Forage breeding for changing environments and production systems: an overview

Smith, K. F.; Spangenberg, Germán

doi:10.1071/cpv65n11_fo

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Therefore, the evaluation of multi-harvest forage breeding trials is time-consuming and expensive. In this context, proper statistical methods that accurately predict the true genotypic values is crucial [1]. As the genotypes experience different environmental conditions over time, it is expected that differential gene expression occurring throughout the growing season.…”

Section: Introductionmentioning

confidence: 99%

Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data

Filho

Barrios

Santos

et al. 2023

Preprint

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Genotype selection for dry matter yield (DMY) in perennial forage species is based on repeated measures over time. Repeated measurements in forage breeding trials generate longitudinal datasets that must be properly analyzed giving a useful interpretation in the genotype selection process. In this study, we have presented a random regression (RRM) approach for selecting genotypes based on longitudinal DMY data generated from ten breeding trials and three perennial species, alfalfa (Medicago sativa L.), guineagrass (Megathyrsus maximus), and brachiaria (Urochloa spp.). We also proposed the estimation of adaptability based on the area under the curve and stability based on the curve coefficient of variation. Our results showed that RRM always approximated the (co)variance structure into an autoregressive pattern. Furthermore, RRM can offer useful information about longitudinal data in forage breeding trials, where the breeder can select genotypes based on their seasonality by interpreting reaction norms. Therefore, we recommend using RRM for longitudinal traits in breeding trials for perennial species.

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Section: Introductionmentioning

confidence: 99%

Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data

Filho

Barrios

Santos

et al. 2023

Preprint

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“…Cultivar development in turfgrass is based on data collected over several years, environments, and grown under various management practices, making the selection cycle slower when compared to annual plants. Therefore, advanced statistical methods are key to predict the genetic value of breeding lines as accurately as possible (Smith & Spangenberg, 2014). These statistical methods need to take into account the spatial variation and correlation within a trial, as well as the temporal correlation between repeated measures, to improve the accuracy in the selection process (Faveri et al, 2015; Gouveia et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Breeding for drought tolerance in perennial ryegrass (Lolium perenne L.) and tall fescue (Lolium arundinaceum [Schreb.] Darbysh.) by exploring genotype by environment by management interactions

et al. 2023

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Background: Drought-tolerant turfgrass cultivars are becoming more important each year as landscape irrigation practices are becoming more restrictive. Methods: In this study, we quantified the level of drought tolerance (DT) in perennial ryegrass and tall fescue genotypes across years under different mowing (MH) and fertility (FL) rates. A total of 48 ryegrass and 38 tall fescue genotypes were planted in 2011 at NexGen, Albany, OR. The trial was divided into three subtrials: (i) DT; (ii) MH and DT; and (iii) FL and DT. Results: The results showed that there was significant genetic variance for most of the traits in both species. The genotype × year interaction was predominantly simple, indicating small changes in genotype ranking for both species. Lower MH improved DT in perennial ryegrass, whereas higher MH resulted in greater DT in tall fescue. For both species, higher MH improved the population mean for turf quality. Higher FL inputs led to higher turf quality for both species, but it did not affect the DT response. Conclusions: Using a selection index, six genotypes for perennial ryegrass and four for tall fescue were selected.

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“…The increase in demand for meat and dairy over the last few decades has led to an intensification of forage based farming. Breeding for the improvement of forage yield and nutrient composition of grassland forage species adds value to these industries (Smith and Spangenberg, 2014;Capstaff and Miller, 2018;Gebremedhin et al, 2019). The length of time required to develop stable new forage cultivars can however take up to 10-15 years (Lee et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks

et al. 2020

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Breeding higher yielding forage species is limited by current manual harvesting and visual scoring techniques used for measuring or estimation of biomass. Automation and remote sensing for high throughput phenotyping has been used in recent years as a viable solution to this bottleneck. Here, we focus on using RGB imaging and deep learning for white clover (Trifolium repens L.) and perennial ryegrass (Lolium perenne L.) yield estimation in a mixed sward. We present a new convolutional neural network (CNN) architecture designed for semantic segmentation of dense pasture and canopies with high occlusion to which we have named the local context network (LC-Net). On our testing data set we obtain a mean accuracy of 95.4% and a mean intersection over union of 81.3%, outperforming other methods we have found in the literature for segmenting clover from ryegrass. Comparing the clover/vegetation fraction for visual coverage and harvested dry-matter however showed little improvement from the segmentation accuracy gains. Further gains in biomass estimation accuracy may be achievable through combining RGB with complimentary information such as volumetric data from other sensors, which will form the basis of our future work.

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Forage breeding for changing environments and production systems: an overview

Cited by 5 publications

References 13 publications

Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data

Assessing genotype adaptability and stability in perennial forage breeding trials using random regression models for longitudinal dry matter yield data

Breeding for drought tolerance in perennial ryegrass (Lolium perenne L.) and tall fescue (Lolium arundinaceum [Schreb.] Darbysh.) by exploring genotype by environment by management interactions

Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks

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