Interaktionen zwischen Genotyp und Ort sind ein Vorteil von lokaler Züchtung von Ackerbohnen (Vicia faba L.)

Organic farming is based on low productivity environments regarding the restricted supply of inputs. Thus, to maximize the response of selection, the local breeding approach was put forward for organic agriculture. To analyse this approach, efforts of organic farmers and scientists were combined in a pertinent, participatory breeding approach using spring faba bean as crop. A set of genotypes with different levels of heterozygosity and heterogeneity were tested in four organic farms and one conventional location. Results for grain yield showed that the genotype-location interaction was highly significant and contributed to a large extent to the yield variation. The findings indicated that indeed local breeding programs could give greater genetic gains for organic cropping systems than traditional, formal plant breeding programs

Effectiveness of spatial analysis in Cryptomeria japonica D. Don (sugi) forward selection revealed by validation using progeny and clonal tests

International audienceAbstractKey messageAccurate evaluation of genetic performances of trees is crucial in order to improve the efficiency of forest tree breeding. We revealed that spatial analysis is effective for predicting individual tree breeding values at the forward selection stage ofCryptomeria japonicaD. Don (sugi) breeding program by using a novel validation approach.ContextIn the process of selecting genetically superior trees for breeding, appropriate handling of environmental effects is important in order to precisely evaluate candidate trees. Spatial analysis has been an effective statistical approach for genetic evaluation at sites with heterogeneous microenvironments. However, the efficiency of spatial analysis on forward selection has not been validated on a practical scale to date.AimsThis study aimed to reveal the effectiveness of spatial analysis, which incorporates spatially autocorrelated residuals into mixed models, for the prediction of breeding values at the forward selection stage by validation using progeny or clonal tests of forward-selected individuals.MethodsTree height was analyzed by ordinary randomized complete block design models and spatial models incorporating spatially autocorrelated residuals in a linear mixed model framework, and model selection was conducted at thirty Cryptomeria japonica D. Don breeding population sites having various topographical ruggedness. For validation, three clonal tests and one progeny test of individuals selected from three and four breeding populations, respectively, were used. The effectiveness of forward selection using the two models was evaluated based on the correlation between individual breeding values at the stage of forward selection and genotypic and breeding values that were estimated by clonal and progeny tests.ResultsSpatial models were more predictive than ordinary models in all cases. Spatial correlation parameters tend to increase with the topographical ruggedness index of each site. The correlation coefficients between breeding values at the time of forward selection and genotypic or breeding values evaluated in succeeding clonal and progeny tests were significantly higher in spatial models than in ordinary models in six out of nine cases.ConclusionValidation using progeny and clonal tests of forward-selected individual trees revealed that spatial analysis is more effective for the evaluation of genetic performance of individuals at the stage of forward selection in Cryptomeria japonica

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

A Novel Discipline in Embryology — Animal Embryo Breeding

The modern animal biotechnologies, such as animal cloning, transgenesis, sex determination, stem cells, designing new livestock, must be performed on animal gametes including sperm and oocytes, and embryos based on embryology theory. Currently, some key biotechnologies in embryology have become the most powerful tool for animal scientists and breeders to improve genetic construction of animal herds. Here, authors put forward a new concept of Animal Embryo Breeding Science to describe this discipline formation, development, and application in animal genetic improvement and breeding. The relationship of embryo breeding with other disciplines has been profiled. Thus, animal scientists and breeders can easily understand and apply embryo breeding theory and related key techniques to accelerate animal improvement speed, to modify genetic construction of animal population, and to design and create new animal individual or breed

Genomic Selection for Processing and End-Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program

Citation: Battenfield, S. D., Guzman, C., Gaynor, R. C., Singh, R. P., Pena, R. J., Dreisigacker, S., . . . Poland, J. A. (2016). Genomic Selection for Processing and End-Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program. Plant Genome, 9(2), 12. doi:10.3835/plantgenome2016.01.0005Wheat (Triticum aestivum L.) cultivars must possess suitable end-use quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely because of amount of seed needed and expense. Without testing and selection, many undesirable materials are advanced, expending additional resources. Here, we develop and validate whole-genome prediction models for end-use quality phenotypes in the CIMMYT bread wheat breeding program. Model accuracy was tested using forward prediction on breeding lines (n = 5520) tested in unbalanced yield trials from 2009 to 2015 at Ciudad Obregon, Sonora, Mexico. Quality parameters included test weight, 1000-kernel weight, hardness, grain and flour protein, flour yield, sodium dodecyl sulfate sedimentation, Mixograph and Alveograph performance, and loaf volume. In general, prediction accuracy substantially increased over time as more data was available to train the model. Reflecting practical implementation of genomic selection (GS) in the breeding program, forward prediction accuracies (r) for quality parameters were assessed in 2015 and ranged from 0.32 (grain hardness) to 0.62 (mixing time). Increased selection intensity was possible with GS since more entries can be genotyped than phenotyped and expected genetic gain was 1.4 to 2.7 times higher across all traits than phenotypic selection. Given the limitations in measuring many lines for quality, we conclude that GS is a powerful tool to facilitate early generation selection for end-use quality in wheat, leaving larger populations for selection on yield during advanced testing and leading to better gain for both quality and yield in bread wheat breeding programs

An integrated molecular and conventional breeding scheme for enhancing genetic gain in maize in Africa

Open Access Journal; Published online: 06 Nov 2019Maize production in West and Central Africa (WCA) is constrained by a wide range of interacting stresses that keep productivity below potential yields. Among the many problems afflicting maize production in WCA, drought, foliar diseases, and parasitic weeds are the most critical. Several decades of efforts devoted to the genetic improvement of maize have resulted in remarkable genetic gain, leading to increased yields of maize on farmers’ fields. The revolution unfolding in the areas of genomics, bioinformatics, and phenomics is generating innovative tools, resources, and technologies for transforming crop breeding programs. It is envisaged that such tools will be integrated within maize breeding programs, thereby advancing these programs and addressing current and future challenges. Accordingly, the maize improvement program within International Institute of Tropical Agriculture (IITA) is undergoing a process of modernization through the introduction of innovative tools and new schemes that are expected to enhance genetic gains and impact on smallholder farmers in the region. Genomic tools enable genetic dissections of complex traits and promote an understanding of the physiological basis of key agronomic and nutritional quality traits. Marker-aided selection and genome-wide selection schemes are being implemented to accelerate genetic gain relating to yield, resilience, and nutritional quality. Therefore, strategies that effectively combine genotypic information with data from field phenotyping and laboratory-based analysis are currently being optimized. Molecular breeding, guided by methodically defined product profiles tailored to different agroecological zones and conditions of climate change, supported by state-of-the-art decision-making tools, is pivotal for the advancement of modern, genomics-aided maize improvement programs. Accelerated genetic gain, in turn, catalyzes a faster variety replacement rate. It is critical to forge and strengthen partnerships for enhancing the impacts of breeding products on farmers’ livelihood. IITA has well-established channels for delivering its research products/technologies to partner organizations for further testing, multiplication, and dissemination across various countries within the subregion. Capacity building of national agricultural research system (NARS) will facilitate the smooth transfer of technologies and best practices from IITA and its partners

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits