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Waterlogging (WL) is an important abiotic stress, severely affecting plant growth and development, inhibiting root respiration and degradation of chlorophyll, senescence of leaves and chlorosis leading to substantial yield loss. These intensities of yield losses generally depend on the duration of WL and crop growth stages. Maize being a dry land crop is particularly sensitive to WL. Systematic screening techniques to identify parameters linked with tolerance are not well established which serves as a major bottleneck in the identification of promising genotypes. In this study, 120 maize inbred lines belonging to diverse genetic backgrounds were evaluated for WL tolerance both at pre-emergence as well as the seedling stage. Results based on percentage germination at pre-emergence and percentage survival at the seedling stage under WL established that pre-germination tolerance is independent of seedling stage tolerance. Membership function value based on WL tolerance coefficient of shoot and root fresh weights, dry weights, lengths, root surface area, shoot area and root volume was used to identify tolerant lines. Established mathematical models were used and identified root dry weight as a single reliable parameter to judge the tolerance level of genotypes. The use of BLPSI and ESIM selection indices as well as MTSI to judge the stability as well as genetic worth of genotypes further strengthens the selection efficiency. Lines thus performing best across all the models included I 185, I 172 and SE 503 and were identified as tolerant lines for WL. A combination of these different selection approaches would further strengthen selection efficiency and is believed to be a rapid and effective selection approach.

Genomic selection (GS) in plant breeding aims to identify individuals with superior genetic merit while maintaining genetic diversity within populations. In plant breeding, considering multiple traits simultaneously makes optimizing selection complex, especially under genetic relatedness constraints. In this study, we propose a binary quadratic programming framework for constructing a multi-trait selection index that maximizes genetic gain while minimizing average pairwise relatedness appropriate for identifying superior candidates for advancement in the breeding pipeline. The approach combines estimated breeding values (EBVs) across multiple traits by applying trait-specific economic weights, while simultaneously accounting for coancestry through the genomic relationship matrix. By formulating the selection problem as a constrained Quadratic Programing Multi-trait Selection Index (QPMSI), our method enables the identification of a fixed number of candidate individuals that jointly optimize selection index values and control genetic relatedness. We evaluated the performance of the proposed method using five real genomic datasets and demonstrated that it provides a more effective balance between selection response and control of genetic relatedness than the Linear Programming Multi-trait Selection Index (LPMSI). In particular, the QPMSI consistently outperformed the LPMSI in terms of the MV metric (gain-to-degree of relatedness ratio), achieving improvements of at least 53.8%. This framework offers a practical and computationally efficient tool for sustainable breeding strategies in multi-trait selection contexts.

In farmed fish, selective breeding for feed conversion ratio (FCR) may be possible via indirectly selecting for easily-measured indicator traits correlated with FCR. We tested the hypothesis that rainbow trout with low lipid% have genetically better FCR, and that lipid% may be genetically related to retention efficiency of macronutrients, making lipid% a useful indicator trait. A quantitative genetic analysis was used to quantify the benefit of replacing feed intake in a selection index with one of three lipid traits: body lipid%, muscle lipid% or viscera% weight of total body weight (reflecting visceral lipid). The index theory calculations showed that simultaneous selection for weight gain and against feed intake (direct selection to improve FCR) increased the expected genetic response in FCR by 1·50-fold compared with the sole selection for growth. Replacing feed intake in the selection index with body lipid%, muscle lipid% or viscera% increased genetic response in FCR by 1·29-, 1·49- and 1·02-fold, respectively, compared with the sole selection for growth. Consequently, indirect selection for weight gain and against muscle lipid% was almost as effective as direct selection for FCR. Fish with genetically low body and muscle lipid% were more efficient in turning ingested protein into protein weight gain. Both physiological and genetic mechanisms promote the hypothesis that low-lipid% fish are more efficient. These results highlight that in breeding programmes of rainbow trout, control of lipid deposition improves not only FCR but also protein-retention efficiency. This improves resource efficiency of aquaculture and reduces nutrient load to the environment.

Introduction: As a consequence of habitat loss and degradation, the leopard cat (Prionailurus bengalensis) in China has become endangered and in need of urgent protection. In situ conservation of leopard cats must be based on an understanding of their habitat selection patterns. We studied the summer habitat of leopard cats using line-transect surveys in the northern Taihang Mountain region surrounding Beijing, China. We compared used plots with non-used plots in elevation, tree canopy, and 20 other ecological variables, and used Vanderploeg＆Scavia's resource selection index (VSI) to analyze habitat preferences. Outcomes/others: Results show that tree canopy, tree height, tree density, and stump quantity of used plots were significantly lower than non-used plots in summer, and that leopard cats preferred habitats located on northern, flat slopes with lower slope, shrub-dominated, dry soil, and less fallen-wood. Leopard cats had a strong tendency to use habitats near human disturbance areas with moderate levels of disturbance intensity. Conclusion: The results suggest that future conservation efforts should emphasize: (1) strengthening the protection and management of forest fringe shrub habitats to improve summer habitat suitability, and (2) environmental education and animal protection campaigns to promote community biodiversity conservation.

One of the most important effects of climatic changes is increasing temperatures and expanding water deficit stress in tropical and subtropical regions. As the fourth most important cereal crop, barley (Hordeum vulgare L.) is crucial for food and feed security, as well as for a sustainable agricultural system. The present study investigates 56 promising barley genotypes, along with four local varieties (Norooz, Oxin, Golchin, and Negin) in four locations to identify high-yielding and adapted genotypes in the warm climate of Iran. Genotypes were tested in an alpha lattice design with six blocks, which were repeated three times. Traits measured were the number of days to heading and maturity, plant height, thousand kernels weight, and grain yield. A combined analysis of variance showed the significant effects of genotypes (G), environments (E), and their interaction (GEI) on all measured traits. Application of the additive main-effect and multiplicative interaction (AMMI) model to the grain yield data showed that GEI was divided into three significant components (IPCAs), and each accounted for 50.93%, 30.60%, and 18.47%, respectively. Two selection indices [Smith–Hazel (SH) and multiple trait selection index (MTSI)] identified G18, G24, G29, and G57 as desirable genotypes at the four test locations. Using several BLUP-based indices, such as the harmonic mean of genotypic values (HMGV), the relative performance of genotypic values (RPGV), and the harmonic mean of the relative performance of genotypic values (HMRPGV), genotypes G6, G11, G22, G24, G29, G38, G52, and G57 were identified as superior genotypes. The application of GGE analysis identified G6, G24, G29, G52, and G57 as the high-yielding and most stable genotypes. Considering all statistical models, genotypes G24, G29, and G57 can be used, as they are well-adapted to the test locations in warm regions of Iran.

Tree breeders must almost always address multiple traits. That entails choosing selection methods and weighing up known or assumed economic-worth functions for traits. Classically, selection methods include independent culling levels, sequential culling for traits within generations, culling for different traits in successive generations (tandem selection), and selection indices, but elements of individual methods can be used in combination. Adverse genetic correlations among economic traits create a need to know the comparative economic worth of gains in different traits and can severely constrain feasible breeding goals. A traditionally cited optimal method is the Smith-Hazel selection index (and more generalised variants) based on good genetic-parameter information and known, linear economic-worth functions. The theoretical optimality, however, depends on various assumptions which are often tacit and unrecognised, and violated. Optimally cost-efficient selection will also depend on evaluation costs and ages of expression for different traits and may need to go well beyond selection indices. More complex criteria of optimality may also arise, making true optimisation very challenging and often unachievable. Where the genetic gain is delivered as mixes of segregants produced by seed propagation, non-linearities of economic-worth functions may not be readily exploited. However, non-linearities that include intermediate optima for trait values, or optima that are conditional on values for other traits (namely restricted domains in ‘multi-dimensional space’), might be exploited much better in clonal forestry systems. With adverse genetic correlations and differences among environments in the expression of genetic variation in individual traits, optimal solutions may entail selection for specific environments or production systems, on a finer scale for deployment than in breeding populations. Genomic selection offers accelerated genetic gains, and may mitigate effects of adverse genetic correlations, but depends strongly on high-quality phenotypic information on the expression of different traits at different ages in specific environments. This technology and its potential combination with gene editing promise enhancements of multi-trait selection. Remote-sensing technologies can now yield huge volumes of field data and much expanded candidate populations, but in some trade-off with effective heritabilities of remote-sensed phenotypes.

Regulations imposed on the use of chemical insecticides call for the development of environmental-friendly pest management strategies. One of the most effective strategies is the push–pull system, which takes advantage of the behavioral response of the insect to the integration of repellent stimuli; it expels the pest out of the main crop (push), while attracting stimuli (attractants) pull the pest to an alternative crop or trap (pull). The objective of this study was to design a push–pull system to control Spodoptera frugiperda in maize crops (Zea mays) in Morelos, Mexico. Data on reproductive potential, larvae development, food consumption and olfactometry were used to obtain a Trap Plant Selection Index (TRAPS) based on Principal Component Analysis. This TRAPS was used to select the most suitable plants. The degree of repellency of potential plants to be used as the trap crop was studied with four-way olfactometers. S. frugiperda females oviposited more eggs on Brachiaria hybrid cv. Mulato II, Panicum maximum cv. Mombasa and Panicum maximum cv. Tanzania than on Z. mays, regardless of the fact that these plants delayed the development of their offspring. Dysphania ambrosioides, Tagetes erecta and Crotalaria juncea were less attractive to S. frugiperda females. Therefore, the former plants could be used as crop traps, and the latter as intercropped repellent plants in a push–pull system.

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) map genomic regions associated with resistance to each disease using genome-wide association study (GWAS). A diversity panel of 196 spring wheat genotypes was evaluated for their reaction to stripe rust at eight environments, leaf rust at four environments, leaf spot at three environments, common bunt at two environments, and grain yield at five environments. The panel was genotyped with the Wheat 90K SNP array and a few KASP SNPs of which we used 23,342 markers for statistical analyses. The RLPSI analysis performed by restricting the expected genetic gain for yield displayed significant (p < 0.05) differences among the 3125 economic weights. Using the best four economic weights, a subset of 22 of the 196 genotypes were selected as potential parents with resistance to the four diseases and acceptable grain yield. GWAS identified 37 genomic regions, which included 12 for common bunt, 13 for leaf rust, 5 for stripe rust, and 7 for leaf spot. Each genomic region explained from 6.6 to 16.9% and together accounted for 39.4% of the stripe rust, 49.1% of the leaf spot, 94.0% of the leaf rust, and 97.9% of the common bunt phenotypic variance combined across all environments. Results from this study provide valuable information for wheat breeders selecting parental combinations for new crosses to develop improved germplasm with enhanced resistance to the four diseases as well as the physical positions of genomic regions that confer resistance, which facilitates direct comparisons for independent mapping studies in the future.

A set of thirty-six wheat cultivars were grown for two consecutive years under low and high nitrogen conditions. The interactions of cultivars with different environmental factors were shown to be highly significant for most of the studied traits, suggesting the presence of wider genetic variability which may be utilized for the genetic improvement of desired trait(s). Three cultivars, i.e., RAJ 4037, DBW 39 and GW 322, were selected based on three selection indices, i.e., tolerance index (TOL), stress susceptibility index (SSI), and yield stability index (YSI), while two cultivars, HD 2967 and MACS 6478, were selected based on all four selection indices which were common in both of the study years. According to Kendall’s concordance coefficient, the consistency of geometric mean productivity (GMP) was found to be highest (0.778), followed by YSI (0.556), SSI (0.472), and TOL (0.200). Due to the high consistency of GMP followed by YSI and SSI, the three selection indices could be utilized as a selection tool in the identification of high-yielding genotypes under low nitrogen conditions. The GMP and YSI selection indices had a positive and significant correlation with grain yield, whereas TOL and SSI exhibited a significant but negative correlation with grain yield under both high and low nitrogen conditions in both years. The common tolerant genotypes identified through different selection indices could be utilized as potential donors in active breeding programs to incorporate the low nitrogen tolerant genes to develop high-yielding wheat varieties for low nitrogen conditions. The study also helps in understanding the physiological basis of tolerance in high-yielding wheat genotypes under low nitrogen conditions.

Motivated by the high turnover rate of the Eurasian eagle owl (Bubo bubo) population in the south of the province of Limburg, the Netherlands, which is linked to extremely high concentrations of PCBs (polychlorinated biphenyls) and DDE (dichlorodiphenyldichloroethylene) found in owl carcasses, a habitat suitability (HS) assessment for this region was conducted to identify possible sources of PCBs in the environment. Twelve environmental characteristics (ECs) that are known to influence the presence of the species were selected. With each EC, a suitability index (SI) was associated and a uninorm was used to aggregate these individual SIs into one overall HS index value. The HS assessment was validated using GPS tracking data of six adult eagle owls. Further, Ivlev’s electivity index and Manly’s habitat selection index were used to compare the area used with what is available in the landscape. To describe the former, we considered both the probability of occurrence and the home range of the tracked individuals. The resulting HS map shows that quarries and vegetation structures, such as hedgerows or solitary trees, are the main attractors for the species, though also forest edges, orchards, and tree and fruit nurseries attract the species in the study area. Hence, further field sampling campaigns to identify possible sources of poisoning should focus on parcels with these land covers. Such a prioritization of parcels becomes possible using our approach.