Agricultural Field Experiments

Petersen, R. G.

doi:10.1201/9781482277371

Cited by 156 publications

(45 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A new set of crosses were produced in every year. There were multiple trials nested within each experimental site, and the trial design was randomized complete block design 35 .…”

Section: Methodsmentioning

confidence: 99%

Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat

Tsai

Janss

Andersen³

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Genome-wide association study (GWAS) and genomic prediction (Gp) are extensively employed to accelerate genetic gain and identify QTL in plant breeding. In this study, 1,317 spring barley and 1,325 winter wheat breeding lines from a commercial breeding program were genotyped with the illumina 9 K barley or 15 K wheat SNP-chip, and phenotyped in multiple years and locations. For GWAS, in spring barley, a QTL on chr. 4H associated with powdery mildew and ramularia resistance were found. There were several SNPs on chr. 4H showing genome-wide significance with yield traits. In winter wheat, GWAS identified two SNPs on chr. 6A, and one SNP on chr. 1B, significantly associated with quality trait moisture, as well as one SNP located on chr. 5B associated with starch content in the seeds. The significant SNPs identified by multiple trait GWAS were generally the same as those found in single trait GWAS. GWAS including genotype-location information in the model identified significant SNPs in each tested location, which were not found previously when including all locations in the GWAS. for Gp, in spring barley, GP using the Bayesian Power Lasso model had higher accuracy than ridge regression BLUP in powdery mildew and yield traits, whereas the prediction accuracies were similar using Bayesian Power Lasso model and rrBLUP for yield traits in winter wheat. Both wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) are major cereal crops worldwide, ranking as the second and fourth in total grain production. The global wheat and barley production amounted to 736 and 146 million metric tons in 2016, with 21% and 41% contributed by European countries, respectiely 1. Due to their economic role worldwide, the advancement of economically important traits of wheat and barley is indispensable, in order to meet the food demands of growing human population. Recent developments in genome sequencing technologies allow plant breeders to characterize the genetic architecture of economically important cereal crops. The reference genomes are now available in both spring barley and winter wheat 2-4 , raising the possibilities for plant breeders to introduce genome-assisted selection to both crops. Several high-density SNP microarrays enabling association mapping, and increase the resolution of quantitative trait loci (QTLs) mapping using hundreds of thousands of genetic polymorphisms throughout the entire genome, are now available. For example, there are several SNP arrays with the varying number of genetic variants customized for both barley and wheat 5-9. Traditionally, the molecular-assisted breeding, using molecular biology techniques, such as gene identification and gene functional characterization, to explain the genetic mechanism of economically important traits has been used in crops. However, several studies evidenced that most traits of commercial interest in wheat and barley are highly polygenic, with many QTLs that each only account for a small proportion of total genetic variances 10,11. This severely limits the applicatio...

show abstract

“…A new set of crosses were produced in every year. There were multiple trials nested within each experimental site, and the trial design was randomized complete block design 35 .…”

Section: Methodsmentioning

confidence: 99%

Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat

Tsai

Janss

Andersen³

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…For some GM perennial plants (e.g. trees), the plants themselves may be more appropriate experimental units than are field plots (Petersen, 1994). Care should be taken to choose an experimental design that does not suffer unduly from loss of plants during the trial.…”

Section: Experimental Designmentioning

confidence: 99%

Guidance on the environmental risk assessment of genetically modified plants

2010

EFSA Journal

329

261

View full text Add to dashboard Cite

This document provides guidance for the environmental risk assessment (ERA) of genetically modified (GM) plants submitted within the framework of Regulation (EC) No. 1829/2003 on GM food and feed or under Directive 2001/18/EC on the deliberate release into the environment of genetically modified organisms (GMOs). This document provides guidance for assessing potential effects of GM plants on the environment and the rationales for the data requirements for a comprehensive ERA of GM plants. The ERA should be carried out on a case‐by‐case basis, following a step‐by‐step assessment approach. This document describes the six steps for the ERA of GM plants, as indicated in Directive 2001/18/EC, starting with (1) problem formulation including hazard identification; (2) hazard characterisation; (3) exposure characterisation; (4) risk characterisation; (5) risk management strategies; and (6) an overall risk evaluation. The scientific Panel on Genetically Modified Organisms (of the European Food Safety Authority (EFSA GMO Panel) considers seven specific areas of concern to be addressed by applicants and risk assessors during the ERA (1) persistence and invasiveness of the GM plant, or its compatible relatives, including plant‐to‐plant gene transfer; (2) plant‐to‐micro‐organism gene transfer; (3) interaction of the GM plant with target organisms and (4) interaction of the GM plant with non‐target organisms, including criteria for selection of appropriate species and relevant functional groups for risk assessment; (5) impact of the specific cultivation, management and harvesting techniques; including consideration of the production systems and the receiving environment(s); (6) effects on biogeochemical processes; and (7) effects on human and animal health. Each specific area of concern is considered in a structured and systematic way following the above‐mentioned steps (1 to 6). In addition, the guidance document is supplemented with several general cross‐cutting considerations (e.g. choice of comparator, receiving environment(s), general statistical principles, long‐term effects) that need to be considered in the ERA.

show abstract

“…In the northeastern U.S., the chipping potato (Solanum tuberosum) variety Katadin is included as a standard in trials because it is prone to internal necrosis (J. Sieczka, personal communication). By agreeing upon some of fects, and 4) competition or border effects may be larger in small plots (Petersen, 1994). Different strategies have been suggested to estimate minimum plot size needed to minimize variance per plot based upon soil heterogeneity, error estimates from previous experiments and costs of the trial (Petersen, 1994;Sills and Nienhuis, 1993;Smith, 1938).…”

Section: Strategies To Enhance Rigor Of Variety Trialsmentioning

confidence: 99%

“…By agreeing upon some of fects, and 4) competition or border effects may be larger in small plots (Petersen, 1994). Different strategies have been suggested to estimate minimum plot size needed to minimize variance per plot based upon soil heterogeneity, error estimates from previous experiments and costs of the trial (Petersen, 1994;Sills and Nienhuis, 1993;Smith, 1938). Developing these types of localized estimates for vegetable experimental plot sizes would improve the efficiency of testing and rigor of experimental design.…”

Section: Strategies To Enhance Rigor Of Variety Trialsmentioning

confidence: 99%

“…Statistical programs, such as SAS (SAS Institute, Cary, N.C.) and Minitab (Minitab Inc, State College, Pa.), can integrate power, standard error and desired differences to provide a recommended replication number needed to detected that difference. It is important to remember that precision of a trial is usually improved faster by increasing the number of replicates, not increasing plot size (Petersen, 1994) and general performance information is optimized by increasing the number of locations, not the number of replications within a location (Talbot, 1997). Because information gained on variety performance from multiple sites is more useful than from individual sites, two replicates per location is commonly adequate.…”

Section: Strategies To Enhance Rigor Of Variety Trialsmentioning

confidence: 99%

See 1 more Smart Citation

Vigor or Rigor? The Competing Goals of Variety Trials

Rangarajan¹

2002

horttech

View full text Add to dashboard Cite

Both growers and vegetable seed companies have had long-term historic relationships with public agriculture extension educators and faculty to conduct unbiased evaluation of vegetable varieties. Reductions in both the number of vegetable seed companies as well as university human resources has led to questions about the viability and appropriateness of publicly-funded variety evaluation programs. Field based extension educators and regional staff have taken more leadership to evaluate varieties, but this often results in fragmented or repetitive trials with limited long term integration of data. Statewide vegetable extension specialists must provide the leadership in coordinating these trials to enhance the rigor of data collection and analysis. Fundamental to enhancing rigor is improving regional coordination and collaboration. The calculation of stability estimates for new and older varieties is most efficiently and quickly achieved through regional collaborations. Initial efforts should improve uniformity of trials by creating common evaluation methods for yield and qualitative evaluations (e.g., color, appearance), including two standard varieties (one local and one regional, long-term standard), standardizing field establishment practices, and selecting experimental designs and plot sizes to improve labor efficiency. These regionally coordinated trials will improve the ability to publish this type of applied research and demonstrate new levels of efficiency for university administrations. In the long term, carefully designed comparisons of genotypic performance among different environments could suggest new directions for university breeding programs as well as cropping systems research.

show abstract

Agricultural Field Experiments

Cited by 156 publications

References 0 publications

Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat

Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat

Guidance on the environmental risk assessment of genetically modified plants

Vigor or Rigor? The Competing Goals of Variety Trials

Contact Info

Product

Resources

About