Poultry biodiversity for alternative farming systems development

Poultry biodiversity represents a key factor to improve poultry resilience and promote sustainable and low input farming systems. The EU and member states promote protection of livestock biodiversity and the development of alternative farming through funding projects such as “Local Chicken Breeds in Alternative Production Chain: Welfare, Quality and Sustainability” (funded by the Italian Ministry of Research and University). The aim of the present research was to identify among five different poultry genotypes Bionda Piemontese (BP), Robusta Maculata (RM), RM x Sasso (RMxS), BP x Sasso (BPxS) and a commercial hybrid (Ross 308) the best suitable breed in terms of productivity and welfare for alternative housing system. A total of 300 (60 x genotype), 21 days old male birds were randomly allotted in two housing systems: 1) standard intensive farming (controlled environment, 33 kg/m2 and standard diet) and 2) free-range (“natural” environmental conditions, 21 kg/m2, access to outdoor area and low-input diet). Slaughtering was performed at 81 days of age. During the trial, the productive performance and behaviour of the animals were evaluated. The housing system, the genotype and their interaction significantly affected many of the studied variables, showing broiler not the ideal genotype for extensive farming system, which is more suited for low/medium performance strains.

AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trails (METs)

The stability and high yielding of Vigna subterranea L. Verdc. genotype is an important factor for long-term development and food security. The effects of G × E interaction on yield stability in 30 Bambara groundnut genotypes in four different Malaysian environments were investigated in this research. The experiment used a randomized complete block design with three replications in each environment. Over multiple harvests, yield component traits such as the total number of pods per plant, fresh pods weight (g), hundred seeds weight (g), and yield per hectare were evaluated in the main and off-season in 2020 and 2021. Stability tests for multivariate stability parameters were performed based on analyses of variance. For all the traits, the pooled analysis of variance revealed highly significant (p < 0.01) variations between genotypes, locations, seasons, and genotypes by environment (G × E interaction). A two-dimensional GGE biplot was generated using the first two principal components (axis 1 and axis 2), which accounted for 94.97% and 3.11% difference in GEI for yield per hectare, respectively. Season and location were found to be the most significant causes of yield heterogeneity, accounting for 31.13% and 14.02% of overall G + E + G × E variation, respectively, according to the combined study of variance. The GGE biplot revealed that the three winning genotypes G1, G3, and G5 appear across environments whereas AMMI model exposed genotypes viz G18, G14, G7, G3, G1, and G5 as best performer. Based on ideal genotype ranking genotype G1 was the best performer, with a high mean yield and high stability in the tested environment. According to the AEC line, genotypes G1 and G3 were extremely stable, while genotypes G2 and G4 were low stable, with a high average yielding per hectare. A GGE and AMMI biplot graphically showed the interrelationships between the tested environment and genotypes, classified genotypes into three categories as well as simplifying visual evaluations, according to this investigation. According to our results, breeding could improve yield production, and the genotypes discovered could be recommended for commercial cultivation.

BIPLOT ANALYSIS FOR IDENTIFICATION OF SUPERIOR GENOTYPES IN A RECOMBINANT INBRED POPULATION OF WHEAT UNDER RAINFED CONDITIONS

Physiological traits of wheat genotypes and their trait relation to drought conditions are important to identify the genotype in target environments. Thus, genotype selection should be based on multiple physiological traits in variable environments within the target region. This study was conducted at Punjab Agricultural University during rabi crop seasons 2012-13 and 2013-14 to study the recombinant inbred lines (RILs) of wheat genotypes derived from traditional landraces and modern cultivars (C518/2*PBW343) based on various morpho-physiological traits. A total of 175 RILs were selected for this study based on various tolerance indices. The genotype by trait (GT) biplot analysis was applied to data from seven high-yielding RILs grown under irrigated (E1) and rainfed environments (E2). The GGE biplot explained 100% of the total variation for chlorophyll content, grain filling period, peduncle length, water-soluble carbohydrates, grain number, grain yield, and 95.1% for canopy temperature, 94.9% for thousand-grain weight. GT-biplots indicated that the relationships among the studied traits were not consistent across environments, but they facilitated visual genotype comparisons and selection in each environment. RIL 84 and RIL108 were close to the average environment (ideal genotype) for all traits studied except chlorophyll content. A well-performing genotype with great environmental stability is called an "ideal genotype. Among all entries, these genotypes performed well. Therefore, among the traits studied, grain filling period, peduncle length, canopy temperature, water soluble carbohydrates, and 1000 grain weight contributed to grain yield under a stress environment. Furthermore, it may be used as a donor material in breeding programs and QTLs mapping.

Pre-emptive Breeding Against Karnal Bunt Infection in Common Wheat: Combining Genomic and Agronomic Information to Identify Suitable Parents

Wheat (Triticum aestivum L.) is the most widely grown cereal crop in the world and is staple food to half the world’s population. The current world population is expected to reach 9.8 billion people by 2050, but food production is not expected to keep pace with demand in developing countries. Significant opportunities exist for traditional grain exporters to produce and export greater amounts of wheat to fill the gap. Karnal bunt, however, is a major threat, due to its use as a non-tariff trade barrier by several wheat-importing countries. The cultivation of resistant varieties remains the most cost-effective approach to manage the disease, but in countries that are free of the disease, genetic improvement is difficult due to quarantine restrictions. Here we report a study on pre-emptive breeding designed to identify linked molecular markers, evaluate the prospects of genomic selection as a tool, and prioritise wheat genotypes suitable for use as parents. In a genome-wide association (GWAS) study, we identified six DArTseq markers significantly linked to Karnal bunt resistance, which explained between 7.6 and 29.5% of the observed phenotypic variation. The accuracy of genomic prediction was estimated to vary between 0.53 and 0.56, depending on whether it is based solely on the identified Quantitative trait loci (QTL) markers or the use of genome-wide markers. As genotypes used as parents would be required to possess good yield and phenology, further research was conducted to assess the agronomic value of Karnal bunt resistant germplasm from the International Maize and Wheat Improvement Center (CIMMYT). We identified an ideal genotype, ZVS13_385, which possessed similar agronomic attributes to the highly successful Australian wheat variety, Mace. It is phenotypically resistant to Karnal bunt infection (&lt;1% infection) and carried all the favourable alleles detected for resistance in this study. The identification of a genotype combining Karnal bunt resistance with adaptive agronomic traits overcomes the concerns of breeders regarding yield penalty in the absence of the disease.

Stability and G × E Analysis of Oil Yield of Sunflower Single Cross Hybrids In Diverse Environments

Multi-environment trials have a fundamental role in selection of the best genotypes across different environments before its commercial release. This study was carried out to identify high-yielding stable sunflower genotypes using the graphical method of the GGE biplot. For this purpose, 11 new hybrids along with four cultivars were evaluated in a randomized complete block design with four replications across 8 environments (combination of years and locations) during 2018–2020 growing seasons. The results indicated that genotype (G), environment (E) and genotype × environment (G×E) effects were significant for oil yield. The G, E and G×E interaction effects accounted for 51.94, 9.50 and 18.67% of the total variation, respectively. Results of biplot analysis showed that the first and second principle components accounted 45.9% and 20.4%, respectively, and in total 66.3% of oil yield variance. GGE biplot analysis indicated two major mega-environments of sunflower testing locations in Iran. Based on the hypothetical ideal genotype biplot, the genotypes G3 and G5 were better than the other genotypes for oil yield and stability, and had the high general adaptation to all environments. Ranking of genotypes based on the ideal genotype from the most appropriate to most inappropriate genotypes is as follows: G5 ˃ G3 ˃ G8 ˃ G14 ˃ G6 ˃ G2 ˃ G13 ˃ G12 ˃ G10 ˃ G11 ˃ G1 ˃ G7 ˃ G4 ˃ G15 ˃ G9. Furthermore, ranking the environments based on the ideal environment introduced Sari location as the best environment. Therefore, the Sari location can be used as suitable test location for selecting superior genotypes of sunflower in Iran. Generally, our results showed the efficiency of the graphical method of the GGE biplot for selection of the genotypes that are stable, high yielding, and responsive.

Environmental trial of pea breeding acessions

The aim of the study was to determine the information content and adequacy of the ecological testing points of the pea breeding material and to identify the "ideal" genotype. Materials and methods. The breeding material in the experiments was represented by cultivars bred at the PPI nd.a. V.Ya. Yuriev (Tsarevych, Oplot, Otaman, Metsenat, Korvet, Haiduk, and Malakhit) and ten breeding lines (SL 11-129, SL 11-213, SL 11-55, SL 11-58, SL 10-37, SL 11-32, SL 10-132, SL 09-118, SL 11-166, and SL 11-176). In addition there was one cultivar (Svit) bred at the Plant Breeding and Genetics Institute. All the cultivars are leafless, semi-dwarf, mid-ripening, except for Tsarevych (mid-early). The field experiments were carried out in accordance with the methods of field experimentation, using the conventional pea growing technology. The seeding rate was 1.2 million germinable seeds/ha; the plot area was 10 m2. To evaluate the accessions for the variability in different environments, we used a regression model developed by S.A. Eberhart and W.A. Russel, where the regression coefficient is an indicator of the genotype-environment interaction. This model is included in "Guidelines for Environmental Trials of Corn". Results and discussion. Thus, comparing the regression coefficient in pea cultivars Oplot, Tsarevych, Haiduk, Korvet, and Metsenat, we could conclude that these accessions were highly intensive in the OSES conditions and extensive in the PPI NAAS conditions (except for Metsenat). Regarding the regression coefficient in the breeding lines, none of them had a regression coefficient of 1.0. Over the study period, the regression coefficient was 1.4 only in line SL 11-58 (PPI NAAS) and 1.2 (OSES), characterizing this line as intensive regardless of the place of cultivation. Taking into account that the regression coefficient values of <1 are intrinsic to extensive accessions, lines SL 10-132 (RC = 0.4) and SL 11-176 (RC = 0.8) are preferred. Because these accessions also have a high genotypic effect. In addition, the regression coefficient in breeding line SL 09-118 was 0.9, with a genotypic effect of 0.07. Such combination of the indicators characterizes the line as relatively stable, with sufficient potential performance, and this breeding line will not be demanding to growing conditions similar to the OSES ones. Conclusions. Thus, the evaluation of both cultivars and breeding lines in the environmental trial showed that the pea breeding at the Plant Production Institute named after VYa Yuryev had a significant potential to create cultivars that would be well-adapted to both eastern and southern conditions, and that environmental trials remained an effective tool for assessing breeding material and selecting accessions with the maximum fulfillment of the genetic potential

Understanding of Yield Stability in Jack Bean (Canavalia ensiformis L.) Genotypes using AMMI and GGE bi-plot Models

Background: Jack bean is an under-exploited legume species, a source of food, medicine and cover crop. By virtue of its adaptive nature to low fertility soils, it is one of the few pulses that grow well on highly leached, nutrient depleted, lowland tropical soils. But, in India, crop improvement work is very little done. Stability of yield is a major criterion for farmer’s acceptability of any variety and there are several methods to estimate the stability and G x E interaction effects of a genotype across seasons. Among these, AMMI analysis is the most recent and widely exploited in different crops for the identification of stable genotypes. In this context, yield stability of 10 accessions of jack bean is studied to identify the stable genotypes.Methods: The experiment was conducted with 10 Jack bean genotypes in RCBD with two replications under rain fed conditions during 2017-2020 in Kharif for four seasons. The data was subjected to analysis of variance and then taken for AMMI and GGE analysis for identification of stable genotypes.Result: The combined analysis of variance revealed that there was highly significant variation (p less than 0.01) in grain yield and environments and genotype interaction among the genotypes. The average bean yield of the genotypes was 533.1 grams per plant. The highest and the lowest mean yield was recorded in PSR-12202 and CHMJB-02 respectively which was corroborated by the AMMI bi-plot as well. Similar to the AMMI bi-plot, the GGE bi-plot also confirmed that PSR-12202 was the stable genotype across the environments, whereas, G1, G2, G3, G4, G6, G7 and G8 were the other genotypes with low yields in some or all the environments. Kharif, 2018 and Kharif, 2020 are discriminating environments and are declared as the most representative than Kharif, 2017 and Kharif, 2019. Generally, PSR-12202 was the ideal genotype with higher mean yield and relatively good stability; G5 was the moderately good yielding genotype and the most unstable genotype; Whereas, G1, G2, G3, G4, G6, G7 and G8 were the poorly yielding and unstable genotypes. Both AMMI and GGE bi-plot are able to establish the genotypic stability and these models can be exploited for judging the genotypes for their GEI in other crops as well.

Stability Assessment of Single-Cross Maize Hybrids Using GGE-Biplot Analysis

Grain yield potential of new maize hybrid varieties across target environments contributes to the uptake of these varieties by farmers. Evaluation of single-cross hybrids developed from test crossing introgressed inbred lines bred for three distinct environments to elite tropical inbred line testers was carried out. The study&rsquo;s objective was to assess grain yield stability and genotype adaptability of the single-cross hybrids across South African environments relative to adapted commercial hybrid checks. One hundred and twenty-two introgressed inbred lines developed using the pedigree breeding program were crossed to four tropical elite inbred line testers using line &times; tester mating design to obtain 488 experimental single cross hybrids. Subject to availability of adequate seed for evaluation, a panel of 444 experimental single-cross hybrids was evaluated using an augmented design in two experiments defined as Population A and B for the study&rsquo;s convenience in South African environments. Data for grain yield (t/ha) performance for experimental single-cross hybrids and commercial check hybrids in Population A and B across environments and individual environments identified experimental single-cross hybrids that had significant comparable grain yield (t/ha) performance relative to best commercial check hybrid (PAN6Q445B) on the market. The selected experimental single-cross hybrids 225, 89, 246 and 43 (Population A) and 112 (Population B) also had a better average rank position for grain yield (t/ha) relative to best commercial check hybrid. These selected experimental single-cross hybrids had a grain yield (t/ha) advantage range of 0.9-6.7% for Population A and 7.3% for Population A and B, respectively, relative to the adapted commercial check hybrid. GGE biplot patterns for which won-where for Population A indicated that at Potchefstroom Research Station and Ukulinga Research Station experimental single-cross hybrids 127 and135 were the vertex (winning) hybrids. Cedera Research Station did not have a vertex hybrid for Population A. For Population B, experimental single-cross hybrids 112, 117 and 18 were the vertex hybrids at Cedera Research Station, Ukulinga Research Station and Potchefstroom Research Station, respectively. Experimental single-cross hybrid 257 was identified as ideal genotype for Population A, while experimental single-cross hybrid 121 in Population B was the ideal genotype. Ideal environments were also identified as Ukulinga Research Station for Population A, and Cedera Research Station for Population B. Average-environment coordination (AEC) view of the GGE biplot in Population A indicated that experimental single-cross hybrids 1 was highly stable across environments. In comparison, Population B experimental single-cross hybrid 161 was highly stable across environments. In conclusion, selected single-cross hybrids in the current study can also be advanced for further evaluation with a possibility for identifying high yielding and stable single-cross hybrids for variety registration and release in target environments in South Africa.

AMMI and GGE Biplot Analysis for G×E Interaction of Wheat Genotypes under Different Irrigation and Sowing Condition

Background: AMMI analysis showed that genotype, environment and genotype-environment interaction had a highly significant variation for 20 wheat genotypes analyzed over four environments. ASV ranking revealed G15 (RVW-4275) as a stable genotype while G3 (RVW-4263) and G9 (RVW-4269) as unstable genotypes. GGE biplot analysis for environment interrelationship revealed that E1 (Irrigated timely sown), E2 (Restricted irrigation timely sown) were correlated forming one group and E3 (Irrigated late sown), E4 (Restricted irrigation late sown) were correlated forming another group. Polygon view showed that G9 (RVW-4269) was found stable and better performing in E1, G12 (RVW-4272) was stable under the E2 environment and G3 (RVW-4263) was stable in E3. Ideal genotype graph with concentric circles having ideal genotype at the center and genotypes G12(RVW-4272), G18(RVW-4278), G13(RVW-4273), G11(RVW-4271), G10 (RVW4270) present in a concentric circle close to the center can to considered as stable and desirable genotypes.Methods: In the present study the plant material comprised of 20 wheat genotypes. These genotypes were randomly allocated in different replication under different environmental condition. The field trial was evaluated at four different environments viz., E1- Irrigated timely sown, E2- Restricted irrigation is timely sown (RI- 2 irrigation), E3- Irrigated late sown, E4- Restricted irrigation late sown during Rabi season of 2016-2017 at research farms, college of agriculture, Gwalior, MP. The genotype main effects and genotype × environment interaction effects (GGE) model and additive main effects and multiplicative interaction (AMMI) model were two statistical approaches used to determine stable genotype in R software.Result: Highly significant difference was seen for genotype and G×E interaction in our study, revealing that genotype yield output was highly impacted by G×E. In all four environments and G3, G9 as unstable genotypes in all four environments, ASV ranking revealed G15 as a stable genotype. For further breeding, these genotypes G12, G18, G13, G11, G2, G10 and G15 may be used to grow genotypes adapted to conditions of partial irrigation or drought stress.

Soybean breeding through induced mutation in Vietnam.

In Vietnam, soybean is one of the traditional crops and plays an important role in crop rotation, soil improvement and meeting the nutritional needs of humans and livestock. With the aim of generating genetic variability in soybean and creating new soybean varieties to meet the needs of production, induced mutation research has been carried out since the 1980s and has gained outstanding achievements. Induction of modified traits and their incorporation into an ideal genotype was achieved by judicious use of the induced mutation technique. So far, outstanding soybean varieties such as DT84, DT90, DT99, DT2008 and several promising lines have been developed in Vietnam by incorporating desirable traits like high and stable yield (2.0-3.5 t/ha), good quality, drought tolerance, disease resistance (rust, powdery mildew, downy mildew), short growth duration (70-100 days), wide adaptability and suitability for cropping systems and ecological regions in the whole country. The most outstanding variety, DT84, occupies over 50% of the total production area and 80% in Central and North Vietnam (about 70,000-80,000 ha/year). These varieties have also been used as materials for developing several additional improved soybean varieties. Thus, induced mutation research has played an important role in improving soybean varieties in Vietnam.