Agricultural Intensification Causes Decline in Insect Biodiversity

The world’s population exceeded 7 billion in late 2011 and it is expected to reach 9.3 billion by 2050. Meanwhile, demand for food is predicted to increase between 50 and 100% by 2050. To meet the food demands of the increasing population, agricultural intensification practices including growing monocultures of high-yielding crop varieties and increased applications of fertilizers and pesticides have been used to increase productivity. These practices, however, impact negatively on biodiversity of existing flora and fauna, particularly causing huge declines in insect biodiversity. This chapter reviews present state of knowledge about agricultural intensification practices and global decline of insect biodiversity (i.e., pest and beneficial insect species) in intensive agricultural system and point out the likely drivers of these declines. It concludes the review by examining sustainable agricultural intensification practices that could be used to mitigate these biodiversity declines while maintaining productivity in intensive agricultural systems

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

Influence of planting date and cultivar on pod-sucking bug infestation and yield of soybean in northern Ghana

Soybean cultivation in Ghana is on a rapid increase because of its importance as food and cash crop. However, its production is constrained by pod-sucking insects that attack the pods and seeds. Field studies were conducted to deterimine the effect of planting date and cultivar on pod-sucking bug (PSB) infestation and yield of soybean. Four soybean varieties, four planting dates and two insecticide spraying regimes were evaluated. The results showed that soybean planted early (i.e. mid-June) suffered less PSB attack resulting in low seed damage and high yields. This suggests that mid-June is the best period to plant soybean to avoid PSBs for maximum yields in northern Ghana. Planting should be completed by mid July to avoid poor yields. The genotypes TGX 1799-8F and TGX 1834-5E consistently suffered less insect attack and can therefore be incorporated in an IPM package because of their relative resistance to insect pests. Keywords: Glycine max (L.) Merrill, Host plant resistance, Pod sucking bugs, Planting date, Insecticide control, Integrated pest managemen

Integrated peanut aflatoxin management for increase income and nutrition in Northern Ghana

Aflatoxins contamination in peanut seeds remains a major challenge in Ghana. This study evaluated aflatoxin levels in peanut samples from farmer storage units, and participatory on-farm research trials. In all, 240 respondents were covered from six main producing districts in northern Ghana through a multi-stage sampling approach. Samples were analysed for total aflatoxins using the indirect Enzyme Linked Immunosorbent Assay technique. Overall, total aflatoxins in the farmer stored nuts showed wide variations across communities and districts. At 20 ppm permissible level, 92.9% of samples (n = 240) from farmer stored peanuts and 98.7% of samples (n = 150) from the on-farm demonstrations were classified as safe at 4–8 weeks after harvest. Therefore, sustainable reduction of aflatoxins to safe limits is possible through greater collaboration among the value chain actors. Low-cost good agricultural practices within the remit of the growers should be prioritized alongside public awareness programmes

Field efficacy of genetically modified FK 95 Bollgard II cotton for control of bollworms, Lepidoptera, in Ghana

Abstract Background Cotton (Gossypium hirsutum L.) cultivation in Ghana is constrained by bollworms that damage squares (flower buds) and developing bolls, resulting in loss in seed cotton yield. Control of these insects is heavily dependent on insecticides that are costly and also pose health and environmental risks to users. Potential alternative control strategies have focused on using cotton genetically modified with the soil-borne bacterium Bacillus thuringiensis Berliner (Bt) that confer resistance against these pests. This study evaluated the field efficacy of the genetically modified FK 95 Bollgard II (FK 95 BG II) cotton for control of bollworms in Ghana. Results Results showed that bollworm densities in the FK 95 BG II cotton were lower compared with those in the FK 37 conventional cotton. However, populations of the natural enemies, ladybird beetles Coccinella undecimpunctata L and lacewings Chrysoperla carnea [Stephens] were higher in the Bt compared with the conventional technology of pest management. On average, seed cotton yields were higher in the FK 95 BG II compared to those in the FK 37. Net profit and cost–benefit ratios also were higher for the Bt technology compared with the conventional practice, indicating that farmers would benefit more if they adopt the Bt technology of cotton pest management. Conclusion The Bt cotton technology of pest management was more effective and economical than the conventional practice of wholly relying on insecticides and was a better management option for bollworm in cotton in the savanna ecology of Ghana