Evaluation of Selected Groundnut (Arachis hypogaea

Groundnut, the most important grain legume in Ghana, is largely cultivated under rainfed conditions within the Guinea savanna zone of the country. The pods and haulms are important sources of income for smallholder farmers in the region. There is an emerging market for groundnut haulms as livestock feed in Ghana. A population of 30 groundnut genotypes were evaluated for yield (pod and haulm) and its components as well as good haulm nutritive value. High significant differences were observed among the genotypes for all agronomic traits. Average pod yield ranged from 1.6 to 5.7 t/ha with SAMNUT 23 and ICGV-IS 13081 being the most productive. Eight out of the 30 genotypes produced haulm yields above 8 t/ha. There was no significant difference among genotypes for in vitro gas production, digestible organic matter, ash, neutral detergent fibre, and metabolizable energy. However, crude protein, crude fibre, and acid detergent fibre were significantly different. Crude protein content was highest (12.53%) in GAF 1723 and lowest (8.00%) in ICGV-IS 08837. Genotypes GAF 1723, ICGV 00064, and ICGV-IS 13998 combined good pod/haulm yield with high haulm nutritive quality. Their utilization will improve farmers’ income and livelihoods in the Guinea savanna of Ghana

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.Instituto de Patología VegetalFil: Jordan, David L. North Carolina State University. Department of Crop and Soil Sciences; Estados UnidosFil: Buol, Greg S. North Carolina State University. Department of Crop and Soil Sciences; Estados UnidosFil: Brandenburg, Rick L. North Carolina State University. Department of Entomology and Plant Pathology; Estados UnidosFil: Reisig, Dominic. North Carolina State University. Department of Entomology and Plant Pathology; 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: Brandford Mochiah, Moses.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: Paredes, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Paredes, Juan Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Monguillot, Joaquín Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Monguillot, Joaquín Humberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Rhoads, James. University of Georgia. Feed the Future Innovation Lab for Peanut; Estados Unido

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

Gene action and combining ability studies for grain yield and its related traits in cowpea (Vigna unguiculata)

Identification of superior genotypes from variability generated via hybridization and understanding the nature of the gene action controlling grain yield and related traits are crucial for cowpea varietal improvement. A field experiment was conducted at the Savannah Agricultural Research Institute, Tamale-Ghana in the 2016 cropping season to examine the combining ability, genotypic and phenotypic correlations for grain yield and other agronomic characters in 25 cowpea genotypes (5 parents and 20 hybrids derived from a diallel cross of the parents). The result indicated that the general combining ability and specific combining ability varied for all characters measured signifying the prominence of both additive and non-additive genetic components in the present study. Non-additive gene action was important for grain yield, canopy width at maturity, plant height (PLHTF), number of seeds per pod, pod weight and days to 50% flowering (DFF). On the other hand, additive gene action was important for days to maturity (DM) and pod length. Parents PADI-TUYA and IT86D-610 were observed to be good general combiners for grain yield and other traits while IT86D-610 × PADI-TUYA, SONGOTRA × PADI-TUYA and IT86D-610 × SARC 57–2 were identified as promising specific combiners for grain yield and related traits. Selection criteria to improve the grain yield of cowpea should focus on plants with long peduncles, high canopy width and many pods per plant as these traits have high genetic correlation with grain yield

Monitoring and Modelling Analysis of Maize (Zea mays L.) Yield Gap in Smallholder Farming in Ghana

Modelling and multiple linear regression were used to explore the reason for low maize yield in the Atebubu-Amantin and West Mamprusi Districts of Ghana, West Africa. The study evaluated maize yields on twenty farms against measures of soil fertility, agronomic attributes and soil water availability. Correlations between yield, soil fertility, rain, crop density, and weed biomass, were low, and no single factor could explain the low yields. A 50-year virtual experiment was then set up using the Agricultural Production Systems Simulator (APSIM) to explore the interactions between climate, crop management (sowing date and nitrogen fertilization) and rooting depth on grain yield and nitrate (NO3-N) dynamics. The analysis showed that a lack of optimal sowing dates that synchronize radiation, rainfall events and nitrogen (N) management with critical growth stages explained the low farm yields

High-Throughput Plant Phenotyping (HTPP) in Resource-Constrained Research Programs: A Working Example in Ghana

In this paper, we present a procedure for implementing field-based high-throughput plant phenotyping (HTPP) that can be used in resource-constrained research programs. The procedure relies on opensource tools with the only expensive item being one-off purchase of a drone. It includes acquiring images of the field of interest, stitching the images to get the entire field in one image, calculating and extracting the vegetation indices of the individual plots, and analyzing the extracted indices according to the experimental design. Two populations of groundnut genotypes with different maturities were evaluated for their reaction to early and late leaf spot (ELS, LLS) diseases under field conditions in 2020 and 2021. Each population was made up of 12 genotypes in 2020 and 18 genotypes in 2021. Evaluation of the genotypes was done in four locations in each year. We observed a strong correlation between the vegetation indices and the area under the disease progress curve (AUDPC) for ELS and LLS. However, the strength and direction of the correlation depended upon the time of disease onset, level of tolerance among the genotypes and the physiological traits the vegetation indices were associated with. In 2020, when the disease was observed to have set in late in medium duration population, at the beginning of the seed stage (R5), normalized green-red difference index (NGRDI) and variable atmospheric resistance index (VARI) derived at the beginning pod stage (R3) had a positive relationship with the AUDPC for ELS, and LLS. On the other hand, NGRDI and VARI derived from images taken at R5, and physiological maturity (R7) had negative relationships with AUDPC for ELS, and LLS. In 2021, when the disease was observed to have set in early (at R3) also in medium duration population, a negative relationship was observed between NGRDI and VARI and AUDPC for ELS and LLS, respectively. We found consistently negative relationships of NGRDI and VARI with AUDPC for ELS and LLS, respectively, within the short duration population in both years. Canopy cover (CaC), green area (GA), and greener area (GGA) only showed negative relationships with AUDPC for ELS and LLS when the disease caused yellowing and defoliation. The rankings of some genotypes changed for NGRDI, VARI, CaC, GA, GGA, and crop senescence index (CSI) when lesions caused by the infections of ELS and LLS became severe, although that did not affect groupings of genotypes when analyzed with principal component analysis. Notwithstanding, genotypes that consistently performed well across various reproductive stages with respect to the vegetation indices constituted the top performers when ELS, LLS, haulm, and pod yields were jointly considered

Genomics, genetics and breeding of tropical legumes for better livelihoods of smallholder farmers

Legumes are important components of sustainable agricultural production, food, nutrition and income systems of developing countries. In spite of their importance, legume crop production is challenged by a number of biotic (diseases and pests) and abiotic stresses (heat, frost, drought and salinity), edaphic factors (associated with soil nutrient deficits) and policy issues (where less emphasis is put on legumes compared to priority starchy staples). Significant research and development work have been done in the past decade on important grain legumes through collaborative bilateral and multilateral projects as well as the CGIAR Research Program on Grain Legumes (CRP‐GL). Through these initiatives, genomic resources and genomic tools such as draft genome sequence, resequencing data, large‐scale genomewide markers, dense genetic maps, quantitative trait loci (QTLs) and diagnostic markers have been developed for further use in multiple genetic and breeding applications. Also, these mega‐initiatives facilitated release of a number of new varieties and also dissemination of on‐the‐shelf varieties to the farmers. More efforts are needed to enhance genetic gains by reducing the time required in cultivar development through integration of genomics‐assisted breeding approaches and rapid generation advancement