South Dakota Soil Classification Key

South Dakota has many different kinds of soils. To keep the characteristics and qualities of these soils in mind, grouping them systematically into a classification scheme is necessary. The classification of soils: (1) aids in remembering characteristics of individual soils, (2) clarifies relationships between soils, (3) aids in discovering new facts, (4) clarifies relationships between soils and their environment, and (5) aids in a person\u27s ability to predict properties of unknown soil based on similar, known soils

The Influence of Biochar Production on Herbicide Sorption Characteristics

Biochar is the by-product of a thermal process conducted under low oxygen or oxygen-free conditions (pyrolysis) to convert vegetative biomass to biofuel (Jha et al., 2010). There are a wide variety of end-products that can be manufactured depending on processing parameters and initial feedstocks (Bridgewater, 2003). The pyrolytic process parameters such as temperature, heating rate, and pressure can change the recovery amounts of each end-product, energy values of the bio-oils, and the physico-chemical properties of biochar (Yaman, 2004)

Atrazine and Alachlor Adsorption Characteristics to Benchmark Soil Series in Eastern South Dakota

Corn, grain sorghum, and soybean are grown on about six million acres in eastern South Dakota each year. Two herbicides used routinely for weed control are atrazine(6-chloro-N-ethyl-N’-(1-methylethyl)-1,3,5-triazine-2,4-diamine) in corn and grain sorghum and alachlor (2-chloro-N-(2,6-diethylphenyl)-N-(methoxymethyl)acetamide) in all three crops. Six benchmark soil series that include a majority of the cropped acres treated with these herbicides are the Egan, Moody, Nora, and Brandt silty clay loams, and Clarno and Enet loams. Batch adsorption studies determined atrazine and alachlor binding characteristics to these soils and aids in assessing the amount of herbicide available for movement. These data also provide a basis for future use and management decisions for these and other related herbicides in similar soils. Soils from three horizons (A, B, and C) for each soil type were treated with atrazine or alachlor at four herbicide concentrations. Atrazine and alachlor sorption partition coefficients differed most in A horizon soils and ranged from 2.16 to 5.35 µmol1-1/n L1/nKg-1 for atrazine and 1.95 to 5.78 µmol1-1/n L1/nKg-1 for alachlor. Atrazine binding to A horizon soils ranked as Brandt \u3eEgan = Moody \u3e Enet = Clarno \u3e Nora. Alachlor binding to A horizon soils ranked as Brandt \u3eMoody \u3e Nora \u3e Enet \u3e Clarno. B and C horizon soils had lower binding for both herbicides; the sorption partition coefficient for atrazine ranged from 0.12 to 1.9 µmol1-1/n L1/nKg-1 while alachlor ranged from 0.43 to 1.64 µmol1-1/n L1/nKg-1. These data indicate that some soil types would be more susceptible to herbicide leaching than others. Once the herbicide moves through the A horizon, it may move rapidly through the lower soil profile (because of the decrease in binding capacity), and therefore, increase the vulnerability of the aquifer to contamination. Best management practices for these herbicides are being investigated to limit their movement through soil

VPH-2 Risk Factor Analysis for the Transmission of Classical Swine Fever in West Timor, Indonesia

Classical Swine Fever (CSF) is a serious and highly infectious viral disease of domestic pigs and wild boar (Paton and Greiser-Wilke 2003). The causative agent, Classical Swine Fever Virus (CSFV) is a small (40±60 nm) enveloped ribonucleic acid (RNA) virus with a single stranded genome with positive polarity (Horzinek et al. 1971; Moennig and Greiser-Wilke 2008). The virus is one of three pestiviruses that forms a group of economically important pathogens (Moennig et al. 1990) belonging to the Flaviviridae family. It has a close antigenic relationship with the other pestiviruses - bovine viral diarrhoea virus (BVDV) and border disease virus (BDV), as demonstrated by immunodiffusion and immunofluorescence tests, and their similar morphology and nucleic acid homology (Wengler 1991; Wengler et al. 1995). Indonesia was free from CSF until 1993. Between 1994 and 1996 thousands of pigs were reported to have died from the disease in the Indonesian regions of North Sumatera, Jakarta, Bali, Central Java, and North Sulawesi (Satya and Santhia 2000). An outbreak of CSF was reported in Dili, East Timor in August 1997 and the disease then spread to the Kupang district of West Timor in March 1998 (Satya and Santhia 2009) and subsequently to all districts of Timor (Santhia et al. 1997; Santhia et al. 1998).              The existence of CSF in an area and the potential for introducing the disease into a new area can be associated with the presence of certain risk factors. Identification of these risk factors is important in understanding the transmission of disease and for developing effective prevention, control and eradication programs. Farmers are a valuable source of information about potential risk factors and associated management and husbandry practices linked with disease as they often have many years of experience in raising or trading livestock. This knowledge can be used to identify risk factors for disease.The objective of the study was to identify potential risk factors associated with CSF infection in West Timor. In particular factors involved in the management and husbandry of pigs were investigated

Management of Occupational Manganism: Consensus of an Experts' Panel

Studies and Research Projects / Report R-417, Montréal, IRSST http://www.irsst.qc.ca/en/_publicationirsst_100134.html (Lucchini R was a member of the Expert Panel

Geostatistical Characterization of the Spatial Distribution of Adult Corn Rootworm (Coleoptera: Chrysomelidae) Emergence

Geostatistical methods were used to characterize spatial variability in western ( Diabrotica virgifera virgifera LeConte) and northern ( Diabrotica barberi Smith & Lawrence) corn rootworm adult emergence patterns. Semivariograms were calculated for adult emergence density of corn rootworm populations in fields of continuous corn and rotated (corn/soybean) corn. Adult emergence densities were generally greater for northern corn rootworms than for western corn rootworms. The spatial structures of the adult rootworm emergence were aggregated as described by spherical spatial models for western corn rootworm and exponential models for northern corn rootworm. Range of spatial dependence varied from 180 to 550 m for western corn rootworm and 172 to 281 m for northern corn rootworm. Semivariograrn models were used to produce contour density maps of adult populations in the fields, based on grid sampling of actual emerging adult populations

Field Scale Variability of Nitrogen and δ15N in Soil and Plants

Understanding the factors that influence soil and plant nitrogen (N) spatial variability may improve our ability to develop management systems that maximize productivity and minimize environmental hazards. The objective of this study was to determine the field (65 ha) scale spatial variability of N and δ15N in soil and corn (Zea mays). Soil, grain, and stover samples were collected from grids that ranged in size from 30 by 30 m to 60 by 60 m. Plant samples, collected following physiological maturity in 1995, were analyzed for total N and δ15N. Soil samples, collected prior to planting in the spring of 1995 and 1996, were analyzed for inorganic‐N, total N, and δ15N. All parameters showed strong spatial relationships. In an undrained portion of the field containing somewhat poorly and poorly drained soils there was a net loss of 95 kg N ha‐1, while in an adjacent area that was tile drained there was a net gain of 98 kg N ha‐1. Denitrification and N mineralization most likely were responsible for losses and gains, respectively. Differences between the N balances of these areas (193 kg N ha‐1) provide a relative measure of the impact of tile drainage on plant N availability and greenhouse gas production in a wet year

Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks

The human brain has a complex, intricate functional architecture. While many studies primarily emphasize pairwise interactions, delving into high-order associations is crucial for a comprehensive understanding of how functional brain networks intricately interact beyond simple pairwise connections. Analyzing high-order statistics allows us to explore the nuanced and complex relationships across the brain, unraveling the heterogeneity and uncovering patterns of multilevel overlap on the psychosis continuum. Here, we employed high-order independent component analysis (ICA) plus multivariate information-theoretical metrics ($O$-information and $S$-information) to estimate high-order interaction to examine schizophrenia using resting-state fMRI. The results show that multiple brain regions networks may be altered in schizophrenia, such as temporal, subcortical, and higher-cognitive brain regions, and meanwhile, it also shows that revealed synergy gives more information than redundancy in diagnosing schizophrenia. All in all, we showed that high-order dependencies were altered in schizophrenia. Identification of these aberrant patterns will give us a new window to diagnose schizophrenia.Comment: 7 pages, 4 figures, Accepted to InfoCog@NeurIPS 2023 (https://sites.google.com/view/infocog-neurips-2023/home

Development of a novel duplex lateral flow test for simultaneous detection of casein and ß-lactoglobulin in food

Milk by-products such as whey and caseinate are widely used as ingredients or processing aids in food industry. However, since they could cause allergic reactions they are included in Allergen Control Plans. ß-Lactoglobulin is the major whey protein and caseins are main proteins in milk. Selection of a unique target to analyze the presence of milk in foods could be insufficient when the source of milk proteins is unknown. A new test based on lateral flow immunocromatography that combines the simultaneous and independent detection of both proteins (ß-lactoglobulin and casein)in one rapid test was developed. The assay was validated according to AOAC guidelines being able to detect ß-lactoglobulin (0.5 ppm), casein (2 ppm), whey and powder milk (1–5 ppm). No cross-reactivity was found with a panel of 38 food commodities. The method is a rapid and suitable tool to identify milk proteins in processed food, ingredients, and rinsing water

Factors Influencing Spatial Variability of Soil Apparent Electrical Conductivity

Soil apparent electrical conductivity (ECa) can be used as a precision farming diagnostic tool more efficiently if the factors influencing ECa spatial variability are understood. The objective of this study was to ascertain the causes of ECa spatial variability in soils developed in an environment with between 50 and 65 cm of annual rainfall. Soils at the research sites were formed on calcareous glacial till parent materials deposited approximately 10,000 years ago. Soil samples (0–15 cm) collected from at least a 60 by 60 m grid in four fields were analyzed for Olsen phosphorus (P) and potassium (K). Elevation was measured by a carrier phase single frequency DGPS and ECa was measured with an EM 38 (Geonics Ltd., ON, Canada) multiple times between 1995 and 1999. Apparent electrical conductivity contained spatial structure in all fields. Generally, the well drained soils in the summit areas and the poorly drained soil in the toeslope areas had low and high ECa values, respectively. The landscape differences in ECa were attributed to: (i) water leaching salts out of summit areas and capillary flow combined with seepage transporting water and salts from subsurface to surface soils in toeslope areas; (ii) lower water contents in summit than toeslope soils; and (iii) water erosion which transported surface soil from summit/shoulder areas to lower backslope/footslope areas. A conceptual model based on these findings was developed. In this model, topography followed a sine curve and ECa followed a cosine curve. Field areas that did not fit the conceptual model were: (i) areas containing old animal confinement areas; (ii) areas where high manure rates had been applied; and (iii) areas where soils were outside the boundary conditions of the model, i.e., soils not developed under relatively low rainfall conditions in calcareous glacial till with temperatures ranging between mesic and frigid. This research showed that the soil forming processes as well as agricultural management influenced ECa and that by understanding how landscape position influences salt loss and accumulation, water redistributions following precipitation, and erosion areas that do not fit the conceptual model can be identified. This information can be used to improve soil sampling strategies