Forage Breeding Programs Research Articles

Exploring genetic variability in fodder quality of forage sorghum in the North – Eastern regions of India

This study investigated genetic variability, character association, and genetic diversity in ninety-five forage sorghum genotypes, evaluated using a randomized block design for seven fodder quality traits. Analysis of variance revealed significant differences among genotypes for all traits. Genotypes such as G90 (424B), G23 (334B), G87 (CSV21F), G76 (330B), and G45 (373B) exhibit desirable fodder quality traits. Notably, G90 (424B) and G47 (NSS5B) stand out for their superior green forage yield per plant and fodder quality, making them prime candidates for varietal development programs. Crude protein emerged as a crucial selection factor for fodder quality. Green forage yield per plant showed minimal association with the other quality traits. A strong positive correlation was observed among in vitro organic matter digestibility, metabolic energy, crude protein, and ash content. D2 analysis identified five clusters, with genotypes from clusters V and IV recommended for crossing to produce superior transgressive segregants for fodder quality. Acid detergent fibre, crude protein, and ash content significantly contributed to genetic divergence. Considering these selected traits and genotypes, they could be invaluable in future sorghum forage breeding programs aimed at enhancing fodder quality traits. Keywords: Correlation, genetic diversity, Path analysis, Sorghum Fodder quality

Hyperspectral Data Can Differentiate Species and Cultivars of C3 and C4 Turf Despite Measurable Diurnal Variation

The ability to differentiate species is not adequate for modern forage breeding programs. The measurement of persistence is currently a bottleneck in the breeding system that limits the throughput of cultivars to the marketplace and prevents it from being selected as a trait. The use of hyperspectral data obtained through remote sensing offers the potential to reduce guesswork by identifying the distribution of pasture species, but only if such data alone can distinguish the subtle differences within species, i.e., cultivars. The implementation of this technology faces many challenges due to the spectral and temporal variability of species. To understand the spectral variability between and within species groups, differentiation using hyperspectral data from monoculture plots of turf species was utilized. Spectral data were collected over a year using an ASD FieldSpec® and canopy pasture probe (CAPP). The plots consisted of monocultures of various species, and cultivars (a total of 10 plots). Linear discriminant analysis (LDA) was conducted on the full spectrum and reduced band data. This technique successfully differentiated the species with high accuracy (>98%). We demonstrate the potential of hyperspectral data and analysis techniques to accurately separate differences down to cultivar level. We also show that diurnal variation is measurable in the spectra but does not preclude differentiation.

Genotype × environment interaction patterns of dry matter yield in meadow brome, orchardgrass, tall fescue, and timothy evaluated at harsh winter sites

AbstractBackgroundGenotype × environment interaction (GEI) slows genetic gains and complicates selection decisions in plant breeding programs. Forage breeding program seed sales often encompass large geographic regions to which the cultivars may not be adapted. An understanding of the extent of GEI in perennial, cool‐season forage grasses will facilitate improved selection decisions and end‐use in areas with harsh winters.MethodsWe evaluated the dry matter yield of nine meadow brome (Bromus biebersteinii Roemer & J. A. Schultes), nine orchardgrass (Dactylis glomerata L.), seven tall fescue (Lolium arundinaceum (Schreb.) Darbysh.), and 10 timothy (Phleum pratense L.) cultivars or breeding populations at seven high latitude and/or elevation locations in Canada and the United States from 2019 to 2021.ResultsFor each of the species, we found significant differences among the genotypes for dry matter yield across environments and found significant levels of GEI. Using site regression analysis and GGE biplot visualizations, we then characterized the extent of the interactions in each species. Except for tall fescue, there was little evidence for the broad adaptation of genotypes across locations.ConclusionsThis research adds further evidence to the limitations of perennial, forage breeding programs to develop widely adapted cultivars and the need to maintain regional breeding efforts.

Protein and Carbohydrate Fractionation to Evaluate Perennial Ryegrass (Lolium perenne L.) Accessions

Precise information about carbohydrates and proteins in relation to their utilization in the rumen is useful for the breeding purposes of perennial ryegrass cultivars used for animal nutrition. The objective of the current study was to evaluate 20 diploid perennial ryegrass accessions from the intermediary heading stage. The ruminal kinetics of different carbohydrate and protein fractions of grasses and legumes are important for forage breeding programs. The Cornell Net Carbohydrate and Protein System (CNCPS) was used to provide such information. Accession-based variation can be observed after considering dynamic degradation processes. Variation among the 20 accessions was observed. Ruminally digested (RDC) and undigested (UDC) carbohydrate and ruminally degraded (RDP) and undegraded (UDP) protein contents, total carbohydrate and total protein contents, and carbohydrate and protein fractions for the first cut and annual averages show significant differences. Although the variation was smaller for the protein fractions, the calculated usable protein content (uCP, sum of UDP and amount of synthesized microbial protein in the rumen) in the small intestine is mainly influenced by decreasing the neutral detergent fiber (NDF) and increasing the sugar content among cultivars. Carbohydrate and protein fractionation is suitable for characterizing perennial grass accessions as it uses parameters relevant to ruminant nutrition, allowing a step forward in forage plant breeding for forage quality. To conclude, using parameters related to ruminal degradation kinetics should favor the selection of accessions with higher amounts of ruminally digested carbohydrates (RDC). The selection of accessions based on protein quality (proportion of UDP) is less favorable for achieving a higher usable protein content.

‘Newell’ bermudagrass: A public release from the USDA Cynodon collection

AbstractWarm‐season perennial grasses are the backbone of the pasture‐based livestock industry in the lower southeastern United States, and bermudagrass (Cynodon spp.) is the most widely planted forage species, covering ∼15 million ha. The genus Cynodon is native to southern Africa, and germplasm collections possess high genetic and phenotypic variability. The USDA National Plant Germplasm System maintains a collection of bermudagrass plant introductions (PIs) in Griffin, GA, and USDA‐ARS, Tifton, GA, maintains additional germplasm. Multi‐location trials were established in four states (Florida, Georgia, North Carolina, and Oklahoma) to screen Cynodon germplasm for herbage accumulation (HA), nutritive value (NV), and bermudagrass stem maggot (BSM) (Atherigona reversura Villeneuve). Due to the large genotype × environment interaction for HA, we focused on selecting accessions adapted to South Georgia and Florida, and further studies were performed in Florida. Several PIs showed improved HA and NV compared with ‘Tifton 85’. PI 316510, originally introduced from Ingelheim, Germany, produced high HA in Citra, FL, and Tifton, GA, with improved NV traits. In addition, PI 316510 had faster establishment and similar BSM tolerance to Tifton 85. We confirmed PI 316510 as tetraploid (2n = 4x = 36) through chromosome counts and flow cytometry, and it is genetically distinct from other commercial cultivars. PI 316510 has been publicly released under the name ‘Newell’, and it is vegetatively propagated. Planting material can be requested from the UF‐IFAS Forage Breeding program.

A spatial-based approach applied to early selection stages in a forage breeding program

A spatial-based approach applied to early selection stages in a forage breeding program

Phenotyping of Urochloa humidicola grass hybrids for agronomic and environmental performance in the Piedmont region of the Orinoquian savannas of Colombia

AbstractIn the low fertility acid soils of the Orinoquian savannas of Colombia, Urochloa humidicola cv. Tully or Humidicola is one of the most widely planted tropical forage grasses for improving livestock productivity. Low nutritional quality of this grass limits sustainable livestock production in this region. In this study, we conducted a phenotypic evaluation under field and greenhouse conditions of one of the first hybrid populations of U. humidicola generated from the forage breeding program of CIAT. Our objective was to identify a set of new hybrids of U. humidicola that combine improved productivity and nutritional quality plus the biological nitrification inhibition (BNI) trait/ability to reduce nitrogen (N) losses via leaching and nitrous oxide (N2O) emissions. To this end, we tested 118 hybrids (planted in pots) in the greenhouse for over 6 months and measured potential nitrification rates (NR) using soil microcosm incubation. NR values observed ranged from 0.27 to 5.75 mg N‐NO3− kg soil−1 day−1. Later, 12 hybrids with different levels of NR were selected and field‐tested in the Orinoquia region over a 4 years period (2013–2017) for dry matter production, nutrition quality (crude protein, in vitro digestibility and fibres content) and NR in each year. In the rainy season of 2018, two hybrids with superior agronomic performance and contrasting field level NR (Uh08/1149 and 0450) were subjected to analysis of soil‐borne N2O emissions after fertilization during 13 days. The NR values recorded were not directly correlated with the forage quality parameters evaluated, however, the two grasses with the lowest NR values were among those with the highest biomass production, crude protein content, and N uptake. The grass hybrid Uh08/1149 and the germplasm accession CIAT 16888 were found as materials with superior forage value, with production of 14.1 and 14.6 tons dry matter ha−1 year−1 (up to 8% higher than the cv. Tully), crude protein of 11.5 and 9.1% per cut (up to 20% higher than the cv. Tully), and N uptake of 31.6 and 25.7 kg N ha−1 cut−1 (up to 30% higher than the cv. Tully). Additionally, these two grasses are likely to exhibit high‐BNI ability, with potential to improve N use efficiency in managed pastures.

Breeding forages with climate resiliency in temperate/tropical transition zones

AbstractAs the earth's climate changes, the ability of breeders to select for traits within forage species that impart adaptability to these changes will be critical for the maintenance of grassland agricultural systems. Temperate ‐ tropical climate transition zones (between 27 and 31° N and S latitude) have proven to be ideal zones for breeding species with variable climate adaptation. Programs located in these regions have the advantage of exposure to alternating extreme warm and cold temperatures, drought and flood conditions, and a multitude of biotic pests including fungi, viruses, insects and nematodes. In this paper we will present examples of how forage breeding programs in north‐central Florida have capitalized on these distinct advantages for selection of cultivars with resiliency to changing climate variables. Breeding programs in red clover (Trifolium pratense L.) and annual ryegrass (Lolium multiflorum Lam.) will be discussed as examples for moving cool season species into warmer climates. Species attributes that contribute to climate resiliency will be identified and described. The ability to identify small changes in genetic photoperiod responses in these regions, are illustrated as an advantage when the objective is development of earlier or later maturity. Transition zones also provide suitable environments for biotic stresses, from both tropical and temperate areas, including fungal diseases, nematodes, and insects, offering desirable field environments for screening and genetic improvement.

Registration of three new bmr12 sorghum mutants from an ethyl methane sulfonate–induced BTx623 mutant population

AbstractThree new sorghum (Sorghum bicolor L.) brown midrib (bmr) genetic stocks BTx623bmr12‐30 (Reg. no. GS‐790, PI 699404), BTx623bmr12‐34 (Reg. no. GS‐789, PI 699405), and BTx623bmr12‐35 (Reg. no. GS‐788, PI 699406) were developed for release by USDA–ARS in Lubbock, TX. The primary purpose for the release of these stocks is to provide an extensive source of (bmr12) variants for use in forage breeding programs. The genetic stocks were isolated from the pedigreed sorghum ethyl methane sulfonate–mutagenized sorghum in BTx623 background. The derived mutations were analyzed by DNA sequencing and confirmed by KASP (Kompetitive Allele Specific PCR) markers. The confirmed bmr mutants were also analyzed for stem and leaf lignin and carbohydrate contents. Furthermore, physiological and agro‐morphological evaluations were performed under control and field conditions. All genetic stocks displayed the reddish‐brown pigmentation in the leaf midrib, confirming the bmr phenotype. The bmr12 mutants flowered 4–5 d earlier than the wild type BTx623. Physical and compositional characterization of seeds from the three genetic stocks revealed no negative consequence of bmr on grain structure. Notably, these bmr12 mutants had 14–24% greater total seed protein than wild type BTx623. The three new bmr12 alleles provide public and private breeders crucial and novel genetic stock sources for use in breeding bmr forage sorghum.

The Extent and Economic Significance of Cultivated Forage Crops in Developing Countries

Forage grasses and legumes are the principal source of nutrition for most ruminant livestock in developing countries. Raising yields of forage crops can increase the availability and affordability of livestock products as well as reduce pressure on increasingly scarce land resources by enabling greater herd densities on existing pasture. However, the economic significance of cultivated forage crops in developing countries is not well-understood. We provide estimates of the present area and production value of cultivated forage crops as well as review evidence on the extent of adoption of CGIAR-derived improved varieties of cultivated forage species and their economic impact in developing countries. There are at least 159 million hectares under cultivated forage crops producing yield worth around $63 billion per year (at 2014-2016 prices). Latin America accounts for about 85% of this forage crop area. CGIAR forage breeding programs have developed and helped disseminate improved varieties of Brachiaria, Stylosanthes, Vigna unguiculata, and Calliandra spp., which by 2015 had been adopted on over 12 million hectares producing economic benefits of over $5.8 billion/year.

CONSERVACIÓN DE GERMPLASMA ex situ REVISADA A LA LUZ DE MECANISMOS Y MÉTODOS DE GENÉTICA

Plant genetic resources for food and agriculture are ex situ conserved in germplasm banks as samples (accessions) of natural or naturalized populations, either as the originally sampled propagules (mainly seeds) or their multiplications. The premises underlying ex situ conservation are that (a) it is the safest and cheapest alternative for germplasm preservation for future generations and (b) accessions are representative of the genetic diversity encountered in nature. In the past decades, ideas, alternatives and considerations have been put forward on the topic, and protocols have been devised for plant germplasm sampling, conservation and multiplication. However, limitations in the management efficiency of germplasm banks have been pointed out by international organizations. In our opinion, germplasm banks in general need to revise their functioning and management at the light of principles and methods of Genetics. To that end, it is necessary to consider the reproductive biology of higher plants -whose genetic consequences at both the individual plant and the population levels are not always either fully understood or taken into account in devising the protocols-, the genetic structures of wild and cultivated populations, and the course of the genetic material in the populations. In this paper, we discuss the three topics and provide an example of a national forage breeding program, from germplasm bank accessions as the germplasm of origin to the obtainment of commercial cultivars. Finally, we present a proposal as a base for discussion among curators, researchers and breeders. Key words: accessions, breeding, genetic resources, germplasm banks, population genetics

CONSERVACIÓN DE GERMPLASMA ex situ REVISADA A LA LUZ DE MECANISMOS Y MÉTODOS DE GENÉTICA

Plant genetic resources for food and agriculture are ex situ conserved in germplasm banks as samples (accessions) of natural or naturalized populations, either as the originally sampled propagules (mainly seeds) or their multiplications. The premises underlying ex situ conservation are that (a) it is the safest and cheapest alternative for germplasm preservation for future generations and (b) accessions are representative of the genetic diversity encountered in nature. In the past decades, ideas, alternatives and considerations have been put forward on the topic, and protocols have been devised for plant germplasm sampling, conservation and multiplication. However, limitations in the management efficiency of germplasm banks have been pointed out by international organizations. In our opinion, germplasm banks in general need to revise their functioning and management at the light of principles and methods of Genetics. To that end, it is necessary to consider the reproductive biology of higher plants -whose genetic consequences at both the individual plant and the population levels are not always either fully understood or taken into account in devising the protocols-, the genetic structures of wild and cultivated populations, and the course of the genetic material in the populations. In this paper, we discuss the three topics and provide an example of a national forage breeding program, from germplasm bank accessions as the germplasm of origin to the obtainment of commercial cultivars. Finally, we present a proposal as a base for discussion among curators, researchers and breeders. Key words: accessions, breeding, genetic resources, germplasm banks, population genetics

Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield

Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

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

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 for LDMY and from 413.07 to 506.56 kg·ha 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.

Genome-wide diversity and structure variation among lablab [Lablab purpureus (L.) Sweet] accessions and their implication in a Forage breeding program

Most orphan crops have not been fully sequenced, hence we rely on genome sequences of related species to align markers to different chromosomes. This hinders their utilisation in plant population improvement programs. Utilising the advances in the science of sequencing technologies, the population structure, relatedness, and genetic diversity among accessions can be assessed quickly for better exploitation in forage breeding programs. Using DArTseq technology, we studied the genetic and structural variation in 65 Lablab purpureus (L.) Sweet conserved gene-bank accessions using 9320 DArTseq-based SNPs and 15,719 SilicoDart markers. These markers had a low discriminating ability with mean polymorphic information content (P.I.C.) of 0.14 with DArTseq-based SNPs and 0.13 with SilicoDart markers. However, the markers had a high mean call rate of 73% with DArTseq-based SNPs and 97% with SilicoDart markers. Analysis of molecular variance revealed a high within populations variance (99.4%), indicating a high gene exchange or low genetic differentiation (PhiPT = 0.0057) among the populations. Structure analysis showed three allelic pools in variable clusters of ΔK = 3 and 6. Phylogenetic tree of lablab accessions showed three main groups with variable membership coefficients. Most pairs of accessions (40.3%) had genetic distances between 0.10 and 0.15 for SilicoDart markers, while for DArTseq-based SNPs, (46.5%) had genetic distances between 0.20 and 0.25. Phylogenetic clustering and minimum spanning analysis divided the 65 accessions into three groups, irrespective of their origin. For the first time, this study produced high-density markers with good genom coverage. The utilisation of these accessions in a forage program will base on the information from molecular-based grouping. The outcomes uncovered the presence of noteworthy measure of variety in Uganda, CIAT and ILRI accessions, thus demonstrating an opportunity for further marker-trait-association studies.

The history and distribution of nodulating Paraburkholderia, a potential inoculum for Fynbos forage species

AbstractLegumes in the Fynbos vegetation of the Western Cape of South Africa have emerged as candidates for domestication, particularly for their adaptation to acidic and infertile soils. However, South African rhizobia have been shown to be very diverse and unique, and a detailed understanding of them is essential to success in forage breeding programs that seek to exploit these “new” legumes. Symbionts of legumes in South Africa that belong to traditional rhizobial genera have been shown to have a unique origin for their symbiotic loci in comparison to members sampled from other regions of the world. Some of the legume tribes in the Fynbos have also been shown to associate predominantly with unique species in the Betaproteobacterial genus Paraburkholderia. The rhizobial members of this genus have two main centres of diversity, of which South Africa is one. In this centre, the legume hosts are principally from the Papilionoideae subfamily while hosts from the mimosoid clade (now in the Caesalpinioideae) are abundant in the South American centre. Not only do these rhizobia differ in terms of host, but their symbiotic loci also show separate origins. The dominance and uniqueness of the Paraburkholderia symbionts, in the context of indigenous South African legumes, makes understanding the history and factors that affect the distribution of this genus essential if successful adaptation and effective nodulation of these legumes in Agriculture are to be achieved globally.

Design of an Unmanned Ground Vehicle and LiDAR Pipeline for the High-Throughput Phenotyping of Biomass in Perennial Ryegrass

Perennial ryegrass biomass yield is an important driver of profitability for Australian dairy farmers, making it a primary goal for plant breeders. However, measuring and selecting cultivars for higher biomass yield is a major bottleneck in breeding, requiring conventional methods that may be imprecise, laborious, and/or destructive. For forage breeding programs to adopt phenomic technologies for biomass estimation, there exists the need to develop, integrate, and validate sensor-based data collection that is aligned with the growth characteristics of plants, plot design and size, and repeated measurements across the growing season to reduce the time and cost associated with the labor involved in data collection. A fully automated phenotyping platform (DairyBioBot) utilizing an unmanned ground vehicle (UGV) equipped with a ground-based Light Detection and Ranging (LiDAR) sensor and Real-Time Kinematic (RTK) positioning system was developed for the accurate and efficient measurement of plant volume as a proxy for biomass in large-scale perennial ryegrass field trials. The field data were collected from a perennial ryegrass row trial of 18 experimental varieties in 160 plots (three rows per plot). DairyBioBot utilized mission planning software to autonomously capture high-resolution LiDAR data and Global Positioning System (GPS) recordings. A custom developed data processing pipeline was used to generate a plant volume estimate from LiDAR data connected to GPS coordinates. A high correlation between LiDAR plant volume and biomass on a Fresh Mass (FM) basis was observed with the coefficient of determination of R2 = 0.71 at the row level and R2 = 0.73 at the plot level. This indicated that LiDAR plant volume is strongly correlated with biomass and therefore the DairyBioBot demonstrates the utility of an autonomous platform to estimate in-field biomass for perennial ryegrass. It is likely that no single platform will be optimal to measure plant biomass from landscape to plant scales; the development and application of autonomous ground-based platforms is of greatest benefit to forage breeding programs.

An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p < 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p < 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale.

Utilization of Molecular Marker Based Genetic Diversity Patterns in Hybrid Parents to Develop Better Forage Quality Multi-Cut Hybrids in Pearl Millet

Genetic diversity of 130 forage-type hybrid parents of pearl millet was investigated based on multiple season data of morphological traits and two type of markers: SSRs (Simple sequence repeats) and GBS identified SNPs (Genotyping by sequencing-Single nucleotide polymorphism). Most of the seed and pollinator parents clustered into two clear-cut separate groups based on marker based genetic distance. Significant variations were found for forage related morphological traits at different cutting intervals (first and second cut) in hybrid parents. Across two cuts, crude protein (CP) varied from 11% to 15%, while in vitro organic matter digestibility (IVOMD) varied from 51% to 56%. Eighty hybrids evaluated in multi-location trial along with their parents for forage traits showed that significant heterosis can be realized for forage traits. A low but positive significant correlation found between SSR based genetic distance (GD between parents of hybrid) and heterosis for most of the forage traits indicated that SSR-based GD can be used for predicting heterosis for GFY, DFY and CP in pearl millet. An attempt was made to associate marker-based clusters with forage quality traits, to enable breeders select parents for crossing purposes in forage breeding programs.

Prospects for Measurement of Dry Matter Yield in Forage Breeding Programs Using Sensor Technologies

Increasing the yield of perennial forage crops remains a crucial factor underpinning the profitability of grazing industries, and therefore is a priority for breeding programs. Breeding for high dry matter yield (DMY) in forage crops is likely to be enhanced with the development of genomic selection (GS) strategies. However, realising the full potential of GS will require an increase in the amount of phenotypic data and the rate at which it is collected. Therefore, phenotyping remains a critical bottleneck in the implementation of GS in forage species. Assessments of DMY in forage crop breeding include visual scores, sample clipping and mowing of plots, which are often costly and time-consuming. New ground- and aerial-based platforms equipped with advanced sensors offer opportunities for fast, nondestructive and low-cost, high-throughput phenotyping (HTP) of plant growth, development and yield in a field environment. The workflow of image acquisition, processing and analysis are reviewed. The “big data” challenges, proposed storage and management techniques, development of advanced statistical tools and methods for incorporating the HTP into forage breeding systems are also reviewed. Initial results where these techniques have been applied to forages have been promising but further research and development is required to adapt them to forage breeding situations, particularly with respect to the management of large data sets and the integration of information from spaced plants to sward plots. However, realizing the potential of sensor technologies combined with GS leads to greater rates of genetic gain in forages.