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Sherstneva O, Abdullaev F, Kior D, Yudina L, Gromova E, Vodeneev V. Prediction of biomass accumulation and tolerance of wheat seedlings to drought and elevated temperatures using hyperspectral imaging. FRONTIERS IN PLANT SCIENCE 2024; 15:1344826. [PMID: 38371404 PMCID: PMC10869465 DOI: 10.3389/fpls.2024.1344826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 01/23/2024] [Indexed: 02/20/2024]
Abstract
Early prediction of important agricultural traits in wheat opens up broad prospects for the development of approaches to accelerate the selection of genotypes for further breeding trials. This study is devoted to the search for predictors of biomass accumulation and tolerance of wheat to abiotic stressors. Hyperspectral (HS) and chlorophyll fluorescence (ChlF) parameters were analyzed as predictors under laboratory conditions. The predictive ability of reflectance and normalized difference indices (NDIs), as well as their relationship with parameters of photosynthetic activity, which is a key process influencing organic matter production and crop yields, were analyzed. HS parameters calculated using the wavelengths in Red (R) band and the spectral range next to the red edge (FR-NIR) were found to be correlated with biomass accumulation. The same ranges showed potential for predicting wheat tolerance to elevated temperatures. The relationship of HS predictors with biomass accumulation and heat tolerance were of opposite sign. A number of ChlF parameters also showed statistically significant correlation with biomass accumulation and heat tolerance. A correlation between HS and ChlF parameters, that demonstrated potential for predicting biomass accumulation and tolerance, has been shown. No predictors of drought tolerance were found among the HS and ChlF parameters analyzed.
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Affiliation(s)
- Oksana Sherstneva
- Department of Biophysics, N.I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
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Chen C, Powell O, Dinglasan E, Ross EM, Yadav S, Wei X, Atkin F, Deomano E, Hayes BJ. Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits. THE PLANT GENOME 2023; 16:e20390. [PMID: 37728221 DOI: 10.1002/tpg2.20390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 08/01/2023] [Accepted: 08/29/2023] [Indexed: 09/21/2023]
Abstract
Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such, genomic prediction in sugarcane presents an interesting case for machine learning (ML) methods, which are purportedly able to deal with high levels of complexity in prediction. Here, we investigated deep learning (DL) neural networks, including multilayer networks (MLP) and convolution neural networks (CNN), and an ensemble machine learning approach, random forest (RF), for genomic prediction in sugarcane. The data set used was 2912 sugarcane clones, scored for 26,086 genome wide single nucleotide polymorphism markers, with final assessment trial data for total cane harvested (TCH), commercial cane sugar (CCS), and fiber content (Fiber). The clones in the latest trial (2017) were used as a validation set. We compared prediction accuracy of these methods to genomic best linear unbiased prediction (GBLUP) extended to include dominance and epistatic effects. The prediction accuracies from GBLUP models were up to 0.37 for TCH, 0.43 for CCS, and 0.48 for Fiber, while the optimized ML models had prediction accuracies of 0.35 for TCH, 0.38 for CCS, and 0.48 for Fiber. Both RF and DL neural network models have comparable predictive ability with the additive GBLUP model but are less accurate than the extended GBLUP model.
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Affiliation(s)
- Chensong Chen
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Queensland, Australia
| | - Owen Powell
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Queensland, Australia
| | - Eric Dinglasan
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Queensland, Australia
| | - Elizabeth M Ross
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Queensland, Australia
| | - Seema Yadav
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Queensland, Australia
| | | | | | | | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Queensland, Australia
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Atta K, Mondal S, Gorai S, Singh AP, Kumari A, Ghosh T, Roy A, Hembram S, Gaikwad DJ, Mondal S, Bhattacharya S, Jha UC, Jespersen D. Impacts of salinity stress on crop plants: improving salt tolerance through genetic and molecular dissection. FRONTIERS IN PLANT SCIENCE 2023; 14:1241736. [PMID: 37780527 PMCID: PMC10540871 DOI: 10.3389/fpls.2023.1241736] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/14/2023] [Indexed: 10/03/2023]
Abstract
Improper use of water resources in irrigation that contain a significant amount of salts, faulty agronomic practices such as improper fertilization, climate change etc. are gradually increasing soil salinity of arable lands across the globe. It is one of the major abiotic factors that inhibits overall plant growth through ionic imbalance, osmotic stress, oxidative stress, and reduced nutrient uptake. Plants have evolved with several adaptation strategies at morphological and molecular levels to withstand salinity stress. Among various approaches, harnessing the crop genetic variability across different genepools and developing salinity tolerant crop plants offer the most sustainable way of salt stress mitigation. Some important major genetic determinants controlling salinity tolerance have been uncovered using classical genetic approaches. However, its complex inheritance pattern makes breeding for salinity tolerance challenging. Subsequently, advances in sequence based breeding approaches and functional genomics have greatly assisted in underpinning novel genetic variants controlling salinity tolerance in plants at the whole genome level. This current review aims to shed light on physiological, biochemical, and molecular responses under salt stress, defense mechanisms of plants, underlying genetics of salt tolerance through bi-parental QTL mapping and Genome Wide Association Studies, and implication of Genomic Selection to breed salt tolerant lines.
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Affiliation(s)
- Kousik Atta
- ICAR-Indian Agricultural Research Institute, New Delhi, India
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Saptarshi Mondal
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
| | - Shouvik Gorai
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Aditya Pratap Singh
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
- School of Agriculture, GIET University, Gunupur, Rayagada, Odisha, India
| | - Amrita Kumari
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | - Tuhina Ghosh
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Arkaprava Roy
- ICAR-Indian Agricultural Research Institute, New Delhi, India
- ICAR- National Institute of Biotic Stress Management, Raipur, India
| | - Suryakant Hembram
- WBAS (Research), Government of West Bengal, Field Crop Research Station, Burdwan, India
| | | | - Subhasis Mondal
- Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, West Bengal, India
| | | | | | - David Jespersen
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
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Breeding Bread-Making Wheat Varieties for Organic Farming Systems: The Need to Target Productivity, Robustness, Resource Use Efficiency and Grain Quality Traits. Foods 2023; 12:foods12061209. [PMID: 36981136 PMCID: PMC10048768 DOI: 10.3390/foods12061209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/29/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
Agronomic protocols (rotation, tillage, fertilization and crop protection) commonly used in organic and conventional crop production differ significantly and there is evidence that modern varieties developed for conventional high-input farming systems do not have the combination of traits required for optimum performance in organic farming systems. Specifically, there is evidence that prohibition on the use of water-soluble, mineral N, P and K fertilizers and synthetic pesticide inputs in organic farming results in a need to revise both breeding and selection protocols. For organic production systems, the focus needs to be on the following: (i) traits prioritized by organic farmers such as high nutrient use efficiency from organic fertilizer inputs, competitiveness against weeds, and pest and disease resistance, (ii) processing quality parameters defined by millers and bakers and (iii) nutritional quality parameters demanded by organic consumers. In this article, we review evidence from variety trials and factorial field experiments that (i) studied to what extent there is a need for organic farming focused breeding programs, (ii) investigated which traits/trait combinations should be targeted in these breeding programs and/or (iii) compared the performance of modern varieties developed for the conventional sector with traditional/older varieties favored by organic farmers and/or new varieties developed in organic farming focused breeding programs. Our review focuses on wheat because there have been organic and/or low-input farming focused wheat breeding programs for more than 20 years in Europe, which has allowed the performance of varieties/genotypes from organic/low-input and conventional farming focused breeding programs to be compared.
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Semagn K, Crossa J, Cuevas J, Iqbal M, Ciechanowska I, Henriquez MA, Randhawa H, Beres BL, Aboukhaddour R, McCallum BD, Brûlé-Babel AL, N'Diaye A, Pozniak C, Spaner D. Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2747-2767. [PMID: 35737008 DOI: 10.1007/s00122-022-04147-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/28/2022] [Indexed: 06/15/2023]
Abstract
This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico
| | | | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Izabela Ciechanowska
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Maria Antonia Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, R6M 1Y5, Canada
| | - Harpinder Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Brian L Beres
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Reem Aboukhaddour
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Brent D McCallum
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, R6M 1Y5, Canada
| | - Anita L Brûlé-Babel
- Department of Plant Science, University of Manitoba, 66 Dafoe Road, Winnipeg, MB, R3T 2N2, Canada
| | - Amidou N'Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
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Ni P, Anche MT, Ruan Y, Dang D, Morales N, Li L, Liu M, Wang S, Robbins KR. Genomic Prediction Strategies for Dry-Down-Related Traits in Maize. FRONTIERS IN PLANT SCIENCE 2022; 13:930429. [PMID: 35845649 PMCID: PMC9280646 DOI: 10.3389/fpls.2022.930429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-down rate is estimated using kernel moisture content at physiological maturity and at harvest time; however, measuring kernel water content at physiological maturity, which is sometimes referred as kernel water content at black layer formation (BWC), is time-consuming and resource-demanding. Therefore, inferring BWC from other correlated and easier to measure traits could improve the efficiency of breeding efforts for dry-down-related traits. In this study, multi-trait genomic prediction models were used to estimate genetic correlations between BWC and water content at harvest time (HWC) and flowering time (FT). The results show there is moderate-to-high genetic correlation between the traits (0.24-0.66), which supports the use of multi-trait genomic prediction models. To investigate genomic prediction strategies, several cross-validation scenarios representing possible implementations of genomic prediction were evaluated. The results indicate that, in most scenarios, the use of multi-trait genomic prediction models substantially increases prediction accuracy. Furthermore, the inclusion of historical records for correlated traits can improve prediction accuracy, even when the target trait is not measured on all the plots in the training set.
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Affiliation(s)
- Pengzun Ni
- Shenyang Key Laboratory of Maize Genomic Selection Breeding, Liaoning Province Research Center of Plant Genetic Engineering Technology, College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
- College of Agronomy, Shenyang Agricultural University, Shenyang, China
| | - Mahlet Teka Anche
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Yanye Ruan
- Shenyang Key Laboratory of Maize Genomic Selection Breeding, Liaoning Province Research Center of Plant Genetic Engineering Technology, College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Dongdong Dang
- Shenyang Key Laboratory of Maize Genomic Selection Breeding, Liaoning Province Research Center of Plant Genetic Engineering Technology, College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Nicolas Morales
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Lingyue Li
- Shenyang Key Laboratory of Maize Genomic Selection Breeding, Liaoning Province Research Center of Plant Genetic Engineering Technology, College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Meiling Liu
- Shenyang Key Laboratory of Maize Genomic Selection Breeding, Liaoning Province Research Center of Plant Genetic Engineering Technology, College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Shu Wang
- College of Agronomy, Shenyang Agricultural University, Shenyang, China
| | - Kelly R. Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
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Budhlakoti N, Kushwaha AK, Rai A, Chaturvedi KK, Kumar A, Pradhan AK, Kumar U, Kumar RR, Juliana P, Mishra DC, Kumar S. Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops. Front Genet 2022; 13:832153. [PMID: 35222548 PMCID: PMC8864149 DOI: 10.3389/fgene.2022.832153] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/10/2022] [Indexed: 12/17/2022] Open
Abstract
Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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Affiliation(s)
- Neeraj Budhlakoti
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Anil Rai
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K K Chaturvedi
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | | | | | - D C Mishra
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sundeep Kumar
- ICAR- National Bureau of Plant Genetic Resources, New Delhi, India
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Sandhu KS, Merrick LF, Sankaran S, Zhang Z, Carter AH. Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs. Front Genet 2022. [PMCID: PMC8814369 DOI: 10.3389/fgene.2021.829131] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Karansher S. Sandhu,
| | - Lance F. Merrick
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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