Flower yield performance and stability of various Rosa damascena Mill. landraces under different ecological conditions

The flower yield stability of Damask rose as an important medicinal and aromatic plant at different environments has not been well documented. In order to evaluate flower yield and stability, 35 landraces of Damask rose were studied at 8 locations in Iran during 2007–2008. Analysis of variance revealed significant differences (p ≤ 0.01) among landraces (G), environments (E), locations (L) and for landrace × environment (GE) and landrace × location (GL) interactions. Both GE and GL interactions were mainly crossover, a large portion of which was accounted for by non-linear (unpredictable) component. The landraces of IS9, YZ2, WA1, IS7 and IS1 with 3120.63, 2941.63, 2894.62, 2769.15 and 2716.92 kg/ha respectively produced the highest flower yield among studied landraces. Kerman with average flower yield of 3635.46 kg/ha produced the highest yield among studied locations. According to the results, most of landraces that originated from temperate, warm temperate and arid regions produced higher flower yield than those from cool, cool temperate, semi-arid and humid regions. The landraces of YZ2, IS5, IS8, IS4, KZ1, AR1_, IS3 and BA1 were stable and YZ2, IS5, IS8, IS4, KZ1, AR1 IS6, IS3, BA1, IS10 and YZ1 were adaptable landraces for flower yield according to Eberhart and Russell (1966) model. The presence of some high flower yield and stable landraces such as YZ2 and IS5 suggests that a genotype can demonstrate high flower yield and stability for yield simultaneously. Thus, simultaneous selection for flower yield and stability using nonparametric methods could be possible. In addition, taking into consideration flower yield and stability potential, the landraces of YZ2, IS5, IS8, IS4 and KZ1 as general stable, adaptable, and high flower yield are recommended. Furthermore, the landraces of IS9 and WA1 as high flower yield and specific adaptable landraces can be recommended for temperate and arid areas and the landraces of IS7 and IS1 for semitemperate and cool areas.     

Effects of stocking density on haematological parameters,growth and fin erosion of great sturgeon (Huso huso) juveniles

The influence of stocking density (1, 2, 4, 6 and 8 kg m^?2) was investigated on haematological parameters, growth and fin erosion of great sturgeon (Huso huso) juveniles for a period of 8 weeks. The mean weight of fish at the start of trial was 93.13±1.04 g. After 8 weeks of rearing, the mean weight was 362.4, 319.7, 267, 242.1 and 211.1 in densities 1–8 kg m^?2 respectively. The results of this study showed that growth parameters, including condition factor, weight, weight gain, feed conversion ratio, specific growth rate and body weight increase, had a statistically significant difference among treatments (P<0.05). A significant difference (P<0.05) was observed in haematocrit, but the other haematological parameters, including red blood cells, white blood cells (WBC), haemoglobin concentration, mean corpuscular haemoglobin, mean corpuscular volume, mean corpuscular haemoglobin concentration and differential WBC count, showed no significant effect with stocking density (P>0.05). At the end of the experiment, stocking density had no significant effect on plasma cortisol and glucose concentration. The fin length (dorsal, anal, ventral, pectoral and caudal) of fish was measured to calculate the fin index. According to this index, dorsal, anal, ventral and pectoral fins showed no significant difference among treatments (P>0.05), but the erosion of the caudal fin was significantly different between fish held on 6 and 8 kg m^?2 (P<0.05). These results showed that rearing density has a major effect on the growth indices of H. huso. Unlike many other fish, great sturgeon exhibited lower stress responses to high stocking density. This indicates that they are more tolerable to rearing conditions in high stocking densities. With respect to the various effects that density causes on growth, fin erosion and physiological and haematological parameters, better understanding of these phenomena considering different levels of density could have a beneficial impact on many rearing steps of this species.     

Concentration of selected heavy metals in spices,dry fruits and plant nuts

Different spices, dry fruits and plant nuts commonly consumed in Pakistan were assayed for the heavy metals cadmium, lead, copper, zinc, iron and manganese by the potentiometric stripping analysis and AA spectrophotometry. The results revealed wide variation in heavy metal content among different biological materials. Mixed spices generally exhibited higher value for trace metals specially lead (6.6–9.2 µg/g), cadmium (0.65–1.34 µg/g), iron (142.3–285.0 µg/g) and zinc (64.2–65.8 µg/g). Dry fruits contained relatively lesser amounts of heavy metals than plant nuts. Almonds contained higher levels of lead (1.02 µg/g) and cadmium (0.24 µg/g) than other nuts and dry fruits.     

RESPONSES OF WHEAT PLANTS IN TERMS OF SOIL WATER CONTENT,BULK DENSITY,SALINITY, AND ROOT GROWTH UNDER DIFFERENT PLANTING SYSTEMS AND VARIOUS IRRIGATION FREQUENCIES

The effects of irrigation water rates and seed bed shapes on changes in soil water and salinity status, bulk density, root growth and dry matter (DM) weights of wheat plants (Triticum aestivum L.) were investigated with a split plot design in a field trial in Zahak Agricultural Research Station in Sistan, Iran in 2005. Irrigation intervals after 80 and 160 mm evaporation from class A evaporation pan were used as main plot. Flat surface, single, triple, and six-row beds with a 20 cm row space were used as subplots. Each treatment was replicated four times. Volumetric soil water content and soil electrical conductivity (EC) were measured using Time Domain Reflectometry (TDR) at 0 —20, 20 —40 and 40 —60 cm depths at nine different times during the growing season. Soil water contents were also measured at 0 —10 and 10 —20 cm depths using standard sampling rings at four different times. The three and six-row beds increased the EC of the saturated paste extract with the more frequent irrigation intervals in this coarse textured soil. Soil water content, DM, and root density were always greater with the more frequent irrigations (shorter irrigation intervals). Root density was greatest in 0 —20 cm depth with the single row bed treatment. Grain yield and root density were greatest with single row bed treatment due to the bed shape at the root development stage (possibly due to a reduced mechanical resistance). A greater soil water content by the short irrigation interval increased grain yield and root density via reducing mechanical resistance. With the loamy sand, bulk density and mechanical resistance increased rapidly after cultivation. Bed shape at root development stage might have enhanced root growth and the crop yields. Apparently, mechanical resistance was the most limiting factor with these loamy sand soils than salinity.     

Calibration of Hargreaves–Samani equation for estimating reference evapotranspiration in semiarid and arid regions

The Penman–Monteith (FAO-56 PM) equation is suggested as the standard method for estimating evapotranspiration (ET₀) by the International Irrigation and Drainage Committee and Food and Agriculture Organization (FAO). On the other hand, the Hargreaves–Samani (HS) equation is an alternative method compared with the FAO-56 PM equation. In the present study, the original coefficient C of the HS equation is calibrated based on the FAO-56 PM equation for estimating the reference ET₀ from 15 meteorological stations in central Iran (about 170,000 km²) under semiarid and arid conditions. After calibration, the new values for C are ranged from 0.0018 to 0.0037. The mean bias error (MBE), the root mean square error (RMSE), and the ratio of average estimations of ET₀ (R) values for all stations are ranged from 0.12 to 5.38, ?5.35 to 1.15 mm d^?1 and 0.64 to 1.28 for the HS equation and from 0.12 to 2.48, ?2.2 to 0.60 mm d^?1, and 1.00 to 1.05 for the calibrated Hargreaves–Samani equation (CHS), respectively. Results indicate that the average RMSE and MBE values are decreased by 40% and 66%, respectively. Relationships for calibrating the C coefficient on the basis of annual average of daily temperature range (ΔT) and wind speed (V) are proposed, calibrated, and validated. Hence, the CHS equation can be used for ET₀ estimates with acceptable accuracy instead of the FAO-56 PM method.     

Characterizing Spatial Variability of Soil Textural Fractions and Fractal Parameters Derived from Particle Size Distributions

Soil particle size distribution (PSD) is a fundamental physical property affecting other soil properties. Characterizing spatial variability of soil texture is very important in environmental research. The objectives of this work were: 1) to partition PSD of 75 soil samples, collected from a flat field in the University of Guilan, Iran, into two scaling domains using a piecewise fractal model to evaluate the relationships between fractal dimensions of scaling domains and soil clay, silt, and sand fractions and 2) to assess the potential of fractal parameters as an index used in a geostatistical approach reflecting the spatial variability of soil texture. Features of PSD of soil samples were studied using fractal geometry, and geostatistical techniques were used to characterize the spatial variability of fractal and soil textural parameters. There were two scaling domains for the PSD of soil samples. The fractal dimensions of these two scaling domains (D₁ and D₂) were then used to characterize different ranges of soil particle sizes and their relationships to the soil textural parameters. There was a positive correlation between D₁ and clay content (R² = 0.924), a negative correlation between D₁ and silt content (R² = 0.801), and a negative correlation between D₂ and sand content (R² = 0.913). The geometric mean diameter of soil particles had a negative correlation with D₁ (R² = 0.569) and D₂ (R² = 0.682). Semivariograms of fractal dimensions and soil textural parameters were calculated and the maps of spatial variation of D₁ and D₂ and soil PSD parameters were provided using ordinary kriging. The results showed that there were also spatial correlations between D₁ and D₂ and particle size fractions. According to the semivariogram models and validation parameters, the fractal parameters had powerful spatial structure and could better describe the spatial variability of soil texture.     

Site-Specific Nitrogen Management in Rice Using Remote Sensing and Geostatistics

Proper doses of nitrogenous fertilizer are most important for rice production system because a large part of the nitrogen may be lost if it is not applied judiciously. A study was conducted covering five blocks of Balasore and two blocks of Bhadrak districts. Soil samples were collected randomly, and field visit was conducted during peak vegetative stage of rice. Two approaches have been used in this study for estimating the site-specific nitrogen (N) requirement in the study area. In one approach, geostatisical analysis and kriging was used to develop the soil test–based N recommendation map by which a minimum of 72 kg N ha^?1 and maximum of 94 kg N ha^?1 were recommended. In a second approach, remote sensing was used and N recommendation map was developed using the moderate-resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and normalized difference vegetation index (NDVI) satellite data, and a minimum requirement of 60 kg N ha^?1 and maximum of 120 kg N ha^?1 was estimated through this approach.     

Proton toxicity interferes with the screening of common bean (Phaseolus vulgaris L.) genotypes for aluminium resistance in nutrient solution

Common bean (Phaseolus vulgaris L.) proved to be very sensitive of low pH (4.3), with large genotypic differences in proton sensitivity. Therefore, proton toxicity did not allow the screening of common bean genotypes for aluminium (Al) resistance using the established protocol for maize (0.5 mM CaCl₂, 8 μM H₃BO₃, pH 4.3). Increasing the pH to 4.5, the Ca²⁺ concentration to 5 mM, and addition of 0.5 mM KCl fully prevented proton toxicity in 28 tested genotypes and allowed to identify differences in Al resistance using the inhibition of root elongation by 20 μM Al supply for 36 h as parameter of Al injury. As in maize, Al treatment induced callose formation in root apices of common bean. Aluminium‐induced callose formation well reflected the effect of Ca supply on Al sensitivity as revealed by root‐growth inhibition. Aluminum‐induced callose formation in root apices of 28 bean genotypes differing in Al resistance after 36 h Al treatment was positively correlated to Al‐induced inhibition of root elongation and Al contents in the root apices. However, the relationship was less close than previously reported for maize. Also, after 12 h Al treatment, callose formation and Al contents in root apices did not reflect differences in Al resistance between two contrasting genotypes, indicating a different mode of the expression of Al toxicity and regulation of Al resistance in common bean than in maize.     

Methods to interpolate soil categorical variables from profile observations: Lessons from Iran

The paper compares semi-automated interpolation methods to produce soil-class maps from profile observations and by using multiple auxiliary predictors such as terrain parameters, remote sensing indices and similar. The Soil Profile Database of Iran, consisting of 4250 profiles, was used to test different soil-class interpolators. The target variables were soil texture classes and World Reference Base soil groups. The predictors were 6 terrain parameters, 11 MODIS EVI images and 17 physiographic regions (polygon map) of Iran. Four techniques were considered: (a) supervised classification using maximum likelihoods; (b) multinominal logistic regression; (c) regression-kriging on memberships; and (d) classification of taxonomic distances. The predictive capabilities were assessed using a control subset of 30% profiles and the kappa statistics as criterion. Supervised classification and multinominal logistic regression can lead to poor results if soil-classes overlap in the feature space, or if the correlation between the soil-classes and predictors is low. The two other methods have better predictive capabilities, although both are computationally more demanding. For both mapping of texture classes and soil types, the best prediction was achieved using regression-kriging of indicators/memberships (κ = 45%, κ = 54%). In all cases kappa was smaller than 60%, which can be explained by the preferential sampling plan, the poor definition of soil-classes and the high variability of soils. Steps to improve interpolation of soil-class data, by taking into account the fuzziness of classes directly on the field are further discussed.     

Nitrogen fertilizer–induced mineralization of soil organic C and N in six contrasting soils of Bangladesh

A 90‐day laboratory incubation study was carried out using six contrasting subtropical soils (calcareous, peat, saline, noncalcareous, terrace, and acid sulfate) from Bangladesh. A control treatment without nitrogen (N) application was compared with treatments where urea, ammonium sulfate (AS), and ammonium nitrate (AN) were applied at a rate of 100 mg N (kg soil)^–1. To study the effect of N fertilizers on soil carbon (C) turnover, the CO₂‐C flux was determined at nine sampling dates during the incubation, and the total loss of soil carbon (TC) was calculated. Nitrogen turnover was characterized by measuring net nitrogen mineralization (NNM) and net nitrification (NN). Simple and stepwise multiple regressions were calculated between CO₂‐C flux, TC, NNM, and NN on the one hand and selected soil properties (organic C, total N, C : N ratio, CEC, pH, clay and sand content) on the other hand. In general, CO₂‐C fluxes were clearly higher during the first 2 weeks of the incubation compared to the later phases. Soils with high pH and/or indigenous C displayed the highest CO₂‐C flux. However, soils having low C levels (i.e., calcareous and terrace soils) displayed a large relative TC loss (up to 22.3%) and the added N–induced TC loss from these soils reached a maximum of 10.6%. Loss of TC differed depending on the N treatments (urea > AS > AN >> control). Significantly higher NNM was found in the acidic soils (terrace and acid sulfate). On average, NNM after urea application was higher than for AS and AN (80.3 vs. 71.9 and 70.9 N (kg soil)^–1, respectively). However, specific interactions between N‐fertilizer form and soil type have to be taken into consideration. High pH soils displayed larger NN (75.9–98.1 mg N (kg soil)^–1) than low pH soils. Averaged over the six soils, NN after application of urea and AS (83.3 and 82.2 mg N (kg soil)^–1, respectively) was significantly higher than after application of AN (60.6 mg N (kg soil)^–1). Significant relationships were found between total CO₂ flux and certain soil properties (organic C, total N, CEC, clay and sand content). The most important soil property for NNM as well as NN was soil pH, showing a correlation coefficient of –0.33** and 0.45***, respectively. The results indicate that application of urea to acidic soils and AS to high‐pH soils could be an effective measure to improve the availability of added N for crop uptake.