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Morales N, Anche MT, Kaczmar NS, Lepak N, Ni P, Romay MC, Santantonio N, Buckler ES, Gore MA, Mueller LA, Robbins KR. Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize. Genetics 2024; 227:iyae037. [PMID: 38469622 DOI: 10.1093/genetics/iyae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 12/02/2023] [Accepted: 02/18/2024] [Indexed: 03/13/2024] Open
Abstract
Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.
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Affiliation(s)
- Nicolas Morales
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Mahlet T Anche
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas S Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas Lepak
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - Pengzun Ni
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenhe District, Shenyang, Liaoning Province, PR China
| | - Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Nicholas Santantonio
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Lukas A Mueller
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- Boyce Thompson Institute, Ithaca, NY 14853, USA
| | - Kelly R Robbins
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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Mróz T, Shafiee S, Crossa J, Montesinos-Lopez OA, Lillemo M. Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2024; 44:5. [PMID: 38230361 PMCID: PMC10789716 DOI: 10.1007/s11032-024-01449-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/08/2024] [Indexed: 01/18/2024]
Abstract
With abundant available genomic data, genomic selection has become routine in many plant breeding programs. Multispectral data captured by UAVs showed potential for grain yield (GY) prediction in many plant species using machine learning; however, the possibilities of utilizing this data to augment genomic prediction models still need to be explored. We collected high-throughput phenotyping (HTP) multispectral data in a genotyped multi-environment large-scale field trial using two cost-effective cameras to fill this gap. We tested back to back the prediction ability of GY prediction models, including genomic (G matrix), multispectral-derived (M matrix), and environmental (E matrix) relationships using best linear unbiased predictor (BLUP) methodology in single and multi-environment scenarios. We discovered that M allows for GY prediction comparable to the G matrix and that models using both G and M matrices show superior accuracies and errors compared with G or M alone, both in single and multi-environment scenarios. We showed that the M matrix is not entirely environment-specific, and the genotypic relationships become more robust with more data capture sessions over the season. We discovered that the optimal time for data capture occurs during grain filling and that camera bands with the highest heritability are important for GY prediction using the M matrix. We showcased that GY prediction can be performed using only an RGB camera, and even a single data capture session can yield valuable data for GY prediction. This study contributes to a better understanding of multispectral data and its relationships. It provides a flexible framework for improving GS protocols without significant investments or software customization. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-024-01449-w.
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Affiliation(s)
- Tomasz Mróz
- Department of Plant Sciences, Norwegian University of Life Sciences, NO-1432 Ås, Norway
| | - Sahameh Shafiee
- Department of Plant Sciences, Norwegian University of Life Sciences, NO-1432 Ås, Norway
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico Veracruz, CP 52640 Texcoco, Edo. de Mexico Mexico
- Colegio de Postgraduados, CP 56230 Montecillos, Edo. de Mexico Mexico
| | | | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, NO-1432 Ås, Norway
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Sakurai K, Toda Y, Hamazaki K, Ohmori Y, Yamasaki Y, Takahashi H, Takanashi H, Tsuda M, Tsujimoto H, Kaga A, Nakazono M, Fujiwara T, Iwata H. Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data. FRONTIERS IN PLANT SCIENCE 2023; 14:1201806. [PMID: 37476172 PMCID: PMC10354427 DOI: 10.3389/fpls.2023.1201806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 06/15/2023] [Indexed: 07/22/2023]
Abstract
Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant's stability under drought stresses. We built a genomic prediction model (MT RRM model ) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MT RRM models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MT RRM model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.
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Affiliation(s)
- Kengo Sakurai
- Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
| | - Yusuke Toda
- Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
| | - Kosuke Hamazaki
- Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
| | - Yoshihiro Ohmori
- Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
| | - Yuji Yamasaki
- Arid Land Research Center, Tottori University, Tottori, Japan
| | - Hirokazu Takahashi
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan
| | - Hideki Takanashi
- Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
| | - Mai Tsuda
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
- Tsukuba Plant Innovation Research Center, University of Tsukuba, Tsukuba, Japan
| | | | - Akito Kaga
- Soybean and Field Crop Applied Genomics Research Unit, Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Mikio Nakazono
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan
| | - Toru Fujiwara
- Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
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