Crop Science最新文献

{"title":"Chromosome 1 QTLs associated with response to bacterial leaf spot in Beta vulgaris","authors":"Audrey K. Morrison,&nbsp;Irwin L. Goldman","doi":"10.1002/csc2.21448","DOIUrl":"10.1002/csc2.21448","url":null,"abstract":"<p>Bacterial leaf spot (BLS), caused by <i>Pseudomonas syringae</i> pathovar <i>aptata</i> (<i>Psa</i>), is a seedborne, foliar disease affecting members of the Amaranthaceae and Cucurbitaceae families, including table beet and Swiss chard crops. There is no known resistance to BLS in beet or chard. A diversity panel, modified from the Wisconsin Beta Diversity Panel (WBDP) and comprised of 219 accessions from the <i>Beta vulgaris</i> crop complex, was assembled and genotyped for single nucleotide polymorphism data. These accessions were screened by foliar inoculation of <i>Psa</i> and visually evaluated for percentage of diseased leaf tissue. Overall, sugar beet and <i>Beta vulgaris</i> subsp. <i>maritima</i> accessions had the lowest BLS response, whereas table beet accessions had the largest range of responses. Phenotypic means were adjusted using best linear unbiased estimates, and two different software programs, GWASpoly and GAPIT3, were utilized to conduct a genome-wide association study (GWAS). Leaf color was found to be significantly associated with and correlated with BLS response scores, and was used as a covariate in GWAS analysis. An association with BLS response was detected on chromosome 1 in the full WBDP, explaining upward of 21% of the variation in the phenotype. The marker associated with this quantitative trait locus (QTL), Chr1_61344476, showed an additive relationship between dosage and BLS response. Eleven candidate genes, described and annotated in sugar beet, were associated with this QTL. Some of these include F Box domains, RNA-binding proteins, and calcium-dependent kinases, all of which have roles in plant defense responses. Marker Chr1_61344476 may be useful in breeding for BLS resistance in members of the <i>Beta vulgaris</i> crop complex.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relating spatial turfgrass quality to actual evapotranspiration for precision golf course irrigation","authors":"Karem Meza,&nbsp;Alfonso F. Torres-Rua,&nbsp;Lawrence Hipps,&nbsp;Kelly Kopp,&nbsp;Chase M. Straw,&nbsp;William P. Kustas,&nbsp;Laura Christiansen,&nbsp;Calvin Coopmans,&nbsp;Ian Gowing","doi":"10.1002/csc2.21446","DOIUrl":"10.1002/csc2.21446","url":null,"abstract":"<p>Golf courses are increasingly affected by water scarcity and climate change. An understanding of spatial variability of actual evapotranspiration (ET_a) and turfgrass quality (TQ) site-specific management zones (SSMZ) is important for the implementation of precision turfgrass management. Therefore, the main objectives of this study were to quantify the relationship between remotely sensed TQ and ET_a estimates and to evaluate the spatial variations of TQ and ET_a at a golf course in Utah. Ground-based normalized difference vegetation index was collected using a TCM-500 sensor, and aerial multispectral and thermal imagery data were acquired from unpiloted aircraft systems (UAS) in 2021, 2022, and 2023. A remote sensing TQ-random forest (RF) model was developed using six datasets of UAS spectral indices and the RF algorithm. The spatial data were analyzed to determine the correlation between TQ and ET_a estimates. The TQ and ET_a SSMZ were created and integrated with irrigation heads on the golf course using the Thiessen polygons tool. Results demonstrated that TQ-RF model was accurate within a root mean square error of 0.05. The correlation between TQ-RF and ET_a was stronger for fairways (<i>R</i>²= 0.74), tees (<i>R</i>²= 0.66), and roughs (<i>R</i>²= 0.75) as compared to greens (<i>R</i>²= 0.25) and the driving range (<i>R</i>²= 0.36) on July 20, 2022. Actual evapotranspiration SSMZ, in combination with TQ-RF SSMZ, is useful for irrigation scheduling, addressing the question of how much and where to irrigate. This study demonstrates the ability of TQ-RF and ET_a SSMZ to identify spatial variation for the purpose of landscape irrigation management in semi-arid areas.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An in-silico approach exploring sorghum source:sink balance across sorghum hybrids: How many leaves are enough?","authors":"Lucia Marziotte,&nbsp;Ana J. P. Carcedo,&nbsp;Laura Mayor,&nbsp;P. V. Vara Prasad,&nbsp;Joaquín A. Peraza,&nbsp;Ignacio A. Ciampitti","doi":"10.1002/csc2.21449","DOIUrl":"10.1002/csc2.21449","url":null,"abstract":"<p>Previous literature documented an imbalance for sorghum [<i>Sorghum bicolor</i> (L.) Moench] between source (leaves) and sink (grains), favoring the source. Therefore, reducing leaf number, anticipating maturity, and placing the dry-down with more favorable environment might be advantageous for producers to fit another crop in the rotation. The aims of this study were to (1) evaluate via in-silico the effects of leaf removal during the grain filling and (2) explore those impacts using a field dataset for sorghum yield. For the first objective, the APSIM (Agricultural Production Systems Simulator) sorghum model was tested with four hybrids across 12 locations in the United States (2015–2023) resulting in an RRMSE (relative root mean squared error) of 25% for yield. As a second step, an APSIM defoliation module was developed using field data of one site-year, demonstrating an RRMSE of 17% for yield. As a last step, the model was used to simulate the effect of sequential defoliations on yield, across 38 years of weather data (1984–2022), without showing any yield penalties when removing up to four leaves after flowering. Leaf area removal after flowering indicated a positive imbalance in source:sink ratio (i.e., source excess). For the second objective, a field dataset from 21 sorghum hybrids with different attainable leaf numbers and cycle duration did not result in significant yield differences. Early maturity hybrids with fewer leaves give farmers the opportunity to intensify crop sequences. Less focus in sorghum improvement for early relative to late maturing hybrids has been reported; therefore, there is still ample room for future yield gains.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QTL mapping of stem rust resistance in a Bill Brown/Gage winter wheat population","authors":"Tadele T. Kumssa,&nbsp;P. S. Baenziger,&nbsp;M. N. Rouse,&nbsp;Waseem Hussain,&nbsp;Vikas Belamkar,&nbsp;Stephen N. Wegulo,&nbsp;Jesse Poland","doi":"10.1002/csc2.21445","DOIUrl":"10.1002/csc2.21445","url":null,"abstract":"<p>The wheat (<i>Triticum</i> spp.) stem rust pathogen, <i>Puccinia graminis</i> f. sp. <i>tritici</i> Eriks. and E. Henn. (<i>Pgt</i>), has continued to be a devastating biotic stress in wheat production. Over previous decades, scientists have identified several resistance genes effective against <i>Pgt</i>. However, the ever-evolving <i>Pgt</i> and low availability of durable resistance necessitates continuous identification and wise deployment of resistance genes. To elucidate the identity of our previously reported stem rust resistance in hard red winter wheat cultivar Gage, we used recombinant inbred lines (RILs) developed from the cross of Bill Brown × Gage and evaluated them for 3 years for response to six different stem rust pathogen races individually at the seedling stage in the greenhouse and a mixture of these races in the field. Using molecular markers, we determined the genomic regions that affect stem rust resistance in Gage, which identified two quantitative trait loci (QTLs) at the seedling stage and one major QTL at the adult stage, giving insight into why Gage has superior stem rust resistance. The seedling stem rust resistance was from <i>SrTmp</i> and likely from an <i>Sr7</i> allele. QTLs conferring adult plant resistance in Gage were mainly from <i>Sr2</i>, but molecular analysis suggested additional minor-effect QTLs were involved.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21445","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling loose smut resistance in Indian bread wheat germplasm: Gene postulation and pedigree analysis","authors":"Divya Bhandhari,&nbsp;Ritu Bala,&nbsp;Puja Srivastava,&nbsp;Jaspal Kaur,&nbsp;Vineet Kumar Sharma","doi":"10.1002/csc2.21441","DOIUrl":"10.1002/csc2.21441","url":null,"abstract":"<p>The present study is aimed at the postulation of <i>Ut</i> genes in loose smut-resistant bread wheat (<i>Triticum aestivum</i> L.) genotypes and establishing a correlation with their pedigree. Loose smut caused by <i>Ustilago segetum tritici</i> (<i>Ust</i>) is an internal seed-borne disease of wheat that can be managed through chemical seed treatment. However, due to the absence of evident symptoms, seed treatment is not a regular practice in the farming community. Thus, the use of resistant cultivars is an efficient and sustainable approach for the management of loose smut of wheat. The majority of current wheat cultivars are susceptible to loose smut. Therefore, there is a pressing need for the development of resistant cultivars, which requires the identification of resistant donors with known resistant genes. In this study, field screening for 3 years resulted in the identification of 124 bread wheat genotypes conferring stable resistance against <i>Ust</i> race T11. Molecular marker-based identification of <i>Ut</i> genes (<i>Ut4</i>–<i>Ut11)</i> revealed the presence of these genes either singly or in combination in 118 genotypes. Among them, six genotypes showed different combinations of five <i>Ut</i> genes, namely, WH 1218 and HI 1633 (<i>Ut4</i>, <i>Ut6</i>, <i>Ut8</i>, <i>Ut9</i>, <i>Ut11</i>), HD 3377 (<i>Ut4</i>, <i>Ut6</i>, <i>Ut8</i>, <i>Ut9</i>, <i>Ut10</i>), WH 1218 and HI 1633 (<i>Ut4</i>, <i>Ut6</i>, <i>Ut9</i>, <i>Ut10</i>, <i>Ut11</i>), and HD 3226 (<i>Ut4</i>, <i>Ut5</i>, <i>Ut6</i>, <i>Ut9</i>, <i>Ut11</i>). The genotypes with multiple genes for loose smut resistance can be used as donors for transferring the resistance into the high-yielding cultivars. Furthermore, the pedigree of each genotype was analyzed to find the gene source of the postulated <i>Ut</i> genes. None of the genotypes showed consistent association with the gene source of the postulated <i>Ut</i> gene present in the pedigree. Thus, no association between molecular marker-based postulation and pedigree of genotypes was inferred. However, the root pedigree of common parents revealed five putative sources of loose smut resistance, that is, Chris, Thatcher, Federation, New-Thatch, and Ostka-Galicyjska, in most of the genotypes under evaluation in the present study.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance and recovery of turfgrasses irrigated with varying crop coefficients","authors":"Elena Sevostianova,&nbsp;Dawn VanLeeuwen,&nbsp;Matteo Serena,&nbsp;Rossana Sallenave,&nbsp;Bernd Leinauer","doi":"10.1002/csc2.21433","DOIUrl":"10.1002/csc2.21433","url":null,"abstract":"<p>Deficit irrigation is a water conserving practice that involves watering below an estimated evapotranspiration (ET) replacement level. Research is limited to comparing cool-season (CS) and warm-season (WS) turfgrass varieties grown in arid regions under varying deficit irrigation replacement levels. This study investigated the effects of five levels of reference evapotranspiration for short grass (ET_OS) replacement (55%, 70%, 85%, 100%, and 115%) on the performance and fall recovery of several turfgrasses in the southwestern United States. Three years of field research evaluated green cover and visual quality of three CS Kentucky bluegrass (<i>Poa pratensis</i> L.) (four cultivars), tall fescue [<i>Schedonorus arundinaceus</i> (Schreb.)] (three cultivars), and perennial ryegrass (<i>Lolium perenne</i> L.) (three cultivars), and two WS turfgrasses bermudagrass (<i>Cynodon dactylon</i> L.) (three cultivars) and buffalograss <i>Buchloe dactyloides</i> (two cultivars). CS grasses required higher ET_OS replacement than WS grasses to maintain acceptable quality (1–9, ≥6 = minimum acceptable) and coverage. Among CS grasses, Barserati Kentucky bluegrass maintained the best quality and green cover under deficit irrigation and demonstrated the most consistent ability to recover. Notably, bermudagrass performed well under deficit irrigation, maintaining acceptable visual quality and better green cover than CS species like Kentucky bluegrass and tall fescue at lower irrigation levels. Overall, there were significant differences among cultivars, demonstrating the importance of the selection process in drought tolerance. These findings support the promotion of drought-resistant WS grasses to conserve water in arid regions without compromising turfgrass functionality. Future research should focus on variable and seasonal ET_OS for irrigation of turfgrasses and estimating irrigation requirements.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High plant density optimizes leaf stomatal traits for accelerating the stomatal response rate at the lower cotton canopy","authors":"Xilin Li,&nbsp;Xiaoming Li,&nbsp;Tong Zhang,&nbsp;Xiaofei Xue,&nbsp;Yunjing Dai,&nbsp;Zhangying Lei,&nbsp;Daohua He","doi":"10.1002/csc2.21443","DOIUrl":"10.1002/csc2.21443","url":null,"abstract":"<p>Plants are often exposed to fluctuating light from a few seconds to a few minutes due to cloud movements, mutual shading of leaves, and change in the angle of the sun. Slow stomatal response to fluctuating light leads to carbon loss, but the influence of planting density on light fluctuation frequency and on stomatal response and carbon gain has yet to be fully explored. To fill this knowledge gap, we investigated leaf morphology, stomatal anatomy and response rate, nitrogen content, biomass, and yield under low density, moderate density, and high density (HD) of cotton cultivar (<i>Gossypium hirsutum</i> L.). The results showed that higher planting density significantly increased light fluctuation frequency at the lower canopy. Stomatal size significantly decreased with the increase in planting density, while total stomatal density was consistent. Stomatal density had greater plasticity of determining maximum stomatal conductance than stomatal size. Faster stomatal response rate to fluctuating light under HD was attributed to smaller and denser stomata in the abaxial leaf side. Therefore, cotton under HD treatment had faster photosynthetic induction rate under light induction, resulting in greater carbon gain. We conclude that faster stomatal response rate achieved by the optimization of stomatal anatomy, especially the abaxial side, plays a crucial role in obtaining more carbon gain, biomass, and yield under HD cotton field. This finding indicates that selecting varieties with rapid stomatal response traits and planting at appropriate densities may optimize fluctuating light use to achieve higher yields.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive analytics of selections of russet potatoes","authors":"Fabiana Ferracina,&nbsp;Bala Krishnamoorthy,&nbsp;Mahantesh Halappanavar,&nbsp;Shengwei Hu,&nbsp;Vidyasagar Sathuvalli","doi":"10.1002/csc2.21432","DOIUrl":"10.1002/csc2.21432","url":null,"abstract":"<p>We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (<i>Solanum tuberosum</i> L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trade-offs between early planting and yellow rust resistance in wheat: Insights from screening experiments in the Indo-Gangetic plain","authors":"Md. Farhad,&nbsp;Shashi B. Tripathi,&nbsp;Ravi P. Singh,&nbsp;Arun K. Joshi,&nbsp;Pradeep K. Bhati,&nbsp;Uttam Kumar","doi":"10.1002/csc2.21442","DOIUrl":"10.1002/csc2.21442","url":null,"abstract":"<p>Wheat crops (<i>Triticum aestivum</i>) that are conventionally planted may exhibit susceptibility to yellow rust (YR). However, the disease can be mitigated if the crops are planted earlier than the recommended planting time. A wheat screening experiment was carried out at the Borlaug Institute of South Asia located in Ludhiana, Punjab, India. The purpose of the study was to gain a deeper understanding of the adaptation patterns of early planted wheat. Early planting was found to be more advantageous for production potential, as well as phenology, stature, and physiological traits. In a separate experiment, each year, the same number of genotypes were screened for YR by artificially inoculating them with pathogen spores. The well-adapted genotypes for early establishment tend to possess a greater vulnerability to YR infection. Furthermore, the infection type score for the genotype selected for early planting showed a significantly greater proportion of S (susceptible) type reactions than for the genotypes adapted to early planting. Intriguingly, more R (resistant) and moderately resistant types of reactions were observed in early-adapted genotypes than in timely-adapted ones. Therefore, further concentrated research on YR screening is required to assess the possibility of breeding early sown wheat in the northwest part of the Indo-Gangetic region.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust genomic prediction and heritability estimation using density power divergence","authors":"Upama Paul Chowdhury,&nbsp;Ronit Bhattacharjee,&nbsp;Susmita Das,&nbsp;Abhik Ghosh","doi":"10.1002/csc2.21430","DOIUrl":"10.1002/csc2.21430","url":null,"abstract":"<p>This manuscript delves into the intersection of genomics and phenotypic prediction, focusing on the statistical innovation required to navigate the complexities introduced by noisy covariates and confounders. The primary emphasis is on the development of advanced robust statistical models tailored for genomic prediction from single nucleotide polymorphism data in plant and animal breeding and multi-field trials. The manuscript highlights the significance of incorporating all estimated effects of marker loci into the statistical framework and aiming to reduce the high dimensionality of data while preserving critical information. This paper introduces a new robust statistical framework for genomic prediction, employing one-stage and two-stage linear mixed model analyses along with utilizing the popular robust minimum density power divergence estimator (MDPDE) to estimate genetic effects on phenotypic traits. The study illustrates the superior performance of the proposed MDPDE-based genomic prediction and associated heritability estimation procedures over existing competitors through extensive empirical experiments on artificial datasets and application to a real-life maize breeding dataset. The results showcase the robustness and accuracy of the proposed MDPDE-based approaches, especially in the presence of data contamination, emphasizing their potential applications in improving breeding programs and advancing genomic prediction of phenotyping traits.</p>","PeriodicalId":10849,"journal":{"name":"Crop Science","volume":"65 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}