Early Differences in the Intensity of Alternate Bearing Among Selected Pistachio Genotypes

Alternate bearing (alternating years with high and low yields) is a prominent characteristic of ‘Kerman’ pistachio (Pistacia vera L.), the primary California cultivar. The degree of alternate bearing is described by alternate bearing index values from 0 (identical yields every year) to 1 (complete alternate bearing). Two separate and replicated trials designed to evaluate selections from a breeding program were conducted in the southwest (Kern County) and northeast (Madera County) areas of the San Joaquin Valley of California. Yields from the scion genotypes ‘Kerman’, ‘Golden Hills’, ‘Lost Hills’, ‘B5-8’, and ‘B19-1’ on PG1 rootstock were measured from 5- to 9-year-old trees in Kern County and from 5- to 7-year-old ‘Kerman’, ‘Golden Hills’, ‘Lost Hills’, and ‘B5-8’ trees on PG1 and UCB1 rootstock in Madera County. In Kern County, average annual yields among genotypes varied from a low of 208 to a high of 5273 kg·ha−1. Differences in the alternate bearing indices among genotypes were significant and ranged from 0.10 for ‘Lost Hills’ to 0.80 for ‘B19-1’. A similar pattern was observed for alternate bearing indices at the Madera County trial. In this younger trial, scion genotype had more influence on alternate bearing indices than did rootstock. Marked differences in the intensity of alternate bearing of young trees in these two trials suggest that alternate bearing might be amenable to selection in breeding programs. However, the observation that ‘B5-8’, with an alternate bearing index of 0.74, varied significantly from its female parent ‘Kerman’ at 0.36 suggests that inheritance is complex.

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597 PB 179 CROP LOAD-RELATED DEFORMITY OF DEVELOPING PISTACIA VERA CV KERMAN NUTS

HortScience ◽

10.21273/hortsci.29.5.517e ◽

1994 ◽

Vol 29 (5) ◽

pp. 517e-517

Author(s):

Louise Ferguson ◽

Patrick Niven ◽

Andrea Fabbri ◽

Lara Dallo ◽

Walter Bentley ◽

...

Keyword(s):

Significant Loss ◽

San Joaquin Valley ◽

Pistacia Vera ◽

Alternate Bearing ◽

Crop Load ◽

Parenchyma Cells ◽

Cell Layers ◽

Individual Cluster

A deformity designated as `damage by other means' (DBOM) by California pistachio processors appeared in California's San Joaquin Valley orchards in 1990. Incidence, higher during the heavy crop year of this alternate bearing cultivar, was as high as 5% of harvested yield. This represents a significant loss as DBOM nuts cannot be used for shelling stock. In 1993 ten weekly individual cluster samples from five heavily and five lightly cropped trees demonstrated a higher incidence of DBOM on heavily cropped trees. Further the damage occurred within one month of nut set, was exclusively on subterminal, adaxial positions of the rachis, and, often did not involve the nutmeat unless the deformity was extensive enough to expose the developing nutlet causing desiccation and abscission. Microanatomical studies demonstrated a deterioration of the parenchyma cells that form the inner cell layers of the endocarp (nut shell).

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Matoa (Pometia Pinata Foster): Local Fruit Papua and Strategy Development

Beccariana Botanical Research Bulletin ◽

10.30862/bbrp.v7i1.276 ◽

2010 ◽

Vol 7 (1) ◽

Author(s):

Julius D. Nugroho

Keyword(s):

Genetic Resources ◽

Fruit Tree ◽

Breeding Program ◽

Strategy Development ◽

Local Market ◽

Font Size ◽

Breeding Programs ◽

Irian Jaya ◽

Forest Exploitation ◽

Program Strategy

Matoa (Pometia pinnata) is a local fruit of  Papua (formerly called Irian Jaya) which has high potensial to develop as comercial fruit. Highly significant genetic resources of matoa potentially for breeding program in Papua are being threatened as a result of cutting down trees for fruit harvesting and of forest exploitation for timber. Besides the loss of genetic resources facing now, other major problems should be consider for conservation and domestication of this fruit tree species i.e. lack of silviculture and agronomy knowledge for further breeding programs; matoa production only for local market; and inadequate government policy for matoa breeding program. Strategy developed for matoa conservation and domestication should also concern about time limited due to the fast loss of genetic poll. This paper provides a general overview of strategy for conservation and domestication of Pometia pinnata with special reference to Papua.

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Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing

Sensors ◽

10.3390/s21123971 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3971

Author(s):

Gabriel Silva de Oliveira ◽

José Marcato Junior ◽

Caio Polidoro ◽

Lucas Prado Osco ◽

Henrique Siqueira ◽

...

Keyword(s):

Remote Sensing ◽

Dry Matter ◽

Pearson Correlation ◽

Genetic Correlations ◽

Remote Sensing Data ◽

Breeding Program ◽

Yield Traits ◽

Dry Matter Yield ◽

Breeding Programs ◽

Ground Truth Data

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha−1 for LDMY and from 413.07 to 506.56 kg·ha−1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs.

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Phenotypic and molecular characterization of a set of tropical maize inbred lines from a public breeding program in Brazil

BMC Genomics ◽

10.1186/s12864-021-08127-7 ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Sirlene Viana de Faria ◽

Leandro Tonello Zuffo ◽

Wemerson Mendonça Rezende ◽

Diego Gonçalves Caixeta ◽

Hélcio Duarte Pereira ◽

...

Keyword(s):

Genetic Diversity ◽

Population Structure ◽

Inbred Lines ◽

Breeding Program ◽

Tropical Maize ◽

Maize Breeding ◽

Breeding Programs ◽

The Public ◽

Maize Inbred Lines

Abstract Background The characterization of genetic diversity and population differentiation for maize inbred lines from breeding programs is of great value in assisting breeders in maintaining and potentially increasing the rate of genetic gain. In our study, we characterized a set of 187 tropical maize inbred lines from the public breeding program of the Universidade Federal de Viçosa (UFV) in Brazil based on 18 agronomic traits and 3,083 single nucleotide polymorphisms (SNP) markers to evaluate whether this set of inbred lines represents a panel of tropical maize inbred lines for association mapping analysis and investigate the population structure and patterns of relationships among the inbred lines from UFV for better exploitation in our maize breeding program. Results Our results showed that there was large phenotypic and genotypic variation in the set of tropical maize inbred lines from the UFV maize breeding program. We also found high genetic diversity (GD = 0.34) and low pairwise kinship coefficients among the maize inbred lines (only approximately 4.00 % of the pairwise relative kinship was above 0.50) in the set of inbred lines. The LD decay distance over all ten chromosomes in the entire set of maize lines with r2 = 0.1 was 276,237 kb. Concerning the population structure, our results from the model-based STRUCTURE and principal component analysis methods distinguished the inbred lines into three subpopulations, with high consistency maintained between both results. Additionally, the clustering analysis based on phenotypic and molecular data grouped the inbred lines into 14 and 22 genetic divergence clusters, respectively. Conclusions Our results indicate that the set of tropical maize inbred lines from UFV maize breeding programs can comprise a panel of tropical maize inbred lines suitable for a genome-wide association study to dissect the variation of complex quantitative traits in maize, mainly in tropical environments. In addition, our results will be very useful for assisting us in the assignment of heterotic groups and the selection of the best parental combinations for new breeding crosses, mapping populations, mapping synthetic populations, guiding crosses that target highly heterotic and yielding hybrids, and predicting untested hybrids in the public breeding program UFV.

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Investigation of Genetic Relationships within Miscanthus using SNP Markers Identified using SLAF-Seq

10.21203/rs.3.rs-152687/v1 ◽

2021 ◽

Author(s):

ZHIYONG Chen ◽

Yancen He ◽

Yasir Iqbal ◽

Yanlan Shi ◽

Hongmei Huang ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Genetic Relationships ◽

Female Parent ◽

Snp Markers ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Breeding Programs ◽

Sequencing Method ◽

Specific Locus

Abstract Background: Miscanthus, which is a leading dedicated-energy grass in Europe and in parts of Asia, is expected to play a key role in the development of the future bioeconomy. However, due to its complex genetic background, it is difficult to investigate phylogenetic relationships and the evolution of gene function in this genus. Here, we investigated 50 Miscanthus germplasms: 1 female parent (M. lutarioriparius), 30 candidate male parents (M. lutarioriparius, M. sinensis, and M. sacchariflorus), and 19 offspring. We used high-throughput Specific-Locus Amplified Fragment sequencing (SLAF-seq) to identify informative single nucleotide polymorphisms (SNPs) in all germplasms.Results: We identified 800,081 SLAF tags, of which 160,368 were polymorphic. Each tag was 264–364 bp long. The obtained SNPs were used to investigate genetic relationships within Miscanthus. We constructed a phylogenetic tree of the 50 germplasms using the obtained SNPs, and found that the germplasms fell into two clades: one clade of M. sinensis only and one clade that included the offspring, M. lutarioriparius, and M. sacchariflorus. Genetic cluster analysis indicated that M. lutarioriparius germplasm C3 was the most likely male parent of the offspring.Conclusions: As a high-throughput sequencing method, SLAF-seq can be used to identify informative SNPs in Miscanthus germplasms and to rapidly characterize genetic relationships within this genus. Our results will support the development of breeding programs utilizing Miscanthus cultivars with elite biomass- or fiber-production potential.

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MoBPSweb: A web-based framework to simulate and compare breeding programs

10.1101/2020.07.08.193227 ◽

2020 ◽

Cited By ~ 1

Author(s):

T. Pook ◽

L. Büttgen ◽

A. Ganesan ◽

N.T. Ha ◽

H. Simianer

Keyword(s):

Selection Index ◽

Relevant Information ◽

Simulation Software ◽

Breeding Program ◽

Breeding Value ◽

Long Term Effects ◽

General Applicability ◽

Breeding Programs ◽

Web Based ◽

Value Estimation

ABSTRACTSelective breeding is a continued element of both crop and livestock breeding since early prehistory. In this work, we are proposing a new web-based simulation framework (“MoBPSweb”) that is combining a unified language to describe breeding programs with the simulation software MoBPS, standing for ‘Modular Breeding Program Simulator’. Thereby, MoBPSweb is providing a flexible environment to enter, simulate, evaluate and compare breeding programs. Inputs can be provided via modules ranging from a Vis.js-based flash environment for “drawing” the breeding program to a variety of modules to provide phenotype information, economic parameters and other relevant information. Similarly, results of the simulation study can be extracted and compared to other scenarios via output modules (e.g. observed phenotypes, accuracy of breeding value estimation, inbreeding rates). Usability of the framework is showcased along a toy example of a dairy cattle breeding program on farm level, with comparing scenarios differing in implemented breeding value estimation, selection index and selection intensity being considered. Comparisons are made considering both short and long-term effects of the different scenarios in terms of genomic gains, rates of inbreeding and the accuracy of the breeding value estimation. Lastly, general applicability of the MoBPSweb framework and the general potential for simulation studies for genetics and in particular in breeding are discussed.

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Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

Frontiers in Genetics ◽

10.3389/fgene.2021.637017 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jana Obšteter ◽

Janez Jenko ◽

Gregor Gorjanc

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Breeding Program ◽

Progeny Testing ◽

Initial Training ◽

Training Population ◽

Breeding Programs ◽

Use Of Resources ◽

Dairy Cattle Breeding ◽

Close Relatives

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

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The Sigmocheirini, a xystodesmid milliped tribe in the Sierra Nevada Mountains, California, U.S.A. (Polydesmida: Xystodesmidae)

Insect Systematics & Evolution ◽

10.1163/187631295x00044 ◽

1995 ◽

Vol 26 (3) ◽

pp. 339-360 ◽

Cited By ~ 3

Author(s):

Rowland M. Shelley

Keyword(s):

Sierra Nevada ◽

New Genus ◽

San Joaquin Valley ◽

Western Slope ◽

Kern County ◽

Pigmentation Pattern ◽

Sierra Nevada Mountains

AbstractThe xystodesmid milliped tribe Sigmocheirini occupies a band along the western slope of the Sierra Nevada Mountains and eastern fringe of the San Joaquin Valley from Placer to Kern counties, California. It is comprised of two genera, Sigmocheir Chamberlin, with three species occurring from Placer to Tulare counties, and the monotypic Ochthocelata gen. n., the sole component, O. adynata sp. n., occurring in northern Kern County. The species of Sigmocheir display a distinctive, trimaculate pigmentation pattern with yellow middorsal and paranotal spots; the coloration of O. adynata is unknown. Sigmocheir calaveras Chamberlin is a senior name for S. dohenyi Chamberlin, the spelling of which was subsequently corrected to danehyi and assigned to the new genus, Tuolumnia, a synonym of Sigmocheir. Sigmocheir furcata sp. n. is proposed for forms from the northern generic range. The southernmost species is S. maculifer (Chamberlin), comb. n., transferred from Harpaphe Cook. The Sigmocheirini are related to the sympatric tribe Xystocheirini; relationships within Sigmocheir are hypothesized as maculifer + (calaveras + furcata).

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ARE TODAY’S DAIRY CATTLE BREEDING PROGRAMS SUITABLE FOR TOMORROW’S PRODUCTION REQUIREMENTS?

Canadian Journal of Animal Science ◽

10.4141/cjas80-034 ◽

1980 ◽

Vol 60 (2) ◽

pp. 253-264 ◽

Cited By ~ 4

Author(s):

A. J. McALLISTER

Keyword(s):

Dairy Cattle ◽

Genetic Improvement ◽

Herd Size ◽

Breeding Program ◽

Genetic Trend ◽

Genetic Progress ◽

Breeding Programs ◽

Holstein Breed ◽

Dairy Cattle Breeding ◽

National Production

In the last decade the dairy cattle population has declined to a level of 1.9 million cows in 1978 with about 56% of these cows bred AI and nearly 20% of the population enrolled in a supervised milk recording program. The decline in cow numbers has been accompanied by an increase in herd size and production per cow. The current breeding program of the dairy industry is a composite of breeding decisions made by AI organizations, breeders who produce young bulls for sampling and all dairymen who choose the sires and dams of their replacement heifers. Estimates of genetic trend from 1958–1975 for milk production in the national milk recorded herd range from 21 to 55 kg per year for the four dairy breeds with Holsteins being 41 kg per year. Both differential use of superior proven sires and improved genetic merit of young bulls entering AI studs contribute to this genetic improvement. Various national production and marketing alternatives were examined. Selection is a major breeding tool in establishing a breeding program to meet national production requirements for milk and milk products once the selection goal is defined. AI and young sire sampling programs will continue to be the primary vehicle for genetic improvement through selection regardless of the selection goal. The current resources of milk-recorded cows bred AI is not being fully utilized to achieve maximum genetic progress possible from young sire sampling indicate that the number of young bulls sampled annually in the Holstein breed could be tripled with the existing milk-recorded and AI bred dairy cow population. Expanded milk recording and AI breeding levels could increase the potential for even further genetic improvement. The potential impact of selection for other traits, crossbreeding and the use of embryo transfer of future breeding programs is highlighted.

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Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program

Theoretical and Applied Genetics ◽

10.1007/s00122-020-03696-9 ◽

2020 ◽

Author(s):

Sikiru Adeniyi Atanda ◽

Michael Olsen ◽

Juan Burgueño ◽

Jose Crossa ◽

Daniel Dzidzienyo ◽

...

Keyword(s):

Genomic Selection ◽

Prediction Accuracy ◽

Large Scale ◽

Primary Objective ◽

Breeding Program ◽

Breeding Cycle ◽

Training Set ◽

Maize Breeding ◽

Phenotypic Data ◽

Breeding Programs

Abstract Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. Abstract The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

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