Soil Productivity Ratings and Estimated Yields for Moody County, South Dakota

The objectives of this research: 1.Revise and update Plant Science Pamphlet 38 (Malo et al., 1990) by obtaining current crop and range yield data for each soil mapping unit in Moody County. 2. Develop a crop rating for each soil mapping unit that is suited for crops in Moody County. 3. Develop a grass/range rating for each soil mapping unit in Moody County. 4. Develop a soil productivity rating that ties together the crop and range rating productivity arrays. 5. Prepare a yield/soil productivity table and report for Moody County

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

A Multi-objective Exploratory Procedure for Regression Model Selection

Variable selection is recognized as one of the most critical steps in statistical modeling. The problems encountered in engineering and social sciences are commonly characterized by over-abundance of explanatory variables, non-linearities and unknown interdependencies between the regressors. An added difficulty is that the analysts may have little or no prior knowledge on the relative importance of the variables. To provide a robust method for model selection, this paper introduces the Multi-objective Genetic Algorithm for Variable Selection (MOGA-VS) that provides the user with an optimal set of regression models for a given data-set. The algorithm considers the regression problem as a two objective task, and explores the Pareto-optimal (best subset) models by preferring those models over the other which have less number of regression coefficients and better goodness of fit. The model exploration can be performed based on in-sample or generalization error minimization. The model selection is proposed to be performed in two steps. First, we generate the frontier of Pareto-optimal regression models by eliminating the dominated models without any user intervention. Second, a decision making process is executed which allows the user to choose the most preferred model using visualisations and simple metrics. The method has been evaluated on a recently published real dataset on Communities and Crime within United States.Comment: in Journal of Computational and Graphical Statistics, Vol. 24, Iss. 1, 201

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

Gas-phase Absorptions of C60+: A New Comparison with Astronomical Measurements

Campbell et al. recently revised, by a few tenths of an Å, the wavelengths for their low-temperature laboratory measurements of fullerene C60+ bands. This accounts for the perturbation caused by the He atom to the C60+-He spectrum. Here, we demonstrate that the revised laboratory wavelengths improve coincidence with the 9632, 9577, 9428, 9365, and 9348 diffuse interstellar bands detected towards the stars HD 46711, HD 169454, and HD 183143

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