Preliminary the Diagnosis and Recommendation Integrated System ( DRIS ) Norms for Evaluating the Nutrient Status of Apple

The Diagnosis and Recommendation Integrated System (DRIS) is an important tool for increasing fruit yield and fruit quality. There are still no studies on the use of DRIS for nutritional diagnosis of the apple tree for China conditions. The objectives of this study were to establish norms for apple, to compare mean yield, leaf nutrient contents and variance of nutrient ratios of lowand high-yielding subpopulations. The study covered the apple producing areas of the Wei-bei Loess Plateau in the northwest of China, in 164 orchards selected for their high productivity and employment of excellent management techniques. The concentrations of nitrogen, phosphorus, potassium, calcium, magnesium, iron, manganese and zinc were determined in leaf samples. The data were divided into high-yielding (>45 t/ha) and low-yielding (<45 t/ha) subpopulations and norms were computed using standard DRIS procedures and a preliminary DRIS norms for apple growing in the Wei-bei Loess Plateau are selected. These norms were developed with data from only one region, so data from future surveys and field trials may subsequently be used to enlarge the database allowing the refinement of model parameters. The results elucidate that the DRIS model for apple, developed in this study, is a diagnostic tool that may be used to predict if insufficiencies or imbalances in N, P, K Ca, Mg, Fe, Mn and Zn supplies are occurring in apple production area in the Wei-bei Loess Plateau, China and indeed elsewhere in the other apple production areas with similar c1imatic and soil conditions.


INTRODUCTION
China apple production and export volume rank the first in the world (FAO, 2010).Almost 4.7 million ha of apple are grown around the world and about 44% of those areas are located in China (FAO, 2010).The Weibei Loess Plateau, with advantaged apple producing conditions, is an important apple production area in China.Apple is grown extensively in this area with an average 601,520 ha in production and annual yield reaches nearly 8.6 million ton (SPBS, 2010).In this area, improper use of fertilizers is likely to be the major factors contributing to declining yield and quality, though no local nutrition guidelines are available.The foliar analysis has frequently been used to be an important tool to monitor the nutrient status of plants.
The Diagnosis and Recommendation Integrated System (DRIS) is claimed to have certain advantages over other conventional interpretation tools (Beverly, 1987;Malavolta et al., 1993;Srivastava and Shyam, 2008).DRIS firstly developed for Hevea brasiliensis by Beaufils (1956Beaufils ( , 1973)).It is a method to evaluate plant nutritional through indexes, which provides a means of simultaneously identifying imbalances, deficiencies and excesses in plant nutrients and ranking them in order of importance (Walworth and Sumner, 1986).DRIS uses a comparison of the leaf tissue nutrient concentration ratios of nutrient pairs with norms from a high-yielding group (Soltanpour et al., 1995), different from the traditional methods of leaf diagnosis like the critical level and sufficiency range.
The objective of this study was to establish appropriate DRIS norms for apple in China, seeking to use the DRIS method for its nutritional diagnosis.A survey of apple was conducted to provide a broad database of foliar nutrient concentrations in low-and high-yielding plants from which to ca1culate DRIS norms.

MATERIALS AND METHODS
The research was carried out in the Wei-beiLoess Plateau which is one of the main apple producing areas in China.The Wei-beiLoess Plateau is located in between 34° 36' and 36° 20' North latitude, 106°20' and 110°40' East longitude and the altitude from 800 to 1200 m.The Wei-bei Loess Plateau belongs to Warm and semi-humid continental monsoon climate.The annual rainfall varies between 525 and 730 mm.Mean maximum temperatures range from 34 to 40°C and mean minimum temperatures from -16 to -25°C.The sunshine duration is between 2,300 to 2,500 h.The frost-free period is 170D.
In 2007, a questionnaire was filled out by the farmer of the orchard, including rootstock, spacing, year of planting, pest management, fertilizer management and corrective measures.One hundred and sixty four apple orchards, 19 from Cultivated loessial soils, 68 from Huangshan soils, 22 from Dark loessial soils, 44 from Lou soils, 4 from red soils, 5 from Cinnamon soils, 2 from Skeletol soils, were selected for survey.According to fruit leaf sample standard in China (Gangli et al., 1985), the collection of leaves was accomplished between July and August.Each orchard 25 plants were random selected for their uniformity.
Leaf samples were washed with deionized water, dried at 65°C weighed, milled to 20 mesh for mineral analysis.Total Nitrogen (N) was analyzed by the Nessler procedure (Chapman and Pratt, 1961).Phosphorus (P) was analyzed by the molybdenum yellow method.Potassium (K) was measured by the flame photometer.Calcium (Ca), Magnesium (Mg), Copper (Cu), iron (Fe), Manganese (Mn) and Zinc (Zn) were measured by atomic absorption spectrophotometer.
According to Beaufils (1973) and Walworth and Sumner (1986), the DRIS norms selection was made along the following priorities: • Yield and leaf nutrient concentrations built a databank, which was divided into high-(>45 t/ha) and low-yielding (<45 t/ha) subpopulations.• Calculate the mean, standard deviation, variance and skew for each leaf nutrient concentration for the two subpopulations.• Calculate a variance ratio (V low for low-yielding sub-population/V high for high-yielding subpopulation) for each nutrient concentration and of two ratios involving each pair of nutrients.
• Make sure that the leaf nutrient concentration data for the high-yielding sub-population were relatively symmetrical or un-skewed, so that they provided realistic approximations of the likely range of interactive influences of different nutrients on crop productivity (Ramakrishna et al., 2009).• Select nutrient ratio expressions that had relatively un-skewed distributions in the high-yielding subpopulation (skewness values <1.0).• Select nutrient expressions for which the variance ratios (V low /V high ) were relatively large.• Select equal numbers of expressions for each of the n elements (A, B, C, …… and X) to meet an absolute (orthogonal) requirement of the mathematical model.• The following equations were developed for the calculation of DRIS indexes based on leaf analysis: where, X/A is the actual value of the ratio of X and A in the plant under diagnosis, x/a the value of the norm (the mean value of high-yielding orchards) and CV the coefficient of variation for population of high-yielding orchards.
It was considered that plants present nutritional balance for a given nutrient when the values of the indices, defined for the DRIS methods, are close to zero (Walworth and Sumner, 1987).When nutrients are in a state of imbalance, the negative DRIS index values mean that are undersupplied and positive DRIS index values mean that are oversupplied.The greater negative DRIS index values of the indices the greater the nutrient undersupply and the greater positive DRIS index values of the indices the greater the nutrient oversupply.

RESULTS
The yield of apple from the sampling apple orchards in 2007 ranged from 9.9 to 112.5 t/ha in Fig. 1.The mean productivity of the apple trees correspond end to 13.6 t/ha (SPBS, 2004(SPBS, , 2005(SPBS, , 2006) ) in the last three harvests in the Wei-bei Loess Plateau.It is evident that the average of yield of the sampling apple orchards used in this study (35.8 t/ha) was much superior to the overall average of the area, but the data were highly skewed in favor of very low yields.This meant that Max only 37 of the 164 data points were assigned to the high-yielding subpopulation (≥45 t/ha).
Summary statistics for the apple yield and leaf nutrient concentration data available from the total apple orchard survey are listed in Table 1.The leaf nutrient concentration for the macronutrients N, P, K, Ca and Mg for total population ranged from 22.2 to 31.9, 1.32 to 3.40, 3.44 to 17.69, 5.97 to 40.44 and 1.29 to 8.15 g/kg, respectively.The leaf nutrient concentration for the micronutrients Cu, Fe, Mn and Zn varied from 1.55 to 39.55, 96.5 to 441.83, 48.95 to 160.93 and 12.01 to 68.47 mg/kg dry weight tissue respectively.The mean leaf nutrient concentrations of N, P, K, Ca and Mg were 26.99, 2.38, 9.79, 13.95 and 3.66 g/kg, respectively.The mean leaf nutrient concentrations of Cu, Fe, Mn and Zn were 6.88, 211.26, 87.24 and 27.95 mg/kg, respectively.
In order to verify differences between mean leaf concentrations from high-yielding subpopulation and low-yielding subpopulation, the minimum, the maximum, the mean leaf nutrient concentrations, coefficient of variation and skewness are shown in the Table 2.In the high-yielding subpopulation, the data for the macronutrients N, P, K, Ca and Mg were relatively symmetrical, with having skewness values less than 0.6.The data for the micronutrients, Fe, Mn and Zn were also relatively symmetrical, with having values marginally less than 1.4.Only Cu was highly skewed  with skewness values greater than 2.0 in the lowyielding subpopulation and skewness values nearly 2.0 in the high-yielding subpopulation.This mean Cu was deemed unsuitable for DRIS model development.Then Mean Coefficients of Variance (C.V.'s), skewness values and variances (V low and V high ) for high and lowyielding subpopulations and the variance ratios, V low /V high were calculated in Table 3.
There were four priorities for nutrient ratio expression selection.The first was to ensure (by visual  (Walworth and Sumner, 1986); The second was to ensure that the skewness values in the high-yielding subpopulation were less than 1.0.The third was to select nutrient ratio expressions which the variance ratios (V low /V high ) were relatively large, thereby maximizing the potential for such expressions to differentiate between healthy and unhealthy plants (Walworth and Sumner, 1987).The forth was to select equal numbers of expressions for each of the eight elements (N, P, K, Ca, Mg, Fe, Mn and Zn) to meet an absolute (orthogonal) requirement of the mathematical model.
The mean values (norms) for the chosen ratios (for the high-yielding population) and their associated CVs were adopted as the DRIS (diagnostic) parameters for apple and are showed in Table 4.The selected nutrient ratio expressions were duly in compliance with the four priorities for nutrient ratio expression selection.A total of 16 nutrient ratio expressions, four for each nutrient (N, P, K, Ca, Mg, Fe, Mn and Zn), were finally selected.Some expressions with high variance ratios were omitted, because five suitable nutrient ratio expressions could not be identified for each nutrient, so four were selected instead.

CONCLUSION
In this study, the leaf nutrient concentration in the high-yielding subpopulation had relatively symmetrical distribution, so that they provided realistic approximations of the likely range of interactive influences of different nutrients on crop productivity (Ramakrishna et al., 2009).Additionally, the selected nutrient ratios had relatively large variance ratios (V low /V high ) and, therefore, these nutrient ratios got the maximum potential to differentiate between "healthy" and "unhealthy" plants (Walworth and Sumner, 1987).The selected nutrient ratios also had small C.V.'s in keeping with their diagnostic importance (Walworth and Sumner, 1986).These were given credibility both to the database and to the DRIS model.The useful parameters in DRIS diagnosis selected on apple nutrition based on different researchers were showed in Table 5 (Parent and Granger, 1989;Zhu et al., 1990;Goh and Malakouti, 1992;Jiang Yuanmao and Shu, 1995).Most of the selected ratios as DRIS norms in this study are significantly like to the norms provided for these researches (Table 5).So the DRIS model for apple, developed in this study, is a diagnostic tool that may be used to predict if insufficiencies or imbalances in N, P, K Ca, Mg, Fe, Mn and Zn supplies are occurring in apple production area in the Wei-bei Loess Plateau, China.
However, the calculation procedures for the norms and DRIS indexes are still in developing stage.Most research results have indicated that the more specific is the database for DRIS norms derivation, the more effective the method application is.The criteria for the reference subpopulation definition also demand further studies.There are several ways to select the reference population, but there is no common and standard.Further investigation and field experiments are necessary, to enlarge the model database and allow the refinement of DRIS parameters.As it stands, though, this preliminary DRIS model for apple is one of the best diagnostic tools currently available for simultaneously evaluating the N, P, K Ca, Mg, Fe, Mn and Zn statuses of apple in the Wei-bei Loess Plateau, China and indeed elsewhere in the other apple production areas with similar climatic and soil conditions.

Fig. 1 :
Fig. 1: Frequency distribution of the yield of apple trees (t/ha) in fruits, for the harvests of 2007 of 164 orchards in the Wei-bei loess plateau, China

Table 1 :
Summary statistics for apple tree yield and leaf nutrient concentration data for total yielding population (n = 164) C.V.: Coefficient of variation; S.D.: Standard deviation; Min.: Minimum; Max.: Maximum

Table 2 :
Summary statistics for apple tree yield and leaf nutrient concentration data for high-yielding (n = 36) and low-yielding (n = 127) subpopulations

Table 3 :
Mean, Coefficients of Variance (C.V.'s), skewness values and variances (V low and V high ) for high and low-yielding subpopulations and the variance ratios, V low /V high

Table 4 :
DRIS norms, mean values, Coefficient of Variation (C.V.'s) and variance ratios (V low /V high ) for selected nutrient ratio

Table 5 :
The useful parameters in DRIS diagnosis selected on apple nutrition based on different researchers Researchers DRIS norms