Morphological and molecular identification of Neopestalotiopsis mesopotamica causing tomato fruit rot

Journal of Plant Diseases and Protection - Severe fruit rot symptoms were observed on tomato crop (Lycopersicon esculentum) located in Hamedan province of Iran, in spring 2014. Affected fruits...     

Impacts of land use on soil organic matter and degree of compactness in calcareous soils of central Iran

This study was conducted to investigate the impact of land use (dryland farming, grassland and irrigated farming) on bulk density, (ρ_b) and relative bulk density (ρ_b‐rel), and to study the relationships between ρ_b and ρ_b‐rel, respectively, and soil organic matter content (OM) and soil texture at 100 locations in calcareous soils of central Iran. The ρ_b–rel was expressed as the ratio of ρ_b to a reference bulk density, ρ_bef. By considering ρ_b‐ref an inherent soil property that is dependent on soil texture but not on OM, the combined effects of OM due to land use and compaction (due to agricultural machinery) on the degree of compactness could be explored. Multiple linear regression was used to derive pedotransfer functions for predicting ρ_b and ρ_b‐rel. It was found that ρ_b‐rel is strongly affected by OM, and a strong correlation was obtained between ρ_b‐rel and the ratio of OM to clay content. The predictive performance of the multiple regression models was poorest for irrigated farming, which might be explained by intensive soil disturbance by tillage in irrigated farming. The main effect of land use was on OM, and consequently, the degree of compactness was mainly controlled by OM. The greatest OM and least ρ_b‐rel were measured in irrigated farming. Dryland farming had the least OM and the greatest ρ_b‐rel.     

Land suitability evaluation in damghan plain for barley, using compare and conformity methods (northeast-Iran)

In this study, land suitability evaluation based on FAO guidelines on the land evaluation system has been determined for barley irrigated area of about 3400 ha in the south of plains Damghan. In order to have more reliable soil data, particularity to control the old soil map of the area, a new soil survey was carried out in the area. South soils of Damghan plain were classified based on semi detailed studies of three physiogrphy units, four map units and two order Aridisols and Entisols. Climatic Data were used from Damghan meteorological synoptic stations for climate evaluation for barley. With respect to climate and soil severe limitations in the study area, the results of map units as the assessment methods include the storie, square root and the number of simple constraints were the same as N2 with permanent unsuitable.     

Perusing biochemical antioxidant enzymes as selection criteria under drought stress in wheat varieties

In regard to important roles of enzymatic antioxidant in abiotic stresses, and their practical use as stress indices, the current research was implemented to peruse antioxidant enzymatic activities of different wheat varieties and to find the susceptible and the tolerant varieties based on these indices. The experiment was carried out in the greenhouse of Islamic Azad University, Sanandaj, Iran in 2015. A factorial experiment based on completely randomized design (CRD) with three replications were used to evaluate the effects of different water regimes, including 100% and 50% field capacity (FC) on 25 wheat cultivars. Antioxidant enzyme activities, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbic peroxidase (APX), and grain yield of the wheat varieties were measured. The results showed that under normal irrigation condition, the relationships between grain yield and antioxidant were inversed, but under stressful condition the relationships between wheat grain yield and some important antioxidants such as CAT and SOD were positive. Among antioxidant enzymes, SOD activity showed a higher variation among wheat varieties of this study, indicating that this enzyme is an important trait to be used in the breeding programs. Based on the results of different statistical techniques and comparing relationships among traits for normal irrigation and stress condition, enzymatic antioxidant could be used as practical criteria for screening tolerant genotypes of wheat. On the other side, SOD and CAT resulted in being the most important criteria for achieving higher tolerant genotypes through indirect selection.     

Relationships of barley biomass and grain yields to soil properties within a field in the arid region: Use of factor analysis

Abstract

Understanding the variability of soil properties and their effects on crop yield is a critical component of site-specific management systems. The objective of this study was to employ factor and multiple regression analyses to determine major soil physical and chemical properties that influence barely biomass and grain yield within a field in the arid region of northern Iran. For this purpose, soil samples and crop-yield data were collected from 108 sites, at regular intervals (20×30 m) in a 5.6 ha field. Soil samples were analysed for total nitrogen (TN), available phosphorus (P_ava), available potassium (K_ava), cation-exchange capacity(CEC), electrical conductivity (EC), pH, mean weight diameter of aggregates (MWD), water-stable aggregates (WSA), field capacity volumetric (FC), available water-holding capacity (AWHC), bulk density (BD), and calcium carbonate equivalent (CCE). Results of the factor analysis, followed by regression of biomass and grain yield of barley with soil properties, showed that the regression equations developed accounted for 78 and 73% of the total variance in biomass and grain yield, respectively. Study of covariance analysis among soil variables using factor analysis indicated that some of the variation measured could be grouped to indicate a number of underlying common factors influencing barley biomass and grain yields. These common factors were salinity and sodicity, soil fertility, and water availability. The most effective soil variables to barley production in the study area identified as EC, SAR, pH, TN, P_ava, AWHC, and FC. In this study, factor analysis was effective to identify the groups of correlated soil variables that were significantly correlated with the within field variability in the yield of the barley crop. Our results also suggest that the approach can be applied to other crops under similar soil and agroclimatic conditions.     

Spatial prediction of USDA‐ great soil groups in the arid Zarand region,Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types

The main objectives of this study were to compare binary logistic regression as an indirect approach and multinomial logistic regression as a direct approach to produce soil class maps in the Zarand region of southeast Iran. With indirect prediction, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel‐wise classification by combining the presence or absence of diagnostic horizons. In direct prediction, the dependent variable was the great group itself, so the probability distribution of the great soil groups was directly predicted. Among the predictors, the geomorphology map was identified as an important tool for digital soil mapping approaches as it helped to increase the accuracy. The results of prediction showed larger mean probability values for each great soil group in the areas actually covered by the great soil groups compared with other areas, indicating the reliability of the prediction. In most predictions, the global purity was slightly better than the actual purity for the models; however, both models provided poor predictions for Haplocambids and Calcigypsids. The results showed that soils with better prediction were those much influenced by topographical and geomorphological characteristics and soils with very poor accuracy of prediction were only slightly influenced by topographical and geomorphological characteristics. An advantage of the indirect method is that it gives insight into the causes of errors in prediction at the scale of diagnostic horizons, which helps in the selection of better covariates.     

How Soil Pore Distribution Could Help in Soil Quality Studies as an Appropriate Indicator

Eurasian Soil Science - The present study is aimed at examining the impacts of land use and slope gradient on pore space distribution as well as some soil physicochemical properties. Three land...     

Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming

Spatial information on soil salinity is increasingly needed for decision making and management practices in arid environments. In this article, we attempted to investigate soil salinity variation via a digital soil mapping approach and genetic programming in an arid region, Chah-Afzal, located in central Iran. A grid sampling strategy with 2-km distance was used. In total, 180 soil surface samples were collected and then analyzed. A symbolic regression was then adopted to correlate electrical conductivity (EC_e) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: EC_av and horizontal: EC_ah), Landsat spectral data and terrain attributes derived from a digital elevation model. The accuracy of the genetic programming model was evaluated using root mean square error (RMSE), mean error (ME), and coefficient of determination (R²) based on an independent validation data set (20% of database or thirty soil samples). In general, results showed that EC_ah had the strongest influence on the prediction of soil salinity followed by salinity index wetness index, Landsat Band 3, multi-resolution valley bottom flatness index, elevation, and normalized difference vegetation index. Furthermore, results indicated that the genetic programming model predicted EC_e over the study area accurately (R² = 0.87, ME = ?1.04 and RMSE = 16.36 dSm^?1). Overall, it is suggested that similar applications of this technique could be used for mapping soil salinity in other arid regions of Iran.     

Applying the CSM-CERES-Wheat model for rainfed wheat with specified soil characteristic in undulating area in Iran

Models can provide researchers with an opportunity to understand the multidimensional relationships among factors affecting crop growth. All such factors, including field condition, must be tested using models. The main objective of this study was to apply the Cropping System Model (CSM)-Crop-Environment Resource Synthesis (CERES)-Wheat model to uneven areas for simulating rainfed wheat. First, the model was calibrated at a field cultivated with wheat variety Sardari. The model was then applied to different points at a ‘hummocky’ evaluation site. At this site, 69 sample points from 5 different slope positions (summit, shoulder, backslope, footslope, and toeslope) were specified and physical and chemical soil properties as well as final grain and total aboveground wheat biomass at each of 69 points were measured. The overall results showed that the normalized root-mean-square error varied between 15% and 34% for the top weight; this was considered to be good enough for undulating areas. The model efficiency (EF) also ranged from 0.35 to 0.26 both in simulation of final grain and in top weight. It can be concluded that CSM-CERES-Wheat model can be effectively applied for yield prediction in hills. It should be noted that use of the CSM-CERES-Wheat model is not recommended for estimation of grain yield in individual slope positions unless more detailed water and fertilizer data are also available.