Soil-Saprolite Profiles Derived from Mafic Rocks in the North Carolina Piedmont: II. Association of Free Iron Oxides with Soils and Clays

The association of free Fe oxides with soils and clays from two Enon sandy loam (Ultic Hapludalfs, fine, mixed, thermic) soilsaprolite profiles was studied. Goethite was the dominant Fe oxide identified. Lepidocrocite was detected in trace amounts in some samples. FeCBD/clay ratios were highest in the epipedons of these soils suggesting the concentrating of Fe oxides as a result of aluminosilicate mineral weathering. External (BET-N2) surface area measurements of non-deferrated and deferrated clays were analyzed in conjunction with electron micrographs of selected clay fractions to determine the association of free Fe oxides with aluminosilicate clays as a function of depth in the profile. Free Fe oxides were found to exist mainly as small, discrete clusters in the A and B horizons of both profiles and specific of the clay surface decreased as a result of treatment for Fe removal. However, external surface areas increased for the saprolite (Cr) horizon clays after deferration. One subfraction identified as having an increase in surface area after deferration was fine clay from the Cr2 horizon, Enon (metagabbro) profile. Chemical data and electron micrographs suggest that either partial dissolution of small, poorly crystalline aluminosilicate clays or removal of some Fe or non-Fe oxide aggregating agent results in breakdown of the fine clays into smaller particles of higher net specific surface

Soil-Saprolite Profiles Derived from Mafic Rocks in the North Carolina Piedmont: I. Chemical, Morphological, and Mineralogical Characteristics and Transformations

The chemical, morphological and mineralogical properties of two Enon sandy loam (fine, mixed, thermic Ultic Hapludalfs) soilsaprolite profiles, one formed on gabbro and the other on metagabbro, are compared. Clay skins are scarce and stress cutans common in the argillic horizons of these soils. Iron-manganese concretions are concentrated in soil horizons immediately above the argillic horizons. The high shrink-swell capacities and slow permeabilities of the argillic horizons result in relatively shallow depths to paralithic contact with saprolite. The parent rock from the Enon profile near Albemarle, Stanly County, North Carolina is a medium-grained metagabbro with chlorite, hornblende, quartz, and calcic plagioclase feldspar as the dominant primary minerals. Chlorite weathers to regularly interstratified chlorite-vermiculite, which alters to randomly interstratified chlorite-vermiculite and smectite. Particle size decreases with each mineral alteration. Hornblende weathers to smectite and goethite. Calcic plagioclase feldspar transforms to kaolinite in the saprolite and soil horizons. Quartz is relatively resistant to chemical weathering. The parent rock of the Enon profile near Concord, Cabarrus County, North Carolina is a coarse-grained gabbro with hornblende and calcic plagioclase feldspar as the dominant primary minerals. Hornblende transforms to smectite and goethite. Calcic plagioclase feldspar alters to kaolinite in the saprolite and soil horizon

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