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Ishimori M, Takanashi H, Fujimoto M, Kajiya-Kanegae H, Yoneda J, Tokunaga T, Tsutsumi N, Iwata H. Spatial kernel models capturing field heterogeneity for accurate estimation of genetic potential. BREEDING SCIENCE 2021; 71:444-455. [PMID: 34912171 PMCID: PMC8661485 DOI: 10.1270/jsbbs.20060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 05/19/2021] [Indexed: 06/14/2023]
Abstract
According to Fisher's principles, an experimental field is typically divided into multiple blocks for local control. Although homogeneity is supposed within a block, this assumption may not be practical for large blocks, such as those including hundreds of plots. In line evaluation trials, which are essential in plant breeding, field heterogeneity must be carefully treated, because it can cause bias in the estimation of genetic potential. To more accurately estimate genotypic values in a large field trial, we developed spatial kernel models incorporating genome-wide markers, which consider continuous heterogeneity within a block and over the field. In the simulation study, the spatial kernel models were robust under various conditions. Although heritability, spatial autocorrelation range, replication number, and missing plots directly affected the estimation accuracy of genotypic values, the spatial kernel models always showed superior performance over the classical block model. We also employed these spatial kernel models for quantitative trait locus mapping. Finally, using field experimental data of bioenergy sorghum lines, we validated the performance of the spatial kernel models. The results suggested that a spatial kernel model is effective for evaluating the genetic potential of lines in a heterogeneous field.
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Affiliation(s)
- Motoyuki Ishimori
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
| | - Hideki Takanashi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
| | - Masaru Fujimoto
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
| | - Hiromi Kajiya-Kanegae
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
| | - Junichi Yoneda
- EARTHNOTE Co. Ltd., 1388 Sokei, Ginoza, Okinawa 904-1303, Japan
| | | | - Nobuhiro Tsutsumi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
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Inostroza L, Espinoza S, Barahona V, Gerding M, Humphries A, del Pozo A, Ovalle C. Phenotypic Diversity and Productivity of Medicago sativa Subspecies from Drought-Prone Environments in Mediterranean Type Climates. PLANTS (BASEL, SWITZERLAND) 2021; 10:862. [PMID: 33923365 PMCID: PMC8146503 DOI: 10.3390/plants10050862] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/15/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022]
Abstract
The phenotypic diversity and productivity of a diverse alfalfa (M. sativa subspp.) panel of cultivars, landraces and wild relatives with putative drought tolerance were evaluated in two Mediterranean environments (central Chile and Southern Australia). In Chile, 70 accessions were evaluated in rainfed conditions and in Australia 30 accessions under rainfed and irrigated conditions, during three growing seasons. Large phenotypic variation was observed among and within subspecies for NDVI, stem length, intercepted PAR and forage yield. Principal component analysis indicated that the first two principal components (PC) accounted for 84.2% of total variance; fall dormancy, taxa, and breeding status were closely related to the agronomical performance of alfalfa accessions. Forage yield varied largely among accessions across years and locations. A linear relationship was found between annual forage yield and annual water added to the experiments (R2 = 0.60, p < 0.001). The GxE analysis for forage yield allowed the detection of the highest yielding accessions for each of the two mega-environments identified. The accessions CTA002 and CTA003 showed greater forage yield in both Chile and Australia environments. It is concluded that new breeding lines derived from crosses between cultivated alfalfa (M. sativa subsp. sativa) and wild relatives belonging to the primary (M. sativa subsp. falcata) and tertiary (M. arborea) gene pool, achieve outstanding agronomical performance in drought-prone environments.
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Affiliation(s)
- Luis Inostroza
- CRI-Quilamapu, Instituto de Investigaciones Agropecuaria, Chillán 3780000, Chile
| | - Soledad Espinoza
- CRI-Raihuen Instituto de Investigaciones Agropecuaria, Cauquenes 3690000, Chile; (S.E.); (V.B.)
| | - Viviana Barahona
- CRI-Raihuen Instituto de Investigaciones Agropecuaria, Cauquenes 3690000, Chile; (S.E.); (V.B.)
| | - Macarena Gerding
- Facultad de Agronomía, Universidad de Concepción, Chillán 3780000, Chile;
| | - Alan Humphries
- South Australian Research and Development Institute (SARDI), Adelaide, SA 5000, Australia;
| | - Alejandro del Pozo
- Centro de Mejoramiento Genético y Fenómica Vegetal, Facultad de Ciencias Agrarias, Universidad de Talca, Talca 3460000, Chile;
| | - Carlos Ovalle
- CRI-La Cruz, Instituto de Investigaciones Agropecuaria, La Cruz 228000, Chile
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Biswas A, Andrade MHML, Acharya JP, de Souza CL, Lopez Y, de Assis G, Shirbhate S, Singh A, Munoz P, Rios EF. Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa ( Medicago sativa L.). FRONTIERS IN PLANT SCIENCE 2021; 12:756768. [PMID: 34950163 PMCID: PMC8689394 DOI: 10.3389/fpls.2021.756768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 11/08/2021] [Indexed: 05/11/2023]
Abstract
The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.
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Affiliation(s)
- Anju Biswas
- Department of Agronomy, University of Florida, Gainesville, FL, United States
| | | | - Janam P. Acharya
- Department of Agronomy, University of Florida, Gainesville, FL, United States
| | | | - Yolanda Lopez
- Department of Agronomy, University of Florida, Gainesville, FL, United States
| | | | - Shubham Shirbhate
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States
| | - Aditya Singh
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States
| | - Patricio Munoz
- Department of Horticultural Sciences, University of Florida, Gainesville, FL, United States
| | - Esteban F. Rios
- Department of Agronomy, University of Florida, Gainesville, FL, United States
- *Correspondence: Esteban F. Rios,
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Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis. G3-GENES GENOMES GENETICS 2018; 8:53-62. [PMID: 29109156 PMCID: PMC5765366 DOI: 10.1534/g3.117.300323] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Cassava (Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant.
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