Advanta Seeds (Netherlands)

Abstract Genotype-by-environment interaction (GEI) analysis is vital in cotton breeding to identify high-yielding, stable hybrids adapted to diverse environments. This study evaluated 39 intra-hirsutum cotton hybrids to identify stable, high-performing hybrids with jassid resistance. Jassid screening classified 18 hybrids as resistant and 15 hybrids as moderately resistant under hotspot conditions. Yield stability was evaluated through stability indices, GGE biplots, Y x WAAS (yield vs. weighted average of absolute scores) biplots, and WAASBY (weighted average of absolute scores and best yield) heat maps. The combined ANOVA results showed that genotype and GEI significantly (p < 0.01) influenced seed cotton yield. The stability indices identified H19, H22, H13, H26, and H38 as stable and high-yielding hybrids. GGE biplots highlighted E3 as the most discriminating environment with hybrids H39, H01, and H25 as best performers, and H15, H03, and H19 in E2. The GGE biplots, Y x WAAS biplot, and WAASBY heat map consistently identified H38, H13, H22, and H26 as high-performing stable hybrids. Robust statistical analyses combined with hotspot screening identified H13, H22, and H26 as superior performers in yield and stability with jassid resistance. These hybrids represent valuable resources for breeding programs aimed at yield stability with jassid resistance in cotton.

Recent approaches to entrepreneurship seek to explain regional heterogeneity by exploring the link between knowledge endowment and new firm creation. There are two main gaps in this stream of research. First, entrepreneurship tends to be considered in terms of entry rates rather than in terms of job creation. Second, most empirical studies focus on relatively large geographical areas and overlook the distance at which knowledge externalities dissipate. The present paper exploits data on firms based in the Emilia-Romagna region (Italy) to show that private R&D spillovers are positively associated with the size at entry of innovative firms only for those located close to the R&D activities and that these spillovers dissipate at a few kilometres from the R&D source. Non-linearities are detected only for low-tech sectors.

The question of the spray volume for applying agrochemicals to plants has and still demands studies to continuously search for guiding parameters for technicians due to the several variables involving application technology. This experiment aimed to determine the best spray volume for applying pesticides with a boom sprayer to soybean (Glycine max) and wheat (Triticum aestivum) crops. The experiment had a completely randomized blocks design with five treatments and five replications. In soybean (crop year 2011/12), the treatments were the control (no pesticide application) and spray volume applications of 50, 100, 150, and 200 L ha-1. For wheat (crop year 2012), the treatments were the control and spray volumes of 75, 100, 125, and 150 L ha-1. The variables analyzed were the yield components. The study concluded the need for applying foliar fertilizers and performing the chemical control of diseases and pests in soybean and wheat crops. The spray volumes of 50 L ha-1 for soybeans and 75 L ha-1 for wheat were satisfactory for spraying agrochemicals with a ground bar sprayer on plants.

Abstract Seed health is an indispensable prerequisite of food security. While the toolkit of plant protection products is currently limited, evidence suggests that the seed microbiome could protect seeds from pathogens. Thus, given their possible disease suppressive potential, we tested 11 different pathosystems to achieve the following proof-of-concept: seed microbiomes can be beneficial for seed health through conferring disease suppression. This study focused on beetroot, onion, spinach, pepper, coriander, red fescue and perennial ryegrass seeds, with each crop being challenged with one or two from a total of six pathogens, namely Pythium ultimum (or a Pythium sp.), Setophoma terrestris, Fusarium oxysporum, Phytophthora capsici, Laetisaria fuciformis and a mix of Puccinia sp. isolates. Each seed lot of each crop was tested with and without treatment with a disinfectant as a proxy for comparing intact seed microbiomes with seed microbiomes after partial elimination by disinfection. We found disease suppression in two pathosystems. Beetroot and spinach seed lots were able to suppress disease caused by P. ultimum when their microbiomes were intact but not after seed disinfection. We speculate that this relates to the microorganisms residing on and in the seed. Yet, seed microbiome disease suppression was not found in all pathosystems, highlighting the variation in seed morphology, plant cultivars, pathogens and seed disinfection treatments. A holistic understanding of the characteristics of seeds that harbour suppressive microbiomes as well as the pathogens that are sensitive to suppression could lead to more targeted and informed seed processing and treatment and, consequently, to the sustainable management of seedling diseases.

Abstract In maize breeding, the selection of the candidate inbred lines is based on topcross evaluations using a limited number of testers. Then, a subset of single-crosses between these selected lines is evaluated to identify the best hybrid combinations. Genomic selection enables the prediction of all possible single-crosses between candidate lines but raises the question of defining the best training set design. Previous simulation results have shown the potential of using a sparse factorial design instead of tester designs as the training set. To validate this result, a 363 hybrid factorial design was obtained by crossing 90 dent and flint inbred lines from six segregating families. Two tester designs were also obtained by crossing the same inbred lines to two testers of the opposite group. These designs were evaluated for silage in eight environments and used to predict independent performances of a 951 hybrid factorial design. At a same number of hybrids and lines, the factorial design was as efficient as the tester designs, and, for some traits, outperformed them. All available designs were used as both training and validation set to evaluate their efficiency. When the objective was to predict single-crosses between untested lines, we showed an advantage of increasing the number of lines involved in the training set, by (i) allocating each of them to a different tester for the tester design, or (ii) reducing the number of hybrids per line for the factorial design. Our results confirm the potential of sparse factorial designs for genomic hybrid breeding.

<strong>English — Français — Español — Português — русский — 中文 — हिंदी — বাঙালি</strong> Editable file .odp included.

SummaryPlant‐pathogenic oomycetes secrete effector proteins to suppress host immune responses. Resistance proteins may recognize effectors and activate immunity, which is often associated with a hypersensitive response (HR). Transient expression of effectors in plant germplasm and screening for HR has proven to be a powerful tool in the identification of new resistance genes. In this study, 14 effectors from the lettuce downy mildew Bremia lactucae race Bl:24 were screened for HR induction in over 150 lettuce accessions. Three effectors—BLN06, BLR38 and BLR40—were recognized in specific lettuce lines. The recognition of effector BLR38 in Lactuca serriola LS102 did not co‐segregate with resistance against race Bl:24, but was linked to resistance against multiple other B. lactucae races. Two unlinked loci are both required for effector recognition and are located near known major resistance clusters. Gene dosage affects the intensity of the BLR38‐triggered HR, but is of minor importance for disease resistance.

Broomrape (Orobanche cumana Wallr.) is a root parasite of sunflower that is regarded as one of the most important constraints of sunflower production in the Mediterranean region. Breeding for resistance is the most effective method of control. P-96 is a sunflower line which shows dominant resistance to broomrape race E and recessive resistance to the very new race F. The objective of this study was to map and characterize quantitative trait loci (QTL) for resistance to race E and to race F of broomrape in P-96. A population from a cross between P-96 and the susceptible line P-21 was phenotyped for broomrape resistance in four experiments, two for race E and two for race F, by measuring different resistance parameters (resistance or susceptibility, number of broomrape per plant, and proportion of resistant plants per F(3) family). This population was also genotyped with microsatellite and RFLP markers. A linkage map comprising 103 marker loci distributed on 17 linkage groups was developed, and composite interval mapping analyses were performed. In total, five QTL ( or1.1, or3.1, or7.1 or13.1 and or13.2) for resistance to race E and six QTL ( or1.1, or4.1, or5.1, or13.1, or13.2 and or16.1) for resistance to race F of broomrape were detected on 7 of the 17 linkage groups. Phenotypic variance for race E resistance was mainly explained by the major QTL or3.1 associated to the resistance or susceptibility character ( R(2)=59%), while race F resistance was explained by QTL with a small to moderate effect ( R(2) from 15.0% to 38.7%), mainly associated with the number of broomrape per plant. Or3.1 was race E-specific, while or1.1, or13.1 and or13.2 of were non-race specific. Or13.1, and or13.2 were stable across the four experiments. Or3.1, and or7.1 were stable over the two race E experiments and or1.1 and or5.1 over the two race F experiments. The results from this study suggest that resistance to broomrape in sunflower is controlled by a combination of qualitative, race-specific resistance affecting the presence or absence of broomrape and a quantitative non-race specific resistance affecting their number.

Crop ScienceVolume 44, Issue 1 p. 350-351 Registrations of Cultivars Registration of ‘Applaud’ Perennial Ryegrass S.A. Bonos, Corresponding Author S.A. Bonos bonos@aesop.rutgers.edu Dep. of Plant Biology and Pathology, Cook College, Rutgers Univ., 59 Dudley Rd., Foran Hall, New Brunswick, NJ, 08901Corresponding author (bonos@aesop.rutgers.edu)Search for more papers by this authorK. Hignight, K. Hignight Advanta Seeds Pacific, 33725 Columbus St. SE, P.O. Box 1496, Albany, OR, 97321-0452Search for more papers by this authorR. Stapp, R. Stapp Pennington Seed Co., P.O. Box 290, Madison, GA, 30650Search for more papers by this authorD. Smith, D. Smith Dep. of Plant Biology and Pathology, Cook College, Rutgers Univ., 59 Dudley Rd., Foran Hall, New Brunswick, NJ, 08901Search for more papers by this authorW.A. Meyer, W.A. Meyer Dep. of Plant Biology and Pathology, Cook College, Rutgers Univ., 59 Dudley Rd., Foran Hall, New Brunswick, NJ, 08901Search for more papers by this authorC.R. Funk, C.R. Funk Dep. of Plant Biology and Pathology, Cook College, Rutgers Univ., 59 Dudley Rd., Foran Hall, New Brunswick, NJ, 08901Search for more papers by this author S.A. Bonos, Corresponding Author S.A. Bonos bonos@aesop.rutgers.edu Dep. of Plant Biology and Pathology, Cook College, Rutgers Univ., 59 Dudley Rd., Foran Hall, New Brunswick, NJ, 08901Corresponding author (bonos@aesop.rutgers.edu)Search for more papers by this authorK. Hignight, K. Hignight Advanta Seeds Pacific, 33725 Columbus St. SE, P.O. Box 1496, Albany, OR, 97321-0452Search for more papers by this authorR. Stapp, R. Stapp Pennington Seed Co., P.O. Box 290, Madison, GA, 30650Search for more papers by this authorD. Smith, D. Smith Dep. of Plant Biology and Pathology, Cook College, Rutgers Univ., 59 Dudley Rd., Foran Hall, New Brunswick, NJ, 08901Search for more papers by this authorW.A. Meyer, W.A. Meyer Dep. of Plant Biology and Pathology, Cook College, Rutgers Univ., 59 Dudley Rd., Foran Hall, New Brunswick, NJ, 08901Search for more papers by this authorC.R. Funk, C.R. Funk Dep. of Plant Biology and Pathology, Cook College, Rutgers Univ., 59 Dudley Rd., Foran Hall, New Brunswick, NJ, 08901Search for more papers by this author First published: 01 January 2004 https://doi.org/10.2135/cropsci2004.3500Citations: 1 Publication No. 12180-2-03. Some of this work was conducted as part of the NJAES Project no. 12180, supported by NJAES funds, other grants and gifts. Additional support was received from the U.S. Golf Association-Golf Course Superintendents Association of America Research Fund, and the New Jersey Turfgrass Association. Registration by CSSA. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume44, Issue1January–February 2004Pages 350-351 RelatedInformation