I. INTRODUCTION
Foth H.D (1996) proposed an alternate method of evaluating crop nutrient
status through the use of tissue analysis that developed by Beaufil (1973), called the
“diagnosis and recommendation intergrated system” (DRIS). This system
incorporates tissues analysis with other yield parameter in making
recommendations.
Beaufils proposed that the DRIS methodology should follow four steps :
1. Defines the characters to be improved and all factors that are suspected to affect
those characters.
2. Gather all reliable data available from fields operations and experiments.
3. Relate yield and all external parameters (i.e. light, rainfall, etc)
4. Relate yield and all external parameters (i.e nutrient content)
The DRIS system emphasizes the important of nutrient balance within plant
tissues and state that maximum yield may never be obtained unless the proper
balance or ratio of nutrients is maintained in plant tissue. Having this proper ratio of
all plant nutrient does not in fact guarantee a maximum yield, because the crop
could be destroyed by hail, flooding and other cause. If the plant nutrient ratio is
incorrect, the yield will be lower.
DRIS system has an advantage over trying to determine critical values that
nutrient ratios in plant tissue tend to be rather constant throughout the growing
season. Thus by applying this system, the farmer avoids having to sample the crop at
a precise growth stage and estimate the change that will occur during the rest of
periode
1
Fertilization provides important influence on plant growth and productivity.
That is why in DRIS system must have been known norms or indices that are
associated with the maximum yield in sugarcane plant. Foliar analysis results and its
ratio between each nutrient and field experiment of ferlilizing affected all the
essential data base for high yielding crops.
Issues the appropriate dose per unit area, how to fertilize, how the
frequency and type of application that is the most efficient will be different in every
place, because of differences in soil type, nutrient available in the soil , organic
material content, micro climate, and others. Indicator clearly visible on the size of
the leaves, high stem, broad leaf surface and the number of shoots. The lack of
growth makes this plant retarded, the leaves fall off, and yellowing in color or
necrosis and others. To determine the dose, type of fertilizer, application frequency,
time and method of fertilization and technical agronomy can be obtained with
method i.e. "diagnosis and recommendation integrated system (DRIS)." This method
has been released since developed by Beaufils (1973).
The Diagnosis and Recommendation Integrated System (DRIS) has been
developed over 25 years, originally for rubber trees in the Far East with further
extension to maize, potato and sugarcane in southern Africa. During this period, the
major features in the progress of its concepts and principles have been published.
DRIS represents an original non-specific experimental approach which can be used
for calibrating the yield and quality factors of the plant, soil, environment,
treatments and/or the farming practices as well as their reciprocal chain reactions. A
means now exists for comparing data for a particular crop at various stages of its
development and at various places in the world ( Beaufils, 1976).
2
The Diagnosis and Recommendation Integrated System (DRIS) is a method
to evaluate plant nutritional nutrient statusthat uses a comparison of the leaf tissue
nutrient concentration ratios of nutrient pairs with norms from a high-yielding group
(Soltanpour et al., 1995). The first step to implement DRIS or any other foliar
diagnostic system is the establishment of standard values or norms (Walworth &
Sumner, 1987; Bailey et al., 1997 vide Roberto, 2003).
The evaluation of the nutrient status in eucalypt (Eucalyptus grandis W. Hill
ex Maid.) forests through tissue analyses that reflects water and nutrient flows in the
system, and represents a complementary tool to soil analysis can be helpful to raise
and maintain the forest productivity at high levels has been done by Gualter et al,
(2004). This study compared the use of the Diagnosis and Recommendation
Integrated System (DRIS), Modified- DRIS (M-DRIS), and Compositional Nutrient
Diagnosis (CND) diagnose methods in eucalypt stands in Central-Eastern Minas
Gerais State, Brazil.
Roberto (2003), has studied to evaluate the relationship between the
diagnosis and recommendation integrated system (DRIS) indices and foliar nutrient
concentrations, to establish optimum foliar nutrient concentrations with DRIS and to
validate the DRIS norms for sugarcane crop. Foliar nutrient concentrations from 126
sugarcane commercial fields were analyzed during the 1996/97 season, to calculate
DRIS indices.
According to Roberto Junior, (2003), plant analysis can be a useful tool for
correcting plant nutrient deficiencies and imbalances (Baldock & Schulte, 1996),
optimizing crop production (Walworth and Sumner, 1986), and for evaluating
fertilizer requirements. The Diagnosis and Recommendation Integrated System
3
(DRIS) is a latest approach as an interpretation system of foliar analysis system.
This methodology has received considerable attention since it was developed by
Beaufils (1973).
The DRIS index scale that results from those calculations is continuous and
easy to understand (Baldock & Schulte, 1996). This model is designed to determine
when the nutrient contents of crops are excessive (positive indices), adequate (zero
indices) or deficient (negative indices). Development of the DRIS for use with a
crop involves compiling a database (Payne et al., 1990) from which optimum ratios
(mean and coefficient of variance) for all nutrient combinations are determined
(Snyder et al., 1989), called DRIS norms. Data are collected in "clip cards" . (Memet
Hakim, 2008), blend all this effort is called "diagnosis and recommendation
integrated system (DRIS)."
Irregularities or errors that occur in the analysis of soil it is difficult to
visually detection, but error in the results of the foliar analysis will easily be indicate
on the deficiency symptoms appear on leaves. That is the analysis of leaves (foliar
analysis) is used as a suitable tool in the DRIS. Relation to productivity must be
tested with field experiments such as fertilizer experimental (organic and synthetic
fertilizers), shaving experiment, the hormone, pest and disorder, irrigation and others
have estimated that the interaction and relationship with plant growth in the womb
of nutrient the burly of the leaves, surface of the leaf area (leaf area index).
Low productivity in the sugar cane plant caused by many factors, the one
that is dominant problems is fertilization. The provision of artificial fertilizers which
continue to make the land was to be hard and the trend of low productivity. The use
of organic fertilizer on a continual basis without the assistance of artificial fertilizers
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Limited Productivity
Fertilizer (chemicals fertilizer) (
Manure (organic manure)
Limited Productivity
has become a trend and productivity is also low. However, the use of both will result
in positive synergies that can improve plant productivity.
Figure 1:The influence of chemicals fertilizer and organic manure
The provision of fertilizer of nitrogen in the form of urea and ZA is still
needed in a number of quite a lot, the effects of plant biomass produced a lot of
sugar cane, each year not less than 100 tons of biomass per ha and the resulting
plants did not return to land again. The problem arises of how many doses of organic
manure and chemical fertilizers that are required to get the sugar cane crop plant
growth, nutrient content in the leaves and maximum productivity? These issues will
be discussed further. Besides, how far results of leaf analysis, soil analysis results,
the results of the experiment, the observed symptoms deficiency the result can be
applied in making the recommendations or fertilization, known as "the diagnosis and
recommendation integrated system (DRIS)" more details, replacing a very old way
simple and global.
5
Some authors claim after developing this method in some species of plants,
with DRIS norms ignore cultivation of soil or specific location (Sumner, 1979;
Walworth & Sumner, 1987; Payne et al., 1990), but the other states DRIS norms in a
particular location will more rigorous than the way diagnose burly widely. (Dara et
al., 1992; Jones Junior, 1993). DRIS norms need to be carefully calculated
(Walworth & Sumner, 1987), should be followed in the experiment with a variety.
Preparation diagnose DRIS on onion plants, for example, generally carried
out in the green house or fertilization experiment in the use NxPxKxS design and
NxPxK faktorials to evaluate DRIS norms (Caldwell et al. 1994 vide Roberto 2003)
Norms are more accurate for the treatment and have also identified the nutrient
status of the burly limited due to the resulting increase in production.
Relationship between production and the resulting nutrient status of plant
analysis is a diagnostic criteria. So the relationship between nutrient nutrient status
and DRIS index is an important criteria for DRIS norms validation. If there is a
connection between nutrient nutrient status and DRIS index, the index as a nutrient
ratio can be used to make the diagnosis. This may be a new way to validate DRIS
norms. This model is suitable to show the index of positive or negative between
nutrient status and respective DRIS index, which can be searched for optimum
nutrient content, because nutrient content in the leaves on the zero level does not
reduce production.
This paper is expected to discuss how to obtain optimum dose to plant
sugar cane which is considered a homogeneous in a certain acreage and how to use
it in the process of "diagnosis and recommendation integrated system (DRIS). DRIS
is a method of making fertilizing recommendations in an integrated system through
6
the diagnosis by foliar analysis, soil analysis, the experiment and field observation
(deficiency symptoms) in the field. This system may not generally used yet in the
sugar cane industries in Indonesia.
Formerly, culture of sugar cane is as perennial crop, rotated with rice or
secondary crop. Today sugar cane is mainly planted in dryland, poor soil and no
irrigation at all, and the sugarcane ratoon maintained as it as only. In Indonesia
ratoon age generally between 4-5 years or 4-5 times the planting season, but in some
field until 8- 10 times as a annual crop.
Generally has even become the norm, that ratoon crop productivity is lower
than the new plants (plant cane). Ratoon crop productivity will be lower if no
maintenance , no supplying, no or less fertilizing in ratoon after harvesting periode.
The low productivity of ratoon crop, is a logical result because no intensive care for
more plants. This is because the plant is considered as a ratoon crop residue only. If
ratoon crops is not considered to be the rest of the plant but the plant is hope.
Thinking long-term to treat sugar cane plant, can not be avoided the stitching to
keep the plant population and improving the quality of ratoon to ensure that every
shoot will grow so that the high productivity is expected .
Good ratoon cane means that the harvest can be improved by increasing the
availability of water to the needs for optimum conditions, fertilizer, weeds control
etc. Irrigation needs to be done at the time of a) time of planting, b) time of shoot
growth (tillering), c) plant located in the growth phase. The required amount of
water is identical with the evapotranspiration.
The amount of optimum dose should be sought with the help of DRIS
method. Time fertilization should consider the rainfall that minimazing a lot of
7
ferlilizer leaching, run off and volatile. The lose of fertilizer content will lessen by
using of organic manure.
The results of the foliar analysis generally finished in 1-2 months after leaves
sample delivered.. Data is entered into the clip card along with plant growth and
production data and other information needed. Grouping of unit sample can be done
based on varieties, planting time, soil type, the same productivity. Grouping can be
use in areas or plantations which have uniformities such as planting periode,
varieties, planting catagories, soil type, shaving periods, but it is a region in the near.
8
II. DECRIPTION OF DRIS
2.1. DRIS (Diagnosis and Reccomendation Integrated System)
Generally DRIS measure the macro element such as N, P, K and , micro
element like S for example, ignored. However total biomass in dry calibration can
be calculated as how much fertilizer needs. Corrective treatment is done after an
evaluation and further improved through DRIS result and deficiency assessment.
DRIS model designed to describe the lack of nutrient (insufficiency of
nutrient) at are marked with - (negative), is marked 0 (zero) if sufficient and + (plus)
for the surplus or (excessive). Levels of insufficiency or excess proportionally be
marked with the index (Walworth and Sumner, 1987). Theoretically DRIS index of
–(minus) 1 can indicate the a lack of few nutrient nutrient status for maximum
production. Indeed quite difficult to distinguish whether the provision of this
correction is economically profitable or not, because so many variables, including
the provision of related additional fertilizer. However, if the fertilizer experiment is
made, the economic calculation can be easily done through an approach to "the law
of diminishing return", the point where the technical efficiency and economic
efficiency point can be determined.
Basically the Diagnosis and Recommendation Integrated System (DRIS) aims
to and having objectives as follows :
1) Obtained the highest productivity through recommendations of fertilization
and other technical aspect in order to achieve best results.
2) Comparing the content of the nutrient nutrient status in a certain time and nutrient
status in the previous year.
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3) Comparing the data analysis of soil, leaf analysis, observation deficiency
symptoms and the results of field experiment
4) To evaluate the relationship between the diagnosis and recommendation
integrated system (DRIS) indices and foliar nutrient concentrations, to establish
optimum foliar nutrient concentrations
5) Diagnosis methods dealing on plant tissue analyses play a key role on precise
definition and interpretation of the nutritional plant status, since reveals greater
constancy of nutrient relations, compared separately to each nutrient content, as
well as in relation to the tissue age (Beaufils, 1973).
6) The use of the critical level for the evaluation of crops or forests nutritional
nutrient statusis questionable, since it does not define whether the deficiency is
acute or not, nor if the nutrient is the most limiting when more than one nutrient
is classified as deficient (Baldock & Schulte, 1996).
7) Using a variety of data analysis and results of the experiment as a reference
in improving efficiency in the management of the plant.
Benefits of DRIS are as follows :
1) For planning and evaluation of fertilizer programs and harvest schedules (Elwali
A. M. O. and G. J. Gascho ,1984) , in short and long term periode.
2) Improve efficiency and achieve highest productivity, with DRIS such as
fertilization recommendations, when and how to fertilize, how to cut the rest of
stalk after cutting (shaving) and application of appropriate irrigation.
Making DRIS as a basis to create both short-term and long-term planning.
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2.2. Calculation and Formula
Sakr RW et al (2008) explained the calculation on DRIS in the second
season on Lawngrass ( Zoysia japonica Steud). DRIS was used to rank the
importantance of the various nutrients in limiting plant growth and to estimate the
degree to which each of the limiting nutrient was deficient. The nutrient indices
were calculated using ratio of all analyzed nutrient (N, P, K, CA, Mg, Fe, Mn,Zn
and Cu). The general Formula used to calculate nutrient indices werw given by
Walworth and Sumner, 1987, as follows :
AI = [+ƒ(A/B) – ƒ(A/C) + (A/D) …. + ƒ(A/N)] /Z (1)
Where A ….N are nutrient, AI is index for nutrient A and when A/B > a/b :
ƒ (A/B) = { [ (A/B)/(a/b) ] – 1 } x ( 1000/CV ) (2)
or, whwn A/B < a/b :
ƒ(A/B) = { 1- [ (a/b) / (A/B)]} x (1000/CV) (3)
in which A/B is the observed ratio of two elements in the tissue, a/b is the norm for
that ratio in a large population in high-growth plant, CV is the coefficient of
variation associated with the norm and Z is the number of functions comprising the
nutrient index.
Another Formula as follows :
N index = + (ƒ(N/P + ƒ(N/K)/2
P index = - (ƒ(N/P + ƒ(K/P)/2
K index = +(ƒ(N/P – ƒ(N/K)/2
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Nutrient Ratio (DRIS performance) for example :
N/P = 10.11
N/K = 1.23
K/P = 8.57
Calculation for maximum results : (Foliar Analysis Results, for example)
Nitrogen (%) = 3.18 N/P = 3.18/0.28 = 11.35
Phosphat (%) = 0.28 N/K= 3.18/2.42 = 1.01
Potasium (%) = 2.42 K/P = 2.42/0.28 = 8.64
ƒ (N/P) = ((11.35/10.11)-1)x 100 = 12.26
ƒ (N/K) = ((1.01/1.23) – 1) x 100 = 6.50
ƒ (K/P) = ((8.64/8.57) – 1) x 100 = 0.82
Indices (Index) :
N = ƒ(N/P)-ƒ(N/K)/2 = (12.26 – 6.5)/2 = 9.38
P = ƒ(N/P)-ƒ(K/P)/2 = (12.26 – 0.82)/2 = -6.54
K = ƒ(K/P)-ƒ(N/K)/2 = (0.82 - 6.50)/2 = - 2.14
Source : Tisdale, and Nelson (1985)
For the calculation of the DRIS and Modified DRIS (M-DRIS) functions, Jones’
equation (1981) was used as follows:
f(A/B) = 10 [(A/B) – (a/b)] / s
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,where 10 = sensitivity coefficient (Black, 1993); A/B = dual relation between the
“A” and “B” nutrient concentrations (g kg-1) of the diagnosed subpopulation; a/b =
dual relation between the “a” and “b” nutrient concentrations (g kg-1) of the
reference subpopulation; and s = standard deviation of the dual relation of the
reference subpopulation.
Founded on the values of all DRIS functions, the DRIS index for each
nutrient was calculated as follows:
IA = f = [ f(A/B) - f(B/A) + f(A/C) - f(C/A) + ...... - f(N/A)] / n
,where IA = DRIS index of the nutrient;
S functions; f(A/B) and f(B/A) = DRIS functions in the direct and inverse forms,
respectively; and n = number of DRIS functions (f). Subsequently, the mean
nutritional balance index (IENm) (Wadt et al., 1998b)
To calculate the M-DRIS functions and indices, and the dual relations, the
nutrient contents were taken into account. In analogy to the establishment of the
DRIS indices by means of the nutrient content functions involved in the diagnosis,
the M-DRIS dry matter index was obtained too. The following equation allowed the
calculation of the Izi indices for the CND (Compositional Nutrient Diagnosis)
method:
Izi = (Zi – zi) / szi,
where Izi = index of the multinutrient variables; Zi = multinutrient variable of the
diagnosed sample; zi = mean of the multinutrient variable in the reference
subpopulation; and szi = standard deviation of the multinutrient variable in the
reference subpopulation. As for the IENm (Wadt et al., 1998b) calculated for the
DRIS, this index was computed for the Compositional Nutrient Diagnosis (CND) at
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the different sites. For the DRIS and M-DRIS methods, the function and index
calculations were performed with routines developed in Excel 5.0; zN through zMg
as well as the CND nutrient indices were also established with Excel 5.0
To choose the ratio order of nutrients two criteria were used. The first,
proposed by Nick (1998), called "R value", consists of the calculation of the
correlation coefficients (r) among the productivity values and the relationship
between pairs of nutrients, either in the direct order or in the inverse order. The
order of the relationship that presents the larger absolute value of the correlation
coefficient (r) is selected:
If : │rA/B│ > │rB/A │ then : relationship in the norm = A/B
If : │rA/B│ < │rB/A │ then : relationship in the norm = A/B
where: | r A/B | = absolute value of the correlation coefficient between the
productivity and the ratio among the concentrations of the nutrients A and B of the
population; | r B/A | = same as above for nutrients B and A.
The second criterion, described by Letzsch (1985) and Walworth & Summer
(1987), called "F value", consists of the calculation of the ratio of the variance of the
relationships among nutrients between the reference group (r) and the one of low
productivity (b), either in the direct order or in the inverse order. It is selected the
order of relationship that presents the larger variance ratio between the high and the
low productivity groups:
If [S²(A/B)b / S² (A/B)r ]>[S²(B/A)b / S² (A/B)r] then relationship in the norm = A/B
If [S²(A/B)b / S² (A/B)r ]<[S²(B/A)b / S² (A/B)r] then relationship in the norm = B/A
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where: s2 (A/B)r = Variance of the ratio among the concentrations of the nutrients A
and B of the reference population (r); s2 (A/B)b = same for nutrients A and B of the
population of low productivity (b); s2 (B/A)r = same for nutrients B and A of r; s2
(B/A)b = same for nutrients B and A of b.
The Indices DRIS for the nutrients were calculated as proposed by Beaufils
(1973), Elwali & Gascho (1984) and Jones (1981), using the software Microsoft
Excel. To evaluate the efficiency of the three methods, considering the two criteria
of choice of the ratio order of nutrients, the Indices DRIS for each nutrient were
related with the concentrations of the respective nutrients and their significance of
the correlation were verified, using the Lineal "General Linear Models" (GLM) of
the SAS statistical package (SAS Institute, 1985).
2.3. DRIS Norm (Nutrient Ratio)
The objectives of this study were to establish DRIS norms for sugarcane crop, to
compare mean yield, foliar nutrient contents and variance of nutrient ratios of low-
and high-yielding groups and to compare mean values of nutrient ratios selected as
the DRIS norms of low- and high-yielding groups. Leaf samples (analyzed for N, P,
K, Ca, Mg, S, Cu, Mn and Zn contents) and respective yields were collected in 126
commercial sugarcane fields in Rio de Janeiro State, Brazil and used to establish
DRIS norms for sugarcane. Nearly all nutrient ratios selected as DRIS norms
(77.8%) showed statistical differences between mean values of the low- and high-
15
yielding groups. These different nutritional balances between the low- and high-
yielding groups indicate that the DRIS norms developed in this paper are reliable.
The DRIS norms for micronutrients with high S²l /S²h ratio and low coefficient of
variation found can provide more security to evaluate the micronutrient status of
sugarcane ( Roberto , 2003)
DRIS indices were calculated using two criteria for the choice of the ratio
order of nutrients (F value - ratio of the variance of the relationships among nutrients
between the reference group and the low productivity group, and R value -
correlation coefficients between the productivity values and the relationship between
pairs of nutrients) and three forms to calculate the nutrient functions (methods of
Beaufils, Jones and Elwali & Gascho). The two criteria for the choice of the ratio
order of the nutrients selected different ratios. The concentrations of nutrients
presented positive correlations (P < 0.01) with the respective indices DRIS, except
for N. The defined DRIS norms are applicable for the nutritional diagnosis of apple
Trees, Gilmar (2007).
The DRIS system makes multiple two-way comparisons between the levels
of various plant nutrients and integrates these comparisons into a series of nutrient
indices (Walworth et al., 1986). The DRIS index scale that results from those
calculations is continuous and easy to understand (Baldock & Schulte, 1996). This
model is designed to determine when the nutrient contents of crops are excessive
(positive indices), adequate (zero indices) or deficient (negative indices).
Development of the DRIS for use with a crop involves compiling a database (Payne
et al., 1990) from which optimum ratios (mean and coefficient of variance) for all
16
nutrient combinations are determined (Snyder et al.,1989), called DRIS norms. The
norms it self and the calculation methode between one to another researcher may be
some differences and it call M-DRIS (Modified-DRIS).
DRIS on corn seen in this chart with “direction” mean that the value of ratio
up, down or equal.
Means of significant expression (values at origin in chart) are : N/P = 10.04 , N/K =
1.49, K/P = 6.74. Ratio N/P = 10.04 with symbol is slight higher than norm 8.7,
N/K = 1.49 with symbol is lower than norm 1.5 and K/P = 6.74 with symbol is
higher than 5,6 as a norm.
Data mention below based on the selection of a plot in an NPK factorial Experiment,
diagnosing the requirement and satisfying it by selecting the plot in which the
required elemen is applied.
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Figure 2 : DRIS chart for obtaining the qualitative order of requirement for NPK in corn. Source : Sumner, Solutions, 22(5):68 (1978)
Table 1 : Use of DRIS norms in diagnosing NPK requirements of corn. (Leaf sample taken at Tassseling-China)
Treatment Leaf Composition Form of Expression Chart Reading DRIS Indices Corn
Yield*
N,P2O5,K2O
N P K N/P N/K K/P N P K N P K
Lb/A % % % - - - - - - - - - %
0-0-0 2.80 0.21
2.20 13.33 1.27 10.48 7 -22 15 28
0-50-0 3.20 0.28
1.10 11.43 3.20 3.57 31 13 -44 49
0-50-60 2.93 0.28
0.26 10.46 1.13 9.29 -6 -9 15 55
0-100-60 2.60 0.26
2.44 10.00 1.07 9.38 -9 -8 17 60
100-100-60 3.16 0.33
2.45 9.58 1.29 7.42 -5 -1 6 75
200-100-60 3.40 0.34
2.40 10.00 1.42 7.06 -1 -1 2 100
*Persentage of the highest valueSumber : Sumner, 1978 vide Zen-hong Shu, 1991
Forms of expression is DRIS norm or nutrient ratio calculated as follows
N/P at 0-0-0 = 2.80/0.21 = 13.33
N/P at 0-50-0 = 3.20/0.28 = 11.43, etc
Table 2 : Comparison of DRIS norm for various values obtained by rationing critical Values
Crop SourceNorm values
N/P N/K K/P
CornCritical valuesDRIS
10.079.93
1.611.58
6.406.06
Sugarcane
Critical valuesDRISCritical valuesDRIS
9.768.718.428.20
1.721.541.521.51
5.675.635.535.46
SoybeanCritical valuesDRIS
12.8413.77
2.312.43
5.555.97
TeaCritical valuesDRIS
14.7315.05
2.402.43
6.136.28
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PotatoCritical valuesDRIS
10.9512.70
0.8120.871
13.4815.06
SorghumCritical valuesDRIS
8.919.25
3.854.05
3.854.05
Source : Zen-hong Shu and Yu Ching (1991)
The relationship between yield and plant nutrient concentration is a premise
to use the plant analysis as diagnostic criterion. So, the relationship between nutrient
concentration and DRIS indices may be a valuable criterion to validate the DRIS
norms. If there is a relationship between plant nutrient concentration and DRIS
index, this index can be used to make nutritional diagnosis. Probably, this is a new
way to validate DRIS norms. This fitted model between nutrient concentration and
respective DRIS index probably shows negative and positive DRIS index, and it
could be used to determine optimum foliar concentration, because the nutrient foliar
concentration at null DRIS index possibly do not limit crop yield. If the crop shows
nutrient concentrations higher or lower than this optimum value, the crop shows
positive or negative DRIS indices respectively, which indicate yield limitation by
nutritional excess or deficiency. (Roberto, 2003). Comparison norms of nutrient
ratio of DRIS between some crop and sugarcane as follows :
Table 3 :. Sugarcane leaf nutrient critical values and optimum ranges.
NutrientCritical Value OptimumRange
% %Nitrogen (N) 1.80 2.00 - 2.60Phosphorus (P) 0.19 0.22-0.30Potassium (K) 0.90 1.00-1.6Calcium (Ca) 0.20 0.20-0.45Magnesium (Mg) 0.12 0.15-0.32Sulfur (S 0.13 0.13-0.18Silicon (Si) 0.50 >0.70
mg/kg mg/kgIron (Fe) ----- 50-105
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Manganese (Mn) ----- 12-100Zinc (Zn) 15 16-32Copper (Cu) 3 4-8Boron (B) 4 15-20Molybdenum 0.05 -----
Source : From Anderson and Bowen (1990), except for Si values (J. M. McCray, unpublished data). All values are fromFlorida except S and Mo, which are from Louisiana.
DRIS norms have been developed in several areas. The crop has been shown
to respond well to "in crop" corrections made following DRIS analysis.
Table 4. Sugarcane TVD leaf blade lamina norms from Florida and South Africa § †
Nutrient Ratio Florida South Africa
N/P 8.706 8.197
N/K 1.526 1.511
K/P 5.633 5.464
Ca/N 0.151 0.128
Ca/P 1.314 1.146
Ca/K 0.222 0.205
Ca/Mg 1.373 1.158
Mg/N 0.113 0.116
Mg/P 0.984 0.962
Mg/K 0.163 0.186§ Data from Beaufils and Sumner (1976) and Elwali and Gascho (1983). † TVD = top visible dewlap.. Source : Gascho, 2000
In table 3, The norms developed in Florida on muck soils are quite similar to
those developed in South Africa on mineral soils Gascho (2000) These ratios
illustrates the norm of DRIS in the sugarcane crop. There are relationship between
table 1 and table 3.
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III. DRIS Application on Sugarcane
Relationships between DRIS index and leaf analysis in the sugar cane has
been developed and DRIS indices for the entire set database with DRIS norms. A
field experiment required to reinforce DRIS norms results. (Reis Junior & Monnerat,
2003). The leaf samples from 3 fertilizer experiment was carried out as follows :
Table 5. Nutrient content on sugar cane leaves, DRIS indices, nutrient balance Index (NBI) and yield of three sugarcane fields
Variable N-org P K Ca Mg S Cu Mn Zn NBI Yield ----------------- (g kg-1) ------------------ -(mg kg-1) - (Mg ha⁻¹)
BambuzalFoliar content 12.8* 2.06 11.2 4.63* 2.89 1.67* 5.50* 67.0 11.4* 50 77.3DRIS indices -6 1 -2 10 6 -4 8 -2 -11
Duas Barras IIFoliar content 12.6* 1.37* 10.0 3.38* 1.65* 0.78* 4.17* 139.5 9.15*160 39.0DRIS indices 4 -10 4 9 -8 -50 7 56 -12
FazendinhaFoliar content 10.9* 2.51 8.81*7.22 3.54 1.08* 5.26* 64.4 14.4* 158 73.5DRIS indices - 19 16 -15 34 17 -41 5 -4 7 *Concentration below sufficient range.At, Bambuzal, Duas Barras II and Fazendinha, in Rio de Janeiro, Brazil. 1996/1997. Source : Roberto, (2003)
Table 6. Soil chemical analysis of the fertilizer trials
Field pH P K Ca Mg Al H+Al Sand Sand Silt Clay Coarse fine - (mg kg-1) - ------------ (mmolc kg-1) --- ---------------- (g kg-1) -------
0-200 mm layer1 6.2 3 140 39 42 1 29.7 30 140 290 5402 4.8 4 150 20 17 13 61.0 237 116 47 6003 5.6 4 103 62 34 0 54.5 48 89 343 520
200-400 mm layer1 6.2 1 112 29 41 0 24.8 30 90 280 6002 4.7 2 45 10 10 15 46.0 295 177 28 5003 5.9 3 65 54 28 0 49.5 45 99 316 540
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At Bambuzal (field 1), Duas Barras II (field 2) and Fazendinha (field 3), in the State of Rio de Janeiro, Brazil. 1997/1998. Source : Roberto, (2003)
Experiment over in the year 1996/1997 was conducted in 3 different places.
DRIS index is calculated from the results of leaf analysis. Coefficient sensitivity
scale to 10. At the end of the experiment the amount of production in each "field" or
be on the treatment plot fertilization and leaf analysis results from each location
shown in the table above. This foliar analysis results are compared with the scale of
the proposal written by Orlando Filho & Campos (1975a, 1975b), Orlando Filho &
Haag (1976), Orlando Filho & Zambello Junior (1977), Orlando Filho et al. (1979,
1980a, 1980b, 1980c) and Malavolta et al. (1997) as explained by Roberto (2003b).
Table 7 : Critical level considering the criterion of sufficiency range (Basso et al., 1986) and obtained for combinations of methods of choice for the ratio order among nutrients (R and F values) and DRIS indices (methods Beaufils, Jones and Elwali & Gascho).
Nutrient Sufficiency range Beaufils Jones Elwali & Gascho R F R F R F N (g kg-1) 20.0 25.9 32.9 28.3 23.6 27.7 45.5P " 1.5 1.7 1.7 1.7 1.7 1.7 1.7K " 12.0 15.8 18.7 17.3 15.3 15.9 17.8Ca " 11.0 12.3 12.0 12.3 12.6 12.7 12.6Mg " 2.5 3.4 3.3 3.4 3.4 3.4 3.4Fe (mg kg-1) 50 152 174 138 114 143 172Mn " 30 230 215 143 260 241 220Zn " 20 48 34 35 38 40 39Cu " 5 9 10 10 9 9 9B " 41 38 42 39 35 38 41
Source : Gilmar ,2007
After that will be seen whether the data foliar analysis results are
"reasonable" or the "unnatural". This is a very important observation made before
the field checks by visual symptom. Ratio of nutrient status as a new norm will
usable be in the future, especially in sugarcane crop in Indonesia.
22
The correlation coefficient among the DRIS indices in apple tree obtained
(Table 3) indicates that positive and significant correlations are verified (P < 0.01)
between the concentrations of nutrients and the respective DRIS indices, except for
N. Lower relationships among the DRIS indexes for N and the concentration of N
have also been observed for other perennial crops like cherry and hazelnut (Righetti
et al., 1988), citrus (Salvo, 2001) and coffee plant (Reis Jr. et al., 2002). For N, the
DRIS index is strongly dependent on the concentration of the other nutrients in the
leaves, while for the other nutrients the DRIS indices are dependent on their own
concentrations. The “R-value” presented better adjustments among the DRIS indices
and the concentrations of the nutrients for the methods Beaufils (1973) and Elwali &
Gascho (1984), while the “F value” presented better adjustment among the DRIS
indices and the concentrations of the nutrients for the method Jones (1981).
Soil testing remains an excellent preplant practice but its reliability for the
successive ratoon crops has been questioned. Results of foliar analyses are usually
interpreted on the basis of the Critical Nutrient Level (CNL) approach. Recently, the
Diagnosis and Recommendation Integrated System (DRIS) was introduced as an
alternative approach for foliar diagnosis. Fertilization according to the DRIS
significantly increased both cane and sugar yields compared with those obtained
when fertilization was guided by foliar analysis using the CNL approach or soil
testing. Differences were not significant between foliar analysis using the CNL
approach and soil testing. The high cane and sugar yields obtained by DRIS-guided
fertilization were attributed to the better nutrient balance as revealed by DRIS
indices late in the season. Foliar analysis using the DRIS proved to be a better guide
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for fertilization of sugarcane than the soil testing method currently in use. (Elwali,
1984)
When the sugar cane crop is a perennial crop, harvesting only once a year,
but for long periode, the culture patern is changing from as annual crop. Planting
sugar cane with this long term pattern, must be thinking, plan and act as long term
program. That is why every year supplying of plant is need to be done to make plant
population normaly again. DRIS favourable once used on sugar cane plants, if the
ratoon kept some more years . How many years until the sugar cane ratoon crops can
be, of course this depends on the existence of the disease and the level of
productivity. This problem until now are still debated, it seems a need to research
the development or long-term, so it can answer the questions above.
Clip card very useful for DRIS system (attached), the nutrient content is a
collection of historical land use data, the cultivation of soil, planting material,
maintenance, treatment of various plants, foliar analysis, productivity etc.. Fertilizer
reccomandation of DRIS application used in the next year, so when the new crop
(plant cane) planted this year, the recommendations applied to the first ratoon crop,
when the recommendations made in the first ratoon crop this year, then the
application is applied to the second ratoon crop, and so on.
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IV. DISCUSSION
4.1. Recommendation of Fertilization
In a method of foliar analysis, main reason is to see the results of analysis or
nutrient content, how many the nutrient can absorbed by leaves, this analysis results
shows the real events happening in the crop. In Maintenance Program, to improve
productivity must have additional doses if the nutrient status shows "medium or fair"
content, If nutrient status shows "good" then the maintenance dose can be given as
usual. So the dosage of fertilizer depending on the nutrient status of the plant leaf
compare with growth conditions in the plant. In principle, the sugarcane productivity
will depend be on fertilizer dose and othe factor which should be adjusted according
to the planning of it.. In addition, given the many external factors that affect the
correction of the symptoms of deficiency, will affect be the amount of fertilizer. The
type of fertilizer, can be considered for most suitable type of and the most
economical in the sugar cane.
Table 8: Guidelines according to the nutrient nutrient status of leaf
Kriteria Nutrient Nutrient status(Kg)ZA SP 36 KCl Kies. Kompos
+Baik-
200 150 200 0 2.000
+Medium-
300 250 300 50 5.000
+Kurang-
400 350 400 100 10.000
Source : Memet Hakim (2008)1.ZA =21 % N, SP36 =36 % P205, KCl =60 % K20,Kies =30 % MgO2. Dosis this will increase or decrease in accordance with the results of field inspection and consideration of productivity
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From above table, clearly visible differences in the needs of burly according to the
level of productivity from 1 ton to 150 tons per ha. In this table visible element
needs relatively little phosphate, this may be due to a lot of elements that can be
burly phosphate absorbed from the soil.
Table 9: Comparison of macro elements taken from sugar cane in the soil
Description Nutrient taken from sugar cane in the soil1 ton cane 70 ton
cane100 ton
cane150 ton cane
N ZA Urea
1.04.762.10
70333146
100476210
150714315
P2O5 RP SP 36
0.62.311.68
42161117
60231168
90346252
K2O KCl (MoP)
2.253.82
157262
225382
337573
Source: Processed from Sundaran (1998)
The provision of fertilizer phosphate larutnya natural resources is low in long-term
benefit, because in addition to a lower price, release element is a little bit. However,
the seasonal sugar cane (only one plant continues demolished) the use of natural
phosphate fertilizer is not recommended.
On the other hand needed to improve productivity burly additional elements, such as
those listed in the table above. The amount of fertilizer given to the land which will
require improved elements burly more.
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Pont of Technical Efficiency
Point of Economical Efficiency
Table 10: Nutrient needed to increase productivity base on biomass
DESCRIPTIONTotal nutrient estimate needed to increase the tons
of cane 1 ton 40 ton 70 ton 100 ton
NZAUrea
2.2510.74.9
904320
157749343
2251.070490
P2O5RPSP 36
0.63.31.7
24137
422312
603317
K2OKCl (MoP)
1.252.0
5080
87140
125200
Source: Sundaran B, 1998
Benchmark and comparison the most relevant is the fertilization experiment
in the field. A very simple experiment you can use randomized block design
(Random Block Design). This experiment is very important, just one experiment on
the sugar factory of each time. How do I respond N, P and K on the different levels
of productivity will be seen clearly. With this experiment point the technical and
economic efficiency can be searched.
Figure 3: Graph of Technical and economic efficiency point
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Each region is determined at this time with the general recommendations, so far
from efficient. With so many elements burly terbuang and approach is very rough,
not the focus.
4.2. Nutrient status Norm
Norm matrix elements are set based on the foliar analysis and observations
from a number of symptoms deficiency trial or in the DRIS index. This norm can be
different depending on the location of every sampling techniques and procedures of
analysis leaves dilaboratorium. Uniformity in procedures and the making of this
analysis leaves, akan can facilitate optimum search for the actual elements burly.
Table 11: The guidelines / criteria nutrient nutrient status of cane
Criteria Nutrient Status
N P K Mg Fe
+high
-2.00 0.12 2.30 0.12 0.30
+medium
-1.70 0.10 2.20 0.10 0.25
+Low
-1.40 0.08 2.10 0.80 0.20
Source : Memet Hakim (2008)
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4.3. Considerations in the DRIS application
4.3.1. The biomass
The amount of biomass produced by the influence of the size of the dose of
fertilizer, as well as the organic material. Simply can compare between biomass
produced sugar cane is around 100 tons, while rubber is only about 3 tons for
example.
4.3.2. The soil fertility
Various types of land planted to sugar cane, it will generate growth that is also
different. DRIS patterns in productivity are expected the same, but the fertilization
and multiple-dose treatment in treatment plants may be different.
4.3.3. The Plant Growth
The growth of the plants with the measured parameters such as the following:
a. Tillering capacity, so the number of shoots per clump or per m
b. Heght of stem
c. Stem diameter, depend on the varieties, stem diameter can growth normaly or not
d. Leaf Area Index or leaf area (width, length and number of leaves)
4.3.4. The Nutrient Deficiency
Although the results of leaf analysis showed likes optimum, but it does not mean
there may be no symptoms deficiency. In many cases there are found any
differences between foliar analysis results and in the reality.
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4.3.5. Crop Productivity Approach
Higher productivity is the indicator of low size of the plant biomass
produced. Biomass produced will affect the demand of biomass in accordance with
the scale it self. In the pattern of "maintenance program" all the nutrient must be
returned to the soil, so that the fertility of soil protected from impoverishment of soil
fertility. This the model produces the same level of productivity with the previous
year.
In the pattern of "downgrade program" replacement of nutrient less than they
should. Generally this happens if the price of fertilizer such as high and not balanced
with the sale price of production. This pattern of results as a decrease in production
but still in the economic threshold limit.
In the pattern of "upgrade program" have elements of production planning as
the desire to increase productivity will be calculated. The result is the expected
increase in productivity, but in terms of the farm more profitable. Maximum yield
can be obtained with this model.
Thus the actual productivity of sugarcane crop may be arranged depending on the
thoughts of “recommendator” it self. In practice recommendator not to think like
that described above, because of limited insight.
4.3.6. Considerations Pest, Deseases and Disorder
In general, the disease attacks the plant if the plants lack the element
potassium. Potassium elements can strengthen cell walls so that the plant
contains enough elements of this will be more resistant against the attack of
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the disease. Varieties are sensitive to a particular disease, even though given
enough elements potassium then remain infected with the disease easily. So
the selection of resistant varieties from main disease are important, in
addition to adding the element potassium.
Nonequlibrium nutrient of the macro or micro element, limit crop
productivity because of the smallest nutrient will limit production (Liebig’s
minimum law). Symptoms of lack of nutrient (deficiency) is basically also a
disease, this can easily be visually, call as disorder. Corrective action is done
to restrict the attack of disease including lack of nutrient with an additional
elements beyond the planned time or simultaneously with the planned time.
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IV. CONCLUSION
Diagnosis and recommendation integrated system (DRIS) in the sugar cane
plant released since developed by Beaufils (1973), has opened a new horizon in
making recommendations for fertilization. DRIS indices is varied, with some use up
to 10 positive or negative , but more simple and easy to use to scale the index 4 in
associated with nutrient deficiencies. DRIS is very suitable to sugar cane crop that is
planted widely, among others, on the sugar cane plantations in Indonesia or known
as sugar factory. Observation result by Roberto, 2003 state that as follows :
Mean yield, foliar nutrient concentrations and Variance of nutrient ratios are not
similar in the low- and high-yielding groups Nearly all nutrient ratios selected as
DRIS norms show statistical differences between mean values in the low- and high-
yielding groups but These different nutritional balances indicate that the DRIS
norms developed in this paper are reliable.
DRIS use on the sugar cane has not been uniform and there is no uniformity
of some modified DRIS that has been developed. Anyway DRIS as a tool to make
recommendations of fertilization with soil analysis, leaf analysis, the assessment of
symptoms of deficiency and field experiments. Analysis of soil can not be relied
upon because of their accuracy can not show its impact, so if it is not visible it is
difficult to check. . Foliar analysis results easily to be check with nutrient deficiency
symptom in the field. Nutrient content ratio in DRIS as a current method is hoped
will be more reliable in practice.
Foliar analysis, held once a year, from each Leaf Sample Unit. The existence
of human error in the foliar analysis can be quickly corrected with the observation
32
in the symptoms of deficiency. Foliar analysis results is a fact of the nutrient
absorption to leaves and how long these absorption affecting the growth. Ongoing
research is needed to obtain the appropriate norms and the desired results.
33