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.
