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The aim of this study was to evaluate the genetic variability and performance of the agronomic traits of wheat cultivars in the southern region of Minas Gerais for the purpose of choosing parents for a rainfed wheat breeding program. We evaluated 78 wheat cultivars in two locations in Minas Gerais regarding different agronomic traits. The statistical analyses were carried out using Henderson's mixed-model approach. The genetic values of the cultivars were predicted, and the heritabilities and accuracies were estimated for selection of cultivars. The cultivar x location interaction was decomposed into its simple and complex fractions. The genetic and environmental correlations between the traits were estimated, and selection was made for multiple traits using the sum of the standardized variables (Z-index). The genetic variance was significant for all the traits, and the cultivar x location interaction was significant. By the Z-index, 15 wheat cultivars more adapted were identified. [ABSTRACT FROM AUTHOR]

This research aimed to select the best cultivars for both supplementary irrigated and rain fed conditions. A group of 17 recently released cultivars were tested employing randomized complete block design with three replications under supplementary irrigation and rain fed conditions at Sanliurfa and in Adiyaman locations in 2019-20. Grain yield with five agro-morphological and six quality traits were scored. Results indicated that entries differed for all traits under study. Index selection based on rank sums of all traits showed that durum wheat cultivars of Sümerli (1st), Artuklu (2nd), Zühre (3th), Perre (4th), and Saricanak-98 (5th) ranked at first five place for both conditions. Spearman's average rank coefficient of correlation between ranks of sums of all traits for supplementary irrigation vs. rain fed condition was 73.52%. This was only 56.8% for grain yields for supplementary irrigation vs. rain fed condition. Mean yield (7,256.26 kg. ha-1) of supplementary irrigation was higher than that of (5,097.56 kg. ha-1) rain fed condition by 42.34%. It was concluded that genotypes 8 (Sümerli), 7 (Artuklu), and 3 (Zühre) perform well in both environments and may be used directly in irrigated or in rain fed conditions in the studied locations. Furthermore, high performing varieties for both supplementary irrigation and rain fed conditions can be picked up better by index selection. This must be referred for better variety selection in breeding programs. [ABSTRACT FROM AUTHOR]

Female fertility is essential to any beef breeding program. However, little genetic gain has been made due to long generation intervals and low levels of phenotyping. Days to calving (DC) is a fertility trait that may provide genetic gain and lead to an increased weaning rate. Genetic parameters and correlations were estimated and compared for DC across multiple joinings (first, second and third+) and lactation status (lactating and non-lactating) for a tropical composite cattle population where cattle were first mated as yearlings. The genetic correlation between first joining DC and mature joining DC (third+) was moderate–high (0.55–0.83). DC was uncorrelated between multiparous lactating and non-lactating cows (rG = −0.10). Mature joining DC was more strongly correlated with second joining lactating DC (0.41–0.69) than with second joining non-lactating DC (−0.14 to −0.16). Thus, first joining DC, second joining DC and mature joining DC should be treated as different traits to maximise genetic gain. Further, for multi-parous cows, lactating and non-lactating DC should be treated as different traits. Three traits were developed to report back to the breeding programs to maximise genetic gain: the first joining days to calving, the second joining days to calving lactating and mature days to calving lactating. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agronomica Sinica is the property of Crop Science Society of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mining & Metallurgy (10057854) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional criss-cross algorithm for the linear programming problem is shown to be finite when s-monotone index selection rules are used. The set of s-monotone index selection rules, among others, include the Last In First Out (LIFO) and the Most Often Selected Variable rule (MOSV). The advantage of applying the s-monotone index selection rule is the flexibility it provides in selecting the pivot element while still preserving the guarantee for finiteness. Such flexibility may be used to improve the numerical stability of the algorithm. [ABSTRACT FROM AUTHOR]

Simple Summary: Breeders use breeding goals to guide genetic gain in a population in a desired direction. Breeding goals consist of economically interesting traits, in which each trait receives an economic value. For example, to increase the size of a piglet litter, breeders use a breeding goal that includes the trait "number of live piglets in a litter" for a specific day after birth. While the litter size is selected using the trait "number of live piglets," it is composed of two traits: "total number born" and "number of dead piglets." The current study used simulations to illustrate that selection for litter size could be improved by selecting for the latter two traits rather than the former. This approach corrects for the fact that these two traits are genetically related to each other, but they also have genetic differences. Further, splitting one trait into two traits allows breeders to focus on the specific elements of a trait. For example, organic pig breeders could select for better piglet welfare by splitting "number of live piglets" into two traits, giving a negative economic value to the number of dead piglets. Selection for the number of living pigs on day 11 (L11) aims to reduce piglet mortality and increase litter size simultaneously. This approach could be sub-optimal, especially for organic pig breeding. This study evaluated the effect of selecting for a trait by separating it into two traits. Genetic parameters for L11, the total number born (TNB), and the number of dead piglets at day 11 (D11) were estimated using data obtained from an organic pig population in Denmark. Based on these estimates, two alternative breeding schemes were simulated. Specifically, selection was made using: (1) a breeding goal with L11 only versus (2) a breeding goal with TNB and D11. Different weightings for TNB and D11 were tested. The simulations showed that selection using the first breeding scheme (L11) produced lower annual genetic gain (0.201) compared to the second (TNB and D11; 0.207). A sensitivity analysis showed that the second scheme performed better because it exploited differences in heritability, and accounted for genetic correlations between the two traits. When the second breeding scheme placed more emphasis on D11, D11 declined, whereas genetic gain for L11 remained high (0.190). In conclusion, selection for L11 could be optimized by separating it into two correlated traits with different heritability, reducing piglet mortality and enhancing L11. [ABSTRACT FROM AUTHOR]

Selecting for multiple traits as opposed to a single trait has become increasingly important in genomic selection. As one of the most popular approaches to multitrait genomic selection, index selection uses a weighted average of all traits as a single breeding objective. Although intuitive and effective, index selection is not only numerically sensitive but also structurally incapable of finding certain optimal breeding parents. This paper proposes a new selection method for multitrait genomic selection, the L-shaped selection, which addresses the limitations of index selection by normalizing the trait values and using an L-shaped objective function to find optimal breeding parents. This algorithm has been proven to be able to find any Pareto optimal solution with appropriate weights. Two performance metrics have also been defined to quantify multitrait genomic selection algorithms with respect to their ability to accelerate genetic gain and preserve genetic diversity. Computational experiments were conducted to demonstrate the improved performance of L-shaped selection over-index selection. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate differences in the performance of offspring of boars selected with an index emphasizing resilience and boars selected based on a traditional index, emphasizing feed efficiency and carcass quality (traditional) index vs. a customized (resilience) index. The resilience index was identical to the traditional index, except that extra emphasis was placed on piglet vitality (increased by 66%), growth rate (decreased by 14%), and feed intake (increased substantially by 5,157%). Sows were mated to either boars selected based on the resilience index or boars selected on the traditional index. Weaned offspring were vaccinated for Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) and experimentally infected with PRRSV RFLP 1-7-4 four weeks later. Offspring were allocated to pens ( n ~ 27 pigs/pen; n  = 27 pens/group) by sire-selection group for a total of 1,458 pigs in 54 pens. The weight of each pen was recorded on 0, 42, and 110 d postinfection (DPI) and used to calculate average daily gain (ADG), average daily feed intake (ADFI), and feed conversion ratio (FCR). Mortalities were recorded from 0 to 110 DPI and necropsies were routinely performed to characterize pathogens present within the barn. Pigs classified as full value (i.e., >104 kg and void of defects) were slaughtered and hot carcass weight (HCW), backfat, loin depth, and lean weight were obtained from the slaughter plant. Effects of progeny group on performance, carcass characteristics, and mortality rate were estimated with a mixed linear model. Differences between progeny groups in ADG ( P  > 0.27), HCW ( P  = 0.68), backfat ( P  = 0.13), or loin depth ( P  = 0.39), and mortality rate ( P  = 0.29) were not detected. From 0 to 42 DPI, offspring of boars selected based on the resilience index had higher ADFI (0.06 kg/d, P  = 0.01) and higher FCR (0.12, P  = 0.01). In summary, results from this study do not support selection of boars for increased feed intake, piglet viability, and robustness in order to prevent losses caused by PRRSV, but selection response was only measured after one generation of male selection. The impact of multiple generations of selection, or the development of an index including traits derived from data collected under disease-challenged conditions should be explored. The data collected for this study are a valuable resource to explore additional genetic selection strategies for enhanced resilience to a multifactorial PRRS challenge., Competing Interests: E.A.L., D.H., and S.D. are employed by Pipestone Applied Research or were at the time of project completion, a company with a vested interest in conducting research trials. J.D and J.M.E.E. are employed by Topigs Norsvin USA, a company that markets breeding stock to the commercial swine industry., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Green development is the way leading to ecological civilization. Green development index is essencial for evaluating the level of ecological civilization, as well as for assessing the construction degree of beautiful China and a well-off society in an all-round way. It is of great significance to comprehensively, rapidly and dynamically obtain the data of green development index and carry out accurate evaluation. However, the traditional investigation methods are difficult to meet the evaluation requirements in terms of data timeliness and coverage. Satellite remote sensing technology is expected to play an important role in green development evaluation due to its advantages of comprehensive, dynamic, rapid and large-scale data acquisition. Focusing on the scientific connotation and objectives of green development, the green development index acquisition, evaluation methods and evaluation systems were systematically summarized in China and abroad. The index selection, technical process and evaluation method of green development evaluation were put forward by using satellite remote sensing technology. Finally, the application test was carried out in Yan'an-Yulin area. The results show that the degree of green development in the area gradually decreases from south to northwest, and the value in Yan'an area is usually higher than that in Yulin area; the ecological environment in the northwestern and southeastern Yulin area is fragile, and the corresponding degree of green development is relatively low. Based on the small-scale application, the following outlook is made in conjunction with the needs of green and high-quality development assessment in western China. It is suggested to build a monitoring network and big data platform for green development in western China, and give full play to the supporting role of natural resources data in the scientific decision-making of critical engineering and major projects of China western development. [ABSTRACT FROM AUTHOR]

Evaluating urban water security is good for finding shortcomings that constrain urban development. This paper introduces the information substitutability index selection method to refine the index system, reducing the 24 indicators to 10 indicators, and using constant weights to check the index system before and after selection. Constant weight modified by the idea of variable weight, and the model is validated with Xi'an City as an example. The results show that the urban water safety level in Xi'an increases year after year and finally stabilizes at level II. The shortcomings are the amount of water resources per capita, the amount of groundwater recharge, the proportion of secondary industry, and the effective irrigation area. There is no change in water security level before and after index selection. After simplifying the index system, the method of information substitutability index selection can retain much of the information of the original index system. The method of variable weight can more realistically reflect the role played by each indicator in different states. Compared to the method of constant weight, the method of variable weight shows greater variability. [ABSTRACT FROM AUTHOR]

Copyright of Agriscientia is the property of Revista AgriScientia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.

Abstract Plantation‐grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce (Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single‐ and multi‐trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate‐to‐high heritability and low genotype‐by‐environment interactions. Weevil resistance was genetically positively correlated with tree height, height‐to‐diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested (GBLUP, threshold GBLUP, Bayesian ridge regression, BayesCπ) was high and did not differ among each other. Multi‐trait models performed similarly as single‐trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi‐trait GS models. A GS index that corresponded to the breeders’ priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high‐quality, weevil‐resistant Norway spruce reforestation stock with high accuracy achieved from single‐trait or multi‐trait GS.

Female fertility is essential to any beef breeding program. However, little genetic gain has been made due to long generation intervals and low levels of phenotyping. Days to calving (DC) is a fertility trait that may provide genetic gain and lead to an increased weaning rate. Genetic parameters and correlations were estimated and compared for DC across multiple joinings (first, second and third+) and lactation status (lactating and non-lactating) for a tropical composite cattle population where cattle were first mated as yearlings. The genetic correlation between first joining DC and mature joining DC (third+) was moderate–high (0.55–0.83). DC was uncorrelated between multiparous lactating and non-lactating cows (rG = −0.10). Mature joining DC was more strongly correlated with second joining lactating DC (0.41–0.69) than with second joining non-lactating DC (−0.14 to −0.16). Thus, first joining DC, second joining DC and mature joining DC should be treated as different traits to maximise genetic gain. Further, for multi-parous cows, lactating and non-lactating DC should be treated as different traits. Three traits were developed to report back to the breeding programs to maximise genetic gain: the first joining days to calving, the second joining days to calving lactating and mature days to calving lactating.

Tobacco (Nicotiana tabacum L.) is one of the industrial plants and is important in the economy of many countries. Plant breeders are usually interested in modifing and breeding several traits simultaneously in their breeding programs. Index selection is the selection of several traits simultaneously based on a simple index. In order to construct and evaluate tobacco genotypes using selection indices, 92 oriental and water pipe's tobacco genotypes were studied in terms of agro-biological traits using a randomized complete block design with three replications under normal (absence of broomrape) and presence of broomrape conditions during two successive years in Urmia Tobacco Research Centre. Selection indices including optimal selection (Smith-Hazel), Base (Brim), desired gain (Pesek-Baker) and Robinson were calculated based on all the studied traits (including: flowering date, plant height, number of leaves, leaf area, fresh and dry weights of leaf, fresh and dry weights of root, fresh and dry weights of aerial parts) for studied genotypes in both normal (absence of broomrape) and broomrape presence conditions. The results of the direct and correlated response of traits showed that the genotypes with more value for leaf area and fresh weight of leaf and aerial parts had potentially a higher performance. Selection based on Smith-Hazel and Brim indices which had the highest selection efficiency (DH) in both normal (absence of broomrape) and presence of broomrape, resulted in increasing the leaf area and fresh weight in normal conditions and leaf area and fresh weight of aerial parts in broomrape stress conditions. On the other hand, fresh weight of leaf and aerial parts were the traits with highest correlated response with dry weight of leaf, therefore, these two indices with the highest correlation with breeding value and with the highest relative efficiency of selection were presented as the best indices. Accordingly, genotype 24 (H.T.I) was introduced as the best genotype under normal (without broomrape stress) and broomrape stress conditions. [ABSTRACT FROM AUTHOR]

Selection for the number of living pigs on day 11 (L11) aims to reduce piglet mortality and increase litter size simultaneously. This approach could be sub-optimal, especially for organic pig breeding. This study evaluated the effect of selecting for a trait by separating it into two traits. Genetic parameters for L11, the total number born (TNB), and the number of dead piglets at day 11 (D11) were estimated using data obtained from an organic pig population in Denmark. Based on these estimates, two alternative breeding schemes were simulated. Specifically, selection was made using: (1) a breeding goal with L11 only versus (2) a breeding goal with TNB and D11. Different weightings for TNB and D11 were tested. The simulations showed that selection using the first breeding scheme (L11) produced lower annual genetic gain (0.201) compared to the second (TNB and D11; 0.207). A sensitivity analysis showed that the second scheme performed better because it exploited differences in heritability, and accounted for genetic correlations between the two traits. When the second breeding scheme placed more emphasis on D11, D11 declined, whereas genetic gain for L11 remained high (0.190). In conclusion, selection for L11 could be optimized by separating it into two correlated traits with different heritability, reducing piglet mortality and enhancing L11.

Our study aimed to assess the potential of indices, including grain protein deviation and protein yield, to simultaneously improve grain yield and protein content in triticale. Our analysis of 1.218 triticale genotypes revealed significant genotypic variation and high heritabilities for all traits. We analysed the suitability of the indices for phenotypic selection for both high grain yield and increased protein content. We show that some genotypes were selected irrespective of the applied index, but also observed considerable differences between the indices. To uncover the genetic architecture of grain yield, protein content and derived indices, we performed genome‐wide association mapping and biparental QTL mapping. This revealed many pleiotropic QTL with opposite effects on grain yield and protein content and a complex inheritance of all traits, suggesting that the genetic architecture limits the potential of marker‐assisted selection. Nevertheless, the use of these indices is recommended for breeding of special‐purpose cultivars, like high‐protein cultivars for on‐farm feed production. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop. [ABSTRACT FROM AUTHOR]

Experimental genotype selection in a forage crop cultivar development program is complex because the ultimate aim is increase performance and sustainability in animal production. The objectives of this study were (1) to identify traits with greater direct and indirect effects on yield of high nutritional value leaf mass (NLM) in Urochloa sp. hybrids and (2) to assess indirect gain from selection for these traits with greater effects through selection indexes and by genotype by yield*trait (GYT) biplot analysis using NLM as a basic variable. We evaluated 96 interspecific hybrids, from a gene pool among Urochloa ruziziensis, Urochloa brizantha and Urochloa decumbens species, in an experiment laid out in a randomized complete block design. A series of agronomic and nutritional value traits were measured. Path analysis and GYT were performed using NLM as the basic variable, and different strategies using selection indexes were adopted. The leaf dry matter and field green weight (FGW) traits exhibited greater direct effects on NLM. All selection strategies proved to be effective in obtaining gains in the NLM variable. GYT analysis and the selection index with weights corresponding to the relative direct effects to each trait on the NLM were the strategies that resulted in a greater correlated response for NLM. Indirect selection for NLM via FGW or the index with the FGW and regrowth capacity traits proved to be viable strategies for selection of Urochloa genotypes in the initial stages of the breeding cycles due to their practicality and lower requirement regarding traits to be measured. [ABSTRACT FROM AUTHOR]

Plantation‐grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce (Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single‐ and multi‐trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate‐to‐high heritability and low genotype‐by‐environment interactions. Weevil resistance was genetically positively correlated with tree height, height‐to‐diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested (GBLUP, threshold GBLUP, Bayesian ridge regression, BayesCπ) was high and did not differ among each other. Multi‐trait models performed similarly as single‐trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi‐trait GS models. A GS index that corresponded to the breeders' priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high‐quality, weevil‐resistant Norway spruce reforestation stock with high accuracy achieved from single‐trait or multi‐trait GS. [ABSTRACT FROM AUTHOR]

A large fraction of the microprocessor energy is consumed by the data movement in the system. One of the reasons is the inefficiency in the conventional cache design. Cache blocks larger than a word are used in conventional caches to exploit spatial locality. However, many applications only use a small part of a cache block before its eviction. Transferring and storing unused data wastes bandwidth, energy, and limited cache space. Prior work on fine-grained caches can reduce data access and storage granularity to reduce the amount of unused data. However, small data blocks typically require greater metadata and control overhead. Sharing the common bits among tags of fine-grained blocks can reduce the metadata overhead but the constraints on which fine-grained blocks can share tag bits can cause fragmentation. This work proposes scrabble, a fine-grained cache that can merge multiple non-contiguous fine-grained blocks into a variable size merged block. The length of the shared tag is maximized to reduce the metadata overhead. The space utilization is improved by supporting merged blocks with variable size. The control overhead can be reduced by moving the merged block together from memory to the last level cache. For applications with poor spatial locality, Scrabble cache can achieve more than 40 percent of performance improvement. Even for application with good spatial locality, the speedup is still more than 7 percent. In general, for an evaluated set of benchmarks, Scrabble cache achieves an average of 2.41× effective capacity over the baseline cache with the same cache capacity which leads to a 16.7 percent performance improvement and an 11 percent on-chip energy reduction. As compared to a state-of-the-art fine-grained cache, Scrabble cache achieves a 1.25× effective capacity, a 7.9 percent speedup, and a 5.8 percent on-chip energy reduction. [ABSTRACT FROM AUTHOR]

• The narrow-sense heritability were low for growth traits, moderate for 1,8-cineole content and high for leaf essential oil concentration. • The genetic relationships between growth, oil concentration and 1,8-cineole content were independent. • Simultaneous improvement of oil concentration and 1,8-cineole in M. cajuputi subsp. cajuputi breeding program was possible by using index selection. • The index selection simultaneously improving 1,8-cineole content by 2.18% and leaf oil concentration by 27.36%. Melaleuca cajuputi is a multi-purpose tree, best known as an aromatic and medicinal plant for the production of medicinal essential oil that has been commercially used. Among three subspecies cajuputi , cumingiana and platyphylla , M. cajuputi subsp. cajuputi is the main source of cajuput oil industry. Breeding program of M. cajuputi subsp. cajuputi aimed to improve leaf oil yield and 1,8-cineole content has been implemented for the last two decades. In this study, genetic parameters for growth (height and diameter), leaf oil concentration and 1,8-cineole content in the full-sib family progeny trial including 39 families planted in Playen, Gunungkidul, Yogyakarta, Indonesia were examined. Narrow-sense heritability were low for growth traits (h i 2 = 0.10 – 0.13) but high for leaf essential oil concentration (0.50) and moderate for 1,8-cineole content (0.21). There were no genetic correlations among growth, oil concentration and 1,8-cineole content. Index selection was used to improve simultaneously both oil concentration and 1,8-cineole content in the M. cajuputi subsp. cajuputi breeding program. The genetic gains for oil concentration increased from −1.19 to 29.67% while the gains for 1,8-cineole content decreased from 6.19 to 0.07%. The scenario that improved 1,8-cineole content by 2.18% was the most optimal selection as genetic responses of leaf oil concentration reached 27.36%. [ABSTRACT FROM AUTHOR]

There is little evidence to show that mortality rates during the period after weaning are improving over time in Australian sheep. The average mortality rate of Merino lambs during the post-weaning period has been estimated to be 5.2%. The present study explored the potential for producers to breed for improved survival rates during the post-weaning period and the potential impact this would have on key production traits. A total of 122 526 weaner survival (mortality) records were obtained from 18 Merino flocks, between 1989 and 2014, encompassing a wide variety of Australian Merino sheep types and production systems. The heritability of weaner survival from a sire model was estimated to be 0.07 ± 0.01 and was significantly greater than zero. The survival of lambs post-weaning was significantly influenced by weaning weight, with higher survival rates observed in the heavier lambs. The phenotypic relationship with weight indicates that selection for heavier weaning and post-weaning weights, and in turn larger growth rates, will improve survival rates. There is genetic variation in weaner survival not explained by the relationship with weaning weight. Weight-corrected weaner survival was antagonistically genetically correlated with fleece weight. Due to these antagonistic genetic relationships selection based on popular MERINOSELECT indexes is leading to a very small reduction in the survival rate of lambs after weaning through to the post-weaning stage. To prevent a decline in weaner survival, producers are advised to record weaner survival and include it in their breeding objective. This study explored the potential for producers to breed for improved survival rates in Australian Merino sheep during the post-weaning period and reported the survival trait to be lowly heritable. Unfavourable genetic correlations with key production traits mean that selection based on popular MERINOSELECT indexes is leading to a very small reduction in the survival rate of lambs after weaning. To prevent a decline in survival, producers are advised to record weaner survival and include it in their breeding objective. [ABSTRACT FROM AUTHOR]

As the dynamic kernel of natural gas pipeline infrastructure, centrifugal compressor unit and its status of health hold a crucial impact on the regular operation of the pipeline system. In order to keep track of the state of the centrifugal compressor unit, monitoring parameters are taken as the indexes for state evaluation. However, with the increasing complexity of monitoring systems, how to select appropriate parameters among numerous monitoring parameters and to establish a rational index system becomes an urgent problem in the process of state evaluation. In traditional, the indexes are selected with expert experience, ignoring the correlation between the monitoring parameters, which results in information redundancy in state evaluation. Besides, the monitoring parameters of auxiliary systems are usually not taken into consideration. For improving the performance of the index system, namely to reduce the information redundancy and to broaden the coverage, a time lag based correlation analysis model (TLCAM) is proposed in this paper for index selection. In the TLCAM, Erdem correlation coefficient (ECC), which takes the lag difference into account, is used to depict the coupling correlation between the monitoring parameters. Meanwhile, a neighborhood extreme value method (NEVM) is proposed to calculate the ECC. Hereafter, the monitoring parameters are classified through incorporating the advantage of information entropy into the density-peak based clustering (CDP). Consequently, an index system with less redundancy and wider coverage is established for the state evaluation of centrifugal compressor unit. At the end, correlation analysis and sensitivity analysis are performed on the two index systems respectively obtained from expert knowledge and TLCAM to compare their performance. The result shows that the index system established by using TLCAM is more sensitive to some early faults and contains less information redundancy. Moreover, it covers more critical components of the whole unit. [ABSTRACT FROM AUTHOR]

Sorghum is one of the world’s major crops, expresses traits for resilience to climate change, and can be used for several purposes including food and clean fuels. Multiple-trait genomic prediction and selection models were implemented using genotyping-by-sequencing single nucleotide polymorphism markers and phenotypic data information. We demonstrated for the first time the efficiency genomic selection modelling of index selection including biofuel traits such as aboveground biomass yield, plant height, and dry mass fraction of the fresh material. This work also sheds light, for the first time, on the promising potential of using the information from the populations grown from seed to predict the performance of the populations regrown from the rhizomes—even two winter seasons after the original trial was sown. Genomic selection modelling of the optimum index selection including the three traits of interest (plant height, aboveground dry biomass yield, and dry mass fraction of fresh mass material) was the most promising. Since the plant characteristics evaluated herein are routinely measured in cereal and other plant species of agricultural interest, it can be inferred that the findings can be transferred in other major crops.

The initial introduction of Pinus elliottii (PEE) to China occurred in the 1930s, and the planting of this conifer species has now attained close to 3 million ha in the subtropical zone of southern China. A large-scale genetic improvement program for PEE was implemented in southern China to produce fast-growing trees with high wood quality to address the severe shortage of timber production over the last two decades. In this paper, selection for stem volume, basic wood density (DEN) and modulus of elasticity (MOE) was based on the Smith-Hazel index, and a total of approximately 2 000 individual trees from 158 PEE open-pollinated families were selected at 22 years of age. The DEN and MOE for each tree were determined by non-destructive evaluation techniques using the Pilodyn and Hitman Director ST300® acoustic velocity device. The heritabilities and genetic and phenotypic correlations for the traits that were measured were estimated using the residual maximum likelihood approach in the flexible mixed modelling program ASReml-R. The results showed that the heritability estimates for the wood properties were between 0.292 and 0.309, and the heritabilities of the growth traits ranged from 0.129 to 0.216. The genetic correlation between the DENP and acoustic velocity (V ) with MOEP was 0.45 and 0.95, respectively. An indirect selection based on V was observed to be highly effective for determination of MOE. It indicated that V can be integrated into tree improvement programs as a useful index of MOE by ranking candidate families or individuals within the selection population. The genetic correlations between the growth traits and wood properties were not significant. By contrast, the phenotypic correlations between them were significantly positive, but the correlation coefficients were very low. The appropriate selection index (I4), which placed 10 times as much weight on DEN and MOE as the equal emphsis method, was determined as the appropriate selection index. [ABSTRACT FROM AUTHOR]