Rates of inbreeding and genetic diversity in Iranian Holstein Cattle

The accumulation of inbreeding and the loss of genetic diversity is a potential problem in Holstein dairy cattle. The goal of this study was to estimate inbreeding levels and other measures of genetic diversity, using pedigree information from Iranian Holstein cattle. Edited pedigree included 1 048 572 animals. The average number of discrete generation equivalents and pedigree completeness index reached 13.4 and 90%, respectively. The rate of inbreeding was 0.3% per year. Effective number of founders, founder genomes, non‐founders and ancestors of animals born between 2003 and 2011 were 503, 15.6, 16.1 and 25.7, respectively. It was proven that the unequal founder contributions as well as bottlenecks and genetic drift were important reasons for the loss of genetic diversity in the population. The top 10 ancestors with the highest marginal genetic contributions to animals born between 2003 and 2011 and with the highest contributions to inbreeding were 48.20% and 63.94%, respectively. Analyses revealed that the most important cause of genetic diversity loss was genetic drift accumulated over non‐founder generations, which occurred due to small effective population size. Therefore, it seems that managing selection and mating decisions are controlling future co‐ancestry and inbreeding, which would lead to better handling of the effective population size.     

Oxidative Stability of Spray-Dried Microencapsulated Fish Oils with Different Wall Materials

Multilayer microencapsulation of fish oil was evaluated by using fish gelatin (treated with or without microbial transglutaminase [MTGase]), chitosan, and maltodextrin for its ability to control lipid oxidation. This study showed that a mixture containing gelatin and maltodextrin obtained the highest percentage of core release. The oxidative stability of the formulated mixtures and the bulk oils was investigated during a period of 60 days. The peroxide value (PV) was assessed as a parameter for primary oxidation products, and p-anisidine (p-AV) and thiobarbituric acid reactive sub-stances (TBARS) was used to analyze secondary oxidation compounds. Observation of oxidation products showed that combinations of gelatin and maltodextrin made by adding MTGase and a mixture of gelatin and chitosan were able to increase the oxidative stability, and increases in PV and p-AV and TBARS were found for all oils.     

Stimulation in the movement and uptake of phosphorus in response to magnetic P solution and arbuscular mycorrhizal fungi in Ocimum basilicum

A greenhouse experiment was conducted to assess the effects of magnetic field, arbuscular mycorrhizal fungi (AMF), and phosphorus (P) concentration in the nutrient solution (0, 5, 10, 20, or 40 mg L^?1) on the mobility and accumulation of P in soil and plant tissues of basil (Ocimum basilicum L.). The experiment was designed as a factorial combination and treatments were arranged in a completely randomized design with four replicates. Magnetic field increased water-soluble P in the soil and P concentration in plant shoot by 30.0% and 13.0%, respectively, in comparison to the control. The application of magnetic field and inoculation of AMF at 10 mg P L^?1 increased the P translocation efficiency by 23.3% and 17.8%, respectively. Overall, our results demonstrated that the use of magnetic field and AMF could be an effective tool for enhancing of uptake and movement efficiency of P even at low concentrations.     

Estimation of yield and dry matter of rapeseed using logistic model under water salinity and deficit irrigation

The inadequacy of most models to simulate crop growth and yield is due to complexity, difficulty to understand, and lack of input data. Therefore, several simple crop growth models are presented to reduce these failures. In this investigation, yield and aboveground dry matter (DM_above) of rapeseed were simulated by two logistic growth models that were based on days after planting (DAP) and growing degree days (GDD) under water salinity and deficit irrigation, in a 2-year experiment. Data of first and second year were used for calibration and validation of the model, respectively. The coefficients of logistic function were determined as a function of irrigation water salinity and sum of applied water and rainfall in spring of the first year. Results indicated that logistic function based on GDD-predicted DM_above during growing season more accurately than logistic function based on DAP. Furthermore, seed yield of rapeseed was estimated based on harvest index with a good accuracy. Therefore, logistic function based on GDD that is based on the cumulative heat units can be used for different weather conditions and planting dates to determine rapeseed DM_above and yield under water salinity and deficit irrigation.     

A model to predict the dry matter and yield of rapeseed under salinity and deficit irrigation

In this investigation, a model was developed to predict dry matter, seed yield and other crop parameters of rapeseed under deficit irrigation and salinity by using soil water and salt budget and other simple plant physiological relationships. Two-year experimental data were used. In calibration and validation of the presented model, results indicated that the model was able to estimate evapotranspiration, soil water content, leaf area index, evaporation, crop transpiration, dry matter and seed yield of rapeseed properly. The advantage of this model is its simplicity and easy calibration in other areas and climate conditions and it can be used to estimate yield and other crop parameters with common measurable data in the field. Prediction of crop yield by this model can be used for better management of agronomic systems to reduce administrative costs and in different environmental conditions. Finally, under scarce data, arid and semi-arid environments, this model is proposed to be used by irrigation managers and agricultural advisors.     

Soil erodibility and its prediction in semi-arid regions

ABSTRACT

Pedotransfer functions (PTFs) have been used to save time and cost in predicting certain soil properties, such as soil erodibility (K-factor). The main objectives of this study were to develop appropriate PTFs to predict the K-factor, and then compare new PTFs with Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE) K-factor models. The K-factor was measured using 40 erosion plots under natural rainfall in Simakan Watershed, an area of 350 km² in central of Iran. The Regression Tree (RT) and Multiple Linear Regression (MLR) were used to develop PTFs for predicting the K-factor. The result showed that the mean of measured K was 0.01 t h MJ^?1 mm^?1. The mean K value predicted by USLE and RUSLE was 2.08 and 2.84 times more than the measured K, respectively. Although calcium carbonate was not considered in the original USLE and RUSLE K-factors, it appeared in the advanced PTFs due to its strong positive significant impact on aggregate stability and soil infiltration rate, resulting in decreased K-factor. The results also showed that the RT with R² = 0.84 had higher performance than developed MLR, USLE and RUSLE for the K estimation.     

Effects of Phosphate Dip Treatment on Quality of Rutilus frisii kutum Fillets During Ice Storage

The use of polyphosphates as a partial replacement of salt in different meat products to improve water holding capacity yield and retard oxidative rancidity has been noticed for many years. Therefore, in this study, effects of polyphosphates dip treatment in 5% solution with three different compounds of phosphate—including tetrasodium pyrophosphate (TSPP), sodium tripolyphosphate (STPP), and a mixture of them on kutum (Rutilus frisii kutum) fillets stored in ice was investigated for 18 days. Phosphate pretreatments showed desirable modal effects on fillets. In particular, the use of TSPP and the mixture of TSPP and STPP solutions were found to delay the fish fillet color changes and improved textural characteristics. An increase in water holding capacity accompanied with a decrease in hardness was observed in samples pretreated with polyphosphates. In total, the results indicated that dipping kutum fillets in polyphosphates solution was the most effective method to improve the quality properties of fillets during ice storage when compared with other phosphate-treated groups.     

Application of an Adaptive Neural-Based Fuzzy Inference System Model for Predicting Leaf Area

Leaf Area (LA) is a key index of plant productivity and growth. A multiple linear regression technique is commonly applied to estimate LA as a non-destructive and quick method, but this technique is limited under the realistic situation. Thus, it is indispensable to elaborate new models for estimation. In this research, the performance of the Adaptive Neural-Based Fuzzy Inference System (ANFIS) in predicting the LA of 61 plant species (C) was investigated. Four parameters including leaf length (L), leaf width (W), C, and specific coefficient (K) for each plant were selected as input data to the ANFIS model and the LA as the output. Seven different ANFIS models including different combinations of input data were constructed to reveal the sensitivity analysis of the models. The normalized root mean square error (NRMSE), mean residual error (MRE), and linear regression were applied between observed LA and estimated LA by the models. The results indicated that ANFIS4-K_2min which employed all input data was the most accurate (NRMSE = 0.046 and R² = 0.997) and ANFIS1 which employed only the K input was the worst (NRMSE = 0.452 and R² = 0.778). In ranking, ANFIS4-K_2ave, ANFIS4-K_1min, ANFIS4-K_1ave, ANFIS3, and ANFIS2 ranked second, third, fourth, fifth, and sixth, respectively. The sensitivity analysis indicated that the predicted LA is more sensitive to the K, followed by L, W, and C. The results displayed that estimations are slightly overestimated. This study demonstrated that the ANFIS model could be accurate and faster alternative to the available laborious and time-consuming methods for LA prediction.     

德国中部Soiling云杉-山毛榉混交林冠层组成对土壤CO2排放的影响

It was hypothesized that soil respiration can be affected by canopy composition. Hence, admixture of trees as a common forest management practice may cause significant change in the carbon cycling. This study was conducted in a mixed spruce-beech stand at Solling forest in central Germany to investigate the effect of canopy composition on soil respiration. The canopy cover was classified in four major canopy classes (pure beech, pure spruce, mixed and gap), and the area under each canopy class was identified as a sub-plot. Soil respiration in each sub-plot (n=4) was measured monthly from Jun 2005 to July 2006. Results show significant difference in annual soil respiration between the beech (359 g·m⁻²·a⁻¹ C) and gap (211 g·m⁻²·a⁻¹ C) sub-plots. The estimation of the total below-ground carbon allocation (TBCA) based on a model given by Raich and Nadelhoffer revealed considerably higher root CO₂ production in the beech sub-plot (231 g·m⁻²·a⁻¹ C) compare to the gap sub-plot (51 g·m⁻²·a⁻¹ C). The contribution of the root respiration to the total soil respiration was higher in the soil under the beech canopy (59%) compared with the soil in the gap (29%). The findings suggested that the condition under the beech canopy may cause more desirable micro-site for autotrophic respiration and consequently higher CO₂ release into the atmosphere.