Breeding Trials Research Articles

Efficient crop model parameter estimation and site characterization using large breeding trial data sets

Scientists have estimated that global crop production needs to double by 2050 to supply the demand for food, feed, and fuel. To reach this goal, novel methods are needed to increase breeding potential yield rates of gain as well as on-farm yields through enhanced management strategies. Both of these tasks require the ability to predict plant performance in multiple, dynamic environments based on a knowledge of cultivar characteristics (critical short day lengths, maximum leaf photosynthetic rates, pod fill durations, etc.) that are ultimately linked to genetics. Because of this linkage, we refer to such traits as genotype-specific parameters (GSP's). Using industry-provided yield and weather data from 353 site-years, we estimated seven primary CROPGRO-Soybean GSP's for each of 182 varieties. The data set had two shortcomings. First, no planting dates were supplied, rendering unknowable the environment actually experienced by the crop. Second, soil data were provided only for the top 20cm, which is inadequate to specify the root environment and water supply availability. Therefore, additional edaphic information was acquired. A novel optimization algorithm was developed that simultaneously estimates GSP's and planting dates, while tuning layered soil water-holding properties. The optimizer, which we have named the holographic genetic algorithm (HGA), uses both externally supplied constraints and its own analysis of data structure to reduce what would otherwise be a search over 2000 dimensions to a much smaller number of overlapping 1- to 3-D problems. Two types of runs were performed. The first was preceded by an independent component analysis (ICA) of published GSP's. The subsequent training sought good component scores rather than the GSP's themselves. The second, separate factor (SF) approach allowed all GSP's to vary separately. This makes parameters unconstrained and more evenly distributed. Results showed that HGA works quite well with the CROPGRO-Soybean model to estimate the cultivar and site-specific parameters from breeding trial data. The quality of the calibrations and evaluations were similar across both run types with RMSE values being ca. 5.6% of the maximum yields. Moreover, the GSP's for a variety can be used to predict its yield in trials not used in that cultivar's calibration. Finally, despite high dimensionality, the GSP's, planting dates, and soil properties for all lines and sites converged concurrently in <58 iterations, demonstrating great utility for use with big data sets.

STUDY OF TREE-TO-TREE YIELD VARIABILITY AMONG SEEDLING-BASED CACAO POPULATIONS IN AN INDUSTRIAL PLANTATION IN CÔTE D'IVOIRE

SUMMARYIn Côte d'Ivoire, the world's largest cocoa producer, cacao (Theobroma cacaoL.) is usually grown from seed. The genetics consist of a mix of amelonado, trinitario and selected hybrids. This mix of varieties generates high phenotypic variabilities, including variability in tree productivity, within cacao populations in both smallholder and industrial plantations. Tree-to-tree variability in yield has been reported in cacao breeding trials under different environmental conditions. However, it has never been considered a limiting factor for agronomical performance of seedling-based cacao plantations. Around 10 000 cacao trees from seven plots under different environmental conditions in a cacao plantation in Côte d'Ivoire were monitored for 2 years. Pod production of individual trees was recorded and annual average tree pod yields were assessed. High heterogeneity in cacao-tree yields was observed in all plots, with coefficients of variation ranging from 56 to 102%. The distribution of cacao-tree yields in each plot was positively skewed. Analysis of these distributions showed that unproductive trees represented a significant proportion of cacao tree populations (7%), and the 20% least productive trees accounted for 3% of production. The 20% most productive trees were responsible for 46% of the total pod production of a plot. This heterogeneity reflects a major imbalance in the agronomical performances of low- and high-yielding trees and also represents possible efficiency gaps in seedling-based cacao plantations, which could be overcome through innovative corrective strategies, opening new pathways for improvement of cacao-based cropping systems.

Large numbers of explanatory variables, a semi-descriptive analysis

Data with a relatively small number of study individuals and a very large number of potential explanatory features arise particularly, but by no means only, in genomics. A powerful method of analysis, the lasso [Tibshirani R (1996) J Roy Stat Soc B 58:267-288], takes account of an assumed sparsity of effects, that is, that most of the features are nugatory. Standard criteria for model fitting, such as the method of least squares, are modified by imposing a penalty for each explanatory variable used. There results a single model, leaving open the possibility that other sparse choices of explanatory features fit virtually equally well. The method suggested in this paper aims to specify simple models that are essentially equally effective, leaving detailed interpretation to the specifics of the particular study. The method hinges on the ability to make initially a very large number of separate analyses, allowing each explanatory feature to be assessed in combination with many other such features. Further stages allow the assessment of more complex patterns such as nonlinear and interactive dependences. The method has formal similarities to so-called partially balanced incomplete block designs introduced 80 years ago [Yates F (1936) J Agric Sci 26:424-455] for the study of large-scale plant breeding trials. The emphasis in this paper is strongly on exploratory analysis; the more formal statistical properties obtained under idealized assumptions will be reported separately.

Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle

While new technologies can capture high-resolution normalized difference vegetation index (NDVI), a surrogate for biomass and leaf greenness, it is a challenge to efficiently apply this technology in a large breeding program. Here we validate a high-throughput phenotyping platform to dynamically monitor NDVI during the growing season for the contrasting wheat cultivars and managements. The images were rapidly captured (approximately 1ha in 10min) by an unmanned aerial vehicle (UAV) carrying a multi-spectral camera (RedEdge) at low altitude (30–50m, 2–5cm2 pixel size). NDVIs for individual plots were extracted from the reflectance at Red and Near Infrared wavelengths represented in a reconstructed and segmented ortho-mosaic. NDVI measured by UAV and RedEdge camera were strongly correlated with those measured by hand held GreenSeeker (R2=0.85) but were offset with UAV readings about 0.2 units higher and more compressed. The high-throughput phenotyping platform captured the variation of NDVI among cultivars and treatments (i.e. irrigation, nitrogen and sowing). During the growing season, the NDVI approached saturation around flowering time (∼0.92), then gradually decreased until maturity (∼0.35). Strong correlations were found between image NDVI around flowering time and final yield (R2=0.82). Given that the image NDVI includes signals from background (soil and senescenced leaves), ground cover from a high resolution hand-held camera was used to adjust the NDVI from UAV. This slightly increased the correlation between adjusted NDVI and yield (R2=0.87). The high-throughput phenotyping platform in this study can be used in agronomy, physiology and breeding to explore the complex interaction of genotype, environment and management. Data fusion from ground and aerial sampling improved the accuracy of low resolution data to integrate observations across multiple scales.

Registration of ‘UA 5814HP’ Soybean with High Yield and High Seed‐Protein Content

‘UA 5814HP’ (Reg. No. CV‐522, PI 679957) soybean [Glycine max (L.) Merr.] was developed and released by the Arkansas Agricultural Experiment Station in 2014 as a high‐yielding and high‐protein late maturity group V conventional cultivar adapted to Arkansas and other southern states. UA 5814HP is an F4 selection from the cross of ‘R95‐1705’ and ‘S00‐9980‐22’. In the 2013–2014 Regional Quality Traits Tests for maturity group V, UA 5814HP was tested in 15 environments and yielded 4277 kg ha−1, which is comparable to the check (‘5601T’, ‘5002T’/‘Ellis’, and ‘Osage’) mean across 2 yr, 4374 kg ha−1. In the 2015 State Variety Tests conducted in Arkansas and other southern states (31 environments), UA 5814HP had an average yield (3723 kg ha−1) similar to that of ‘REV 57R21’ (Terral Seed) (3798 kg ha−1). In the Arkansas Soybean Breeding trials (12 environments; 2011–2015), UA 5814HP had higher seed‐protein content (455 g kg−1) than the check cultivars, ‘AG 5606’ (415 g kg−1, 2011–2013) and ‘AG 5831’ (395 g kg−1, 2014). UA 5814HP has a determinate growth habit, purple flowers, tawny pubescence, tan pod walls, and brown seed hilum. It is resistant to stem canker, frogeye leaf spot, and charcoal rot and is susceptible to races 2 and 5 of soybean cyst nematode, southern root‐knot nematode, and reniform nematode.

Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model

Key messageA flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials.Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.

On-board Breeding Trial of Hilsa (Tenualosa ilisha, Ham. 1822) and Testing of Larval Rearing in Bangladesh

The Hilsa shad, Tenualosa ilisha commonly known as Hilsa is one of the most commercially important fish species in South Asian countries. For the conservation of Hilsa it is necessary to establish a standard breeding and culture protocol along with the present Hilsa management activities. The on board breeding trial was conducted during 10 October 2016 to 02 November 2016 which was the peak breeding time of Hilsa for the year 2016. The male and female Hilsa broods were collected from the River Meghna using BFRI experimental net during afternoon and late evening of full moon and new moon time. A total of six breeding trials were conducted in which 13 pairs of selected Hilsa broods were used for the breeding trials. For the breeding trial, both eggs and milt were collected through stripping and then the eggs were mixed with the milt immediately. The fertilized eggs were transferred to a plastic hatching jar for incubation providing mild water circulation, aeration and shade to protect penetration of direct sunlight for controlling temperature i.e., to maintain congenial environment. During the incubation period, the eggs were observed for 24 hrs to study the embryonic and larval development stages of Hilsa. After 4.2-4.5 hrs of fertilization morula stage of embryonic development was identified and after 8-8.5 hrs. of fertilization 18-myotome stage of embryonic development was identified from the sixth breeding trial. After that, no embryonic development of egg was observed up to 12 hrs after fertilization and the fertilized eggs were found to be dead filled with fungus at the end. The water quality parameters were found in good range during the breeding trial though the temperature was found to be fluctuated from the optimum ranges needed for the incubation of fertilized eggs. Although it was not possible to be succeeded completely in artificial breeding of Hilsa, the experience of on board breeding trial in the major breeding ground of River Meghna will give the necessary insight for future works.

Conditional Ablation of Progesterone Receptor Membrane Component 2 Causes Female Premature Reproductive Senescence.

The nonclassical progesterone receptors progesterone receptor membrane component (PGRMC) 1 and PGRMC2 have been implicated in regulating cell survival of endometrial and ovarian cells in vitro and are abundantly expressed in these cell types. The objective of this study was to determine if Pgrmc1 and Pgrmc2 are essential for normal female reproduction. To accomplish this objective, Pgrmc1 and/or Pgrmc2 floxed mice (Pgrmc2fl/fl and Pgrmc1/2fl/fl) were crossed with Pgr-cre mice, which resulted in the conditional ablation of Pgrmc1 and/or Pgrmc2 from female reproductive tissues (i.e.,Pgrmc2d/d and Pgrmc1/2d/d mice). A breeding trial revealed that conditional ablation of Pgrmc2 initially led to subfertility, with Pgrmc2d/d female mice producing 47% fewer pups/litter than Pgrmc2fl/fl mice (P = 0.001). Pgrmc2d/d mice subsequently underwent premature reproductive senescence by parities 2 to 5, producing 37.8% fewer litters overall during the trial compared with Pgrmc2fl/fl mice (P = 0.020). Similar results were observed with Pgrmc1/2d/d mice. Based on ovarian morphology and serum P4, the subfertility/infertility was not due to faulty ovulation or luteal insufficiency. Rather an analysis of midgestation implantation sites revealed that postimplantation embryonic death was the major cause of the subfertility/infertility. As with our previous report of Pgrmc1d/d mice, Pgrmc2d/d and Pgrmc1/2d/d mice developed endometrial cysts consistent with accelerated aging of this tissue. Given the timing of postimplantation embryonic demise, uterine decidualization may be disrupted in mice deficient in PGRMC2 or PGRMC1/2. Overall, this study revealed that Pgrmc1 and/or Pgrmc2 are required for the maintenance of uterine histoarchitecture and normal female reproductive lifespan.

Residual Variance–Covariance Modelling in Analysis of Multivariate Data from Variety Selection Trials

Field trials for variety selection often exhibit spatial correlation between plots. When multivariate data are analysed from these field trials, there is the added complication in having to simultaneously account for correlation between the traits at both the residual and genetic levels. This may be temporal correlation in the case of multi-harvest data from perennial crop field trials, or between-trait correlation in multi-trait data sets. Use of parsimonious yet plausible models for the variance–covariance structure of the residuals for such data is a key element to achieving an efficient and inferentially sound analysis. In this paper, a model is developed for the residual variance–covariance structure firstly by considering a multivariate autoregressive model in one spatial direction and then extending this to two spatial directions. Conditions for ensuring that the processes are directionally invariant are presented. Using a canonical decomposition, these directionally invariant processes can be transformed into a set of independent separable processes. This simplifies the estimation process. The new model allows for flexible modelling of the spatial and multivariate interaction and allows for different spatial correlation parameters for each harvest or trait. The methods are illustrated using data from lucerne breeding trials at several environments.

A Hierarchical Bayesian Estimation Model for Multienvironment Plant Breeding Trials in Successive Years

In agriculture and plant breeding, multienvironment trials over multiple years are conducted to evaluate and predict genotypic performance under different environmental conditions and to analyze, study, and interpret genotype × environment interaction (G × E). In this study, we propose a hierarchical Bayesian formulation of a linear–bilinear model, where the conditional conjugate prior for the bilinear (multiplicative) G × E term is the matrix von Mises–Fisher (mVMF) distribution (with environments and sites defined as synonymous). A hierarchical normal structure is assumed for linear effects of sites, and priors for precision parameters are assumed to follow gamma distributions. Bivariate highest posterior density (HPD) regions for the posterior multiplicative components of the interaction are shown within the usual biplots. Simulated and real maize (Zea mays L.) breeding multisite data sets were analyzed. Results showed that the proposed model facilitates identifying groups of genotypes and sites that cause G × E across years and within years, since the hierarchical Bayesian structure allows using plant breeding data from different years by borrowing information among them. This model offers the researcher valuable information about G × E patterns not only for each 1‐yr period of the breeding trials but also for the general process that originates the response across these periods.

Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars

Climate change is expected to impact global food supply and food security by affecting growing conditions for agricultural production. Process-based dynamic growth models are important tools to estimate crop yields based on minimum inputs of climate, soil, crop management, and crop cultivar parameters. Using region-specific cultivar parameters is critical when applying crop models at a global scale because cultivars vary in response to climate conditions, soils, and crop management. In this study, parameters were developed for modern cultivars representing all 17 CIMMYT wheat Mega Environments (MEs) using field experimental data and genetic cultivar relationships for the CROPSIM-CERES model in DSSAT v 4.5 (Decision-Support System for Agrotechnology Transfer). Cultivar performance was tested with independent CIMMYT breeding trial field experiments across several locations. Then crop simulations were carried out at 0.5×0.5° pixels for global wheat-growing areas, using cultivars representing MEs, soil information, region-specific crop management, and initial soil conditions. Aggregated simulated wheat yields and production were compared to reported country yields and production from Food and Agriculture Organization (FAO) statistics, resulting in a Root Mean Square Error (RMSE) of 1.3t/ha for yield and 2.2Mt/country for country production. Some of the simulated errors are relatively large at country level because of uncertainties in pixel information for climate, soil, and crop management input and partly because of crop model uncertainties. In addition, FAO yield statistics have uncertainties because of incomplete farm reports or poor estimates. Nevertheless, this new cultivar-specific, partially-validated global baseline simulation enables new studies on issues of food security, agricultural technology, and breeding advancement impacts combined with climate change at a global scale.

Preliminary Success on Hormone Induced Breeding of Striped Mullet (Mugil cephalus L.) in Bangladesh

Induced breeding trial of two years old striped mullet (Mugil cephalus L.) was conducted during winter season in a commercial fish hatchery at Cox’s Bazar, Bangladesh. Adult or sub-adults of M. cephalus were collected from the wild and reared in the saline ponds from August to December 2014. The broods were injected using carp PG, HCG and LRH-A2 after 48 hrs of acclimatization in the hatchery tanks. The effective doses of carp PG 30mg/kg body weight, LRH-A2150?g with a combination of 0.3ml Domperidone and 0.5 ml Calcium injection, and HCG dose of 30,000 IU in case of female and 5,000 IU in case of male were found to spawning success. The GSI value ranged between 7.92-12.38 and egg diameter of matured fish ranged between 563-594?m and that of fertilized egg between 650-680?m with fecundity calculated as 780 to 900 nos/g. The fish started spawning between 44-48 hrs and cell division was observed after the first hour of spawning, however, mass mortality occurred after 6 hrs of spawning.Bangladesh J. Zool. 43(2): 279-289, 2015

Long-Term Field Evaluation Reveals Huanglongbing Resistance in Citrus Relatives.

Citrus huanglongbing (HLB) is a destructive disease with no known cure. To identify sources of HLB resistance in the subfamily Aurantioideae to which citrus belongs, we conducted a six-year field trial under natural disease challenge conditions in an HLB endemic region. The study included 65 Citrus accessions and 33 accessions belonging to 20 other closely related genera. For each accession, eight seedling trees were evaluated. Based on quantitative polymerase chain reaction analysis of the pathogen titers and disease symptoms, eight disease-response categories were identified. We report two immune, six resistant, and 14 tolerant accessions. Resistance and tolerance observed in different accessions may be attributed to a multitude of factors, including psyllid colonization ability, absence of pathogen multiplication, transient replication of the bacterium, lack of pathogen establishment in the plant, delayed infection, or recovery from infection. Most citrus cultivars were considered susceptible: 15 citrons, lemons, and limes retained leaves in spite of the disease status. Resistance and high levels of field tolerance were observed in many noncitrus genera. Disease resistance/tolerance was observed in Australian citrus relative genera Eremocitrus and Microcitrus, which are sexually compatible with citrus and may be useful in future breeding trials to impart HLB resistance to cultivated citrus.

Conditional Ablation of Progesterone Receptor Membrane Component 1 Results in Subfertility in the Female and Development of Endometrial Cysts.

Progesterone (P4) is essential for female fertility. The objective of this study was to evaluate the functional requirement of the nonclassical P4 receptor (PGR), PGR membrane component 1, in regulating female fertility. To achieve this goal, the Pgrmc1 gene was floxed by insertion of loxP sites on each side of exon 2. Pgrmc1 floxed (Pgrmc1(fl/fl)) mice were crossed with Pgr(cre) or Amhr2(cre) mice to delete Pgrmc1 (Pgrmc1(d/d)) from the female reproductive tract. A 6-month breeding trial revealed that conditional ablation of Pgrmc1 with Pgr(cre/+) mice resulted in a 40% reduction (P = .0002) in the number of pups/litter. Neither the capacity to ovulate in response to gonadotropin treatment nor the expression of PGR and the estrogen receptor was altered in the uteri of Pgrmc1(d/d) mice compared with Pgrmc1(fl/fl) control mice. Although conditional ablation of Pgrmc1 from mesenchymal tissue using Amhr2(cre/+) mice did not reduce the number of pups/litter, the total number of litters born in the 6-month breeding trial was significantly decreased (P = .041). In addition to subfertility, conditional ablation of Pgrmc1 using either Amhr2(cre/+) or Pgr(cre/+) mice resulted in the development of endometrial cysts starting around 4 months of age. Interestingly, pregnancy attenuated the formation of these uterine cysts. These new findings demonstrate that PGR membrane component 1 plays an important role in female fertility and uterine tissue homeostasis.

Cost and accuracy of advanced breeding trial designs in apple.

Trialing advanced candidates in tree fruit crops is expensive due to the long-term nature of the planting and labor-intensive evaluations required to make selection decisions. How closely the trait evaluations approximate the true trait value needs balancing with the cost of the program. Designs of field trials of advanced apple candidates in which reduced number of locations, the number of years and the number of harvests per year were modeled to investigate the effect on the cost and accuracy in an operational breeding program. The aim was to find designs that would allow evaluation of the most additional candidates while sacrificing the least accuracy. Critical percentage difference, response to selection, and correlated response were used to examine changes in accuracy of trait evaluations. For the quality traits evaluated, accuracy and response to selection were not substantially reduced for most trial designs. Risk management influences the decision to change trial design, and some designs had greater risk associated with them. Balancing cost and accuracy with risk yields valuable insight into advanced breeding trial design. The methods outlined in this analysis would be well suited to other horticultural crop breeding programs.

Red and white Chinook salmon: genetic divergence and mate choice

Chinook salmon (Oncorhynchus tshawytscha) exhibit extreme differences in coloration of skin, eggs and flesh due to genetic polymorphisms affecting carotenoid deposition, where colour can range from white to bright red. A sympatric population of red and white Chinook salmon occurs in the Quesnel River, British Columbia, where frequencies of each phenotype are relatively equal. In our study, we examined evolutionary mechanisms responsible for the maintenance of the morphs, where we first tested whether morphs were reproductively isolated using microsatellite genotyping, and second, using breeding trials in seminatural spawning channels, we tested whether colour assortative mate choice could be operating to maintain the polymorphism in nature. Next, given extreme difference in carotenoid assimilation and the importance of carotenoids to immune function, we examined mate choice and selection between colour morphs at immune genes (major histocompatibility complex genes: MHC I-A1 and MHC II-B1). In our study, red and white individuals were found to interbreed, and under seminatural conditions, some degree of colour assortative mate choice (71% of matings) was observed. We found significant genetic differences at both MHC genes between morphs, but no evidence of MHC II-B1-based mate choice. White individuals were more heterozygous at MHC II-B1 compared with red individuals, and morphs showed significant allele frequency differences at MHC I-A1. Although colour assortative mate choice is likely not a primary mechanism maintaining the polymorphisms in the population, our results suggest that selection is operating differentially at immune genes in red and white Chinook salmon, possibly due to differences in carotenoid utilization.

Addressing huge spatial heterogeneity induced by virus infections in lentil breeding trials.

BackgroundSpatial heterogeneity can have serious effects on the precision of field experimentation in plant breeding. In the present study the capacity of the honeycomb design (HD) to sample huge spatial heterogeneity was appraised. For this purpose, four trials were conducted each comprising a lentil landrace being screened for response to viruses.ResultsHuge spatial heterogeneity was reflected by the abnormally high values for coefficient of variation (CV) of single-plant yields, ranging 123–162 %. At a given field area, increasing the number of simulated entries was followed by declined effectiveness of the method, on account of the larger circular block implying greater intra-block heterogeneity; a hyperbolic increasing pattern of the top to bottom entry mean gap (TBG) indicated that a number of more than 100 replicates (number of plants per entry) is the crucial threshold to avoid significant deterioration of the sampling degree. Nevertheless, the honeycomb model kept dealing with variation better than the randomized complete block (RCB) pattern, thanks to the circular shape and standardized type of block that ensure the less possible extra heterogeneity with expanding the area of the block.ConclusionsOwing to the even and systematic entry allocation, breeders do not need to be concerned with the extra spatial heterogeneity that might induce the extra surface needed to expand the size of the block when many entries are considered. Instead, they could improve accuracy of comparisons with increasing the number of replicates (circular blocks) despite the concomitant greater overall spatial heterogeneity.

Genome-wide association study identifies three key loci for high mesocarp oil content in perennial crop oil palm.

GWAS in out-crossing perennial crops is typically limited by insufficient marker density to account for population diversity and effects of population structure resulting in high false positive rates. The perennial crop oil palm is the most productive oil crop. We performed GWAS for oil-to-dry-mesocarp content (O/DM) on 2,045 genotyped tenera palms using 200K SNPs that were selected based on the short-range linkage disequilibrium distance, which is inherent with long breeding cycles and heterogeneous breeding populations. Eighty loci were significantly associated with O/DM (p ≤ 10−4) and three key signals were found. We then evaluated the progeny of a Deli x AVROS breeding trial and a 4% higher O/DM was observed amongst those having the beneficial genotypes at two of the three key loci (p < 0.05). We have initiated MAS and large-scale planting of elite dura and pisifera parents to generate the new commercial tenera palms with higher O/DM potential.

Triallel cross designs for comparing a set of test lines with a control line

Partial triallel cross (PTC) designs are suitable for developing crosses between inbred lines to study the genetic nature of quantitative traits in crops, which in turn help identify the strategy and methods of selection essential for breeding. PTCs are useful to compare test lines versus control line with more precision. In this paper, a method for constructing a class of PTC designs for comparing a set of test lines with a control line has been described. The proposed class of designs are variance balanced for estimating the contrasts pertaining to general combining ability effects of half as well as full parents. The suggested construction method doesn't demand deep knowledge in statistics and therefore, the breeders can easily obtain small and efficient triallel cross plans for test lines vs. control line comparisons for their breeding trial.

Referencing laser and ultrasonic height measurements of barleycultivars by using a herbometre as standard

Assessment of plant height is an important factor for agronomic and breeder decisions; however, current field phenotyping, such as visual scoring or using a ruler, is time consuming, labour intensive, costly and subjective. For agronomists and plant breeders, the most common method used to measure plant height is still a meter stick. In a 3-year study, we have adopted a herbometre similar to a rising plate meter as a reference method to obtain the weighted plant height of barley cultivars and to evaluate vehicle-based ultrasonic and laser distance sensors. Sets of 30 spring barley cultivars and 14 and 60 winter barley cultivars were tested in 2013, 2014 and 2015, respectively. The herbometre was well suited as a reference method allowing for an increased area and was easy to handle. The herbometre measurements within a plot showed very low coefficients of variation. Good and close relationships (R2 = 0.59, 0.76, 0.80) between the herbometre and the ultrasonic distance sensor measurements were observed in the years 2013, 2014 and 2015, respectively, demonstrating also increased values of heritability. Hence, both sensors were able to differentiate among barley cultivars in standard breeding trials. For the sensors, we observed a 4-fold faster operating time and 6-fold increase of measurement points compared with the herbometre measurement. Based on these results, we conclude that distance sensors represent a powerful and economical high-throughput phenotyping tool for breeders and plant scientists to estimate plant height and to differentiate cultivars for agronomic decisions and breeding activities potentially being also applicable in other small grain cereals with dense crop stands. Particularly, ultrasonic distance sensors may reflect an agronomically and physiologically relevant plant height information.