Mirhassan Rasoulsiadaghiani; Vali Feiziasl; Ebrahim Sepehr; Mehdi Rahmati; Salman Mirzaee
Abstract
Introduction: In cereal crops, nitrogen is the most important element for maintaining growth status and enhancing grain yield. Nitrogen is an important constituent of the chlorophyll molecule and the carbon-fixing enzyme ribulose-1, 5-bis-phosphate carboxylase/oxygenase. Therefore, providing enough nitrogen ...
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Introduction: In cereal crops, nitrogen is the most important element for maintaining growth status and enhancing grain yield. Nitrogen is an important constituent of the chlorophyll molecule and the carbon-fixing enzyme ribulose-1, 5-bis-phosphate carboxylase/oxygenase. Therefore, providing enough nitrogen to achieve optimal yield is essential. Common chemical analyzes are used to determine the nutrient elements of plants using laboratory methods. Conventional laboratory techniques are expensive, laborious, and time-consuming. Determination of plant biochemical content by remote sensing could be used as an alternative method which reduce the problems of laboratory analyses. Expensive and time-consuming direct determination of the nutritional status of the plant play an important role in the quantitative and qualitative yield of the product. However, exposure to rainfed wheat nutrient stresses (in particular, nitrogen) compared to irrigated wheat resulting in attempts to evaluate these features with acceptable accuracy without the direct measurement. In this regard, remote sensing data and satellite images are of the basic dryland management and optimal wheat production methods. As such, it collects massive information periodically from the surface of the planet, and it is easy to use this timely information to identify the stresses and apply appropriate agronomic methods in order to counteract them or reduce their negative impact on the production of this strategic product. Therefore, the goal of this study was to determine the nitrogen concentration of dryland wheat in the laboratory and its fitting with ETM+ images, evaluate the accuracy of remote sensing in determining the total nitrogen content of the plant and establish a regression relationship to estimate the amount of canopy nitrogen in the plant.
Material and Methods: This research was undertaken in parts of the south of the West Azerbaijan Province in Iran. The sampling was done from 45 dryland wheat fields using a stratified random method in May 2016. The wheat canopy nitrogen was determined using the Kjeldahl method. Satellite images of the ETM+ were downloaded on the USGS website. Then the required pre-processing was performed on images to reduce systematic and non-systematic errors. Statistical analyses were performed by excel and SPSS. Descriptive statistics and correlations were obtained between reflectance data obtained from various satellite bands and nitrogen measured in the laboratory. Correlated variables among the reflectance data of different bands were analyzed by principal component to reduce repeat calculations. The regression relationship between the plant canopy nitrogen and the first principal component has been evaluated using the stepwise regression method. To draw the plant canopy nitrogen, map, the equation was obtained and the ETM+ image has been used for land uses. Finally, the map of canopy N distribution at the studied area was drawn.
Results and Discussion: The results showed that nitrogen content varied from 1.6% to 0.79%, with an average of 1.11%. The normality data was verified by the Shapiro Wilk test. The results of the Pearson correlation showed that the wheat canopy nitrogen has a high correlation with digital number values of all bands of satellite images except band 4, so that it has the highest and the least correlation with band 2 and band 4, respectively. The correlation between remote sensing data in different bands was also evaluated using bi-plot statistics, which results showed a high correlation between all bands except band 4 with the first one of the principal component (PC1). Therefore, only PC1 data has been used to study the regression relationships between wheat canopy nitrogen and remote sensing data. A regression equation between wheat canopy nitrogen and ZPC1 (R2= 0.71) was developed. ZPC1 is obtained according to the following formula: where ZPC1 is the standardized Z parameter, is the average of PC1 and the ????pc1 is the standard deviation of PC1. Finally, the map of canopy N distribution was drawn to the studied area. According to the results of this study, the application of remote sensing data such as Landsat ETM+ data is a very important variable for improving and managing the prediction of wheat canopy nitrogen.
Conclusion: Overall, the results indicated that the remote sensing data provide more accurate and timely information from the drylands of Iran to manage farm fertilization and prevent the decline in yields at critical points. However, proper management to avoid the fertilizer loss by precise and timely application of N-fertilizer is needed.
Salman Mirzaee; MirHassan Rasouli-Sadaghiani; Naser Miran
Abstract
Introduction: Citrus is an important fruit crop cultivated in tropical regions of the world with immense nutritional value and advised on daily basis in diet. In Iran, it is cultivated in high reaches of northern and southern regions. The low productivity has been ascribed mainly to the nutritional health ...
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Introduction: Citrus is an important fruit crop cultivated in tropical regions of the world with immense nutritional value and advised on daily basis in diet. In Iran, it is cultivated in high reaches of northern and southern regions. The low productivity has been ascribed mainly to the nutritional health of the plantations which is the most concern among farmers. To plan fertilization efficiently, it is necessary to know the desirable concentration of macro and micro nutrient in tissues that are representative of the plant’s nutritional status. Traditionally, to determine the optimum fertilizer doses the most appropriate method was to apply fertilizer on the basis of soil test and crop response studies (Regar and Singh, 2014) which defied the synergistic and antagonistic effects in relative availability of different essential nutrients from soil. The foliar nutrient concentration is considered most pertinent and reliable method to judge the well-being of a tree as it represents the in situ condition in a holistic way and is a very powerful tool for nutritional diagnosis to assess deficiency symptoms and make fertilizer recommendations (Filho, 2004). Because of the dynamic nature of the leaf tissue composition, strongly influenced by leaf age, maturation stage, and the interactions involving nutrient absorption and translocation, the tissue diagnosis may be a practice of difficult understanding and utilization (Walworth and Sumner, 1987). The Diagnosis and Recommendation Integrated System (DRIS) developed by Beaufils (1973), expresses the result of foliar analysis through indices, which represent in a continuous numeric scale, the effect of each nutrient in the nutritional balance of plant. DRIS is advantageous as it presents continuous scale and easy interpretation; allows nutrient classification (from the most deficient up to the most excessive); can detect cases of yield limiting due to nutrient imbalance, even when none of the nutrient is below the critical level; and finally, allows to diagnose the plant nutritional balance through an imbalance index (Baldock and Schulte, 1996). Nutritional balance is an important factor in increasing the yield and improving the quality of horticultural products especially Citrus. Hence, the objective of this study was to determining the optimum level of the macro and micro nutrient elements and evaluating the nutritional status of Lisbon lemon and Perl tangerine in Dezful.
Materials and Methods: For this purpose, 30 Lisbon lemon and 30 Perl tangerine gardens were selected randomly from citrus gardens in Dezful. Leaf samples were collected from middle of terminal shoots of current year growth in the periphery of tree from in late September. Leaf samples were washed in detergent followed by tap water and distilled water. Leaves dried under shade and then dried in hot air oven at 70ºC for 48 hours. The dried leaves were grounded to fine powder by using mixer and stored in air tight butter paper bags for nutrient analysis. Kjeldahl method was followed to measure total nitrogen, and phosphorus was measured by vanado-molybdophosphoric yellow colour method using spectronic, while potassium was measured by flame photometric method. Other elements content was determined by atomic absorption system. The gardens were divided into two groups of low and high yielding. All forms expression and their variance into two groups and variance ratio the group of low to high yielding in tow type gardens were calculated. Then using DRIS calibration formula, DRIS index for nutrient elements with low yielding were determined and nutrient balance index (NBI) were calculated.
Results and Discussion: The results showed that the optimum level in Lisbon lemon leaves were 2.97, 0.11, 1.85, 3.88 and 0.17% for N, P, K, Ca, Mg and 200.5, 24.9, 23.9, 68.8, 32.9 mg.kg-1 for Fe, Zn, Mn, Cu and B, respectively. In addition, the optimum level in Perl tangerine leaves were 2.97, 0.09, 1.57, 3.44 and 0.34% for N, P, K, Ca, Mg and 167.2, 32.7, 26.1, 28.0, 48.4 mg.kg-1 for Fe, Zn,Mn, Cu and B, respectively.
Conclusion: In general, based on DRIS indices priority on the macro and micro nutrients as Fe > N > B > K >Mn> Ca > Mg = P > Cu > Zn for Lisbon lemon and B > Fe > K > Cu > N > Ca > Mg >Mn> Zn > P for Perl tangerine were determined. The NBI of all gardens with low yielding was more than zero, indicating an imbalance nutritional in low yielding gardens.
sara kalbali; Shoja Ghorbani-Dashtaki; Mahdi Naderi; Salman Mirzaee
Abstract
Introduction: Rock fragments on soil surfaces can also have several contrasting effects on the hydraulics of overland flow and soil erosion processes. Many investigators have found that a cover of rock fragments on a soil surface can decrease its erosion potential compared to bare soil surface (1, 12 ...
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Introduction: Rock fragments on soil surfaces can also have several contrasting effects on the hydraulics of overland flow and soil erosion processes. Many investigators have found that a cover of rock fragments on a soil surface can decrease its erosion potential compared to bare soil surface (1, 12 and 18). This has mainly been attributed to the protection of the soil surface by rock fragments against the beating action of rain. This leads to a decrease in the intensity of surface sealing, an increase in the infiltration rate, a decrease in the runoff volume and rate, and, hence, a decrease in sediment generation and production for soils covered by rock fragments. Parameters that have been reported to be important for explaining the degree of runoff or soil loss from soils containing rock fragments include the position and size (15), geometry (18), and percentage cover (11 and 12) of rock fragments and the structure of fine earth (16). Surface rock fragment cover is a more important factor for hydroulic properties of surface flows such as flow depth, flow velocity, Manning’s roughness coefficient (n parameter) and flow shear stress and geometrics properties of formed rill such as time, location, number, length, width and depth of rill. Surface rock fragment cover is directly affected soil erosion processes in dry area specially in areas that plant can not grow because of sever dryness and salinity. Also, Surface rock fragment prevent the contact of rain drops to aggregates, decreasing physical degradation by decreasing flow velocity. The objective of this study was to investigate the effect of different surface rock fragment cover on hydraulic properties of surface flows and geometrics properties of formed rill.
Materials and Methods: For this purpose, 36 field plots of 20 meter length and 0.5 meter width with 3% slope were established in research field of agricultural faculty, Shahrekord University. Before each erosion event, topsoil was tilled and smoothed with hand tools to remove soil irregularities and soil sealing, update aggregates which come from deeper soil. Then, for beginning the experiment, surface rock fragment cover is scattered randomly on plot surface. Experiment equipment such as collecting the runoff systems installed at the end of plots. In each experiment after setting the surface flow, surface runoff inter to soil surface and testing continued for 60 minutes after starting runoff. Flow velocity was measured using a dye-tracing technique (potassium permanganate) and depth, width and length of rill were measured using a ruler. Treatments were including four level rock fragment cover (0, 10, 20 and 30%) and three rate runoff (2.5, 5 and 7.5 L min-1) with three replications that experiments were done in a factorial with randomized complete block design. Surface runoff samples were oven-dried and weighed to determine sediment loads. Sediment concentration was determined as the ratio of dry sediment mass to runoff volume, while the erosion rate was calculated as the sediment yield per unit area per period of time.
Results and Discussion: The results of this study showed that surface rock fragment cover plays an important role in water distribution. Based on the results, the positive effects of rock fragment cover on Manning’s n and the negative effect on flow velocity. Increasing surface rock fragment cover increased hydroulic properties such as flow depth, Manning’s n and flow shear stress significantly (p
F. Asadzadeh; manoochehr gorji; A. Vaezi; S. Mirzaee
Abstract
Introduction: Field plots are widely used in studies related to the measurements of soil loss and modeling of erosion processes. Research efforts are needed to investigate factors affecting the data quality of plots. Spatial scale or size of plots is one of these factors which directly affects measuring ...
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Introduction: Field plots are widely used in studies related to the measurements of soil loss and modeling of erosion processes. Research efforts are needed to investigate factors affecting the data quality of plots. Spatial scale or size of plots is one of these factors which directly affects measuring runoff and soil loss by means of field plots. The effect of plot size on measured runoff or soil loss from natural plots is known as plot scale effect. On the other hand, variability of runoff and sediment yield from replicated filed plots is a main source of uncertainty in measurement of erosion from plots which should be considered in plot data interpretation processes. Therefore, there is a demand for knowledge of soil erosion processes occurring in plots of different sizes and of factors that determine natural variability, as a basis for obtaining soil loss data of good quality. This study was carried out to investigate the combined effects of these two factors by measurement of runoff and soil loss from replicated plots with different sizes.
Materials and Methods: In order to evaluate the variability of runoff and soil loss data seven plots, differing in width and length, were constructed in a uniform slope of 9% at three replicates at Koohin Research Station in Qazvin province. The plots were ploughed up to down slope in September 2011. Each plot was isolated using soil beds with a height of 30 cm, to direct generated surface runoff to the lower part of the plots. Runoff collecting systems composed of gutters, pipes and tankswere installed at the end of each plot. During the two-year study period of 2011-2012, plots were maintained in bare conditions and runoff and soil loss were measured for each single event. Precipitation amounts and characteristics were directly measured by an automatic recording tipping-bucket rain gauge located about 200 m from the experimental plots. The entire runoff volume including eroded sediment was measured on storm basis using the collection tanks. The collected runoff from each plot was then mixed thoroughly and a sample was taken for determining sediment concentration by weight. The per-storm soil loss was then obtained.
Results and Discussion: A wide range of rainfall characteristics were observed during the study period.The results indicated that the maximum amount of coefficients of variation (CVs) for runoff and soil loss from replicated plots were 60 and 80 percent, respectively, which were considerably higher than the variability of soil characteristics from these plots. CV of runoff and soil loss data among the replicates decreased as a power function of mean runoff (R2= 0.661, P
S. Mirzaee; S. Ghorbani Dashtaki; H. Khodaverdiloo
Abstract
Introduction: The infiltration process is one of the most important components of the hydrologic cycle. Quantifying the infiltration water into soil is of great importance in watershed management. Prediction of flooding, erosion and pollutant transport all depends on the rate of runoff which is directly ...
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Introduction: The infiltration process is one of the most important components of the hydrologic cycle. Quantifying the infiltration water into soil is of great importance in watershed management. Prediction of flooding, erosion and pollutant transport all depends on the rate of runoff which is directly affected by the rate of infiltration. Quantification of infiltration water into soil is also necessary to determine the availability of water for crop growth and to estimate the amount of additional water needed for irrigation. Thus, an accurate model is required to estimate infiltration of water into soil. The ability of physical and empirical models in simulation of soil processes is commonly measured through comparisons of simulated and observed values. For these reasons, a large variety of indices have been proposed and used over the years in comparison of infiltration water into soil models. Among the proposed indices, some are absolute criteria such as the widely used root mean square error (RMSE), while others are relative criteria (i.e. normalized) such as the Nash and Sutcliffe (1970) efficiency criterion (NSE). Selecting and using appropriate statistical criteria to evaluate and interpretation of the results for infiltration water into soil models is essential because each of the used criteria focus on specific types of errors. Also, descriptions of various goodness of fit indices or indicators including their advantages and shortcomings, and rigorous discussions on the suitability of each index are very important. The objective of this study is to compare the goodness of different statistical criteria to evaluate infiltration of water into soil models. Comparison techniques were considered to define the best models: coefficient of determination (R2), root mean square error (RMSE), efficiency criteria (NSEI) and modified forms (such as NSEjI, NSESQRTI, NSElnI and NSEiI). Comparatively little work has been carried out on the meaning and interpretation of efficiency criteria (NSEI) and its modified forms used to evaluate the models.
Materials and Methods: The collection data of 145 point-data of measured infiltration of water into soil were used. The infiltration data were obtained by the Double Rings method in different soils of Iran having a wide range of soil characteristics. The study areas were located in Zanjan, Fars, Ardebil, Bushehr and Isfahan provinces. The soils of these regions are classified as Mollisols, Aridisols, Inceptisols and Entisols soil taxonomy orders. The land use of the study area consisted of wheat, barley, pasture and fallow land.The parameters of the models (i.e. Philip (18), Green and Ampt (3), SCS (23), Kostiakov (6), Horton (5), and Kostiakov and Lewis (11) models) were determined, using the least square optimization method. All models were fitted to experimental infiltration data using an iterative nonlinear regression procedure, which finds the values of the fitting parameters that give the best fit between the model and the data. The fitting process was performed using the MatLab 7.7.0 (R2008b) Software Package. Then, the ability of infiltration of water into soil models with the mean of coefficient of determination (R2), root mean square error (RMSE), efficiency criteria(NSEI) and modified forms (such as NSEjI, NSESQRTI,NSElnI and NSEiI) were determined and goodness of criteria was compared for the selection of the best model.
Results and Discussion: The results showed the mean of RMSE for all soils cannot always be a suitable index for the evaluation of infiltration of water into soil models. A more valid comparison withNSEI, NSEjI, NSESQRTI, NSElnI indices indicated that these indices also cannot apparently distinguish among the infiltration models for the estimation of cumulative infiltration. These indices are sensitive to the large amount of data. The NSEiI index with giving more weight to infiltration data in shorter times was selected as the most appropriate index for comparing models. According to the NSEiI index, Kostiakov and Lewis, Kostiakov, SCS, Philip, Horton, and Green and Ampt models were the best models in approximately 72.42, 44.83, 26.9, 53.11, 11.73 and 1.0 percent of soils, respectively.
Conclusion: The results of this study indicated that the ability of modified forms of NSE indices in evaluation of infiltration of water into soil models depend on the influence of models from infiltration data values in different time series. This encourages us to be cautious on the application and interpretation of statistical criteria when evaluating the models.
Keywords: Error, Statistical criteria, Infiltration water into soil