نشریه مهندسی زراعی
سال سی و هفتم شماره 2 (زمستان 1393)

Release of urban sewage in the environment is considered a serious problem to the environment and human health while its use in agriculture is recommended as a rich fertilizer in organic matter and major nutrients affecting plant growth. In this study، the use of treated urban sewage sludge، as a cheap organic fertilizer، a reproducible source in the nature and a municipal waste on the chemical properties of soil and essential nutrients of olive plant and soil alone and in combination with chemical fertilizer، was investigated. This study was conducted at Shahid Chamran University of Ahvaz greenhouse with 12 treatments and 3 replications within six months. Effects of sewage sludge، chemical fertilizer and combined effects on various parameters such as ECe، pH، organic matter content and essential nutrients of olive plant and soil and olive physiological characteristics were determined. Soil nutrient concentrations and physiological function of olive plants subjected to sewage sludge increased، and soil pH significantly decreased. The effect of sewage sludge and fertilizer on the vegetative components of the olive plant، including number of lateral branches، number of new leaves، height and leaf dry weight، significantly increased. Sewage sludge improved some soil chemical properties. 150 tons of sewage sludge per hectare without fertilizer treatment had the best effect on the properties of olive plants and soil. Sewage sludge increased the number of leaf، leaf area and organic matter content about twice times comparing with blank treatment but decreased 0. 4 unit pH. Effect of sewage sludge on soil potassium content was fewer than P and N.

The aim of this research was to investigate the effect of grain soaking time (at two levels in 2 and 4 days), grain soaking temperature (at two levels 10 and 25 °C) and grain final moisture content (at three levels 8, 11, 14%w.b.) on the percentage of broken grain during husking and whitening processes for three paddy rice varieties (Hashemi, Hasani and Khazar) which are common in Guilan Province. The results revealed that the effect of main factors and their intractions was significant on the broken grain percentage during husking and whitening processes. The Hashemi variety had the highest broken grain with values 42.99% and 38.48% at husking and whitening processes, respectively. By increasing grain soaking time from two to four days, broken grain mean values at husking and whitening processes decreased significantly from 20.23% to 15.59% and 29.53% to 27.19%, respectively. By increasing soaking temperature from 10 to 25 °C, broken grain mean values at husking and whitening processes decreased significantly from 19.26% to 16.57% and 29.64% to 27.38%, respectively. The increase of final grain moisture content from 8 to 14%w.b. caused a significant decrease of broken grain at husking from mean value 21.39% to 14.88% and a increase significant in broken grains at whitening from mean value 25.91% to 31.05%. Comparison of the mean results of quaternary interactions showed that the least broken grains percent of husking process (4.736%) was obtained at test with Hasani variety, soaking time of four days, soaking temperature of 10°C and grain moisture content of 14 (%w.b.). Also, the hightest broken grain percentage of husking process (56.8%) was obtained at test with Hashemi variety, the soaking time of two days, the soaking temperature of 10 °C and the grain moisture content of 8 w.b.%. The least broken grain percentage of whitening process (9.44 %) was obtained at test with Khazar variety, the soaking time of four days, the soaking temperature of 25 °C and the moisture content of 11 w.b.%. The highest broken grain percentage of whitening process (57.85%) was obtained at test with Hashemi, soaking time of 2 days, soaking temperature of 10 °C and grain moisture content of 14%(w.b.).

This study was conducted to assess the ability of LEACHN model for predicting nitrate and ammonium in the soil profile under corn at the Safiabad Agricultural Research Center, over two growing years. Three irrigation levels (50, 75, and 100% water requirement) and three nitrogen fertilizer levels (150, 225, and 300 kg N ha-1) were applied through fertigation in three equal splits during the first irrigation, 6 to 8 leaves, and before flowering stages. The results of model calibration showed that nitrification and denitrification rates with 0.17, 0.004 per day and a dispersion coefficient equal to 200 mm provided the minimum mean bias error (MBE) and the maximum modeling efficiency (EF). Therefore, the MBE and EF values for nitrate varied from -2.09 to 3.89 and -0.1 to 0.88 respectively. However, model predictions for NH4 mobility through the soil profile were lower than that of nitrate mobility. Comparison between predicted and measured values did not show nitrate leaching below 60 cm in the soil profile in any treatments. Statistical indexes of MBE, EF, and coefficient of variation (CV) for soil moisture content predictions in different depths varied between 0.003 and 0.019, 0.24 and 0.73, and 5.8 and 17.9% respectively showing the high accuracy of model in prediction of soil moisture content.

Today the chemical fertilizers are replaced with organic fertilizers due to the bad effects on human health and agricultural soils. There are barriers against useful utilization of organic fertilizers such as transportation (high volume), high moisture content, uneven distribution of fertilizers, dust production and storage. One of the solutions is densification of manure as pellet or granule. The effects of process parameters were evaluated in laboratory scale rotating drum. The effects of parameters such as drum rotational speed, granulation time, filling percentage of drum and percentage of molasses were investigated on the fracture force of granules. The results showed that all parameters and also interaction of drum rotational speed and granulation time have significant effect on the fracture force. Also, the results showed that with enhancement of granulation time, percentage of drum filling and percentage of molasses, resistance to failure of the granules increased.

Microorganisms play an important role in providing nutrients for plants and also in soil development. This study was carried out to investigate the effect of two strains of Bacillus cereus on the release of potassium and iron from micaceous minerals. An experiment was set up with a completely randomized design and factorial arrangement with three replications. Treatments included two types of mineral (phlogopite and muscovite), two strains of bacteria (PTCC 1247 and PTCC 1665), control and eight time periods. The results indicated that released potassium and iron were highly dependent on the strain of bacterium and also the mineral type. Strain PTCC 1665 could release a higher amount of potassium as compared to PTCC 1247. The amount of released iron increased at the beginning of the experiment but decreased and then increased as time passed. In general, the amount of released iron from phlogopite was higher than that from muscovite.

The present investigation was designed to compare the effect of combination biofertilizer of nitrogen fixing free-living bacteria and plant growth promoting bacteria of Azotobacter. It also aimed to compare the effect of Azospirillum alone and in combination with other plant growth promoting bacteria of Pseudomonas fluorescens and Bacillus subtilis with five nitrogen fertilizer levels on the wheat growth indices and yield.The experiment including biofertilizer and different amounts of nitrogen fertilizer was carried out as factorial in completely randomized design with three replications. At the end of plant growth period, some plant indices such as spike and grain number, crop yield, grain to straw weight ratio, straw yield, and grain nitrogen percentage were measured. The results showed that in the simple effect of bio fertilizer, the most effective factor was related to the biofertilizer consortium containing Azotobacter, Azosprillum, Pseudomonas and Baillus subtilis which respectively increased 8, 22.5, 26.5 and 23.3 % the amounts of grain nitrogen, spike number, straw yield and plant yield in comparison with control treatment. Although in the simple effect of chemical fertilizer on studied indices, 100 kg/ha of nitrogen had the most effect, it didn’t demonstrate any significant difference in comparison with the fertilizer level of 75 kg/ha. The highest amounts of measured indices were related to the interaction of bio fertilizer including Azotobacter, Azosprillum, and Pseudomonas and Bacillus subtilis with nitrogen level of 75kg/ha that could respectively increase 25, 100.4, 53.5, 100.6 and 92.6% of grain and spike number, the nitrogen content of grain, straw and plant yield compared to control plant.

The fertility maps are useful tools to know about the land resources. These maps are essential to do a correct fertilizer recommendation, monitoring the changes of soil fertility level and also to do prediction of toxicity or deficiency of necessary plants nutritional elements in the soil. The present research was carried out to digital mapping of macro and micro elements as well as some important physical and chemical properties of the soil for agricultural service centers of Golestan province to optimize the use of chemical fertilizers in operating plan pattern. The studied areas were under irrigated and rain-fed wheat cultivation and the total surface area was about 520000 hectares. Using based maps with the scale of 1: 50,000, the area was divided into 5200 grids each having one kilometer square area. Using Global Positioning System (GPS) instrument a composite soil sample was taken from the center of any grid and analyzed for major macro- and micronutrients as well as physical and chemical properties of the soil namely; pH, EC,TNV, sand, silt and clay contents. At the time of sampling, some additional information like latitude, longitude, elevation, owner's name, date of sampling, kind of water resources, possibility of water logging, crop history (present and previous) was collected and recorded. After getting the laboratory results, for the above stated data sets, an electronic layer was created for any particular parameter and after doing interpolation, the layers were polygonized. Anisotropy of the data sets in different directions was evaluated by the help of variogram surface operation. Then spatial correlation of each data set was calculated. After variogram analysis, spatial variation of the data sets was studied and a suitable model was selected. Finally, through point interpolation using kriging technique, the digital map of each parameter was established.

In recent years, there has been a great development in the digital soil mapping which has led to production of maps for countries and the continents. Although many studies have been conducted all over the world, few Iranian soil scientists have shown interests in digital mapping. Therefore, in the present research, different data mining techniques (i.e. regression logistic, artificial neural network, genetic algorithm, decision tree and discriminant analysis) were applied to spatial prediction of great group soils in the area covering of 72000 ha in Ardakan. In this area, by using the conditioned Latin hypercube sampling method, location of 187 soil profiles was selected, which was then described, sampled, analyzed and allocated in taxonomic classes according to soil taxonomy of America. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes, Landsat 7 ETM+ data and a geomorphologic surfaces map. Results showed that decision tree model had the highest accuracy while it could increase the accuracy of prediction up to 44% in comparison with discriminant analysis technique. Results also indicated using the taxonomic distances led to improving the overall accuracy of decision tree up to 3%. Results confirmed capability of decision tree, artificial neural networks, genetic algorithm, logistic regression, and discriminant analysis with 70%, 65%, 65%, 55%, and 47% accuracy, respectively. Moreover, results showed that decision tree model could predict soil classes in sub-great group with the overall accuracy of 84.2%.