Use of remote sensing and geographical information systems to identify environmental features that influence the distribution of paramphistomosis in sheep from the southern Italian Apennines

A geographic information system (GIS) was constructed using remote sensing (RS) and landscape feature data together with Calicophoron daubneyi positive survey records from 197 georeferenced ovine farms with animals pasturing in a 3971 km(2) area of the southern Italian Apennines. The objective was to study the spatial distribution of this rumen fluke, identify environmental features that influence its distribution, and develop a preliminary risk assessment model. The GIS for the study area was constructed utilizing the following environmental variables: normalized difference vegetation index (NDVI), land cover, elevation, slope, aspect, and total length of rivers. These variables were then calculated for "buffer zones" consisting of the areas included in a circle of 3 km diameter centered on 197 farms. The environmental data obtained from GIS and RS and from data taken by the veterinarians on the field (stocking rate and presence of streams, springs and brooks on pasture) were analyzed by univariate (Spearman and ANOVA) and multivariate (discriminant) statistical analyses using the farm coprological status (positive/negative) as the dependent variable. Sheep on 32 of the 197 (16.2%) farms, were positive for C. daubneyi, with an average intensity of 52 epg. A multivariate stepwise discriminant analysis model was developed that included moors and heathland, sclerophyllous and coniferous forest vegetation, autumn-winter NDVI and presence of streams, springs and brooks on pasture. The variables entered in the model were also correlated with C. daubneyi positive farms in the univariate tests and are consistent with the environmental requirements of C. daubneyi and its snail intermediate host.     

Development and validation of a soil-based geographic information system model of habitat of Fossaria bulimoides, a snail intermediate host of Fasciola hepatica

Soil maps were investigated for their potential to indicate the spatial distribution of habitat of the amphibious snail, Fossaria bulimoides, the intermediate host of Fasciola hepatica in Louisiana. On a 760 ha farm adjacent to the Gulf of Mexico, most snail habitats clustered along interfaces of cheniers (relict beaches with sandy soils) with marsh (heavy, gleyed clays). A few small foci occurred on the cheniers above interfaces. Virtually no habitats occupied marsh away from these interfaces, on this or several other farms previously examined. Habitat and soil map overlays were analyzed in a raster-based geographic information system (GIS) with a 10 m pixel size; a model of habitat was generated as bands 50–90 m wide at chenier-marsh interfaces. The model was extrapolated to a 29 000 ha study area and tested on 12 additional farms not adjacent to the Gulf. On these farms, snail surveys were carried out as transects on three strata: waveface (WF, former beachfront) and backslope (BS) chenier-marsh interfaces, and chenier. The perimeters of all habitats encountered were outlined. The interface of marsh with chenier backslopes occupied 5.25% of the area of the farms, but held 21.69% of snail habitat area. Additionally, chenier soils of the Hackberry-Mermentau complex (HM) occupied 12.32% of farm area, but held 61.39% of habitat. Habitat area was related by regression analysis to the proportion of farm consisting of HM soils (r² = 0.41, P < 0.05).     

RZWQM simulation of long-term crop production, water and nitrogen balances in Northeast Iowa

Agricultural system models are tools to represent and understand major processes and their interactions in agricultural systems. We used the Root Zone Water Quality Model (RZWQM) with 26 years of data from a study near Nashua, IA to evaluate year to year crop yield, water, and N balances. The model was calibrated using data from one 0.4 ha plot and evaluated by comparing simulated values with data from 29 of the 36 plots at the same research site (six were excluded). The dataset contains measured tile flow that varied considerably from plot to plot so we calibrated total tile flow amount by adjusting a lateral hydraulic gradient term for subsurface lateral flow below tiles for each plot. Keeping all other soil and plant parameters constant, RZWQM correctly simulated year to year variations in tile flow (r² = 0.74) and N loading in tile flow (r² = 0.71). Yearly crop yield variation was simulated with less satisfaction (r² = 0.52 for corn and r² = 0.37 for soybean) although the average yields were reasonably simulated. Root mean square errors (RMSE) for simulated soil water storage, water table, and annual tile flow were 3.0, 22.1, and 5.6 cm, respectively. These values were close to the average RMSE for the measured data between replicates (3.0, 22.4, and 5.7 cm, respectively). RMSE values for simulated annual N loading and residual soil N were 16.8 and 47.0 kg N ha⁻¹, respectively, which were much higher than the average RMSE for measurements among replicates (7.8 and 38.8 kg N ha⁻¹, respectively). The high RMSE for N simulation might be caused by high simulation errors in plant N uptake. Simulated corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yields had high RMSE (1386 and 674 kg ha⁻¹) with coefficient of variations (CV) of 0.19 and 0.25, respectively. Further improvements were needed for better simulating plant N uptake and yield, but overall, results for annual tile flow and annual N loading in tile flow were acceptable.     

Empirical analysis and prediction of nitrate loading and crop yield for corn-soybean rotations

Nitrate nitrogen losses through subsurface drainage and crop yield are determined by multiple climatic and management variables. The combined and interactive effects of these variables, however, are poorly understood. Our objective is to predict crop yield, nitrate concentration, drainage volume, and nitrate loss in subsurface drainage from a corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) rotation as a function of rainfall amount, soybean yield for the year before the corn-soybean sequence being evaluated, N source, N rate, and timing of N application in northeastern Iowa, U.S.A. Ten years of data (1994-2003) from a long-term study near Nashua, Iowa were used to develop multivariate polynomial regression equations describing these variables. The regression equations described over 87, 85, 94, 76, and 95% of variation in soybean yield, corn yield, subsurface drainage, nitrate concentration, and nitrate loss in subsurface drainage, respectively. A two-year rotation under average soil, average climatic conditions, and 125 kg N/ha application was predicted to loose 29, 37, 36, and 30 kg N/ha in subsurface drainage for early-spring swine manure, fall-applied swine manure, early-spring UAN fertilizer, and late-spring split UAN fertilizer (urea ammonium nitrate), respectively. Predicted corn yields were 10.0 and 9.7 Mg/ha for the swine manure and UAN sources applied at 125 kg N/ha. Timing of application (i.e., fall or spring) did not significantly affect corn yield. These results confirm other research suggesting that manure application can result in less nitrate leaching than UAN (e.g., 29 vs. 36 kg N/ha), and that spring application reduces nitrate leaching compared to fall application (e.g., 29 vs. 37 kg N/ha). The regression equations improve our understanding of nitrate leaching; offer a simple method to quantify potential N losses from Midwestern corn-soybean rotations under the climate, soil, and management conditions of the Nashua field experiment; and are a step toward development of easy to use N management tools.     

Simulating management effects on crop production, tile drainage, and water quality using RZWQM-DSSAT

The objective of this study was to explore if more crop-specific plant growth modules can improve simulations of crop yields, and N in tile flow under different management practices compared with a generic plant growth module. We calibrated and evaluated the Root Zone Water Quality Model (RZWQM) with the Decision Support for Agrotechnology Transfer (DSSAT v3.5) plant growth modules (RZWQM-DSSAT) for simulating tillage (NT — no till, RT — ridge till, CP — chisel plow, and MP — moldboard plow), crop rotation {CC — continuous corn, and CS — corn (Zea mays L.)-soybean [Glycine max (L.) Merr.]}, and nitrogen (N) (SA — single application at preplant, and LSNT — late spring soil N test based application) and manure (SM — fall injected swine manure) management effects on crop production and water quality. Data from 1978 to 2003 from a water quality experiment near Nashua (Nashua experiments), Iowa, USA, were used. The model was calibrated using data from one treatment plot and validated for the rest of the plots. Simulated management effects on annual N loading in tile flow were agreeable with measured effects in 85%, 99%, 88%, and 78% of the cases for tillage, crop rotation (CS vs. CC), N application timing (SA vs. LSNT), and swine manure applications (SM vs. SA), respectively. On average, the LSNT plots were simulated to have 359 kg ha^− 1 higher corn yield compared to SA, when the observed increase was 812 kg ha^− 1. Grain yield simulations were not sensitive to differences between RT and NT, between SM and SA treatments, and between CS and CC. We conclude that considering the uncertainties of basic input data, processes in the field, and lack of site specific weather data, the results obtained with this RZWQM-DSSAT hybrid model were not much better than the results obtained earlier with the generic crop growth module.     

Evaluating and predicting agricultural management effects under tile drainage using modified APSIM

An accurate and management sensitive simulation model for tile-drained Midwestern soils is needed to optimize the use of agricultural management practices (e.g., winter cover crops) to reduce nitrate leaching without adversely affecting corn yield. Our objectives were to enhance the Agricultural Production Systems Simulator (APSIM) for tile drainage, test the modified model for several management scenarios, and then predict nitrate leaching with and without winter wheat cover crop. Twelve years of data (1990-2001) from northeast Iowa were used for model testing. Management scenarios included continuous corn and corn-soybean rotations with single or split N applications. For 38 of 44 observations, yearly drain flow was simulated within 50 mm of observed for low drainage (< 100 mm) or within 30% of observed for high drain flow. Corn yield was simulated within 1500 kg/ha for 12 of 24 observations. For 30 of 45 observations yearly nitrate-N loss in tile drains was simulated within 10 kg N/ha for low nitrate-N loss (< 20 kg N/ha) or within 30% of observed for high nitrate-N loss. Several of the poor yield and nitrate-N loss predictions appear related to poor N-uptake simulations. The model accurately predicted greater corn yield under split application (140-190 kg N/ha) compared to single 110 kg N/ha application and higher drainage and nitrate-N loss under continuous corn compared to corn/soybean rotations. A winter wheat cover crop was predicted to reduce nitrate-N loss 38% (341 vs. 537 kg N/ha with and without cover) under 41-years of corn-soybean rotations and 150 kg N/ha applied to corn. These results suggest that the modified APSIM model is a promising tool to help estimate the relative effect of alternative management practices under fluctuating high water tables.     

Managing chronic arthritis.

Many compounds are being investigated for the control of symptoms of osteoarthritis in people and animals. Ideally, treatment should include analgesia, inflammation control, and chondroprotection. With further progress in this area, combination therapies tailored to the needs of the individual animal should enable us to maximize efficacy and minimize side effects. Only a few of the newer therapies and pharmaceutic agents have been investigated in the horse, however. With more rigorous investigation, they may be determined to be ineffective or unsafe. Meanwhile, as much information should be gathered from manufacturers as possible so as to ensure that appropriate recommendations are made.     

Lateral and Vertical Variations of Soil Organic and Inorganic Carbon Content in Aridisols and Entisols of a Rangeland

Eurasian Soil Science - The influence of biological and physicochemical soil properties on the variations in soil organic and inorganic carbon (OC and IC) contents at the soil surface was studied....     

Evidence for a collective intelligence factor in the performance of human groups

Psychologists have repeatedly shown that a single statistical factor--often called "general intelligence"--emerges from the correlations among people's performance on a wide variety of cognitive tasks. But no one has systematically examined whether a similar kind of "collective intelligence" exists for groups of people. In two studies with 699 people, working in groups of two to five, we find converging evidence of a general collective intelligence factor that explains a group's performance on a wide variety of tasks. This "c factor" is not strongly correlated with the average or maximum individual intelligence of group members but is correlated with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group.