Ali Ajili Lahiji
Abstract
Introduction: The study of the nutritional status of orchards is one of the primary priorities for the nutrition of crops and orchards, which is done in different methods. One of these methods is the deviation from the optimum percentage (DOP). Hazelnut is one of the species of the family (Betulaceae) ...
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Introduction: The study of the nutritional status of orchards is one of the primary priorities for the nutrition of crops and orchards, which is done in different methods. One of these methods is the deviation from the optimum percentage (DOP). Hazelnut is one of the species of the family (Betulaceae) which is the fifth most produced nut in the world after cashews, almonds, walnuts, and oaks. Turkey is the world's largest producer with about 70.3% of the total world production of hazelnuts and Italy with 11.9%, the United States with 4.5%, and Azerbaijan with 4.2%, Georgia with 3.8%, Spain with 2.5% of world production, respectively. Other hazelnut producing countries account for 2.8% of the world production and the world hazelnut production in 2018 was about 888,000 tons. In 2018, Iran was the eighth largest producer in the world with a production of 16,000 tons. Guilan province has 18,000 hectares of hazelnut orchards, which constitute 85% of the cultivated area of hazelnut orchards in the country. Since the leaf is the most important part of plant metabolism and the concentration of nutrients in the leaf at certain stages of plant growth and development has a great relationship with yield. Therefore, leaf analysis and interpretation of the results, provided that according to standard methods, can provide good information about the nutritional status of the plant and be used to recommend the appropriate fertilizer. Materials and Methods: The optimal Deviation (DOP) method was used to investigate and identify nutritional deficiency and determine the optimal concentration of nutrients. For this purpose, the number of nutrients such as nitrogen, phosphorus, potassium, manganese, copper, iron, and zinc were determined in 32 hazelnut orchards with growth of the following year branches in the three major hazelnut production cities (Rudsar, Siahkal, and Amlash), in July. To implement this project, 32 orchards over 10 years old were sampled from the dominant cultivars of the region (Gerd cultivar), so that they are different in terms of management and yield. Seventeen orchards in Eshkavrat region of Rudsar, seven orchards in Hazelnut areas of Siahkal, and eight orchards in the Eshkvarat region of Amlash city were selected for sampling. The orchards that had the best yield due to the great management were randomly selected to determine the standard concentration (Cref) and other low-yield and medium-sized orchards were randomly selected to determine the nutritional status. The orchards were divided into low and high-yield groups in August. When the concentration of nutrients in the leaves was relatively constant, about 50 healthy young leaves were sampling in different directions and 10 trees in each orchard. Pest-free samples were transferred to the laboratory and leaf samples were first washed in water and then washed with distilled water. The samples were placed into the oven at 65 °C for drying. The dried samples were completely powdered and passed through a sieve with half a millimeter holes. In leaf analysis, nitrogen nutrients was measured in a more digestible manner by Kjeltec device, phosphorus by spectrophotometry, potassium by flame photometric, manganese, copper, iron, and zinc by dry ash method and atomic absorption spectrometry. Results and Discussion: The results showed that the average concentrations of N, P, Fe, Mn, Zn, Cu in high-yield orchards were higher than the concentration of nutrients in low-yield orchards. The deviation index was calculated from the optimal percentage and the priority of the nutritional needs of hazelnut trees in each garden was determined. Indicators are positive, negative or zero numbers, zero indicates the optimal statue of concentration, a positive value indicates excess nutrient and a negative number indicates nutrient deficiency. According to the indexes of deviation from the optimal percentage, among the elements manganese, nitrogen and iron had the highest negative index, respectively, so that manganese had negative indexes in 78% of orchards and nitrogen had negative indexes in 65% of orchards, and then Iron had negative indexes in 60% of the orchards and phosphorus in 56% of the orchards, zinc in 53% of the orchards and potassium in 50% of the orchards and finally copper in 46% of the orchards had negative indexes, respectively. Conclusion: Optimal concentrations were determined for nitrogen, phosphorus, potassium, iron, manganese, zinc, copper, 3.08%, 0.16%, 0.80%, 570.38 ppm, 175.26 ppm, 42.93 ppm, ppm 17.09 in the leaves. Based on the calculations of the DOP method, the following results were obtained for the priority of feeding the orchards. Mn>N>Fe>P>Zn> K>Cu
Ali Ajili Lahiji; Ali Mohammadi Torkashvand; Abdolmohammad Mehnatkesh; Mir Naser Navidi
Abstract
Introduction In order to land classification for agricultural and natural resources, the most important criterion and factor is the production or yield of the lands. The best way to evaluate a method is to interact between the yield and potential of the product with the land specification involved in ...
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Introduction In order to land classification for agricultural and natural resources, the most important criterion and factor is the production or yield of the lands. The best way to evaluate a method is to interact between the yield and potential of the product with the land specification involved in the production. One of the new methods in the land evaluation for different uses is the modeling or simulation of the intended use. Artificial neural networks are one of the new tools that are used today to simulate yield and determine the effective factors in the production of agricultural crops. New land evaluation methods include different modeling techniques, which these new methods, including simulations, are used in land valuation methods to test the ability of models for determining the relationship mentioned. An artificial neural network is one of the techniques that can do nonlinear analysis. It is important to recognize the most important input parameters to the predictive models of olive yield, which are also considered to be effective variables in production. Through the process of sensitivity analysis, valuable information about the sensitivity of the model to its input variables can be provided to the designer and model architect. In north of Iran, in the southern areas of Gilan Province, the most important olive gardens of the country are located in the southern part of the country. Different soil and water, topographical and climatic factors affect the yield of olive orchards.
Materials and Methods Climatic data, soil and water characteristics, topographic characteristics and leaf nutrition elements as input variables and olive yield were used as output models. Twenty-eight factors were selected as the most important factors or variables affecting the yield of olive orchards. These input variables included soil properties: EC, (TNV), organic carbon percentage, available phosphorus, available potassium, clay percentage and silt percentage; irrigation water characteristics including: EC and content of irrigation water; topographic characteristics including: altitude and the slope; the concentration of nutrients in leaves included: nitrogen, phosphorus, potassium, iron and zinc; climatic factors including: sunny hours, evaporation rate, average temperature and olive yield were considered as output of artificial neural network model.
Results and Discussion Using the MATLAB software by artificial neural network, the best structure of this network was obtained for the component of the yield of olive. The trained structure had 28 input nodes in 5 groups and one output node. The number of hidden nodes, 38 nodes and the most appropriate number of repetitive learning based on the test and error, 20 were determined for olive product yield. After determining the best structure of the neural network with a R-test of 80%, using Hill's sensitivity analysis, the model's response to each of the input variables was studied and the most important factors influencing the yield of olive oil were obtained. Based on the results of sensitivity analysis, the most important parameters affecting the yield of olive are content of leaf nitrogen, soil phosphorus, winter evaporation, summer evaporation, average autumn temperature, summer sunshine, leaf potassium, salinity, salinity, and slope, respectively. In general, this study showed that the amount of nitrogen and phosphorous is the most important factor for the production of olives, and then variables that are related to the amount of water in the soil, (summer evaporation, average autumn temperature, summer sunshine), were identified as important. The concentration of potassium, iron and zinc in the leaf as a nutrient element and water and soil salinity from stressors for plants and slope are important topographical factors that affected the amount of soil depth and soil water content as the most important factors in olive crop production.
Conclusion In brief, the role of micro and macro nutrients and the factors affecting the maintenance of water in the soil and providing moisture for the plant. In addition to nutrients, gardeners should consider soil and water salinity, which is a stressor for plants, as well as slope, which is important topography factor that effective on soil depth and water available and nutrients for plants as the most important factors in olive production.
