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      Controlled Environment Ecosystem: A Cutting-Edge Technology in Speed Breeding

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          Abstract

          The controlled environment ecosystem is a meticulously designed plant growing chamber utilized for cultivating biofortified crops and microgreens, addressing hidden hunger and malnutrition prevalent in the growing population. The integration of speed breeding within such controlled environments effectively eradicates morphological disruptions encountered in traditional breeding methods such as inbreeding depression, male sterility, self-incompatibility, embryo abortion, and other unsuccessful attempts. In contrast to the unpredictable climate conditions that often prolong breeding cycles to 10–15 years in traditional breeding and 4–5 years in transgenic breeding within open ecosystems, speed breeding techniques expedite the achievement of breeding objectives and F1–F6 generations within 2–3 years under controlled growing conditions. In comparison, traditional breeding may take 5–10 years for plant population line creation, 3–5 years for field trials, and 1–2 years for variety release. The effectiveness of speed breeding in trait improvement and population line development varies across different crops, requiring approximately 4 generations in rice and groundnut, 5 generations in soybean, pea, and oat, 6 generations in sorghum, Amaranthus sp., and subterranean clover, 6–7 generations in bread wheat, durum wheat, and chickpea, 7 generations in broad bean, 8 generations in lentil, and 10 generations in Arabidopsis thaliana annually within controlled environment ecosystems. Artificial intelligence leverages neural networks and algorithm models to screen phenotypic traits and assess their role in diverse crop species. Moreover, in controlled environment systems, mechanistic models combined with machine learning effectively regulate stable nutrient use efficiency, water use efficiency, photosynthetic assimilation product, metabolic use efficiency, climatic factors, greenhouse gas emissions, carbon sequestration, and carbon footprints. However, any negligence, even minor, in maintaining optimal photoperiodism, temperature, humidity, and controlling pests or diseases can lead to the deterioration of crop trials and speed breeding techniques within the controlled environment system. Further comparative studies are imperative to comprehend and justify the efficacy of climate management techniques in controlled environment ecosystems compared to natural environments, with or without soil.

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          Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

          Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
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            Soil organic matter turnover is governed by accessibility not recalcitrance

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              Machine Learning for High-Throughput Stress Phenotyping in Plants.

              Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
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                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                26 June 2024
                09 July 2024
                : 9
                : 27
                : 29114-29138
                Affiliations
                []Faculty of Agricultural Sciences, Arunachal University of Studies , Namsai, Arunachal Pradesh 792103, India
                []College of Agriculture, Central Agricultural University , Iroisemba, Manipur 795004, India
                [§ ]PG Department of Agriculture, Khalsa College , Amritsar, Punjab 143002, India
                []School of Agricultural Sciences, Joy University , Thirunelveli, Tamil Nadu 627116, India
                []Agricultural Research Station, Agriculture University , Jodhpur, Rajasthan 342304, India
                []Faculty of Biotechnology, Shri Ramswaroop Memorial University , Barabanki, Uttar Pradesh 225003, India
                [7 ]Patanjali Herbal Research Department, Patanjali Research Institute , Haridwar, Uttarakhand 249405, India
                [8 ]ISBM University , Gariyaband, Chhattishgarh 493996, India
                [9 ]Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research , Lucknow, Uttar Pradesh 226002, India
                [10 ]Division of Plant Physiology and Biochemistry, ICAR-Indian Institute of Sugarcane Research , Lucknow, Uttar Pradesh 226002, India
                Author notes
                Author information
                https://orcid.org/0000-0003-3637-8729
                https://orcid.org/0000-0002-9795-5641
                Article
                10.1021/acsomega.3c09060
                11238293
                feb93200-bea7-4fc5-9946-f66b905d05f5
                © 2024 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 14 November 2023
                : 31 May 2024
                : 25 May 2024
                Categories
                Review
                Custom metadata
                ao3c09060
                ao3c09060

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