Diagnosis and Recommendation Integrated System (DRIS) to Assess the Nutritional State of Plants

Ademar Pereira Serra, Marlene Estevão Marchetti, Davi José Bungenstab, Maria Anita Gonçalves da Silva, Rosilene Pereira Serra, Franklyn Clawdy Nunes Guimarães, Vanessa Do Amaral Conrad and Henrique Soares de Morais

Submitted: 29 November 2011 Published: 30 April 2013

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Author Information

Ademar Pereira Serra

Davi José Bungenstab

Marlene Estevão Marchetti

Franklyn Clawdy Nunes Guimarães

Vanessa Do Amaral Conrad

Henrique Soares de Morais

Maria Anita Gonçalves da Silva

Rosilene Pereira Serra

*Address all correspondence to:

1. Introduction

Approximately 80-90% of fresh biomass composition of plants consists of water, and 10-20% of fresh biomass comprises the dry biomass.

The elemental composition of dry biomass of plants consists above 90% of carbon, hydrogen and oxygen, the remains of nutrition composition is made of other essential nutrients to plants, such as: nitrogen, phosphorus, potassium, calcium, magnesium, sulphur, boron, zinc, iron, manganese, nickel, silicon and other elements uptaken from the environment (Epstein & Bloom, 2006).

The nutritional state of plants influence the dry biomass production. The nutritional deficiency of some essential nutrient prevents the maximum potential productive of plants. According to Serra et al. (2011), the fresh and dry biomass production from medicinal plant Pfaffia glomerata Pedersen (Spreng.) was negatively influenced by nitrogen (N) and phosphorus (P) concentration into the plant, furthermore, the limitation of P in soil generated less growth on plant with less biomass yield and expressed visible N and P nutrition deficiency.

The nutritional diagnose of plants consists on determination of nutrients contents, this determination is made with the comparison of the nutrient content with standard values, and this procedure called by leave diagnose that uses information from chemical analyses of plant tissue. However, there is the visible diagnose that is made with visual observation of nutritional deficiency or excess symptoms.

The visual diagnose can be little practical, because, when the deficiency symptoms show in plants, the plant metabolism has been already damaged and the correction of deficiency can note be taken good benefits on increase of yield or better products quality, besides, the deficiency symptoms is shown in plant when the deficiency is acute (Marshner, 1995).

The tissue analyses has been considered the direct way to evaluation the nutritional state of plants, but, to do this evaluate it is necessary a well specific part from the plant to take this diagnose, this specific part is the leaf tissue that is the most used (Malavolta, 2006; Mourão Filho, 2003; Hallmark & Beverly, 1991; Beaufils, 1973).

The leaf tissue is considered the most important part of the plant where the physiologic activate happens and this tissue shows easily the nutritional disturb. To use the leaf tissue is necessary to have the chemical analyses. Furthermore, to assess the nutritional status there is the need to have leaf standard to sample, this leaf standard depend on the crop that intend to evaluate, but, nowadays there are many information about the most cultivated commercial crops.

The leave diagnose can be a useful tool to assess the nutritional status of plant, but, the procedure to analyse the data must be appropriate. Furthermore, because of natural dynamic of the leaf tissue composition that is strengly influenced by leaf age, maturation stage and interaction among nutrients on uptake and translocation into the plant, if all the damages criteria were not observed the leaf diagnose becomes very difficult to understand and used (Walworth & Sumner, 1987).

The interpretation of nutrients contents in leaf analyses can be made by several methods to assess plant nutritional status. To interpretate results of traditional chemical analyses of plant tissue for the assessment of the nutritional status of plants, the methods of critical level and sufficiency range are used more frequently (Beaufils, 1973; Walworth & Sumner, 1987; Mourão Filho, 2004; Serra et al., 2010a,b; Camacho et a., 2012; Serra et al., 2012).

There are other diagnose systems, such as: Compositional Nutrient Diagnosis (CND) (Parent & Dafir, 1992), plant analysis with standardized scores (PASS) (Baldock & Schulte, 1996), these two methods are less studied then critical level and sufficiency range, but there is CND standard published on Serra et al. (2010a,b) for the West region of Bahia, a state in Brazil and other authors (Parent, 2011; Wairegi and Asten, 2012).

The sufficiency range is the most used method of diagnose, and this method consists on optimum ranges of nutrients concentration to establish the nutritional state of crops, otherwise to use the sufficiency range it is necessary to develop regional calibration that is very expensive.

The Diagnosis and Recommendation Integrated System (DRIS) relate the nutrient contents in dual ratios (N/P, P/N, N/K, K/N. ), because of the relation between two nutrients, the problem with the biomass accumulation and reduction of the nutrients concentration in plants with its age is solved (Beaufils, 1973; Walworth & Sumner, 1987; Singh et al., 2000). The use of DRIS on concept of nutritional balance of a plant is becoming an efficient method to assess the nutritional status of plants, this method puts the limitation of nutrients in order of plant demand, enabling the nutritional balance between the nutrient in leaf sample.

Because of several factors that can influence nutrient concentration in plants, Jones (1981) suggests that it is necessary to be critical in relation to reliability of DRIS standard, because in this way the use of leaf diagnose method can be well used.

2. Diagnosis and Recommendation Integrated System (DRIS)

The Diagnosis and Recommendation Integrated System (DRIS) was developed by Beaufils in 1973, this method consist in dual relation between a pair of nutrients (N/P, P/N, N/K, K/N. ) instead of the use of sufficiency range or critical level that are called univariate methods, because only the individual concentration of the nutrients in leaf tissue is taken into consideration while no information about the nutritional balance is provided. DRIS enables the evaluation of the nutritional balance of a plant, ranking nutrient levels in relative order, from the most deficient to the most excessive.

With the use of dual relation on DRIS, the problem with the effect of concentration or dilution on the nutrients in plants is solved, because, according to Beaufils (1973); Walworth & Sumner (1987) with the growth of leaf tissue, on one hand the concentration of nitrogen, phosphorus, potassium and sulphur decrease in older plants and the concentration of calcium and magnesium increase in older plants on the other hand. When it is used the DRIS method, where the dual ratio is used, the values remain constant, minimizing the effect of biomass accumulation, that is one of the major problem with sufficiency range and critical level method.

It is feasible to find on literature some crops on which DRIS had already been used to assess the nutritional status of plants, such as; pineapple (Sema et al., 2010), cotton (Silva et al., 2009; Serra et al., 2010a,b; Serra et al., 2012), rice (Guindani et al., 2009), potato (Bailey et al., 2009; Ramakrishna et al., 2009), coffee (Nick, 1998), sugarcane (Elwali & Gascho, 1984; Reis Jr & Monnerat, 2002; Maccray et al., 2010), orange (Mourão Filho et al., 2004), apple (Natchigall et al., 2007a,b), mango (Hundal et al., 2005), corn (Reis Jr, 2002; Urricariet et al., 2004), soybean (Urano et al., 2006, 2007), Eucalyptus (Wadt et al., 1998), among other crops.

According to Baldock & Schulte (1996), there are four advantages of DRIS; (1) the scale of interpretation is continuous numeric scale, and easy to use, (2) put the nutrients in order of the most deficiency to the most excessive, (3) identify cases where the yield of plant is been limited by into factor as nutritional status and (4) the Nutritional Balance Index (NBI) give a result of combined effects of nutrients. Nevertheless, the disadvantage of this methodology is that the DRIS index is not independent, because one nutrient concentration can have hard influence on the other DRIS index for one nutrient but this problem can be corrected in parts with a hard selection of the nutrient that will compound the DRIS norms.

3. DRIS norms

To be feasible the use of DRIS to assess the nutritional status of plants, the first step is establish the DRIS norms or standard. The DRIS norms consist on average and standard deviation of dual ratio between nutrients (N/P, P/N, N/K, K/N, etc.) obtained from a crop reference population (Table 1), but, it is necessary that the crop reference shows high yield (Beaufils, 1973). This method has been followed along the years (Jones, 1981; Alvarez V. & Leite, 1999; Silva et al., 2009; Maccray et al., 2010; Serra et al., 2010a,b; Serra et al., 2012).

The data bank to compose the DRIS norms is formed by the crop yield and chemical analysis of leaf tissue, and this information can be obtained from commercial crop or experimental units. The size of the data bank is not a factor that is directly related to the quality of the DRIS norms (Walworth et al., 1988; Sumner, 1977).

Walworth et al. (1988) observed that, when they used 10 data to establish the DRIS norms, the results obtained were more accurate then the use of a large number of data. What is more important to improve efficiency on DRIS norms is the quality of the data, because it is not accepting the use of sick plants to compose the data bank to establish the DRIS norms.

To make part of the DRIS norms, the rations between nutrients can be selected by the direct form (N/P) or reverse (P/N), but, there is more than one way to change the ratio that is going to compose the DRIS norms. Bataglia et al. (1990) used the entire dual ratio without selecting the direct or reverse form, and other researchers used the transformation by natural log (Beverly, 1987; Urano et al., 2006, 2007; Serra et al., 2010a,b; Serra et al., 2012).

With many ways to select the ratio to compose the DRIS norms there is a necessity to establish the most efficiency way for each crop that results in a better efficiency of the system. Silva et al. (2009) tested the dual ratio selection using the “F” value (Jones, 1981; Letzsch, 1985; Walworth & Sumner, 1987) and “r” value (Nick, 1998) in cotton crop, on his turn, Silva et al. (2009) did not test the criterion of choice the ratio by log transformation or the use of all nutrient ratio as it were made by Alvarez V. & Leite (1999) and Serra et al. (2010a,b).

Results obtained by Serra et al. (2012) showed that the use of “F” value or log transformation in nutrient ratio to define the norms produced different DRIS index, furthermore, when the DRIS index is interpret by Beaufils ranges the difference observed among index was reduced, showed less difference between the two groups of norms.

Following the premises of DRIS proposed by Beaufils (1973), it is feasible to change the dual ratio (A/B or B/A) that is more important to compose the DRIS norms. This way it is expected that the dual ratio from crop with high-yielding (reference population), composed with healthy plants, shows less variation than the population of plants with low-yielding (non-reference population), thus, the relation between variance ratio method, the F value, was defined as the variance ratio of low-yielding (non-reference) and high-yielding population (reference), and the order of the ratio with the highest value was chosen among the variance ratios (Jones, 1981; Letzsch, 1985; Walworth & Sumner, 1987).

The utilization of the relationship between variance ratio method (“F” value) from low-yielding and high-yielding is the most used method to define the DRIS norms. The method “F” value is defined on the data bank divided into two groups (non-reference and reference), and the choice of ratio directly (A/B) or inverse (B/A) defined by relationship between variances from the two populations, in which the ratio chosen will result arises from the following analysis (Jones, 1981; Letzsch, 1985; Walworth & Sumner, 1987):

S 2 A B n o n - r e f e r e n c e p o p u l a t i o n S 2 A B r e f e r e n c e p o p u l a t i o n > S 2 B A n o n - r e f e r e n c e p o p u l a t i o n S 2 B A r e f e r e n c e p o p u l a t i o n E1

Then: the dual ratio that will make part of the DRIS norms will be A/B, on the another it will be B/A. S 2 is the variance of the dual ratio of the reference population and non-reference.

Besides the selection of forward or reverse ratio to compose the DRIS norms, the same principle can be selected with regard to the significance of F value, which can be 1%, 5% or 10% (Wadt, 1999), and feasible to use all dual ratio, which was selected by the largest ratio of variances, without the rigour of significance (Beaufils, 1973; Jones, 1981; Walworth & Sumner, 1987; Serra, 2011).

One can observe on literature that there are not any consensus about which methodology is more efficient to use. Jones (1981) did not select for significance, but he selected by the biggest reason of variances, as well as Raghupathi et al. (2005); Guindani et al. (2009); Sema et al. (2010); Serra et al. (2012). However, Wadt (2005) used the “F” value for the selection of dual ratio with a significance of 10%, excluding from the norms the dual ratio that was with significance above this value.

When selecting the dual ratio by significance of the “F” value, the sum of DRIS indexes does not give a zero value, in this case some nutrients can remain with a larger number of dual ratio than those with fewer ratios. However, Wadt et al. (1999) concludes that the rigour of the selection by the significance of “F” value generates greater efficiency for the diagnosis, in studies made with coffee crop ( Coffea canephora Pierre).