Diallel analysis of pod yield and 100 seeds weight in peanut (Arachis hypogaea L.) using GRIFFING and HAYMAN methods.

Objectives: The lack of information on yield and yield components are an obstacle in peanut (Arachis hypogaea L.) breeding for productivity improvement in BURKINA FASO. This work is to elucidate the mode of heritability of genes that govern 100 seed weight and pod yield, and identify the best parents for recommendation in hybridization programs.Materials and methods: A 6 x 6 full diallel with breeding lines such as GM656, NAMA, PC79-79, AS, SH470P and CN94C; was conducted. Two models were used, one of GRIFFING (1956) and the second of HAYMAN (1954), to detect the general (GCA) and specific (SCA) combining ability. GCA/SCA 1 for 100 seed weight and GCA / SCA 1 for pod yield. D-H1 difference reveals existence of an over dominance for 100 seed weight and partial dominance for pods yield. Heritability in the narrow sense is 48.7% for the pod yield and 55.3% for the weight of 100 seeds.Conclusion and Application of results: These results show that the pod yield is governed by genes with additive effects and non-additive effects with more additivity effects. However, the weight of 100 seed is essentially governed by genes with additive and non-additive effects with a preponderance of non-additive effects and dominance. HAYMAN graphical analysis indicates that CN94C, SH470P, AS have a lot more genes that control pods yield and 100 seed weight. These breeding lines may be recommended in hybridization for improvement of productivity. The importance of Reciprocal effects (RCE) shows that we must consider maternal effects in hybridization for breeding programs. In these programmeKeys word: Arachis hypogaea L. Yield; 100 seed weight, General Combining Ability; Specific Combining Ability

Phytochemical analysis of Ziziphus mucronata Willd. extract and screening for antifungal activity against peanut pathogens

International audienceChemical analysis of aqueous extract of Ziziphus mucronata Willd. was determined by liquid chromatography–mass spectrometry (LC-MS) analysis. Among the 11 compounds found, catechin, rutin (quercetin 3-O-rutinoside), delphinidin-3-glucoside, isoquercetin (hyperoside) and quercitrin (quercetine3,7-O-L-dirhanmopyranoside) were identified as the major phenolics components in this aqueous plant extract. To elute the target compounds, the fractionation of crude extract was carried out on solid phase extraction (SPE) columns. The different fractions (from FZ1 to FZ5) obtained after fractionation were evaluated in vitro against economically important foliar fungal pathogens of peanut, including Cercospora arachidicola, Phaeoisariopsis personata and Puccinia arachidis. The treatments with Z. mucronata fractions were compared with negative control (water) and standard solutions of cathechin and rutin (1 mg/mL). All the fractions recorded an inhibitory effect, firstly on conidial germination and germ tube elongation, secondly on disease evolution on peanut leaves previously inoculated by fungi; the level of efficiency of inhibition varied from 40.55 (FZ1 against C. arachidicola) to 57.14% (FZ2 and FZ3 against P. arachidis). Then, spores of P. arachidis seemed to be more sensitive to the treatment

EXPERIMENTATION OF AN APPLICATION OF EARLY DIAGNOSIS AND INVENTORY OF SOYBEAN DISEASES (GLYCINE MAX (L.) MERR.) IN BURKINA FASO

<p>Glycine max (L.) Merr also known as soya or soybean plays an important role in legume production in Burkina Faso. Every year, the country produces an average of 30,000 tonnes of soybean. It is grown for its oilseeds, which are rich in protein, fat, minerals and vitamins, making it an important food and feed crop. In addition, soya production is profitable for growers because it provides a real source of income through marketing operations. The lack of fertile land, adequate rainfall and phytosanitary protection in soya cultivation are not conducive for efficient production. Ignorance and lack of knowledge of the diseases encountered in soya production make it even more difficult to protect the crop, which further limits production.In order to improve knowledge of soybean diseases in Burkina Faso, an inventory of diseases associated with this crop was carried out using a plant pathology diagnostic application. In this study, the Plantix-Crop Doctor application, based on artificial intelligence with deep learning, was used in an Alpha Lattice experimental device. A disease identification form from the  Quebec Agriculture and Agri-Food Research Centre  was used as a reference. Among the diseases identified were Septoria leaf spot, grey leaf spot, anthracnose, bacterial blight, soybean blight, sudden death syndrome, downy mildew, powdery mildew and soybean rust. This list provides a database of soybean diseases that must be controlled by methods that consider environmental protection. The Plantix - your crop doctor application can be relied on to diagnose soybean diseases so that they can be treated at an early stage.</p><p> </p&gt