Breeding groundnut for resistance to rosette disease and its aphid vector, Aphis craccivora Koch in Malawi.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.Groundnut (Arachis hypogaea L.) is one of the most important legume crops in Malawi. However, production among smallholder farmers has declined in recent years. One of the constraints affecting groundnut production is groundnut rosette disease (GRD). Therefore, the main objective of this study was to develop appropriate groundnut cultivars that are resistant to GRD, combined with other traits preferred by farmers, in order to improve income and food security of smallholder farmers in Malawi and beyond. The specific aims were; (i) to assess groundnut cropping systems used by smallholder farmers in Malawi, their varietal preferences, and production challenges (ii) to assess the genetic diversity among groundnut germplasm collected from ICRISAT, the Chitedze gene bank and farmers (iii) to identify sources of resistance to GRD and to its aphid vector (iv) and to understand the type of gene action governing GRD resistance, and to identify groundnut genotypes suitable for use as parents in breeding for GRD resistance. Assessment of groundnut cropping systems used by smallholder farmers, their varietal preferences, and production challenges was done by using a field survey and participatory rural appraisal (PRA) tools. The field survey was done in Lilongwe, Mchinji and Salima while the PRA was done in Kasungu, Lilongwe, and Salima. The assessment of genetic diversity among 106 groundnut genotypes collected from ICRISAT, Chitedze gene bank and farmers was done using 19 SSR markers. High throughput DNA extraction was done followed by polymerase chain reactions (PCR) after which the amplified products were analyzed. Evaluation of genotypes to identify new sources of resistance to GRD and its aphid vector was conducted under two test situations, one with high inoculum levels and one with low inoculum levels. Under high inoculum level, the infector row technique developed by Bock and Nigam (1990) which employs a susceptible variety as a disease spreader was used. While under low inoculum level, an aphid resistant variety instead of the infector row was used to control the aphids. Aphid resistance was studied under field and glasshouse conditions. Plants were planted in rows and at 14 DAS, 2 aphids were place on each plant. Aphid resistance was determined by observing the increase in number of the aphid population on the test plants. Gene action governing inheritance of resistance to GRD was studied under high disease pressure created by using viruliferous aphids. Parents and F2 generations and their reciprocals were used in the study. The trials were laid out in a glasshouse and aphids were infested a week after germination and were killed after 7 days using Dimethoate. Disease data was collected at 7, 14, 21 and 28 days after aphid infestation. The study on groundnut cropping systems, varietal preferences and production challenges revealed that most farmers grew groundnut alongside maize (Zea mayis L.) and beans (Phaseolus vulgaris L.) as food crops and tobacco (Nicotiana tabacum L.) and cotton (Gossypium hirsutum L.) as cash crops. The most preferred groundnut varieties grown by farmers were Chalimbana and CG 7. GRD was observed in half of the fields visited. However, 98% of the farmers interviwed had experienced it in their fields at some point, and 63.3% of the farmers believed that GRD was a major problem. Other challenges noted by farmers included lack of quality seed, poor extension support, lack of inputs, manipulation of the markets by buyers, and the failure of groundnut crops to meet the high standards required by the market. The examination of genetic diversity among 106 groundnut genotypes revealed a total number of 316 alleles with a mean of 17 alleles per locus. Polymorphic information content (PIC) and gene diversity values were high, which indicated that genetic diversity among the groundnut genotypes was high. The analysis of molecular variance indicated that 72.9% of the genetic variation observed in the genotypes was due to the variation between individuals within rather than between specific population groups. The evaluation of genotypes for resistance to GRD revealed five highly resistant genotypes namely ICG 9449, ICG 14705, ICGV-SM 05701, MW 2672 and MW 2694. Farmer preferred genotypes were rated as either moderately resistant or susceptible to GRD. Aphid resistance was only recorded in ICG 12991. Yield and GRD incidence were negatively and moderately correlated, which confirmed that GRD has the potential to reduce yield in groundnuts. The highly resistant genotypes were also high yielding except for genotype ICG 9449. Farmer preferred genotypes CG 7, Chalimbana and Tchayilosi, also gave above average yields, despite high disease incidence levels, which showed that these genotypes have tolerance to GRD. The study on gene action governing GRD resistance revealed information on combining ability effects of GRD resistance. The diallel analysis showed that GCA, SCA, reciprocal, maternal and non-maternal effects were all significant, which indicated that both additive and non-additive gene effects played a role in governing GRD resistance. The significance of SCA and reciprocal effects indicated that maternal parents played an important role in the expression of GRD resistance. However, the additive effects were predominant over non-additive gene effects. Four of the resistant genotypes, ICG 14705, MW 2694, ICGV-SM 05701, and MW 2672, were the best combiners for GRD resistance. Generally, the study indicates that there is still a need to develop new varieties with resistance to GRD having traits preferred by farmers to enhance adoption. There is also a need for breeders to work with extension staff in promoting new varieties and also there is need for extension staff to actively provide information to farmers on production and marketing of groundnut. Groundnut is widely known to have a narrow genetic base which has been a bottleneck to its improvement. However, the high genetic diversity observed in this study provides a basis for selection of appropriate parental genotypes for breeding programmes which can enhance further the broadening of the groundnut genetic base. Identification of the genotypes with high resistance to GRD in this study provides an opportunity to breed more GRD resistant materials. The observation that additive gene effects are predominant in governing GRD resistance means that GRD resistant materials can be improved by introgressing additive genes using recurrent selection breeding procedures. There is also a need to employ molecular techniques which can help in shortening the entire breeding process

Examples of risk tools for pests in Peanut (Arachis hypogaea) developed for five countries using Microsoft Excel

Suppressing pest populations below economically-damaging levels is an important element of sustainable peanut (Arachis hypogaea L.) production. Peanut farmers and their advisors often approach pest management with similar goals regardless of where they are located. Anticipating pest outbreaks using field history and monitoring pest populations are fundamental to protecting yield and financial investment. Microsoft Excel was used to develop individual risk indices for pests, a composite assessment of risk, and costs of risk mitigation practices for peanut in Argentina, Ghana, India, Malawi, and North Carolina (NC) in the United States (US). Depending on pests and resources available to manage pests, risk tools vary considerably, especially in the context of other crops that are grown in sequence with peanut, cultivars, and chemical inputs. In Argentina, India, and the US where more tools (e.g., mechanization and pesticides) are available, risk indices for a wide array of economically important pests were developed with the assumption that reducing risk to those pests likely will impact peanut yield in a positive manner. In Ghana and Malawi where fewer management tools are available, risks to yield and aflatoxin contamination are presented without risk indices for individual pests. The Microsoft Excel platform can be updated as new and additional information on effectiveness of management practices becomes apparent. Tools can be developed using this platform that are appropriate for their geography, environment, cropping systems, and pest complexes and management inputs that are available. In this article we present examples for the risk tool for each country.Fil: Jordan, David L.. University of Georgia; Estados Unidos. North Carolina State University; Estados UnidosFil: Buol, Greg S.. North Carolina State University; Estados UnidosFil: Brandenburg, Rick L.. North Carolina State University; Estados UnidosFil: Reisig, Dominic. North Carolina State University; Estados UnidosFil: Nboyine, Jerry. Council for Scientific and Industrial Research Savanna Agricultural Research Institute; GhanaFil: Abudulai, Mumuni. Council for Scientific and Industrial Research Savanna Agricultural Research Institute; GhanaFil: Oteng Frimpong, Richard. Council for Scientific and Industrial Research Savanna Agricultural Research Institute; GhanaFil: Mochiah, Moses Brandford. Council for Scientific and Industrial Research Crops Research Institute; GhanaFil: Asibuo, James Y.. Council for Scientific and Industrial Research Crops Research Institute; GhanaFil: Arthur, Stephen. Council for Scientific and Industrial Research Crops Research Institute; GhanaFil: Akromah, Richard. Kwame Nkrumah University Of Science And Technology; GhanaFil: Mhango, Wezi. Lilongwe University Of Agriculture And Natural Resources; MalauiFil: Chintu, Justus. Chitedze Agricultural Research Service, Lilongwe; MalauiFil: Morichetti, Sergio. Aceitera General Deheza; ArgentinaFil: Paredes, Juan Andres. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Instituto de Patología Vegetal; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Unidad de Fitopatología y Modelización Agrícola - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Unidad de Fitopatología y Modelización Agrícola; ArgentinaFil: Monguillot, Joaquín Humberto. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigaciones Agropecuarias. Instituto de Patología Vegetal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Singh Jadon, Kuldeep. Central Arid Zone Research Institute, Jodhpur; IndiaFil: Shew, Barbara B.. North Carolina State University; Estados UnidosFil: Jasrotia, Poonam. Indian Institute Of Wheat And Barley Research, Karnal; IndiaFil: Thirumalaisamy, P. P.. India Council of Agricultural Research, National Bureau of Plant Genetic Resources; IndiaFil: Harish, G.. Directorate Of Groundnut Research, Junagadh; IndiaFil: Holajjer, Prasanna. National Bureau Of Plant Genetic Resources, New Delhi; IndiaFil: Maheshala, Nataraja. Directorate Of Groundnut Research, Junagadh; IndiaFil: MacDonald, Greg. University of Florida; Estados UnidosFil: Hoisington, David. University of Georgia; Estados UnidosFil: Rhoads, James. University of Georgia; Estados Unido

The groundnut improvement network for Africa (GINA) germplasm collection: a unique genetic resource for breeding and gene discovery

International audienceCultivated peanut or groundnut (Arachis hypogaea L.) is a grain legume grown in many developing countries by smallholder farmers for food, feed, and/or income. The speciation of the cultivated species, that involved polyploidization followed by domestication, greatly reduced its variability at the DNA level. Mobilizing peanut diversity is a prerequisite for any breeding program for overcoming the main constraints that plague production and for increasing yield in farmer fields. In this study, the Groundnut Improvement Network for Africa assembled a collection of 1,049 peanut breeding lines, varieties, and landraces from 9 countries in Africa. The collection was genotyped with the Axiom_Arachis2 48K SNP array and 8,229 polymorphic single nucleotide polymorphism (SNP) markers were used to analyze the genetic structure of this collection and quantify the level of genetic diversity in each breeding program. A supervised model was developed using dapc to unambiguously assign 542, 35, and 172 genotypes to the Spanish, Valencia, and Virginia market types, respectively. Distance-based clustering of the collection showed a clear grouping structure according to subspecies and market types, with 73% of the genotypes classified as fastigiata and 27% as hypogaea subspecies. Using STRUCTURE, the global structuration was confirmed and showed that, at a minimum membership of 0.8, 76% of the varieties that were not assigned by dapc were actually admixed. This was particularly the case of most of the genotype of the Valencia subgroup that exhibited admixed genetic heritage. The results also showed that the geographic origin (i.e. East, Southern, and West Africa) did not strongly explain the genetic structure. The gene diversity managed by each breeding program, measured by the expected heterozygosity, ranged from 0.25 to 0.39, with the Niger breeding program having the lowest diversity mainly because only lines that belong to the fastigiata subspecies are used in this program. Finally, we developed a core collection composed of 300 accessions based on breeding traits and genetic diversity. This collection, which is composed of 205 genotypes of fastigiata subspecies (158 Spanish and 47 Valencia) and 95 genotypes of hypogaea subspecies (all Virginia), improves the genetic diversity of each individual breeding program and is, therefore, a unique resource for allele mining and breeding