Quantitative Recommendation of Phosphorus Fertilizer Based On The Correlation Between Phosphorus of Plant Organs and Soil Phosphorus In Washington Navel Oranges


 AimMany methods have been proposed to recommend plant nutrients, all of which are qualitative and based on the concentration of the element in the soil or leaves. But, in the "Integrated Plant and Soil System" (IPSS) method, there is a recommendation of fertilizer quantified and based on the correlation between concentration of elements in plant and soil organs. MethodsIn this study, 39 Washington Navel Oranges orchards were selected and in each orchard, three trees were chosen and sampled from roots, stems, leaves, fruit as well as the soil around the roots. Sampling was performed in late March and early May for two consecutive years and phosphorus was measured in the samples. After a correlation analysis between "soil properties and phosphorus of plant organs" with soil phosphorus, those factors that had a significant correlation with soil phosphorus were selected. And based on that, there was determined a regression model between them.ResultsAmong all the studied factors, fruit phosphorus had the highest correlation with soil phosphorus. Based on that, two regression equations were obtained by which the required phosphorus can be calculated.DiscussionsSince the physicochemical properties of each element are different from the other, so the leaf alone cannot be a good indicator to determine the nutritional status of all elements. Therefore, it is more logical that the fertilizer recommendation of each element is based on the correlation between the element in that organ of the plant that has the highest correlation with that element in the soil.


Introduction
Nowadays, without regard to the nutrient elements of the soil, increasing production of crops in any region of the developing world will not be possible. One of the ways to improve the status of soil nutrient elements is application of fertilizers. These compounds, in addition to increasing the production and improving the quality of agricultural products, should not cause pollution of the environment and the accumulation of contaminants in the plant organs (Lu et Li et al. 2020). Among the essential nutrients, P has the greatest impact on the development and progress of reproductive organs, and as long as the P de ciency is not corrected, many products do not respond to nitrogen uptake (Sanchez 2007 Many studies have shown that excessive fertilizer uses by farmers who tend to produce more often does not always contribute to increase yields, but excessive apply causes the waste of fertilizer and its negative effects on the environment (Ju et al. 2009). Contamination of groundwater and surface (Le et al. 2010), greenhouse gas emissions (Zheng et al. 2004), accumulation of nutrient elements (Chen et al. 2006) and nutrient leaching (Zhang et al. 2005). So increasing the use of fertilizers will cause more and more problems for the environment in the world in future. Therefore, it is necessary to nd a suitable fertilizer recommendation system that can not only improve the nutrient requirements for more production, but also helps maintain environmental sustainability (Xu et al. 2014). So far, various methods have been proposed to determine the status of nutrients in soil and plants, including morphological symptoms, soil test and plant analysis (Sajjadi 1992 Dow and Roberts 1982). Although DRIS and DOP methods are more common than other methods, they also have weaknesses. The DOP method is not widely used due to lack of reference numbers for most plants (Lucena 1997). This method requires the collection of a series of data such as climate, topography, soil test, plant species, etc. If the accuracy of each factor is reduced, will be affected on the norm of each element. Also, the amount of the element in the soil or plant is not quantitatively and expressed with terms such as positive and negative (Ciesielska et al. 2002;Garcia-Escudero et al. 2013). In the DRID system, unlike other methods, the interpretation of leaf analysis results does not depend on the physiological age and site of sampling and in this method, the leaf is considered to be the most important place for plant analysis (Beu ls 1973; Beverly et al. 1984;Sumner 1977). E ciency of the DRIS method is when all the nutrients in the plant are examined together, so the limitation of this method is when only one or two elements of the plant are to be examined. Another weakness is the complexity of the method, which results in errors in the interpretation of the results and the recommendation of fertilizer, as well as de ciency or excessive amount of the element in the soil or plant is not expressed quantitatively and instead of that are used terms such as low, high or su cient. As indicated the fertilizer recommendation is often based on the concentration of the element in the soil or plant, and there is less method in which the advice is based on the concentration of the element in the plant and soil (Vasileios et al. 2013). Also, in all available methods, the leaf is considered as the main organ of the plant to study the status of all nutrients, while the method of absorption, transfer, accumulation and role of each element in each organ is different from the other element in a plant. Therefore, it does not seem logical, leaves as an organ to determine the status of all the nutritional elements. Considering the above, and given that citrus fruits are evergreen and require more water and nutrients than deciduous plants, so to recommend fertilizer, there is necessary to use a quantitative method that not only provides plant nutrients but also does not pollute the environment. IPSS method can be a good system for fertilizer management and environmental protection. For these reasons the purpose of this research; 1) Determine the organ that the phosphorus content has the highest correlation with the phosphorus content of the soil. 2) Determine the model in which the fertilization recommendation is based on the relationship between the concentration of the element in the soil and the plant organs which is termed "integrated plant and soil system"(IPSS).

Study area
The study was conducted in Jahrom (N 35, 32; E 28, 29) from March 2019 to June 2020, which is one of the most important citrus cultivation areas in Iran. The climate is arid to the semi-arid, the annual rainfall does not exceed 250 mm, and the average annual air temperature uctuates around 21.24 centigrade. Agriculture, especially citrus and palm farming constitute the main economic activity of the local people.

Sampling and experimental analysis
In this research, 39 Washington Navel Orange (WNO) orchards were selected each from 8 to 10 hectares, and the average age of orchards in different areas of Jahrom was from 8 to 10 years. According to the previous years, yield and applied fertilizers were classi ed into three categories of low, medium and high yield gardens. Of these, 18 orchards were classi ed in a high yield group while more fertilizers were used (gardens in which 220-280 kg of ammonium sulfate and super triple phosphate, 250-300 kg potassium sulfate, and 40-70 kg Magnesium sulfate were consumed in hectare per year and yields of 60 to 70-ton ha -1 ). Meantime 10 orchards were used as the moderate group (in which 100-130 kg of ammonium sulfate and super triple phosphate, 20-40 kg of Magnesium sulfate were consumed in hectare per year and high yield of 30 to 40-ton ha -1 ), And 11 orchards as low yields and low fertilizer consumption (with less than 80-100 kg of ammonium sulfate and super triple phosphate per hectare and yield of 10-20 tons per hectare in a year). In two consecutive years (2019, 2020), sampling was performed twice a year, the rst sampling in late March and the second in the early May. Since 39 orchards were surveyed and three Washington Navel orange trees were chosen in each orchard and each tree was sampled four times in two consecutive years, thus totally 468 trees were sampled. It is very important that the two executive sample have time period correspondence (Estefan et al. 2013). Samples were taken from each side of the trees and from the roots, stems, old and young leaves, fruits and soil around the roots. Samples were packed in paper bags and transported to the laboratory (Carter 1993). Plant specimens were disinfected in 5% sodium hypochlorite, washed with distilled water and exposed to air to be dried (Campbell and Plank 1998;Jones 1998). Samples were Oven dried at 60 ° C, ground and kept in paper bags ( Table 1. First, a correlation matrix was performed between each soil characteristic (pH, EC, OM, CEC), and P of plant organs (roots, stems, young and old leaves, fruit) with soil P ( Table 2). Variables that had a signi cant correlation with soil P were selected and the correlation between those variables and soil phosphorus was investigated using multivariate regression analysis.

Results And Discussion
The   Where: Y 1 = soil available phosphorus, X 1 = phosphorous concentration in old leaves, X 2 = phosphorous concentration in fruits, X 3 = phosphorous concentration in young leaves, X 4 = phosphorous concentration in roots.
Considering that there is a signi cant correlation between P of plant organs, and there is a strong correlation between P of plant organs and also the highest correlation between P of fruit and P of other plant organs ( Table 2). The mentioned multivariate regression equation can be simpli ed. For this purpose, on the right side of the equation, instead of the average P of plant organs (X i ), the ratio between the average P of that organ and fruit P (X 1 ) is set (Table 5). Therefore, the multivariate equation becomes a bivariate equation, and instead of measuring P in all plant organs, only fruit P is measured. As a result, above-mentioned model was more simpli ed while keeping its precision and effectiveness.

Regression model in high yield orchard
In high-yield orchards (n = 216), as in other orchards, there was a signi cant correlation between soil P and P of plant organs, and the highest correlation was among fruit P with soil P and plant organs P. Therefore, a multivariate regression equation was written between soil P and plant organs P. Then, using the Enter method, a signi cant model was obtained (R 2 adj = 0.982, 216 = N, 0.001> P) ( Table 7). Which: Y 2 = soil available phosphorus, X 1 = phosphorous concentration in root, X 2 = phosphorous concentration in fruits, X 3 = phosphorous concentration in old leaves, X 4 = phosphorous concentration in young leaves.
Since P of plant organs had a signi cant correlation, and the highest correlation was between soil available P and fruit.
The mentioned multivariate regression equation can be simpli ed. For this purpose, according to the table 8, on the right side of the equation, instead of the average P of plant organs (X i ), the ratio between the average P of that organ and fruit P (X 2 ) is set. Therefore, the multivariate equation becomes a bivariate equation, and instead of measuring P in all plant organs, only fruit P is measured (Formula B): With arithmetic summation of X1 in the both side of the model: Y 2 = soil available phosphorus (mg Kg -1 ), X2= P concentration in the fruits (mg Kg -1 ) The regression model obtained from four sampling stages of all orchards as well as high-yield orchards showed that there is a signi cant correlation between soil phosphorus and phosphorus of Washington Navel Orange organs. As mentioned, the result of this research is two formulas. Formula A, which shows the correlation between fruit P (X 1 ) and soil P (Y 1 ) in all orchards. The second formula (B), shows the correlation between fruit P (X 2 ) and soil phosphorus in high-yield orchards (Y 2 ). If in Formula (B) instead of X 2 , the average P concentration of the fruit is replaced from Table 8, the amount of soil P (Y 2 ) will be obtained in high-yield orchards. Which is the norm of soil P in this region for orange plants. Also in which orchard probably has P de ciency, in formula (A) instead of X 1 is put the fruit P of that orchard, and the soil P of this orchard (Y 1 ) will be calculated. Therefore, by Subtracting Y 1 from Y 2 , the required P is calculated quantitatively. The following example shows the application of this method and its formulas in fertilizer recommendation: Suppose the concentration of fruit P in one of the citrus orchards of Jahrom is 975 mg kg -1 dry matter, if the distance from one tree to another is 4 meters, the average root depth is 80 -120 cm, the average root expansion radius is 1.2 meters. To supply P, use triple superphosphate fertilizer (30% P) and the e ciency of fertilizer is 78%. To reach the desired level of P, how much of this fertilizer is required in a ten-hectare orchard?
At First, put the amount of fruit phosphorus in formula (A) to obtain the concentration of soil available P (21.068 mg kg -1 ). On the other hand, the average fruit P in high yield orchards is 1273.899 mg kg -1 (table 8), by putting this amount in Formula B, the norm concentration of P in the soils of this region for oranges will be 38.661 mg kg -1 . Therefore, if subtract the amount of soil P from the norm concentration of the area, the amount of P required for that orchard will be 17.593 mg kg -1 soil. As calculated, the amount of P required for each kilogram of soil is 17.593 mg. It is also assumed that superphosphate fertilizer has 30% P and its e ciency in soil is 78%, therefore, P fertilizer require per each hectare is: (IPSS). In this system contrary to the methods mentioned, the recommended fertilizer is based on the correlation between each divisive element in the soil and plant organs. This method (IPSS) is quantitative and fertilizer required for each element is precisely measurable. On the other hand, in most fertilizer recommendation methods, the leaf is an indicator for determining the nutritional status of all the elements and the basis for their fertilizer recommendation.
While, the chemical properties and the role of each element in each of the plant organs is different from other elements.
Therefore, it is more logical that fertilizer recommend is based on the relationship between the concentration of the element in the soil and that plant organ, which has the highest correlation with the concentration of that element in the soil, not merely based on the concentration of phosphorus (element) in the soil or plant leaves. The method used in this study is called Integrated Plant and Soil System (IPSS). In this system contrary to the methods mentioned, fertilizer recommendation is based on the correlation between the element in the soil and plant organs. This method (IPSS) is quantitative and fertilizer required for each element is precisely measurable. On the other hand, in most fertilizer recommendation methods, the leaf is an indicator for determining the nutritional status of all the elements and the basis Another important point to note is that the behavior of elements such as P in the soil is very complex and therefore there are different methods with various extractors to measure P in the soil. But the amount of phosphorus measured in a soil is not the same in any of them (Bray and Kurtz 1945;Lindsay 1979;Mehlich 1984;Soltanpour and Schwab 1977). In IPSS, the amount of soil P is accurately measured only once to determine the Norm concentration of phosphorus in pilot studies. By measuring plant P and correlation between soil P and plant phosphorus can determine the amount of P fertilizer required. Which is another advantage over conventional methods.