Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (EC_p – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R² = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (EC_e – dS/m). Our interpretation based on estimates of EC_e suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.     

Sensitivity,specificity, and predictive values of reagent test strip estimations of blood urea nitrogen

Azostix-reagent-tests(R) strips (Ames, Miles, Inc., Diagnostic Division, Elkhart, IN) were used to measure blood urea nitrogen values in blood samples from 125 dogs and cats at the North Carolina State University, College of Veterinary Medicine. Results of the tests were compared with standard serum urea nitrogen results. Sensitivity, specificity, and negative predictive values were high (86.4, 90.3, and 96.5%, respectively). Positive predictive value was low, 65.5% of the dogs and cats with elevated blood urea nitrogen values were correctly classified as abnormal The test performs well when the prevalence of abnormal values is near 50%.     

Seed production of Chenopodium album in competition with field vegetables

Total weed control within a crop is both difficult and expensive to achieve, so that some weeds will often remain to set seed. The seed production resulting from these weeds will ultimately affect the sustainability of the weed control strategy. If too much is allowed to return each season there could be a gradual, but significant, increase in the potential weed flora over a number of seasons. Field trials were carried out in 2000 and 2001 to quantify the potential magnitude of this weed seed return from Chenopodium album L., grown at two planting densities either in pure stands or in competition with one of two crops (cabbage or onion). Crop and weed weights and weed seed production were notably greater in 2001. Both dry weight and seed production of C. album were suppressed by increasing planting density or by the presence of crop, with cabbage having a more suppressive effect. Despite the plasticity in seed production, a linear relationship was demonstrated between log weed seed production and log weed biomass that was robust over a range of competitive situations with onion and cabbage, at different planting densities and in growing seasons. The study also demonstrated that the relationship could be combined with an existing simple competition model to allow the consequences of incomplete weed control to be assessed in terms of potential weed seed return.     

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R²; 0.29–0.69) outperformed both PTF‐1 (R²; 0.11–0.18) and PTF‐2 (R²; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R²; 0.61–0.86) performed comparably as PTF‐1 (R²; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R²; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).     

Movement rules for juvenile steelhead: dynamic linking of movement behaviour to habitat and density

We incorporated explanatory factors including stream habitat type and fish density into individual‐based models with dynamic connections among adjacent habitat units to infer dispersal behaviour of juvenile steelhead Oncorhynchus mykiss in a Great Lakes watershed. We used mark–recapture data and an inverse modelling approach to estimate daily probability of steelhead moving out of a habitat unit, P(move), according to four competing models. The models used included (i) a null model where all fish had equal movement probability; (ii) a habitat‐dependent model where P(move) depended on the habitat type; (iii) a density‐dependent model of P(move); and (iv) a model where P(move) depended on both density and habitat type. The habitat‐dependent model provided the most parsimonious fit to the observed data according to Akaike's information criteria (AICc). In the null model, P(move) averaged 0.70, whereas P(move) averaged 0.75 in pools, 0.68 in riffles and 0.73 in runs in the habitat‐dependent model.     

SALTIRSOIL: a simulation model for the mid to long‐term prediction of soil salinity in irrigated agriculture

The SALTIRSOIL model predicts soil salinity, sodicity and alkalinity in irrigated land using basic information on soil, climate, crop, irrigation management and water quality. It extends the concept of the WATSUIT model to include irrigation and crop management practices, advances in the calculation of evapotranspiration and new algorithms for the water stress coefficient and calculation of electrical conductivity. SALTIRSOIL calculates the soil water balance and soil solution concentration over the year. A second module, SALSOLCHEM, calculates the inorganic ion composition of the soil solution at equilibrium with soil calcite and gypsum at the soil’s CO₂ partial pressure. Results from comparing predicted and experimentally determined concentrations, observations and predictions of pH, alkalinity and calcium concentration in calcite‐saturated solutions agree to the second significant figure; in gypsum‐saturated solutions the standard difference between observations and predictions is <3% in absolute values. The algorithms in SALTIRSOIL have been verified and SALSOLCHEM validated for the reliable calculation of soil salinity, sodicity and alkalinity at water saturation in well‐drained irrigated lands. In simulations for horticultural crops in southeast Spain, soil solution concentration factors at water saturation, quotients of electrical conductivity (EC₂₅) at saturation to electrical conductivity in the irrigation water, and quotients of sodium adsorption ratio (SAR) are very similar to average measured values for the area.     

Drought Tolerance and Water‐Use Efficiency of Biogas Crops: A Comparison of Cup Plant,Maize and Lucerne‐Grass

The cup plant (Silphium perfoliatum L.) is discussed as an alternative energy crop for biogas production in Germany due to its ecological benefits over continuously grown maize. Moreover, a certain drought tolerance is assumed because of its intensive root growth and the dew water collection by the leaf cups, formed by fused leaf pairs. Therefore, the aim of this study was to estimate evapotranspiration (ET ), water‐use efficiency (WUE ) and the relevance of the leaf cups for the cup plant's water balance in a 2‐year field experiment. Parallel investigations were conducted for the two reference crops maize (high WUE ) and lucerne‐grass (deep and intensive rooting) under rainfed and irrigated conditions. Root system performance was assessed by measuring water depletion at various soil depths. Transpiration‐use efficiency (TUE ) was estimated using a model approach. Averaged over the 2 years, drought‐related above‐ground dry matter reduction was higher for the cup plant (33 %) than for the maize (18 %) and lucerne‐grass (14 %). The WUE of the cup plant (33 kg ha^?1 mm^?1) was significantly lower than for maize (50 kg ha^?1 mm^?1). The cup plant had a lower water uptake capacity than lucerne‐grass. Cup plant dry matter yields as high as those of maize will only be attainable at sites that are well supplied with water, be it through a large soil water reserve, groundwater connection, high rainfall or supplemental irrigation.     

Intracranial arteries of New Zealand rabbits; identification,three-dimensional modelling,and morphometry by computed tomography angiography

The aim of the study was to determine identification, three-dimensional modelling, and morphometry of intracranial arteries in New Zealand Rabbit by using computed tomography angiography. A total of 20 adult New Zealand Rabbits from both sexes were used in the study. General anaesthesia procedure was followed before computed tomography angiography imaging. Rabbits were placed in a prone position for imaging. Computed tomography angiography imaging was performed by injecting contrast agent into vena auricularis marginalis of rabbits. Morphometric measurements of the specified points were taken by MIMICS program using the computed tomography angiography images acquired. Three-dimensional intracranial artery model was prepared from the two-dimensional images on the same program. Measurements were statistically compared in terms of sex and side. It was observed in the study that arteria basilaris was generated by bilateral arteria vertebralis at the basal level of medulla oblongata. In the study, a statistically significant difference was determined only in diameter values of arteria cerebri caudalis dextra et sinistra in both female and male rabbits. On the other hand, no statistically significant difference was determined with respect to the side. Consequently, we think that the data of the present study will contribute to further studies on cerebrovascular pathology, clinicians or researchers.     

Estimation of Runoff,Peak Discharge and Sediment Load at the Event Scale in a Medium‐Size Mediterranean Watershed Using the Annagnps Model

In this paper, the Annualized Agricultural Non‐Point Source (AnnAGNPS) model has been used to estimate runoff, peak discharge and sediment load at the event scale in a Mediterranean watershed. The study area is the Carapelle torrent, Southern Italy (area = 506 km²), where continuous rainfall, streamflow and sediment load data are available. Nineteen flood events have been registered in the period 2007–2009 and were used for the application of the model. The aim of the paper is to evaluate the predictive accuracy of the model at the event scale, in a medium‐size watershed, given the specific conditions of the semi‐arid environments. A sensitivity analysis has been carried out to assign the correct parameterization: the mean normalized output variation of the most meaningful input parameters pointed out the influence of the curve number on runoff, peak discharge and sediment load predictions (values greater than 1); the MN Manning's roughness coefficient and K, C and P factors of the universal soil loss equation showed a moderate influence on sediment load simulations (values between 0·5 and 1). The selection of the Soil Conservation Service synthetic storm types has been based on the observed storm events analysis to improve the peak discharge simulations. The model prediction has proved to be good for runoff (R² = 0·74, NSE = 0·75, W = 0·92) and peak discharge (R² = 0·85, NSE = 0·70, W = 0·94), and satisfactory for sediment yield (R² = 0·70, NSE = 0·63, W = 0·91). The relative error is lower for high events; this result is quite interesting in semi‐arid environments, where most of the annual sediment yield is concentrated in a few, severe events. Copyright © 2013 John Wiley & Sons, Ltd.     

PREDICTIVE INTELLIGENT CONTROL OF THE COLD STORAGE

The predictive intelligent control scheme is proposed, which is based on approximation theory and numerical method of Chcbyshev orthogonal polynomial. It has the practical value to the cold storge using widely electromagnetic valve and can be also applied similar systems in orther area.