Breeding Cycle Research Articles

Advancing utilization of diverse global carrot (Daucus carota L.) germplasm with flowering habit trait ontology.

Biennial vegetable crops are challenging to breed due to long breeding cycle times. At the same time, it is important to preserve a strong biennial growth habit, avoiding premature flowering that renders the crop unmarketable. Gene banks carry important genetic variation which may be essential to improve crop resilience, but these collections are underutilized due to lack of characterization for key traits like bolting tendency for biennial vegetable crops. Due to concerns about introducing undesirable traits such as premature flowering into elite germplasm, many accessions may not be considered for other key traits that benefit growers, leaving crops more vulnerable to pests, diseases, and abiotic stresses. In this study, we develop a method for characterizing flowering to identify accessions that are predominantly biennial, which could be incorporated into biennial breeding programs without substantially increasing the risk of annual growth habits. This should increase the use of these accessions if they are also sources of other important traits such as disease resistance. We developed the CarrotOmics flowering habit trait ontology and evaluated flowering habit in the largest (N=695), and most diverse collection of cultivated carrots studied to date. Over 80% of accessions were collected from the Eurasian supercontinent, which includes the primary and secondary centers of carrot diversity. We successfully identified untapped genetic diversity in biennial carrot germplasm (n=197 with 0% plants flowering) and predominantly-biennial germplasm (n=357 with <15% plants flowering). High broad-sense heritability for flowering habit (0.81 < H2< 0.93) indicates a strong genetic component of this trait, suggesting that these carrot accessions should be consistently biennial. Breeders can select biennial plants and eliminate annual plants from a predominantly biennial population. The establishment of the predominantly biennial subcategory nearly doubles the availability of germplasm with commercial potential and accounts for 54% of the germplasm collection we evaluated. This subcollection is a useful source of genetic diversity for breeders. This method could also be applied to other biennial vegetable genetic resources and to introduce higher levels of genetic diversity into commercial cultivars, to reduce crop genetic vulnerability. We encourage breeders and researchers of biennial crops to optimize this strategy for their particular crop.

Validation of Diets with Tomato Pomace in Complete Cycle Breeding of Tenebrio molitor (L.) (Coleoptera: Tenebrionidae).

By-product-based diets have the potential to improve the environmental and economic sustainability of Tenebrio molitor (Linnaeus, 1758) production. However, evaluations of the efficacy of new diets are generally focused on larval performance, while the effect on adults is poorly understood. This aim of this study was to evaluate diets enriched with tomato pomace over a complete breeding cycle. The results showed that when used as an oviposition substrate, all the tested diets, including tomato pomace (T), outperformed the control bran-yeast diet (WY, 95:5 ratio), possibly due to the presence of cholesterol and linoleic acid. The adults fed with the bran-tomato pomace-brewer's spent grain diet (WTB, 50:27:23 ratio), the bran-tomato pomace-yeast diet (WTY, 50:41:9 ratio), and the bran-tomato pomace diet (WT, 50:50 ratio) produced significantly more larvae than those fed with the WY diet. The WTB diet (despite being yeast-free) performed similarly to the WY control diet during the subsequent larval growth phase, making it suitable for the entire production cycle. In conclusion, the results show that tomato pomace can be used a valid by-product in the formulation of efficient diets for the breeding of T. molitor and also provide an alternative to expensive yeast.

Deep learning-based intelligent precise aeration strategy for factory recirculating aquaculture systems

Factory recirculating aquaculture system (RAS) is facing in a stage of continuous research and technological innovation. Intelligent aquaculture is an important direction for the future development of aquaculture. However, the RAS nowdays still has poor self-learning and optimal decision-making capabilities, which leads to high aquaculture cost and low running efficiency. In this paper, a precise aeration strategy based on deep learning is designed for improving the healthy growth of breeding objects. Firstly, the situation perception driven by computer vision is used to detect the hypoxia behavior. Then combined with the biological energy model, it is constructed to calculate the breeding objects oxygen consumption. Finally, the optimal adaptive aeration strategy is generated according to hypoxia behavior judgement and biological energy model. Experimental results show that the energy consumption of proposed precise aeration strategy decreased by 26.3% compared with the manual control and 12.8% compared with the threshold control. Meanwhile, stable water quality conditions accelerated breeding objects growth, and the breeding cycle with the average weight of 400 g was shortened from 5 to 6 months to 3–4 months.

Genomics-assisted speed breeding for crop improvement: present and future

Global agricultural productivity and food security are threatened by climate change, the growing world population, and the difficulties posed by the pandemic era. To overcome these challenges and meet food requirements, breeders have applied and implemented different advanced techniques that accelerate plant development and increase crop selection effectiveness. However, only two or three generations could be advanced annually using these approaches. Speed breeding (SB) is an innovative and promising technology to develop new varieties in a shorter time, utilizing the manipulation of controlled environmental conditions. This strategy can reduce the generation length from 2.5 to 5 times compared to traditional methods and accelerate generation advancement and crop improvement, accommodating multiple generations of crops per year. Beside long breeding cycles, SB can address other challenges related to traditional breeding, such as response to environmental conditions, disease and pest management, genetic uniformity, and improving resource efficiency. Combining genomic approaches such as marker-assisted selection, genomic selection, and genome editing with SB offers the capacity to further enhance breeding efficiency by reducing breeding cycle time, enabling early phenotypic assessment, efficient resource utilization, and increasing selection accuracy and genetic gain per year. Genomics-assisted SB holds the potential to revolutionize plant breeding by significantly accelerating the identification and selection of desirable genetic traits, expediting the development of improved crop varieties crucial for addressing global agricultural challenges.

A Logistic Regression Model to Predict Malaria Severity in Children

One of the main causes of death around the globe is malaria. Researchers have sought to develop predicting models for malaria outbreaks based on metrological data, climate data and the breeding cycle of plasmodium, the causative agent of malaria. This study predicts the severity of malaria based on environmental and biological factors. A logistic regression model was developed in this study to predict the severity of malaria based on such factors as sickle cell disease, stagnant water, garbage dumps, wet lawns, and the use of treated mosquito nets with an 83.3% accuracy rate. The study was carried out in the Bosomtwe District of Ghana with 417 respondents. It was deduced that although children in the district are highly prone to malaria infection, the severity is very low. The study recommends that not just having a good sample size alone is important during machine learning model development but also having a good sample representation of the various class labels is equally important.

Analyzing the Instabilities in the Venus Atmosphere Using Bred Vectors

AbstractBarotropic, baroclinic, and Rossby‐Kelvin instabilities exist in the Venus atmosphere, as revealed by previous studies using numerical models. This study aims to deepen our understanding of the instabilities in the Venus atmosphere using the breeding of growing modes (BGM) method for the first time. We conducted a 1‐year model simulation as the control run. First, we conducted identical twin experiments in which random perturbations were added to the control run at the initial time, and they grew freely. Perturbations in the upper layer (UL, 70–100 km altitudes) grow faster at the beginning but saturate earlier at a low value compared to those in the lower layer (LL, 40–70 km altitudes). Next, we implemented the BGM method and conducted multiple breeding cycle experiments with different rescaling amplitudes and intervals, the two parameters of the BGM method. The bred vectors (BVs) from these experiments identified instabilities in various regions. With large rescaling amplitudes, the structure and amplitude of the BVs in the LL closely resembled the deviations in the control run, indicating the growth of BVs due to barotropic, baroclinic, and Rossby‐Kelvin instabilities. Composite mean analysis shows larger BV amplitudes in the morning hemisphere at the 60–70 km altitudes in the mid‐latitudes, indicating enhanced baroclinic instability by the thermal tide. Finally, we estimated the intrinsic predictability related to baroclinic instability for Venus is &gt;1 month, which would be longer than that for the Earth of ∼2 weeks.

The complex polyploid genome architecture of sugarcane

Sugarcane, the world’s most harvested crop by tonnage, has shaped global history, trade and geopolitics, and is currently responsible for 80% of sugar production worldwide1. While traditional sugarcane breeding methods have effectively generated cultivars adapted to new environments and pathogens, sugar yield improvements have recently plateaued2. The cessation of yield gains may be due to limited genetic diversity within breeding populations, long breeding cycles and the complexity of its genome, the latter preventing breeders from taking advantage of the recent explosion of whole-genome sequencing that has benefited many other crops. Thus, modern sugarcane hybrids are the last remaining major crop without a reference-quality genome. Here we take a major step towards advancing sugarcane biotechnology by generating a polyploid reference genome for R570, a typical modern cultivar derived from interspecific hybridization between the domesticated species (Saccharum officinarum) and the wild species (Saccharum spontaneum). In contrast to the existing single haplotype (‘monoploid’) representation of R570, our 8.7 billion base assembly contains a complete representation of unique DNA sequences across the approximately 12 chromosome copies in this polyploid genome. Using this highly contiguous genome assembly, we filled a previously unsized gap within an R570 physical genetic map to describe the likely causal genes underlying the single-copy Bru1 brown rust resistance locus. This polyploid genome assembly with fine-grain descriptions of genome architecture and molecular targets for biotechnology will help accelerate molecular and transgenic breeding and adaptation of sugarcane to future environmental conditions.

How Do Escape Distance Behavior of Broiler Chickens Change in Response to a Mobile Robot Moving at Two Different Speeds?

In poultry farming, robots are considered by birds as intruder elements to their environment, because animals escape due to their movement. Their escape is measured using the escape distance (ED) technique. This study analyzes the behavior of animals in relation to their ED through the use of a robot with two speeds: 12 rpm and 26 rpm. The objective is to understand whether the speeds cause variations in ED and their implications for animal stress. A broiler breeding cycle was analyzed (six weeks) through the introduction of the robot weekly. ED analyses were carried out on static images generated from footage of the robot running. The results indicate higher escape distance rates (p < 0.05) peaking midway through the production cycle, notably in the third week. Conversely, the final weeks saw the lowest ED, with the most significant reduction occurring in the last week. This pattern indicates a gradual escalation of ED up to the fourth week, followed by a subsequent decline. Despite RPM12 having shown low ED results, it did not show enough ED to move the animals away from their path of travel, causing bumps and collisions. RPM26 showed higher ED in all breeding phases, but showed ED with no bumps and collisions.

Bioactive Lignan Honokiol Alleviates Ovarian Oxidative Stress in Aging Laying Chickens by Regulating SIRT3/AMPK Pathway.

Aging is not only a key internal cause of age-related diseases in humans but also poses a threat to the productivity of farm animals with longer breeding cycles, such as laying chickens. Various measures were taken to prolong the laying period by reducing oxidative stress to improve poultry ovarian functions. Within the mitochondria, SIRT3, a member of the Sirtuin family, plays an important role in post-translational modifications and the regulation of protein activities involved in energy metabolism and oxidative response. This study aimed to investigate the alleviating effect of a bioactive lignan Honokiol (HKL) on oxidative stress in aging chicken ovaries in order to retard decline in egg production. The results showed that HKL treatment restored the abnormal balance between cell proliferation and apoptosis, and it enhanced the antioxidant capacity of the H2O2-induced small white follicles (SWFs) by activating the SIRT3/AMPK pathway. Moreover, HKL significantly increased total egg production, the number of yellow follicles, and the mRNA expression of yolk synthesis and deposition-related genes, serum estrogen, and antioxidant levels. These findings suggest that HKL holds promise in enhancing the egg productivity of aging laying chickens by promoting yolk deposition and reducing ovarian oxidative stress.

The effect of selenium on the proliferation of bovine endometrial epithelial cells in a lipopolysaccharide-induced damage model

BackgroundEndometritis is a common bovine postpartum disease. Rapid endometrial repair is beneficial for forming natural defense barriers and lets cows enter the next breeding cycle as soon as possible. Selenium (Se) is an essential trace element closely related to growth and development in animals. This study aims to observe the effect of Se on the proliferation of bovine endometrial epithelial cells (BEECs) induced by lipopolysaccharide (LPS) and to elucidate the possible underlying mechanism.ResultsIn this study, we developed a BEECs damage model using LPS. Flow cytometry, cell scratch test and EdU proliferation assay were used to evaluate the cell cycle, migration and proliferation. The mRNA transcriptions of growth factors were detected by quantitative reverse transcription-polymerase chain reaction. The activation of the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) and Wnt/β-catenin pathways were detected by Western blotting and immunofluorescence. The results showed that the cell viability and BCL-2/BAX protein ratio were significantly decreased, and the cell apoptosis rate was significantly increased in the LPS group. Compared with the LPS group, Se promoted cell cycle progression, increased cell migration and proliferation, and significantly increased the gene expressions of TGFB1, TGFB3 and VEGFA. Se decreased the BCL-2/BAX protein ratio, promoted β-catenin translocation from the cytoplasm to the nucleus and activated the Wnt/β-catenin and PI3K/AKT signaling pathways inhibited by LPS.ConclusionsIn conclusion, Se can attenuate LPS-induced damage to BEECs and promote cell proliferation and migration in vitro by enhancing growth factors gene expression and activating the PI3K/AKT and Wnt/β-catenin signaling pathways.

Transcriptome Analysis of Intermittent Light Induced Early Bolting in Flowering Chinese Cabbage.

In flowering Chinese cabbage, early booting is one of the most important characteristics that is linked with quality and production. Through fixed light intensity (280 μmol·m-2·s-1) and fixed intermittent lighting in flowering Chinese cabbage, there was early bolting, bud emergence, and flowering. Moreover, the aboveground fresh weight, blade area, dry weight of blade, and quantification of the leaves in flowering Chinese cabbage were significantly reduced, while the thickness of tillers, tillers height, dry weight of tillers, and tillers weight were significantly increased. The chlorophyll contents and soil-plant analysis and development (SPAD) value decreased in the early stage and increased in the later stage. The nitrate content decreased, while the photosynthetic rate, vitamin C content, soluble sugar content, soluble protein content, phenolic content, and flavonoid content increased, and mineral elements also accumulated. In order to explore the mechanism of intermittent light promoting the early bolting and flowering of '49d' flowering Chinese cabbage, this study analyzed the transcriptional regulation from a global perspective using RNA sequencing. A total of 17,086 differentially expressed genes (DEGs) were obtained and 396 DEGs were selected that were closely related to early bolting. These DEGs were mainly involved in pollen wall assembly and plant circadian rhythm pathways, light action (34 DEGs), hormone biosynthesis and regulation (26 DEGs), development (21 DEGs), and carbohydrate synthesis and transport (6 DEGs). Three hub genes with the highest connectivity were identified through weighted gene co-expression network analysis (WGCNA): BrRVE, BrLHY, and BrRVE1. It is speculated that they may be involved in the intermittent light regulation of early bolting in flowering Chinese cabbage. In conclusion, intermittent light can be used as a useful tool to regulate plant growth structure, increase planting density, enhance photosynthesis, increase mineral accumulation, accelerate growth, and shorten the breeding cycle.

Vietnamese cassava varieties progression across 50 years

Vietnamese cassava varieties constitute the fundamental and pivotal element in the sustainable development programme for cassava. This article aims to encapsulate the advancements made over nearly five decades in breeding and enhancing Vietnamese cassava varieties. It delineates the suitable cassava variety structures for each period and ecological region. The selection of cassava varieties exhibiting high starch yield and disease resistance, coupled with the establishment of a suitable and efficient cassava cultivation model, exemplified by 10T for Vietnamese cassava varieties KM568, KM539, KM537, KM569, and KM94, stands as a cornerstone for sustaining cassava development over the years. Presently, we advocate for farmers to cultivate promising cassava varieties such as KM568 or KM539 (an enhanced version of the International Center for Tropical Agriculture (CIAT) cassava variety C39, refined through multiple breeding cycles from 2004 onwards), KM537, KM569, or HN1 (originally known as TMEB419), alongside popular cassava varieties: KM440, KM419, KM94, KM7, STB1, KM414, KM98-7, KM140, KM98-5, KM98-1. We have conducted Distinctness, Uniformity, and Stability (DUS) and Value for Cultivation and Use (VCU) tests, showcasing outstanding cassava varieties in large-scale farming, thereby providing compelling evidence for the prudent conservation and sustainable development of cassava. Vietnamese cassava progression (1975 to date) has traversed six stages, with five waves of restructuring cassava varieties, aligning with target orientations, farming conditions, and market demands, culminating in 16 popular cassava varieties and four promising cassava varieties KM568, KM539, KM537, and KM569.

Modeling post-Pleistocene megafauna extinctions as complex social-ecological systems

Abstract The role of human hunting behavior versus climate change in the mass extinction of megafauna during the Late Quaternary is much debated. To move beyond monocausal arguments, we treat human–megafauna–environment relationships as social–ecological systems from a complex adaptive systems perspective, to create an agent-based model that tests how human hunting may interact with environmental stress and animal life history to affect the probability of extinction. Using the extinction of Syncerus antiquus in South Africa at 12–10 ka as a loose inspirational case study, we parameterized a set of experiments to identify cross-feedbacks among environmental dynamics, prey life history, and human hunting pressure that affect extinction probability in a non-linear way. An important anthropogenic boundary condition emerges when hunting strategies interrupt prey animal breeding cycles. This effect is amplified in patchy, highly seasonal environments to increase the chances of extinction. This modeling approach to human behavior and biodiversity loss helps us understand how these types of cross-feedback effects and boundary conditions emerge as system components interact and change. We argue that this approach can help translate archaeological data and insight about past extinction for use in understanding and combating the current mass extinction crisis.

Pigs lacking the SRCR5 domain of CD163 protein demonstrate heritable resistance to the PRRS virus and no changes in animal performance from birth to maturity.

Porcine reproductive and respiratory syndrome (PRRS) is one of the world's most persistent viral pig diseases, with a significant economic impact on the pig industry. PRRS affects pigs of all ages, causing late-term abortions and stillbirths in sows, respiratory disease in piglets, and increased susceptibility to secondary bacterial infection with a high mortality rate. PRRS disease is caused by a positive single-stranded RNA PRRS virus (PRRSV), which has a narrow host-cell tropism limited to monocyte-macrophage lineage cells. Several studies demonstrated that the removal of CD163 protein or, as a minimum, its scavenger receptor cysteine-rich domain 5 (SRCR5) precludes the viral genome release, conferring resistance to PRRSV in live animals. Today, very limited information exists about the impact of such edits on animal performance from birth to maturity in pigs. Using CRISPR-Cas9 with dual-guide RNAs and non-homologous end joining (NHEJ), first-generation (E0) pigs were produced with a deletion of exon 7 in the CD163 gene. The selected pigs were bred to produce the next three generations of pigs to establish multiple lines of pigs homozygous for the edited allele, thereby confirming that the CD163 gene with removed exon 7 was stable during multiple breeding cycles. The pigs were evaluated relative to non-edited pigs from birth to maturity, including any potential changes in meat composition and resistance to PRRSV. This study demonstrates that removing the SRCR5 domain from the CD163 protein confers resistance to PRRSV and, relative to unedited pigs, resulted in no detected differences in meat composition and no changes in the growth rate, health, and ability to farrow. Together, these results support the targeted use of gene editing in livestock animals to address significant diseases without adversely impacting the health and well-being of the animals or the food products derived from them.

Sugarcane breeding: a fantastic past and promising future driven by technology and methods.

Sugarcane is the most important sugar and energy crop in the world. During sugarcane breeding, technology is the requirement and methods are the means. As we know, seed is the cornerstone of the development of the sugarcane industry. Over the past century, with the advancement of technology and the expansion of methods, sugarcane breeding has continued to improve, and sugarcane production has realized a leaping growth, providing a large amount of essential sugar and clean energy for the long-term mankind development, especially in the face of the future threats of world population explosion, reduction of available arable land, and various biotic and abiotic stresses. Moreover, due to narrow genetic foundation, serious varietal degradation, lack of breakthrough varieties, as well as long breeding cycle and low probability of gene polymerization, it is particularly important to realize the leapfrog development of sugarcane breeding by seizing the opportunity for the emerging Breeding 4.0, and making full use of modern biotechnology including but not limited to whole genome selection, transgene, gene editing, and synthetic biology, combined with information technology such as remote sensing and deep learning. In view of this, we focus on sugarcane breeding from the perspective of technology and methods, reviewing the main history, pointing out the current status and challenges, and providing a reasonable outlook on the prospects of smart breeding.

Nesting habitat of the Brown-throated Martin Riparia paludicola mauritanica in Morocco

The Brown-throated Martin Riparia paludicola (subspecies mauritanica) of family Hirundinidae is endemic to Morocco and known for its ability to dig tunnels in earthen banks in which it nests. The nature of the sediment and the morphological features of the nest holes and riverbank habitat have not been thoroughly described across the species’ range in Morocco. Here, we present a qualitative and quantitative analysis of the textural characteristics of the sediment chosen by the martins in two successive breeding seasons, using a sample of 104 nests, which covered most of the species’ distribution range in Morocco. We also studied the surrounding landscape and morphological features of a total of 114 nests. Our observations show that besides nesting in natural riverbank habitat, the Brown-throated Martin can use both manmade embankments and substrates (even cement and concrete) to carry out a successful breeding cycle. This study provides important information on the nesting habitat of the Brown-throated Martin across Morocco. We believe that such descriptive work can help with the conservation of this Moroccan endemic by better targeting the right habitat to either protect or restore. The findings also include information useful to assist in creating new nesting sites for the species.

Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles

Key messageWe validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations.Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybrid values across breeding cycles. It relies on two breeding cycles of a maize reciprocal genomic selection scheme involving multiparental connected reciprocal populations from flint and dent complementary heterotic groups selected for silage performances. Selection based on genomic predictions trained on a factorial design resulted in a significant genetic gain for dry matter yield in the new generation. Results confirmed the efficiency of sparse factorial training sets to predict candidate line GCAs and hybrid values across breeding cycles. Compared to a previous study based on the first generation, the advantage of factorial over tester training sets appeared lower across generations. Updating factorial training sets by adding single-cross hybrids between selected lines from the previous generation or a random subset of hybrids from the new generation both improved predictive abilities. The CDmean criterion helped determine the set of single-crosses to phenotype to update the training set efficiently. Our results validated the efficiency of sparse factorial designs for calibrating hybrid genomic prediction experimentally and showed the benefit of updating it along generations.

Sex‐specific resource use by wild turkeys in response to hunting activity

AbstractHunting causes direct mortality and potentially disrupts normal activities of game and non‐game species. As spatial (i.e., selection of hunting areas) and temporal (i.e., only diurnally present) patterns of hunters can become predictable, hunted species may respond accordingly. Risk becomes more dynamic and complex for species that are hunted concurrent with their breeding cycle, and growing literature has noted that wild turkey (Meleagris gallopavo spp.) behaviors can be altered by hunting activity. We allocated global positioning system (GPS) units to 1,500 wild turkey hunters and affixed GPS transmitters to 175 wild turkeys during 2014–2018 on the Webb Wildlife Management Area Complex in South Carolina, USA. We evaluated whether wild turkeys shifted resource selection as a function of hunter resource selection during the progression of hunting seasons. Male wild turkeys avoided areas where stationary hunting bouts occurred during the early hunting season (and selected for these areas before hunting began), whereas females were more likely to select those areas avoided by males by the end of the hunting season. For every 15% increase in predicted probability of an area being hunted, male wild turkeys were 4.16 times less likely to select that area, whereas female wild turkeys were 1.08 times more likely to select that area relative to pre‐season periods when hunters were not on the landscape. Hunting activity induced immediate responses by male wild turkeys as they sought refuge away from hunted areas. Coupled with recent research suggesting hunters are more influential than natural predators in reducing the frequency of male vocalizations and eliciting fleeing and avoidance behavior, our results indicate hunting activity could affect distribution and courtship behaviors of male wild turkeys during their breeding season.

Aspects of Breeding Performance of Scopoli’s Shearwater (Calonectris diomedea): The Case of the Largest Colony in Greece

Here we report, for the first time, aspects of the breeding performance of Scopoli’s Shearwater (Calonectris diomedea) in one of its largest colonies in Europe, i.e., in the Strofades island group. We describe the chronology of the main events in the breeding cycle of this species on Stamfani Island, the largest island of this island group, including the evaluation of breeding performance and the influence of ecological factors (nesting habitat, nest type, nest-entrance orientation, and occupation rate per nest) on breeding success. The Scopoli’s Shearwater colony of Stamfani Island exhibited a high degree of breeding synchrony and nest site tenacity. The data obtained by monitoring 472 nests during five consecutive years (2008–2012), showed a breeding success (fledging per occupied nest per year) of up to 66.6%. In addition, hatching success (chick hatched successfully per egg laid) was 76.9%, and fledging success (fledging young per chick hatched successfully) was 86.8%. We also observed annual variations in breeding performance during that period. These results seemed to be influenced positively by the breeding experience of the pair. Furthermore, the type of nest site and the nest-entrance orientation were found to have an effect on breeding success rates, whereas the nesting habitat did not, indicating low predation risk by rats.

An Optimal Model to Improve Genomic Prediction for Protein Content and Test Weight in a Diverse Spring Wheat Panel

In recent years, genomic selection has been widely used in plant breeding to increase genetic gain. Selections are based on breeding values of each genotype estimated using genome-wide markers. The present study developed genomic prediction models for grain protein content (GPC) and test weight (TW) in a diverse panel of 170 spring wheat lines phenotyped in five environments. Five prediction models (GBLUP, RRBLUP, EGBLUP, RF, RKHS) were investigated. The population was genotyped for genome-wide markers with the Infinium iSelect 90 K SNP assay. Environmental variation was adjusted by calculating BLUPs across environments using the complete random effect GxE model. Both GPC and TW showed high heritability of 0.867 and 0.854, respectively. When using the five-fold cross-validation scheme in the five statistical models, we found that the EGBLUP model had the highest mean prediction accuracy (0.743) for GPC, while the RRBLUP model showed the highest mean prediction accuracy (0.650) for TW. Testing various proportions of the training population indicated that a minimum of 100 genotypes were required to train the model for optimum accuracy. Testing the prediction across environments showed that BLUPs outperformed 80% of the tested environments, even though at least one of the environments had higher prediction accuracies for each trait. Thus, the optimized GS model for GPC and TW has the potential to predict trait values accurately. Implementing GS would aid breeding through accurate early generation selection of superior lines, leading to higher genetic gain per breeding cycle.