Background and ObjectivesNUTRIENT limitations often reduce crop yield in farmers' fields. Identifying the status of rice plant NUTRIENTs in the soil and plant is necessary for plant nutrition management. In rice plant, it is common to use soil chemical properties test to understand the availability of soil NUTRIENTs. Although soil tests based on critical level may indicate the adequacy of NUTRIENTs, sometimes the rice plant in the field exhibit signs of its deficiency. Flooding conditions in rice cultivation leads to chemical and electrochemical changes in some NUTRIENTs, which result in inadequate NUTRIENT uptake by the plant. Solely relying on soil testing in such conditions increase errors in results interpretation and fertilizer recommendations. Therefore, measuring the NUTRIENTs characteristics of both plant and soil can be effective in fertilizer recommendations. One method for interpreting leaf analysis results is the use of COMPOSITIONAL NUTRIENT DIAGNOSIS ((CND)) method. The aim of this study was to establish reference norms, the range of optimal concentration and NUTRIENTs limitation in rice plant using the (CND) method during 2018 growing season in 60 paddy fields of Guilan province, Iran. MethodologySoil and leaf composite samples were collected from farmers' fields in a standard manner and analyzed using appropriate laboratory methods. At harvest time, the yield of each field was determined. NUTRIENT indices were assessed by the COMPOSITIONAL NUTRIENT DIAGNOSIS ((CND)) method. By employing the cumulative function model of the variance ratio of NUTRIENTs and solving third-degree cumulative function equations related to 10 NUTRIENTs along with their residual concentrations, yields corresponding to each of them were calculated in terms of tons per hectare. To categorize the yield community into favorable and unfavorable groups, initially, the yield-NUTRIENT function was plotted and the yield groups were separated by identifying the inflection points of the curve. To validate the COMPOSITIONAL NUTRIENT DIAGNOSIS method, the relationship between the nutrition balance index (r2) and crop yield was evaluated through a scatter plot. Descriptive statistics were conducted using SPSS version 24 and (CND) calculations were performed using Excel software. ResultsBased on the average of available phosphorus concentration and its critical level 47% of the fields exhibited phosphorus deficiency. According to the critical level of available potassium, except for one farm, the available potassium content exceeded the critical level in all fields. Due to the fact that the minimum amount of microNUTRIENTs (zinc, iron, copper, and manganese) was more than their critical level in paddy fields, indicating no deficiency in the studied soils. Available magnesium concentration ranged from 0.8 to 4.6 with the median of 2.4 cmol+/kg. Nearly 75% of the studied soils were deficient in magnesium, based on the critical level of available magnesium (3 cmol+/kg).The average phosphorus concentration in leaves was 0.23% and based on the critical level, there was phosphorus deficiency in two fields. The average potassium concentration in the sample of leaves collected from the fields was 1.57% and based on the critical level, potassium deficiency was observed in rice plant tissue in 55% of the fields. The average magnesium concentration in rice leaf samples was 0.17% and six farms showed deficiency based on the magnesium critical level. The average zinc concentration was 13.69 in rice leaf samples and based on zinc critical level, 58 farms (96%) showed zinc deficiency. Based on the calculated average yields, the target yield was determined as 4133 kg/hectare, and according to the target yield, 43.3% of selected fields were classified as high-yielding and 56.6% were classified as low-yielding group. The (CND) method correctly recognized the balance of NUTRIENTs in rice plant with 55% accuracy. The relationship between yield and NUTRIENTs balance index showed that 31% of fields had nutritional balance, 24% had nutritional unbalanced, 22.4% had yield reduction other than nutritional factors such as climate, root depth limitations, pests, and diseases. ConclusionThe findings showed that: 1- Using only the critical level of soil and plant NUTRIENTs for assessing the nutritional status of soil and plants was not efficient in the studied paddy fields. 2- The (CND) method, due to its consideration of the interactive effects of NUTRIENTs, had greater capability than the critical level method in diagnosing nutritional disorders, NUTRIENT deficiencies, and fertilizer recommendations for the studied paddy fields. However, it was not the most accurate, sensitive and unique because factors other than soil fertility and rice plant nutrition, such as climatic conditions and irrigation, and field management, also influence the growth and development of rice plant and its yield. 3- The most important macroNUTRIENTs and microNUTRIENTs needed in studied paddy soils based on the (CND) method were magnesium and phosphorus (macroNUTRIENTs) and copper and zinc (microNUTRIENTs). 4- The (CND) method can be as an effective tool for interpreting soil and plant analysis in plant nutrition management at paddy farming systems.​​Data Availability StatementData is available on reasonable request from the authors. AcknowledgementsThe authors are thankful to the AREEO for financial supports. Conflict of interestThe authors declare no conflict of interest. Ethical considerations The authors avoided data fabrication, falsification, plagiarism, and misconduct.

COMPOSITIONAL NUTRIENT DIAGNOSIS ((CND)) methods are important to interpret the results of chemical analysis and DIAGNOSIS of nutritional status of plants. In order to evaluate the nutritional status of squash (Lagenaria Vulgaris) through (CND) and to determine NUTRIENT norms for this crop, 122 leaf samples were collected from squash fields of Khoy region and N, P, K, Ca, Mg, Fe, Mn, Zn, Cu and B elements in the samples were analyzed. The squash fields were divided into two high and low yielding groups, based on mathematical and statistical methods and by additive function of variance ratio and then the (CND) indices and norms were calculated. Results of the research showed that there were significant differences between high and low yielding fields in terms of NUTRIENTs’ concentration. Based on (CND) indices, potassium and zinc NUTRIENTs had the most negative indices. (CND) nutritional balance index of low yielding fields were more than zero indicating nutritional imbalances in these fields.

Introduction Quince with the scientific name "Cydonia oblonga Mill." is one of the most important horticultural products in the word including Iran. According to the average production from 1994 to 2020, Iran was the fourth largest quince producer in the world. Isfahan province is one of the most important centers of high quality quince production with 2432 hectares of cultivated area and annual production of 25986 tons. Most of the quince orchards are located in the cities of Natanz and Isfahan. Plant nutrition as an important factor in growth, is a function of NUTRIENTs and environmental conditions interactions. Assessing the nutritional status of plants is based on precise determination of NUTRIENTs and appropriate application method to DIAGNOSIS and interpret the results. Various methods have been used to evaluate the nutritional status of the plant, such as the Critical Value Approach (CVA), the Deviation from Optimum Percentage (DOP), the DIAGNOSIS and Recommendation Integrated System (DRIS) and the COMPOSITIONAL NUTRIENT DIAGNOSIS ((CND)). The (CND) method expresses interactions by considering the ratio of one element to the geometric mean of all elements. Then high and low functional groups are separated, by using mathematical and statistical methods and application of cumulative function of the variance ratio of NUTRIENTs and the chi-square distribution function. Finally, (CND) NUTRIENTs norms and indices such balance index are calculated step by step. Therefore, considering the importance of the quince production in the country and the lack of sufficient knowledge to determine its nutritional status, the present study was conducted with the aim of investigating the nutritional status of quince trees using the (CND) method and determining the NUTRIENTs norms for this product. Materials and Methods  In order to evaluate the nutritional status of quince trees using the (CND) method, 28 orchards were selected in the cities of Isfahan and Natanz. The orchards were selected such a way that they had different ranges of yield. The geographical location was recorded for each orchard. Then random and composite sampling of leaves was done from branches without fruit in July 2018. Concentration of nitrogen phosphorous, potassium, calcium, magnesium, iron, manganese, zinc, copper and boron was measured in quince leaves. At the end of season, the yield was determined for each orchard. The orchards divided into two groups based on high and low yields. The (CND) norms, (CND) nutritional index and nutritional balance index (r2) were computed based on steps of Parent and Dafir. The balance index of nutritional elements (r2) was calculated by Keith-Nilson method based on the Chi-square statistical distribution function (K2) in Excel software.   Results and Discussion According to results of cumulative distribution function of NUTRIENT variance and considering the yield of 23 tons per hectare as the intermediate yield, 25% of the studied orchards were in the high yield group and 75% of the orchards were in the low yield group. After solving the third– rank cumulative function equations of the studied NUTRIENTs, the highest yield was obtained for potassium Fci (VK) = 21.98 and the lowest value was for nitrogen Fci (VN) = 15.37. (CND) standard norms of NUTRIENTs and residual value were described as: V*N= 2.91, V*P= 1.39, V*K= 2.91, V*Ca= 2.13, V*Mg= 1.35, V*Fe= -2.01, V*Mn= -3.12, V*Zn= -3.97, V*Cu= -4.85, V*B= -3.51 and V*Rd= 6.78. The (CND) NUTRIENT index revealed that potassium and nitrogen had the most negative index among macroNUTRIENTs in the low-yield orchard group. The low amount of soil organic matter and the high presence of sand can contribute to the negative nitrogen index. Among the microNUTRIENTs, the iron index was negative in 67.7% of the low-yield orchards. Zinc and copper had the next highest nutritional requirements in most orchards. The presence of calcareous conditions in the soil of the studied orchards may be one of the reasons for this observation. The estimation of the nutritional balance index indicated that the r2 value in orchards with low yield was 60.3% higher than that in high-yield orchards. Conclusion  (CND) nutritional balance index (r2), specially in orchards with low yield was more than zero (20.85), indicating nutritional imbalance in these orchards. Proper management and balanced application of chemical fertilizers should be considered. This can increase the yield and quality of quince production.

Tissue analysis is a useful tool for evaluation and optimizing NUTRIENTs for sugar beet using Deviation from Optimum Percentage (DOP) and COMPOSITIONAL NUTRIENT DIAGNOSIS ((CND)) methods. In order to evaluate the nutritional status of sugar beet farms in Kermanshah Province, this project was conducted in two cropping seasons (2018-21). In each year, 30 different farms with different ranges of soil properties were selected in each region. The project database for the first and second seasons was completed and analyzed and DOP and (CND) indices were calculated for each NUTRIENT element. Compared with the critical NUTRIENT concentration in Kermanshah Province, the results showed that 85%, 10%, 97. 5%, 67%, 93%, 100%, and 5% of farms were deficient in phosphorus, potassium, iron, manganese, zinc, boron and copper, respectively. The average sugar beet yield in this region was about 67. 6 t/ha. Selected sugar beet farms were divided into two groups of high and low yields. The results of (CND) and DOP indices in the Kermanshah province showed that Mg, P, N and K deficiencies were more prevalent among macro-elements and Fe, Mn and Zn deficiencies were observed among micro-elements. However, there was a difference between the two methods in terms of the content of N, Mn, and Fe among the deficient elements. There was a significant correlation (0. 48) between nutritional balance index and yield at 1% probability level. The results obtained in this study can be used to increase the yield and improve the quality of sugar beet in each of the regions.

Tissue NUTRIENT status of high yielding plant populations can be a reference norm for evaluation of growth and achievement of optimal yield. COMPOSITIONAL reference norms derived from generic models are important. Yield data of 382 sugar beet fields was analyzed and, using mathematical approach, cumulative variance ratio function[Fic()], and chi-squared distribution function, the low (n1= 259) and high (n2= 123) yielding subpopulations were separated from each other. Cutoff yield was obtained at 53.980 t.ha-1. Critical chi-squared value was found to be 7.3 and was confirmed by using data of leaf samples from 38 fields and Cate-Nelson statistical procedure. Preliminary (CND) norms of different elements were found as follows: V*N=3.442±0.145, V*P=0.765±0.139,V*K =3.197±0.187, V*Zn=-3.691±0.306, V*Fe=-3.317±0.321, V*Cu=-4.377±0.233, V*B =-3.487±0.293, V*S=1.359±0.283, V*Rd=6.589±0.076. Later, NUTRIENT sufficiency range and critical intervals norms and NUTRIENT indexes for (CND) were determined using analysis results of 38 sugar beet tissue samples. Satisfactory results were obtained when data of many fertilizer trials were investigated to arrive at preliminary validation of norms and indexes. These norms, indexes, NUTRIENT sufficiency ranges, and critical intervals can be used for prediction of growth, yield, and nutrition disorders of sugar beet.

In order to study the nutritional status of orange orchards, Thompson navel sweet orange in north of Iran (east of Mazandaran province) through (CND) method, 133 orchards were studied in summer (August). After selecting and as signing 30 trees in each orchard, plant samples were taken from the trees in a proper time to determine concentration of the elements and their average yield was measured in the harvest season. By using the cumulative distribution function (CDF) of NUTRIENTs variance ratio model in this work, 27 orchards were considered as the high yielding group and 106 orchards were considered as the low yielding group. By solving the third-degree cumulative function equations related to the elements, the related yields were calculated and the cut-off yield was determined 58 ton ha-1. The norms were calculated for 10 elements from average concentrations of high yield groups: N 2. 32± 0. 24%; P 0. 17± 0. 01%; K 1. 74± 0. 24%; Ca 3. 38± 0. 94%; Mg 0. 41± 0. 08%; Mn 40± 18 mg kg-1; Zn 45. 8± 25 mg kg-1; Fe 199. 4± 78 mg kg-1; Cu 14± 6. 7 mg kg-1; B 80± 36 mg kg-1. Measured norms showed that K, P and Mn showed more deficiency in the orchards, respectively.

To study the imbalanced fertilization and to optimize fertilizer use in canola farms in the north of Khuzestan Province, Iran, we used COMPOSITIONAL NUTRIENT DIAGNOSIS ((CND)) and Deviation from Optimum Percentage (DOP) methods. This research covered 30 canola (Brassica Napus) fields during one cropping season (2019) and soil, leaf, and water samples were collected and analyzed, and grain yield was also measured. Nutritional elements including N, P, K, Fe, Cu, Zn and leaf Mn were determined by standard laboratory methods. Yield community were divided into low and high yield groups using the cumulative function of NUTRIENT variance ratio and K-square distribution function considering the intermediate yield of 2882 kg. ha-1. Then, according to the (CND) system method, the standard norms for N, P, K, Fe, Cu, Zn, Mn and residual elements (Rd) were determined as 3. 27± 0. 14, 0. 83± 0. 17, 2. 73± 0. 2,-1. 89± 0. 13,-4. 23± 0. 53,-3. 45± 0. 3,-3. 46± 0. 26, and 3. 27± 0. 14, respectively. The results of (CND) system method showed that the most limiting factors in yield were K (among macro-elements) and Mn (among micro-elements). In DOP method, the optimum levels of N, P and K were 5. 27%, 0. 44%, 2. 72%, and for Fe, Cu, Zn, and Mn were 313, 37, 77, and 67 mg. kg-1 dry weight, respectively. In this method, the order of NUTRIENT deficiency was revealed as Cu> Zn> Mn> N> Fe> P.

the quantity and quality of crops yields, and to protect the environment from the negative effects of excess fertilizers. To achieve these goals, the analysis of these data should be based on a systematic and comprehensive approach, especially in terms of NUTRIENTs’ interactions and separation of NUTRIENTs synergistic and antagonistic effects. DRIS and (CND)clr can diagnose D number of components, while D-1 could be diagnosed in the D-COMPOSITIONAL “ Hilbert space” across (CND)ilr. The objective of this paper was to compare “ Aitchison” and “ Mahalanobis” distances (as a predictor) across ilr coordinates as measures of NUTRIENT imbalance, as well as determination of macro and micro reference norms for sugar beet using (CND)-ilr and “ Pan Balance ” technique for diagnosing NUTRIENTs status. We collected 170 root yield and foliar samples in fall sugar beet fields of Khuzestan province in Iran and analyzed seven NUTRIENTs in leaf (N, P, K, Fe, Mn, Zn, and Cu). Then, NUTRIENTs were arranged into ten balances: ilr1: [Fe|Cu, Zn, Mn], ilr2: [Mn|Zn, Mn], ilr3: [Zn|Cu], ilr4: [P | N], ilr5: [NP | K], ilr6: [Fe | Mn], ilr7: [Zn | CU], ilr8: [Fe, Mn| Zn, Cu], ilr9: [N, P, K|Mn, Zn, Fe, Cu], and ilr10: [FV|N, P, K, Mn, Zn, Fe, Cu], which were computed as isometric log ratios (ilr). Total population of observations’ classification performed by a customized <> procedure (ROC technique) showed that a critical “ Mahalanobis” distance of 4. 2 separated balanced (low yield) from imbalanced (high yield) specimens about yield cut-off of 60. 32 t/ha with test performance of 85%, as measured by the area under the ROC curve for ilr4 to ilr10. Comparing the “ Mahalanobis” distance with the “ Aitchison” distance showed that they were similar. By using Pan balance technique, comparing total NUTRIENT balance between reference (TN) and none reference (TP) group of total fields by Tukey’ s test showed seven significant differences (P ≤ 0. 05), except ilr7. Results showed that in order to increase sugar beet root yield in the study area, it was not necessary to use iron fertilizers and N-fertilization should be reduced, while potassium fertilizer should be increased.

Introduction Grape is one of the most important horticultural products in the world and Iran which has been noticed due to its cultivation area, high economic and nutritional values. Annually, about 68 million tons of grape are produced in the world. Iran, with 309, 000 ha cultivation area and about 3. 3 million tons share of production, is the 11th largest producer of this fruit in the world. Recent studies have shown that plant nutrition and soil fertility have significant effect in the reduced yield quality in the grape fields of our country. Plant nutrition as an influential factor is a function of the interaction of NUTRIENTs and environmental conditions. Assessing the nutritional status of plants is necessary to achieve the relationship between NUTRIENTs availability in the soil, the amount of elements in the plant and yield. Plant analysis method is used to optimize fertilizer application and diagnose plant nutrition disorders. The plant analysis method is useful for evaluating plant nutrition if an appropriate method to be used to diagnose and interpret the results. Tissue NUTRIENT status can be diagnosed by the Critical Value Approach (CVA), the DIAGNOSIS and Recommendation Integrated System (DRIS), and COMPOSITIONAL NUTRIENT DIAGNOSIS ((CND)). Only DRIS and (CND) provide NUTRIENT imbalance indexes, although no threshold value has been validated yet for diagnostic purposes. (CND) method expresses interactions by considering the ratio of one element to all elements. In this method, high and low functional groups are separated with great accuracy with the help of mathematical and statistical methods and the application of the cumulative function of variance ratio of NUTRIENTs and chi-square distribution function. A critical (CND) imbalance index was derived from the chi-square distribution function. Due to the importance of grape production in the country and the lack of required nutritional norms, this study was conducted to investigate the nutritional status of grape fields using the (CND) method. Materials and Methods In order to evaluate the nutritional status of grape fields in the Hamedan province, this study was conducted in the cropping years of 2017-2020. Every year, 40 different orchards were selected in each of the regions. The orchards were selected in such a way that they had different ranges of yield and soil properties. A database containing laboratory and field data was created for each grape field. The geographical location was recorded for the orchards. In each orchard, plant (leaf) samples were prepared and analyzed based on suitable laboratory methods. At the end of the season, the yield and its components were determined by visiting each orchard. Concentrations of nitrogen, phosphorus, potassium, calcium, magnesium, iron, zinc, manganese, and copper were measured in grape leaves. The project database was completed and (CND) indices were calculated for each NUTRIENT element. The selected grape fields were divided into two groups with high and low yield based on yield. The (CND) norms and indexes were computed according to computation steps of Parent and Dafir. The Cate–, Nelson ANOVA procedure was used to partition yield data between two groups by maximizing the between-groups sums of squares to determine the threshold values for (CND) indexes required to compute the critical (CND) r2 value. We used 83 observations for developing the NUTRIENT norms. Results and Discussion The results of the indices calculated by the method of (CND) showed that the grape fields were deficient in nitrogen and potassium among the macroNUTRIENTs and iron and manganese among the microNUTRIENT elements. There was a correlation (0. 25) between nutritional balance index and yield that was significant at 1 percent probability level. Potassium index was negative in 83% of low yield orchards. After potassium, nitrogen had a negative index in 58% of medium and low yield orchards. Phosphorus had the most positive index among macroNUTRIENTs and was positive in most orchards. Among the microNUTRIENTs, manganese, iron, and zinc indices were negative in 59%, 49% and 73% of the orchards, respectively. The presence of calcareous conditions in the soils of the region can be the reason for this deficiency. The boron index was positive in some orchards and negative in some other orchards. Furthermore, in total, the index of unknown factors was negative in 41% of grape fields in Hamadan province. Conclusion The results indicated that management of evaluated orchards was not suitable and application of chemical fertilizers was unbalanced. The results of this study can be used in grape fields to increase yield and product quality. Therefore, it is recommended to use deficient elements in the fertilization program to improve yield.

Background and Objectives: Apples are the world's largest commercial fruit orchard. According to the World Food Agriculture Organization in 2017, Iran is one of the top three apple producers in the world. Nutrition is one of the important factors that affect the quality and quantity of fruit. This study was conducted to evaluate and compare different fertilizer management on the nutritional status of apple trees in 1397-1398 at Razi University of Kermanshah (34° 19′ N and 47° and 7′ E). Materials and Methods: This research was conducted to a complete randomized block design in a four-year-old high-density apple orchard with Gala cultivar based on M9. Different managements included mineral, organic, and integrated systems. In the chemical management, 65 g urea, 38 g triple superphosphate, and 60 g KCl per tree plus the foliar application of ferrous, zinc, and calcium were used. Inorganic management 5 kg compost, 0. 5 kg farmyard manure (cow), and 5 kg biochar per tree were used. The integrated management included chemical fertilizers + biochar, chemical fertilizers +compost, chemical fertilizers + farmyard manure, and compost + biochar. The balance of NUTRIENTs in apple leaf NUTRIENTs was investigated using the COMPOSITIONAL NUTRIENT DIAGNOSIS ((CND)) method. Results: The coefficient of determination (R2) of the third-order equation model for all elements was in the range of 0. 79-0. 89. The cutoff points of the curves (-b/3a) for nitrogen 7. 98, phosphorus 7. 64, potassium 7. 12, calcium 7. 82, magnesium 8. 00, ferrous 7. 87, zinc 7. 98, copper 7. 79, manganese 7. 41 and residual value (R9) 8. 11 kg were obtained. In general, the average fruit yield for different elements are so closed and ranged from 7. 12 to 8. 11 (on average of 7. 77± 0. 31) related to potassium and R9, respectively. According to the Cate-Nelson method, fruit yield equals 8. 75 kg was chosen as critical fruit yield to grouping the groups into high-and low-fruit yield subgroups. The control, farmyard manure, and compost treatments belonged to a low-fruit yield subgroup. While integrated treatments as well as chemical fertilizers and biochar treatments grouped in the high-fruit yield subgroup. The results indicated that in the high-fruit yield subgroup the average of COMPOSITIONAL NUTRIENT DIAGNOSIS norms (V*(X)) were positive for nitrogen, potassium, calcium, and magnesium besides negative for phosphorus, ferrous, copper, and manganese. The balance index (r2) indicated the imbalances of NUTRIENTs under different fertilizer managements. After the application of different fertilizers, some indices were changed from the most negative amounts to the most positive amounts. These changes were found for the ferrous index in chemical management, calcium index in organic management, and nitrogen and phosphorus indices in integrated management (in comparison with the application of only chemical fertilizers). In integrated management calcium, magnesium, and ferrous indices were increased from-0. 17,-0. 42, and-1. 24 in untreated trees to 0. 15, 0. 07, and 0. 15, respectively. Conclusion: It is necessary to consider microNUTRIENTs’ deficiency in the future fertilizer management of the studied apple orchard. Among macroNUTRIENTs calcium, phosphorus, magnesium, and potassium in mineral fertilizer management should be considered. The potassium deficiency was one of the limiting factors to produce apple fruit in organic and integrated fertilizer management. Based on the fruit yield, the application of chemical fertilizers or biochar was recommended. For the integrated management, chemical fertilizer +biochar, chemical fertilizer + farmyard manure, and chemical fertilizer +compost were suggested regarding the available fertilizers.