Grassland Research最新文献

{"title":"Synergies between microsites of plant communities and steady-stage alpine meadows on the Qinghai–Tibetan Plateau","authors":"Li Lin,&nbsp;Xingliang Xu,&nbsp;Guangmin Cao,&nbsp;Fawei Zhang,&nbsp;Yikang Li,&nbsp;Bo Fan,&nbsp;Qian Li,&nbsp;Junjie Huang","doi":"10.1002/glr2.12057","DOIUrl":"10.1002/glr2.12057","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Due to the effects of climate change and overgrazing in recent decades, alternative stable states in the alpine <i>Kobresia</i> meadow degradation process have coexisted in the same geographical and climatic environment, with variations occurring among microsites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used a space-for-time substitution approach to explore the synergies of microsite variation according to its numerical characteristics and the proportion of each stable state at various stages of succession in alpine <i>Kobresia</i> meadows on the Qinghai–Tibetan Plateau.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>(1) The highest average aboveground biomass in summer was 196.2 ± 20.3 g m⁻², with significantly higher levels of biomass in ≤3.65 sheep unit ha⁻¹ than in other levels of grazing intensity, while the parameters showed no significant differences among grazing intensity levels in &gt;3.65 sheep unit ha⁻¹. (2) The importance of plant functional groups, aboveground biomass, and niche breadth of Poaceae and Cyperaceae significantly decreased as the grazing intensity increased. (3) The effects of ≥0°C accumulated temperature, total precipitation, altitude, longitude, and latitude cumulatively contributed less than 20% of the variation in the distribution of functional group characteristics across microsites.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>(1) Overgrazing decreases primary production in alpine <i>Kobresia</i> meadows, but ecosystem responses regulate plant community structure and botanical components so as to partially counteract grazing disturbance. (2) Overgrazing changed the proportion of microsites, which in turn led to regime shift in the plant community and subsequent synergies between the microsites of plant communities and their stable states.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"2 4","pages":"289-298"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/glr2.12057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135153897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic prediction of seasonal forage yield in perennial ryegrass","authors":"Agnieszka Konkolewska, Steffie Phang, Patrick Conaghan, D. Milbourne, Aonghus Lawlor, Stephen Byrne","doi":"10.1002/glr2.12058","DOIUrl":"https://doi.org/10.1002/glr2.12058","url":null,"abstract":"Genomic selection has the potential to accelerate genetic gain in perennial ryegrass breeding, provided complex traits such as forage yield can be predicted with sufficient accuracy.In this study, we compared modelling approaches and feature selection strategies to evaluate the accuracy of genomic prediction models for seasonal forage yield production.Overall, model selection had limited impact on predictive ability when using the full data set. For a baseline genomic best linear unbiased prediction model, the highest mean predictive accuracy was obtained for spring grazing (0.78), summer grazing (0.62) and second cut silage (0.56). In terms of feature selection strategies, using uncorrelated single‐nucleotide polymorphisms (SNPs) had no impact on predictive ability, allowing for a potential decrease of the data set dimensions. With a genome‐wide association study, we found a significant SNP marker for spring grazing, located in the genic region annotated as coding for an enzyme responsible for fucosylation of xyloglucans—major components of the plant cell wall. We also presented an approach to increase interpretability of genomic prediction models with the use of Gene Ontology enrichment analysis.Approaches for feature selection will be relevant in development of low‐cost genotyping platforms in support of routine and cost‐effective implementation of genomic selection.","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86819632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic prediction of seasonal forage yield in perennial ryegrass","authors":"Agnieszka Konkolewska,&nbsp;Steffie Phang,&nbsp;Patrick Conaghan,&nbsp;Dan Milbourne,&nbsp;Aonghus Lawlor,&nbsp;Stephen Byrne","doi":"10.1002/glr2.12058","DOIUrl":"https://doi.org/10.1002/glr2.12058","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Genomic selection has the potential to accelerate genetic gain in perennial ryegrass breeding, provided complex traits such as forage yield can be predicted with sufficient accuracy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this study, we compared modelling approaches and feature selection strategies to evaluate the accuracy of genomic prediction models for seasonal forage yield production.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Overall, model selection had limited impact on predictive ability when using the full data set. For a baseline genomic best linear unbiased prediction model, the highest mean predictive accuracy was obtained for spring grazing (0.78), summer grazing (0.62) and second cut silage (0.56). In terms of feature selection strategies, using uncorrelated single-nucleotide polymorphisms (SNPs) had no impact on predictive ability, allowing for a potential decrease of the data set dimensions. With a genome-wide association study, we found a significant SNP marker for spring grazing, located in the genic region annotated as coding for an enzyme responsible for fucosylation of xyloglucans—major components of the plant cell wall. We also presented an approach to increase interpretability of genomic prediction models with the use of Gene Ontology enrichment analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Approaches for feature selection will be relevant in development of low-cost genotyping platforms in support of routine and cost-effective implementation of genomic selection.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"2 3","pages":"167-181"},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71948237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arachis species: High-quality forage crops—nutritional properties and breeding strategies to expand their utilization and feeding value","authors":"Hui Song,&nbsp;Yafeng Huang,&nbsp;Lanlan Ding,&nbsp;Zhenquan Duan,&nbsp;Jiancheng Zhang","doi":"10.1002/glr2.12059","DOIUrl":"https://doi.org/10.1002/glr2.12059","url":null,"abstract":"<p>Plants of the genus <i>Arachis</i> originated from South America and are cultivated worldwide. The genus <i>Arachis</i> contains 83 species and nine intrageneric taxonomic sections. The cultivated peanut (<i>Arachis hypogaea</i> L.) belongs to the <i>Arachis</i> section, the forage peanut (<i>Arachis pintoi</i> Krapov. &amp; W. C. Greg.) belongs to the <i>Caulorrhizae</i> section, and the perennial peanut (<i>Arachis glabrata</i> Benth.) belongs to the <i>Rhizomatosae</i> section. These three peanut species have been developed for use as fodder crops. This review summarizes the forage value of <i>Arachis</i> species. Forage and perennial peanuts can be intercropped with forage species to feed livestock. The cultivated peanut vines and peanut by-products, such as peanut skins and peanut meal, are also high-quality fodder used to feed sheep, cattle, and poultry. A major limiting factor in terms of adopting forage and perennial peanuts as forage crops is their limited resistance to frosts, resulting from their low winter hardiness. Therefore, the feeding value of cultivated peanuts is higher compared to forage and perennial peanuts. This review suggests that <i>Arachis</i> is a suitable forage crop, focusing on their nutritional properties and breeding to increase their performance under cultivation and feeding value.</p>","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"2 3","pages":"212-219"},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71986770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arachis species: High‐quality forage crops—nutritional properties and breeding strategies to expand their utilization and feeding value","authors":"Hui Song, Yafeng Huang, Lanlan Ding, Zhenquan Duan, Jiancheng Zhang","doi":"10.1002/glr2.12059","DOIUrl":"https://doi.org/10.1002/glr2.12059","url":null,"abstract":"Plants of the genus Arachis originated from South America and are cultivated worldwide. The genus Arachis contains 83 species and nine intrageneric taxonomic sections. The cultivated peanut (Arachis hypogaea L.) belongs to the Arachis section, the forage peanut (Arachis pintoi Krapov. & W. C. Greg.) belongs to the Caulorrhizae section, and the perennial peanut (Arachis glabrata Benth.) belongs to the Rhizomatosae section. These three peanut species have been developed for use as fodder crops. This review summarizes the forage value of Arachis species. Forage and perennial peanuts can be intercropped with forage species to feed livestock. The cultivated peanut vines and peanut by‐products, such as peanut skins and peanut meal, are also high‐quality fodder used to feed sheep, cattle, and poultry. A major limiting factor in terms of adopting forage and perennial peanuts as forage crops is their limited resistance to frosts, resulting from their low winter hardiness. Therefore, the feeding value of cultivated peanuts is higher compared to forage and perennial peanuts. This review suggests that Arachis is a suitable forage crop, focusing on their nutritional properties and breeding to increase their performance under cultivation and feeding value.","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73652137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revitalizing the grassland on the Qinghai–Tibetan Plateau","authors":"Shikui Dong","doi":"10.1002/glr2.12055","DOIUrl":"https://doi.org/10.1002/glr2.12055","url":null,"abstract":"<p>Grassland is the largest ecosystem on the Qinghai–Tibetan Plateau (QTP) and provides multiple ecosystem functions and services. Understanding the endowment of the QTP grassland and how to revitalize it have profound implications for the sustainable use and efficient conservation of these unique and globally valuable ecosystems. In this paper, we highlight the importance of the QTP grassland in regional and global settings, stress the values of the QTP grassland in ecological and socioeconomic dimensions, and emphasize the actions needed to restore degraded grassland in the QTP region. The QTP is the largest single area of alpine grassland in the world and an important gene pool of alpine biological resources. The QTP grassland covers two critical ecoregions for conserving the best and most representative habitats for alpine biodiversity on the planet. The QTP grassland is also regarded as one of the best carriers and objects of socio-ecological systems in the world. To promote the resilience and sustainability of the QTP grassland through adaptation, different parties need to work together to find feasible options to resist shock, stresses, and disturbance and to maintain the fundamental functions and basic structures of the QTP grassland.</p>","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"2 3","pages":"241-250"},"PeriodicalIF":0.0,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71986272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revitalizing the grassland on the Qinghai–Tibetan Plateau","authors":"S. Dong","doi":"10.1002/glr2.12055","DOIUrl":"https://doi.org/10.1002/glr2.12055","url":null,"abstract":"Grassland is the largest ecosystem on the Qinghai–Tibetan Plateau (QTP) and provides multiple ecosystem functions and services. Understanding the endowment of the QTP grassland and how to revitalize it have profound implications for the sustainable use and efficient conservation of these unique and globally valuable ecosystems. In this paper, we highlight the importance of the QTP grassland in regional and global settings, stress the values of the QTP grassland in ecological and socioeconomic dimensions, and emphasize the actions needed to restore degraded grassland in the QTP region. The QTP is the largest single area of alpine grassland in the world and an important gene pool of alpine biological resources. The QTP grassland covers two critical ecoregions for conserving the best and most representative habitats for alpine biodiversity on the planet. The QTP grassland is also regarded as one of the best carriers and objects of socio‐ecological systems in the world. To promote the resilience and sustainability of the QTP grassland through adaptation, different parties need to work together to find feasible options to resist shock, stresses, and disturbance and to maintain the fundamental functions and basic structures of the QTP grassland.","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81219911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia","authors":"Gezahagn Kebede,&nbsp;Walelign Worku,&nbsp;Habte Jifar,&nbsp;Fekede Feyissa","doi":"10.1002/glr2.12056","DOIUrl":"https://doi.org/10.1002/glr2.12056","url":null,"abstract":"The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.The study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.The pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression , the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance , indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones.Therefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"2 3","pages":"182-196"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71940589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia","authors":"Gezahagn Kebede, W. Worku, Habte Jifar, Fekede Feyissa","doi":"10.1002/glr2.12056","DOIUrl":"https://doi.org/10.1002/glr2.12056","url":null,"abstract":"The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.The study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.The pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression , the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance , indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones.Therefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77696448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection of rhizobial strains differing in their nodulation kinetics under low temperature in four temperate legume species","authors":"Emmanuelle D’Amours, A. Bertrand, J. Cloutier, François-P. Chalifour, A. Claessens, S. Rocher, M. Bipfubusa, Chantal Giroux, C. J. Beauchamp","doi":"10.1002/glr2.12054","DOIUrl":"https://doi.org/10.1002/glr2.12054","url":null,"abstract":"","PeriodicalId":100593,"journal":{"name":"Grassland Research","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79657240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}