08-08T23:13:10.358+00:00 60 https://www.frontiersin.org/articles/10.3389/fagro.2026.1863730 https://www.frontiersin.org/articles/10.3389/fagro.2026.1863730 2026-08-07T00:00:00Z Systematic Review Akashdeep SinghDebashis PaulAli BajwaAniruddha Maity 70% dormancy in freshly matured seeds, with Plantaginaceae, Convolvulaceae, Brassicaceae, and Poaceae showing the highest dormancy levels, whereas Onagraceae and Cyperaceae exhibited relatively low dormancy. Annual species generally displayed greater dormancy intensity, while perennial species maintained longer dormancy periods. Seeds produced during spring and summer exhibited greater and longer dormancy than those produced during fall. Cold stratification and hormonal treatments were the most effective approaches for overcoming physiological dormancy, whereas scarification was essential for breaking physical dormancy. Germination responses were strongly regulated by temperature and light, with optimal conditions varying among taxonomic groups.DiscussionWeed seed dormancy is governed by interacting taxonomic and life-history traits that influence species persistence and emergence dynamics. Understanding dormancy mechanisms and dormancy-breaking requirements can improve prediction of weed emergence, optimize stale seedbed and herbicide-timing strategies, and enhance long-term seedbank management. This review highlights the need for a comprehensive global database integrating weed seed dormancy characteristics, dormancy-breaking methods, and adaptive responses to environmental change.]]> https://www.frontiersin.org/articles/10.3389/fagro.2026.1850038 https://www.frontiersin.org/articles/10.3389/fagro.2026.1850038 2026-08-06T00:00:00Z Original Research Sanket J. MoreBhagyashri KawaleYogita RaskarThangasamy ArunachalamSagar M. WayalYogesh P. KhadeSuresh GawandePranjali A. GedamBhushan BibweRajiv KaleVijay Mahajan 70%), with the greatest impact during the rainy season. Economic analysis revealed that pendimethalin followed by polyethylene mulching translated into higher net returns and favorable benefit-cost ratios across seasons. Weed flora exhibited seasonal variation, with dicot dominance in post-rainy and winter seasons and monocot predominance during the rainy season, highlighting the need for season-specific weed management strategies. Correspondingly, herbicide performance varied across seasons: pendimethalin followed by propaquizafop + oxyfluorfen (T1) was more effective in post-rainy and winter seasons, whereas pendimethalin followed by clodinafop-propargyl + oxyfluorfen (T3) performed better during the rainy season. Multivariate and correlation analyses revealed that bulb yield was primarily driven by early weed suppression (WCE at 30 and 60 DAT) and improved morphological and canopy traits rather than biochemical parameters. Integrating pre-emergence pendimethalin with polyethylene mulching provides a robust weed management strategy across diverse production environments. This approach offers a sustainable alternative to herbicide-only programs, particularly for rainy-season onion cultivation. Nevertheless, further research on mulch-specific agronomic practices, multi-location validation, and long-term ecological impacts is essential to fully realize the potential of plasticulture-based systems.]]> https://www.frontiersin.org/articles/10.3389/fagro.2026.1885332 https://www.frontiersin.org/articles/10.3389/fagro.2026.1885332 2026-08-05T00:00:00Z Original Research Katrin KukaMayka Schmitt RahnerPaul KeßelerSylvia KratzElke Bloem https://www.frontiersin.org/articles/10.3389/fagro.2026.1888848 https://www.frontiersin.org/articles/10.3389/fagro.2026.1888848 2026-08-04T00:00:00Z Original Research J. M. MatamorosC. MuñozE. ZagalM. A. Sanchez-MonederoN. Escalona https://www.frontiersin.org/articles/10.3389/fagro.2026.1831957 https://www.frontiersin.org/articles/10.3389/fagro.2026.1831957 2026-08-04T00:00:00Z Original Research Cintia da Silva de OliveiraRikelme Matheus dos Santos RelvasRithielly Machado Rodrigues de AraújoCecília Leão Pereira ResendeLuciana Maria da SilvaKátia Aparecida de Pinho CostaFabricio Rodrigues https://www.frontiersin.org/articles/10.3389/fagro.2026.1894667 https://www.frontiersin.org/articles/10.3389/fagro.2026.1894667 2026-08-03T00:00:00Z Original Research Evanson R. OmuseHonest MachekanoDaniel M. MutyambaiDennis BeesigamukamaChrysantus M. TangaSamwel O. OgajiSevgan SubramanianFrank Chidawanyika https://www.frontiersin.org/articles/10.3389/fagro.2026.1861055 https://www.frontiersin.org/articles/10.3389/fagro.2026.1861055 2026-07-31T00:00:00Z Original Research Julian KirchgesserMousumi HazarikaSilvia Bachmann-PfabeBettina Eichler-LöbermannKlaus J. DehmerMareike KavkaRalf Uptmoor https://www.frontiersin.org/articles/10.3389/fagro.2026.1773812 https://www.frontiersin.org/articles/10.3389/fagro.2026.1773812 2026-07-31T00:00:00Z Original Research A. KeerthikaA. K. ShuklaKamla K. ChoudharyR. S. MehtaM. B. Noor MohamedArvind Singh TetarwalSeeta Ram MeenaVijay Singh Meena https://www.frontiersin.org/articles/10.3389/fagro.2026.1819085 https://www.frontiersin.org/articles/10.3389/fagro.2026.1819085 2026-07-31T00:00:00Z Review Apurva Vikas SabnisÉricmar Avila dos SantosTriin KallavusRiina KaasikSilva VilumetsKristof De SchutterEve Veromann https://www.frontiersin.org/articles/10.3389/fagro.2026.1868962 https://www.frontiersin.org/articles/10.3389/fagro.2026.1868962 2026-07-30T00:00:00Z Perspective Adrianna SzczepaniecLuis CrucesNeha Panwar https://www.frontiersin.org/articles/10.3389/fagro.2026.1889814 https://www.frontiersin.org/articles/10.3389/fagro.2026.1889814 2026-07-30T00:00:00Z Original Research Thornala Omkar SaicharanB. Tirapathi ReddyIrfan AlamTarak HussainJalaluddin Khan 22.1, Bonferroni-corrected p< 0.0083). In addition to disease classification, the framework includes a disease-aware NPK fertilizer optimization engine formulated as a constrained linear program using Bayesian regularized parameters (NPK fertilizer coefficient interactions with disease severity), which were estimated using ordinary least squares regression. According to the results of the nutrient recommendation model, the adjusted-R² value was 0.873 with a root mean square error (RMSE) of 4.2 kg/ha, leading to a decrease in the average absolute fertilizer estimation error from 42.3 kg/ha to 6.1 kg/ha and a large practical effect size (Cohen’s d = 2.83). The real-world applicability of this approach was established through a randomized controlled field trial involving 312 farmers across two growing seasons, Kharif 2023 and Rabi 2023–24, using intent-to-treat analysis. Results clearly show significant agronomic and economic value, with better crop yield (18-24%), average reduction in cost per hectare of ₹12,500, a decrease in soil NPK deviation from 18.4% to 4.9%, and a 3.2x return on investment. Analyses of data showed statistically significant differences in all outcome measures (Wilcoxon Z = 14.2, p<.001; t(311) = 22.1, p<.001). In addition, the SHAP interpretability analysis provides evidence of a strong correlation between learned visual biomarkers and disease characteristics that have been verified by agronomists (κ = 0.89). Comprehensive studies of ablation, sensitivity, and information theory demonstrated that each architecturally derived component is effective and robust. Thus, the PDRPFRS provides an AI-driven solution for precision agriculture that is scalable, interpretable, and economically viable.]]> https://www.frontiersin.org/articles/10.3389/fagro.2026.1897506 https://www.frontiersin.org/articles/10.3389/fagro.2026.1897506 2026-07-29T00:00:00Z Original Research Vrushabh Vijay FiskeyKamsali Sukeswara AchariShreyas BagrechaDevendra Kumar DadhichTejaswini ChandrakarShivangi KaithManjunath S MelavankiSudha YadavShruti SetiaHarjeet SinghPooja MeenaMarthala Bhuvaneswar ReddyShashank PatelAbhishek PatidarRajvir SharmaDileep KumarSudhir KumarVinod KumarJyothi AerpullaChooi-Lin PhooiElisa Azura AzmanKamal GargSanjeev Kumar https://www.frontiersin.org/articles/10.3389/fagro.2026.1816633 https://www.frontiersin.org/articles/10.3389/fagro.2026.1816633 2026-07-29T00:00:00Z Original Research Tharsos Hister GiovanellaGiovana Ghisleni RibasFábio Ricardo Marin https://www.frontiersin.org/articles/10.3389/fagro.2026.1900983 https://www.frontiersin.org/articles/10.3389/fagro.2026.1900983 2026-07-29T00:00:00Z Review Wuletaw TadesseFatima HenkrarKhaoula LahrichiAakash ChawadeAdmas Alemu https://www.frontiersin.org/articles/10.3389/fagro.2026.1848662 https://www.frontiersin.org/articles/10.3389/fagro.2026.1848662 2026-07-28T00:00:00Z Original Research Guy BlommeMuller KamiraElizabeth KearsleyNancy SafariWalter Ocimati https://www.frontiersin.org/articles/10.3389/fagro.2026.1863802 https://www.frontiersin.org/articles/10.3389/fagro.2026.1863802 2026-07-28T00:00:00Z Systematic Review Oumaima MoustaidDina ZanbotChaimae RamdaniMustapha El BouhssiniKarim El Fakhouri https://www.frontiersin.org/articles/10.3389/fagro.2026.1875569 https://www.frontiersin.org/articles/10.3389/fagro.2026.1875569 2026-07-28T00:00:00Z Original Research Wang JingZhao Xiao-LinWang Yu-QingLiu Yue-HanLi Pei-YingTian Xin-ChunMa Song-MeiZhang Yun-Ling https://www.frontiersin.org/articles/10.3389/fagro.2026.1888735 https://www.frontiersin.org/articles/10.3389/fagro.2026.1888735 2026-07-27T00:00:00Z Review José Carlos Barbosa dos SantosCarol Mallory-SmithVictor H. V. Ribeiro https://www.frontiersin.org/articles/10.3389/fagro.2026.1848775 https://www.frontiersin.org/articles/10.3389/fagro.2026.1848775 2026-07-27T00:00:00Z Original Research Richard Ansong OmariMosab HalwaniMakoto KanasugiSonoko D. Bellingrath-KimuraNaoko Ohkama-Ohtsu https://www.frontiersin.org/articles/10.3389/fagro.2026.1773212 https://www.frontiersin.org/articles/10.3389/fagro.2026.1773212 2026-07-27T00:00:00Z Original Research Abdallah R. MakaleSteven F. MwilengaHamida M. BwaizeAndrew K. KabanzaJohn TengaWilson A. NeneBakari R. KidundaGeradina P. Mzena