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RESEARCH ARTICLE
PRELIMINARY DRIS MODEL PARAMETERIZATION TO ACCESS GROUNDNUT (ARACHIS
HYPOGAEA L.) NUTRIENT STATUS IN BENIN (WEST AFRICA)
Gustave D. DAGBENONBAKIN1, Emile C. AGBANGBA2,4, Valentin KINDOMIHOU3, Léonard Elie
Akpo4, Nestor SOKPON2, Brice SINSIN3
1National Institute of Agricultural Research of Benin, 01 B P 884 Cotonou, Benin
Email: dagust63@yahoo.fr
2 Faculty of Agronomy, University of Parakou, 01 BP 123, Parakou, Benin; E-mail: agbaemile@yahoo.fr
3Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi, 03 BP1974
Cotonou, Benin; E-mail: valentin.kindomihou@fsa.uac.bj
4Institute 3Laboratoire d’Ecologie Végétale et Hydro-écologie, Faculté des Sciences et Techniques, Université
Cheikh Anta DIOP, Sénégal, B.P. 5005 Dakar (Sénégal) ; E-mail : leonard.akpo@ucad.edu.sn
ARTICLE INFO ABSTRACT
The use of critical concentration approach to diagnosis the nutrient status of plants is somewhat
erroneous in that ‘critical nutrient concentrations’ are not independent diagnostics, but can vary in
magnitude as the background concentrations of other nutrients increase or decrease in crop tissue.
The Diagnosis and Recommendation Integrated System (DRIS), an alternative is sometimes less
sensitive than the sufficiency range approach to differences caused by leaf position, tissues age,
climate, soil conditions, and cultivar effect because it uses nutrient ratios. The DRIS provides a
reliable means of linking leaf nutrient concentrations to the yield of groundnut, and may be
developed for this crop using existing experimental data. The present study was carried out in the
Upper Catchment of Benin in 2001 and 2002, and grain yield and leaf nutrient concentration data
from organic and inorganic trials were used to establish DRIS norms for N, P, K, Mg, Ca, S and Zn
and statistical parameters for groundnut. The DRIS norms from this study were K/Ca: 1.4, K/S: 15.8,
K/N:0.7, Mg/Ca: 0.2, Mg/K: 0.2, Mg/P: 2.1, Mg/Zn: 159.8, N/Ca: 2, N/S: 23.9, Zn/N: 0.0008, P/K:
0.1, P/N: 0.1, P/S: 1.3, P/Ca: 0.1, P/Zn: 76.1, S/Ca: 0.1, and Zn/Ca: 0.002. Although the database was
relatively small, the norms derived for nutrient ratios of key biological significance, i.e. N/S and
K/N, were within the expected narrow ranges for higher plants, giving credibility to both the
database and the DRIS model. Data from future surveys and field experiments may subsequently be
used to enlarge the database allowing the refinement of model parameters and hopefully an
expansion of the diagnostic scope such as to include other micro-nutrients. The nutrient status
assessment using the selected DRIS norms shows a good nutrient level for N, P, K, Ca, S and Zn in
the groundnut leaves for high yielding as their value was similar to those presented in the literature.
As it stands, this preliminary DRIS model for groundnut offers a good diagnostic tool for evaluating
the N, P, K, Ca, S and Zn status of groundnut crops in Benin.
Copy Right, IJCR, 2012, Academic Journals. All rights reserved.
INTRODUCTION
The use of chemical analysis of plant material for diagnostic
purposes is based on the assumption that causal relationships
exist between growth rate (and yield) and nutrient content in
the shoot dry matter (Marschner, 1997). Critical leaf nutrient
concentrations have frequently been used to diagnose
nutritional status of plants (Tyner, 1946; Viets et al., 1954;
Beaufils et Sumner, 1977). The critical concentration approach
is somewhat erroneous in that ‘critical nutrient concentrations’
are not independent diagnostics, but can vary in magnitude as
the background concentrations of other nutrients increase or
*Corresponding author: dagust63@yahoo.fr
decrease in crop tissue (Walworth and Sumner, 1986; Bailey
1989, 1991, 1993). These criteria have been evaluated for a
wide range of crops (Katyal and Randhawa 1985; Jones et al.
1990; Westfall et al., 1990; Kelling and Matocha 1990).
Walworth and Sumner (1987) underline that foliar analysis is
helpful for assessing plant nutrient status only if adequate
procedures are available for making diagnoses from analytical
data. According to Beaufils (1973) and Walworth and Sumner
(1987), an alternative approach to nutritional status evaluation
is the Diagnosis and Recommendation Integrated System
(DRIS). This method uses a comparison of leaf tissue
concentration ratios of nutrient pairs with norms developed
from high-yielding populations to diagnose nutrient status.
DRIS has been used successfully to interpret the results of
ISSN
: 0975
-
833X
Available online at http://www.journalcra.com
In
ternational Journal of Current Research
Vol. 4, Issue, 04, pp.108-115, April, 2012
INTERNATIONAL JOURNAL
OF CURRENT RESEARCH
Article History:
Received 08th January, 2012
Received in revised form
07th February, 2012
Accepted 14th March, 2012
Published online 30th April, 2012
Key words:
DRIS Preliminary,
DRIS norms,
Grain yield,
Groundnut,
Benin.
foliar analyses for a wide range of crops such as rubber and
sugarcane (Elwali and Gascho 1984), potato (Meldal Johnson
and Sumner, 1990; Mackay et al., 1987;), apple (Szü cs et
al.,1990; Singh et al., 2000), peach (Awasthi et al., 2000),
mango (Raj and Rao, 2006), sweetpotato (Ramakrishna et al.,
2009), grassland swards (Bailey 1997), cauliflower (Hundal et
al., 2003), rice (Singh and Agrawal, 2007), corn (Escano et
al., 1981, Elwali et al., 1985, Soltanpour et al., 1995) ,
tomatoes (Hartz et al., 1998), pineapple ( Angeles et al. 1990;
Teixeira et al., 2009: Agbangba, 2008; Agbangba et al., 2010;
Dagbenonbakin et al., 2010), cotton (Dagbenonbakin et al.,
2009). The DRIS approach was designed to provide a valid
diagnostic irrespective of plant age, tissue origin (Sumner,
1977a, Meldal-Johnsen and Sumner 1990, Bailey 1997, Jones,
1993 Sumner, 1977) cultivar, local conditions (Payne et al.,
1990), or changes in the method of tissue sampling or the time
of sampling (Moreno et al., 1996). The DRIS is sometimes
less sensitive than the sufficiency range approach to
differences caused by leaf position, tissues age, climate, soil
conditions, and cultivar effect because it uses nutrient ratios
(Sanchez et al., 1991). Once DRIS norms have been
established and validated from a large population of randomly
distributed observations, they should be universally applicable
to that crop (Sumner 1977a, 1979) because of for a given
species, there appear to be specific nutrient ratios for
maximum crop performance that transcend local conditions,
such soil, climate and cultivars (Snyder and Kretschmer, 1988,
Snyder et al., 1989). As yet, DRIS has not been applied to
groundnut.
China leads in production of groundnuts having a share of
about 41.5% of overall world production (http://en.wikipedia.
org/wiki/Peanut), followed by India (18.2%) and the United
States of America (6.8%). Groundnut was found to be the first
of important oilseed crops grown in West African countries
followed by rapseed and mustard, sesame, linseed, safflower
and castor. Groundnut is one of the main annual crops in
Benin. The cultivated area of this crop occupies about 100,000
hectares (ha) in Benin. It still occupies an important place as
in central Benin, for instance, groundnut seeds play an
important role in traditional customs as people eat a fried paste
made from its seed alone. Its importance as a cash crop in the
economy of the region is highly documented (Bationo et al,
2007; Labitan, 2010). In the country, the groundnut oil is
edible, used in cookery because it has a mild flavor and a
relatively high smoke point and in the manufacture.
Groundnut kernel is nutritionally rich, high protein content.
Otherwise, whether crops consumption was found as a source
of aflatoxin exposure, dietary exposure to aflatoxin from
groundnut was less than from maize in young children from
Benin and Togo (Egal et al., 2005). Due to its high
monounsaturated content, it is considered healthier than
saturated oils, and is resistant to rancidity. Collines
Department is one of the main areas of production of this crop
and the yield did not exceed 0.90 t.ha-1 in Benin. Although
groundnuts help countering malnutrition and childhood
anemia, however, it importance in bridging the hunger gap in
Benin is less well known in policy circles. It is known that the
major problems of groundnut farmers in Benin are low crop
productivity because this crop took up a larger proportion of
the recovered 15N than the trees (Cattan 1993, Schilling
1997).
The aim of the present study was to develop DRIS model
parameters for groundnut using grain yield and leaf tissue
nutrient concentration data from the 2001 and 2002 through
organic and inorganic fertilizer survey for assessing mineral
nutrient of this crop in the Upper Catchment of Benin.
MATERIAL AND METHODS
Experimental site
Experiments were carried out in 2001 and 2002 at three sites:
Beterou (southern Borgou Department), Dogue (southern
Donga Department), and Wewe (border of southern Borgou
and southern Donga Departments), at a distance of about 45,
87 and 80 km, respectively, from Parakou (Figure 1). The
distribution of the plots at the different sites is shown in figure
2. Soil textures (table 1) at Beterou, Dogue and Wewe taken
in the top 20 cm were loamy sand with 3-10 % of clay and 76-
86 % of sand, and sandy loam with 7-13 % of clay and 73-80
% of sand on all site. Soils in the three locations have low
fertility. The climate on the site is Sudanese-Guinean. The
rainfall distribution is unimodal with two seasons; a rainy
season from mid of April to mid of October, and the
subsequent dry season. The average total annual rainfall is
1167.6 mm. The average temperature is 25°C. First rainfall
begins in March, and becomes significant from May to
September.
Figure 1. Location of the experiment area (Upper Oueme
Catchment)
109 International Journal of Current Research, Vol. 4, Issue, 04, pp.108-115, April,
2012
Figure 2. Map of the distribution of the field plots
at the three sites
Sampling design and chemical analyses
Mature leaves from both the main stem and either cotyledon
lateral branch were sampled at the blooming stage as
recommended by FAO (2000) and Leo M et al. (1973). After
air drying, material was further dried at 70°C to a constant
weight, pre-ground by a Brabender mill and stored dry. Soil
samples, 0-20 cm depth, were collected at each farmer field
before the experimental block was installed. The groundnut
grain was harvested in a (2 x 2) m2 area and repeated thrice
per plot. Plant material was ground by a planetary mill
(Retsch). The following analyses were carried: C, N and S
determined by elemental analysis in the EuroEA 3000. Further
elemental composition was determined after dry ashing in
porcelain crucibles at 550°C in a muffle furnace, dissolving
the ash in concentrated nitric acid, evaporation to dryness on a
sand bath (to precipitate silicate), and taking up with
concentrated nitric acid again, and transferred to volumetric
flasks with several rinses of ultra pure water (MilliporeQ). P
was determined using the molybdo-vanadate blue method,
with a spectral photometer (model Eppendorf
Digitalphotometer 6114) at wavelengths of 465 and 665 µm.
K, Ca, Mg and micronutrients were determined on a Perkin-
Elmer PE 1100 B atomic absorption spectrophotometer
(flame). The soil texture (five fractions) was done by
Robinson pipette (Tran et al., 1978); the pH was determined in
water (a soil/water ratio of 2:1) using a pH meter with glass
combination electrode with a WTW pmx 2000; total N was
determined using the macro Kjeldahl procedure described by
Jackson (1958) with a Gerhardt Vapodest; organic C was
determined using the method described by Walkley and Black
(1934) and the organic matter content calculated by
multiplying organic C by 1.724; C, N, and S were determined
by an automatic Elemental Analyser EuroEA 3000 according
to the Dumas method; P was extracted with calcium-acetat-
lactat-extraction (CAL) and determined by colour
development in the extract with molybdenum blue and
photometric measurement. Micronutrient levels were
determined after extraction of soil samples with 01 N HCl,
adjusted to volume, and filtered through Whatman No1.
Analysis was done with a Perkin-Elmer flame atomic
absorption spectrophotometer, Model 70PE 1100 B.
Development of DRIS model and data analysis
The grain yield and leaf tissue nutrient concentration data
DRIS norms and coefficients of variation (CVs) were derived
according to the procedure of Walworth and Sumner (1987).
Scatter diagrams of yield versus nutrient concentrations and
all conceivable nutrients ratios were constructed and
subdivided into high-yielding and low-yielding sub-
populations with the cut off point between the two
subpopulations set at 1233,50 kg ha-1 (mean + interval of
confidence). The rational for this subdivision is that nutrient
data for high-yielding plants are usually more symmetrical
than those for low-yielding plants (Walworth and Sumner
1986, 1987). The yield at which the separation between the
two sub-populations was set was a compromise between
maximizing the potential for data symmetry in the high-
yielding sub-population (i.e. by excluding data for low-
yielding) (Ramakrishna et al., 2009), yet including as many
data points as possible for statistical credibility (Walworth and
Sumner, 1987).
Mean values or norms for each nutrient expression together
with their associated CVs and variances were then calculated
for the two sub-populations. The mean values in the high-
yielding sub-population of seventeen expressions involving
seven nutrients (N, P, K, Ca, Mg, Zn, and S) were ultimately
chosen as the diagnostic norms for groundnut. The selection
was made along the following priorities. The first was to
ensure that the leaf nutrient concentration data for the high-
yielding sub-population were relatively symmetrical or
unskewed, so that they provided realistic approximations of
the likely range of interactive influences of different nutrients
on crop productivity (Ramakrishna et al., 2009). The second
priority was to select nutrient ratio expressions that had
relatively unskewed distributions in the high-yielding sub-
population (skewness values <1.0). The third priority was to
select nutrient expressions for which the variance ratios (V
low/V high) were relatively large (>1.0), thereby maximizing
the potential for such expressions to differentiate between
‘healthy’ and ‘unhealthy plants’ (Walworth and Sumner
1987). Having evaluated the model parameters, DRIS indices
may then be calculated for nutrients A to N using the
following generalized equations (Bailey et al., 1997a;
Hallmark et al., 1987):
X index =
X
N
X
M
B
X
A
Xffff
110 International Journal of Current Research, Vol. 4, Issue, 04, pp.108-115, April,
2012
Where CV/1
a
x
/
A
X
100
A
X
f when A
X
>
SD
a
x
and CV/
A
X
/
a
x
1100
A
X
fwhen .SD a
x
A
X
A
X is the ratio of concentrations of nutrients X and A in the
sample while a
x, CV, SD are the mean, coefficient of
variation, and standard deviation for the parameter A
X in the
high-yielding population respectively. Similarly, other nutrient
ratios B
X, x
Mand x
N are calibrated against the
corresponding DRIS reference parameters, b
x, b
m and x
n.
Nutrient indices calculated by this formula can range from
negative to positive values depending on whether a nutrient is
relatively insufficient or excessive with respect to all other
nutrients considered. The more negative is the index value for
a nutrient, the more limiting is that nutrient. Descriptive
statistics were determined for grain yield, leaf nutrient
concentration and nutrient ratio expression data using Minitab
statistical software version 14. Descriptive included, means,
medians, minimum and maximum values, variances, CV’s and
skewness values, where a skewness value of zero indicates
perfect symmetry, and values greater than 1.0 indicate marked
asymmetry.
RESULTS
Leaf nutrients concentration statistics
Summary statistics for the grain yield and leaf nutrient
concentration data available from the 2001, 2002 trial are
given in Table 2. The grain yield data ranged from 264.1 kg
ha-1 to 2208.3 kg ha-1 with a mean of 1117.1 kg ha-1 in the
full population. The difference between the low and the high
sub-populations for yield was highly significant (p = 0.001).
The average foliar N contents and K, Ca, and S concentrations
were higher in the high-yielding sub-population than in the
low-yielding sub-population, with the means being
significantly higher (p < 0.01). So, higher nutrient contents
were observed in the high- yielding sub-population.
Only the Mg content was significantly higher in the low-
yielding sub-population (p= 0.023). The Zn content is the
same in the both subpopulation. Leaf N, K, Ca, Mg and Zn; P
and S nutrient levels in the high yielding subpopulation in our
experiments ranked between or are at the limit of the
sufficiency ranges published by Mills et al., (1996) and
Planck, (1989). Thirty five (36) out of eighty three (83) data
points were assigned to the high-yielding subpopulation
(≥1233.50 kg ha-1). As regards the leaf nutrient
concentrations, the data for all the nutrients N, P, K, Ca, S and
Zn were relatively symmetrical as well in the total population
as in the reference one (high yielding population) with 5 of
them having skewness values
Table 1. Overview of soil characteristics (plough layer: 0 – 20 cm)
at the beginning of the experiment (in parenthesis) Standard
deviation
less than 1.0 and hence were deemed suitable for DRIS model
development. The nutrient Mg was highly skewed in the total
population but was relatively symmetrical in the reference
population. As a result, the data sets for all the nutrient N, K,
Ca ,P, Mg and Zn; and S is relevant to parameterize DRIS
model.
Binary nutrients ratio statistics
Binary nutrient ratio combinations of all seven nutrients were
therefore calculated, and summary statistics evaluated for each
of the resulting 42 nutrient ratio expressions (table 3). To
determine which nutrient ratio expressions in table 3 should be
included in the DRIS model, the selection priorities,
previously outlined (above), were sequentially applied. Firstly,
nutrient ratios were selected that had skewness values less
than 1.0, thereby eliminating 4 nutrient ratio expressions.
Physical properties Chemical properties
Clay
Silt
Sand
Texture
P K pH N OM C/N
Sites
-------[%]------ Mg kg-1 Cmolkg-1 ------[%]-----
Lighter soils
Beterou
Mean 6.8 9.7 82.9 11.1 0.25 6.7 0.064 1.53 14.1
(1.1) (1.4) (1.5) (4.3) (0.04) (0.1) (0.009) (0.23) (0.8)
Dogue
Mean 7.2 9.8 81.8 4.0 0.12 6.4 0.058 1.26 12.76
(0.8) (2.4) (2.9) (1.3) (0.03) (0.1) (0.013) (0.21) (0.8)
Wewe
Mean 7.2 11.0
81.2 6.3 0.14 6.6 0.058 1.26 16.7
(0.9) (2.0) (2.0)
LS
(2.5) (0.03) (0.1) (0.016) (0.17) (9.4)
Heavier soils
Beterou
Mean 8.8 11.7
78.2 17.6 0.31 6.7 0.061 1.66 15.5
(1.5) (1.4) (1.5) (11.8 ) (0.07) (0.1) (0.019) (0.69) (2.3)
Dogue
Mean 8.6 13.8
76.7 5.2 0.15 6.4 0.064 1.42 13.1
(0.7) (1.9) (1.8) (3.1) (0.03) (0.1) (0.008) (0.21) (0.5)
Wewe
Mean 9.6 14.2
75.6 8.1 0.20 6.8 0.068 1.47 13.3
(1.8) (1.9) (1.7)
SL
(3.8) (0.07) (0.1) (0.011) (0.27) (2.3)
Table 2. Summary statistics for groundnut yield and leaf nutrient concentration data for total (n=83) and
high-yielding (n=36) sub-populations
Total
yieding
population (n=83)
Low yielding
sub
-
population (n = 47)
High yielding sub
-
population (n = 36) Sufficiency ranges
Parameters Mean
CV
Skew
Mean
CV Skew
Mean
CV
Skew
(Campwell, 2000)
Grain (kg ha-1)
*** 1114.6
48.3
0.24 695.4
33.5 0.2 1661.9
15.2
-0.1
Nutrients (%)
N*** 3.3 15 .0
-0,1 3. 1 17.5 -0.5 3.6 7.2 0.2 3.5 - 4.5
P** 0.2 19.3
1.6 0.2 22. 1 1.2 0.2 11. 4
-0.0 0. 2 - 0.5
K*** 2.2 24 .4
0.2 2.0 23. 9 0.0 2.5 22. 5
0.3 1.7 - 3.0
Ca*** 1.5 36.8
-1.5 1. 3 49.24
-0.9 1.8 10.7
-0.4 0. 5 - 2.0
Mg** 0.5 73 .2
2.8 0.6 80.91
1.9 0.4 17.8
0.8 0.3 - 0.8
S*** 1.2 24.1
-1.7 0. 1 28.6 -1.8 0.2 12.6
1.2 0.2 - 0.35
Nutrient (ppm)
ZnNs 27.5 31.5
0.3 27.3 33.5 0.1 27.7 29.3
0.6 20 - 60
Yield mean and mean of nutrient concentration of low and high
-
yielding groups are significantly different at
111 International Journal of Current Research, Vol. 4, Issue, 04, pp.108-115, April,
2012
Table 3
. Mean values of nutrient ratios for high and low-yielding sub-populations together with their
respective coefficients of Variance CV’s) and variances (low and high), skewness values for the
high-yielding sub-population, and the variance ratios (Vlow/Vhigh)
Low yielding sub
-
population
High yielding sub
-
population
V (low
Ratios
/high)
Mean CV(%)
Median Mini Max Skew
Mean CV(%)
Median Mini Max Skew
N/P
15
.
0
27
.
0
14
.
5
7
.
0
23
.
6
-
0
.
1
18
.
4
13
.
5
18
.
5
13
.
3
24
.
0
0
.
1
2
.
7
N/K 1.6 24.3 1.6 1.0 2.5 0.5 1.6 26.6 1.6 1.0 2.4 0.4 0.8
N/Ca
4
.
1
90
.
6
2
.
3
1
.
6
16
.
3
1
.
7
2
.
0
12
.
5
2
.
0
1
.
6
2
.
7
0
.
9
217
.
9
N/Mg
7
.
5
46
.
9
7
.
6
1
.
2
15
.
2
-
0
.
1
8
.
9
16
.
9
8
.
9
5
.
2
12
.
1
-
0
.
1
5
.
5
N/S
27
.
2
47
.
7
25
.
4
16
.
6
106
.
6
5
.
4
23
.
9
12
.
3
23
.
5
17
.
4
28
.
6
-
0
.
2
19
.
3
N/Zn
1241
.
1
33
.
2
1194
.
2
684
.
3
3136
.
7
2
.
2
1402
.
6
31
.
0
1361
.
8
660
.
3
2218
.
1
0
.
2
0
.
9
P/N 0.1 33.8 0.1 0.0 0.1 1.5 0.1 13.9 0.1 0.0 0.1 0.6 10.1
P/K
0
.
1
38
.
5
0
.
1
0
.
1
0
.
2
1
.
3
0
.
1
25
.
1
0
.
1
0
.
1
0
.
1
0
.
6
4
.
1
P/Ca 0.4 120.0
0.2 0.1 1.6 1.6 0.1 15.4 0.1 0.1 0.2 0.6 646.1
P/Mg
0
.
6
83
.
4
0
.
4
0
.
1
2
.
1
2
.
3
0
.
5
17
.
0
0
.
5
0
.
3
0
.
7
0
.
4
32
.
2
P/S
2
.
1
94
.
1
1
.
6
1
.
0
14
.
3
5
.
7
1
.
3
15
.
1
1
.
3
1
.
0
1
.
9
0
.
8
96
.
1
P/Zn
93
.
1
56
.
7
83
.
0
35
.
5
256
.
8
1
.
7
76
.
1
27
.
7
71
.
5
43
.
6
123
.
3
0
.
4
6
.
3
K/N 0.7 23.8 0.6 0.4 1.0 0.3 0.7 26.2 0.6 0.4 1.0 0.2 1.0
K/P
9
.
8
33
.
4
9
.
9
4
.
1
18
.
2
0
.
5
12
.
2
24
.
0
12
.
2
7
.
5
19
.
0
0
.
2
1
.
3
K/Ca 2.8 96.9 1.5 0.8 11.9 1.8 1.4 28.7 1.2 0.8 2.3 0.5 47.1
K/Mg
4
.
8
46
.
6
4
.
7
0
.
8
9
.
3
-
0
.
2
6
.
0
33
.
9
5
.
5
2
.
6
10
.
7
0
.
7
1
.
2
K/S
18
.
0
52
.
3
15
.
8
8
.
8
69
.
5
4
.
0
15
.
8
20
.
0
15
.
7
9
.
5
21
.
6
0
.
1
8
.
9
K/Zn
792
.
2
25
.
9
818
.
1
386
.
8
1310
.
5
0
.
1
894
.
5
23
.
4
880
.
2
481
.
7
1298
.
2
0
.
0
1
.
0
Ca/N
0
.
4
45
.
6
0
.
4
0
.
1
0
.
6
-
0
.
8
0
.
5
11
.
8
0
.
5
0
.
4
0
.
6
-
0
.
1
9
.
0
Ca/P 6.3 55.8 6.6 0.6 11.1 -0.5 9.2 14.9 9.2 6.4 12.5 0.1 6.7
Ca/K
0
.
6
51
.
7
0
.
7
0
.
1
1
.
2
-
0
.
3
0
.
8
28
.
1
0
.
8
0
.
4
1
.
3
0
.
4
2
.
1
Ca/Mg
3
.
1
51
.
5
3
.
6
0
.
1
5
.
9
-
0
.
8
4
.
4
18
.
3
4
.
3
2
.
9
6
.
4
0
.
7
3
.
8
Ca/S
9
.
7
43
.
6
10
.
9
1
.
3
16
.
5
-
0
.
8
12
.
0
17
.
2
11
.
6
7
.
6
15
.
4
-
0
.
2
4
.
2
Ca/Zn 463.5 56. 2 474.5 78.0 1488.9 1.2 701.2 32.5 717.7 350.4
1265.8
0.5 1.3
Mg/N
0
.
2
99
.
4
0
.
1
0
.
1
0
.
8
1
.
8
0
.
1
19
.
0
0
.
1
0
.
1
0
.
2
1
.
4
94
.
3
Mg/P 2.9 77.5 2.2 0.5 8.5 1.6 2. 1 17.4 2.1 1.4 3.2 0.7 37.8
Mg/K
0
.
3
92
.
9
0
.
2
0
.
1
1
.
2
1
.
9
0
.
2
34
.
1
0
.
2
0
.
1
0
.
4
0
.
9
22
.
6
Mg/Ca 1.5 195.3
0.3 0.2 13.4 2.5 0.2 17.7 0.2 0.2 0.3 0.3 5116.7
Mg/S
6
.
2
117
.
2
3
.
2
1
.
1
29
.
3
2
.
1
2
.
8
23
.
2
2
.
8
2
.
0
4
.
5
1
.
0
125
.
7
Mg/Zn
263
.
6
105
.
5
138
.
7
89
.
8
1042
.
7
2
.
0
159
.
8
29
.
5
154
.
7
81
.
4
264
.
6
0
.
3
34
.
9
S/N
0
.
0
20
.
7
0
.
0
0
.
0
0
.
1
-
1
.
0
0
.
0
12
.
9
0
.
0
0
.
0
0
.
1
0
.
7
2
.
3
S/P 0.6 31.1 0.6 0.1 1.0 -0.5 0.8 14.2 0.8 0.5 1.0 -0.1 2.8
S/K
0
.
1
30
.
4
0
.
1
0
.
0
0
.
1
0
.
1
0
.
1
21
.
3
0
.
1
0
.
0
0
.
1
0
.
8
1
.
9
S/Ca
0
.
2
97
.
2
0
.
1
0
.
1
0
.
8
2
.
2
0
.
1
18
.
9
0
.
1
0
.
1
0
.
1
0
.
9
93
.
5
S/Mg
0
.
3
56
.
3
0
.
3
0
.
0
0
.
9
0
.
9
0
.
4
21
.
0
0
.
4
0
.
2
0
.
5
0
.
0
4
.
8
S/Zn
49
.
9
43
.
5
47
.
6
13
.
0
142
.
2
2
.
0
58
.
7
28
.
9
55
.
5
28
.
1
93
.
6
0
.
3
1
.
6
Zn/N 0.0 27.5 0.0 0.0 0.0 0.3 0.0 33.7 0.0 0.0 0.0 0.8 1.0
Zn/P
0
.
0
43
.
0
0
.
0
0
.
0
0
.
0
0
.
5
0
.
0
28
.
3
0
.
0
0
.
0
0
.
0
0
.
6
2
.
1
Zn/K 0.0 30.1 0.0 0.0 0.0 1.2 0.0 26.0 0.0 0.0 0.0 1.0 2.0
Zn/Ca
0
.
0
84
.
0
0
.
0
0
.
0
0
.
0
1
.
8
0
.
0
02
33
.
9
0
.
0
0
.
0
0
.
0
0
.
7
26
.
3
Zn/Mg 0.0 45.0 0.0 0.0 0.0 -0.7 0.0 30.5 0.0 0.0 0.0 0.6 1.9
Zn/S
0
.
0
47
.
0
0
.
0
0
.
0
0
.
1
2
.
6
0
.
0
32
.
1
0
.
0
0
.
0
0
.
0
1
.
2
3
.
5
CV: Coefficient of variation; Mini: Minimum; Maxi: Maximum; Skew: Skewness
Table 4. DRIS norms, CV’s and skewness values for the high-yielding sub-
population, and variance ratios (Vlow/Vhigh) of nutrient ratio
expressions selected for inclusion in the DRIS model for
groundnut.
Nutrient Ratios
Norms (mean) CV (%)
Skew
V(low/high)
K/Ca 1.4 28.7 0.5 47.1
K/N 0.7 26.2 0.2 1 .0
K/S 15.8 20.0 0.1 8.9
Mg/Ca 0.2 17.7 0.3 5116.7
Mg/K 0.2 34.1 0.9 22.6
Mg/P 2.1 17.4 0.7 37.8
Mg/Zn 159.8 29.5 0.3 34.9
N/Ca 2.0 12.5 0.9 217.9
N/S 23.9 12.3 -0.2 19.3
Zn/N 0.0OO8 33.7 0.8 1.0
P/Ca 0.1 15.4 0.6 646.1
P/K 0.1 25.1 0.6 4.1
P/N 0.1 13.9 0.6 10.1
P/S 1.3 15.1 0.8 96.1
P/Zn 76.1 27.7 0.4 6.3
S/Ca 0.1 18.9 0.9 93.5
Zn/Ca 0.002 33.9 0.7 26.3
CV: Coefficient of variation
;
Skew: Skewness
112 International Journal of Current Research, Vol. 4, Issue, 04, pp.108-115, April,
2012
Secondly, on the basis of the variance ratios (Vlow/Vhigh),
which had ratios greater than 1.0, 17 of the thirty eight
remaining nutrient ratio expressions were ultimately chosen as
DRIS norms for groundnut. There are K/Ca: 1.4, K/S: 15.8,
K/N:0.7, Mg/Ca: 0.2, Mg/K: 0.2, Mg/P: 2.1, Mg/Zn: 159.8,
N/Ca: 2, N/S: 23.9, Zn/N: 0.0008, P/K: 0.1, P/N: 0.1, P/S: 1.3,
P/Ca: 0.1, P/Zn: 76.1, S/Ca: 0.1, and Zn/Ca: 0.002.
DISCUSSION
Admittedly, the database used for the DRIS model
development was relatively small. However, most of the
nutrient content and yields of high and low-subpopulations
were significantly different. This variation is a consequence of
the source of data. All the data were gathered from
fertilization experiments, were soil nutrient availability
changed due to fertilization treatments. The means and
variance of selected nutrient ratios from the subpopulations
were also different. According to Reis and Monnerat (2003),
those differences between nutritional status of high and low-
yielding subpopulations are indicative of reliability of DRIS
norms that will be developed. Moreover, most of the selected
ratio (14 out of 17) has a low coefficient of variation less than
30%. That means probably the nutrients needed to be kept in
such balance within groundnut tissue if grain production is to
be sustained and optimized (Ramakrishna et al., 2009). The
lower coefficient of variation were associated with a great
ratio of variance ratio between the low- and high- yielding
group (Vlow/Vhigh> 1). As pointed out by Bailey et al.
(1997), DRIS norms (nutrient ratios) with large ratio of
variance and small coefficient of variation imply that the
balance between these specific pairs of nutrients could be of
critical importance for crop production. Therefore, nutrient
ratios with a large ratio of variance with a small coefficient of
variation indicate that a high yield should be associated with a
small variation around the average nutrient ratio. The DRIS
norms for K/N (0.7) a nutrient ratios of known physiological
and diagnostic importance had norm values within the
expected narrow ranges for higher plants, i.e. 0.6–0.9 (Elwali
and Gascho 1984; Meldal-Johnsen and Sumner 1980; Stevens
and Watson 1986; Amundson and Koehler 1987; Jones et al.
1990; Kelling and Matocha 1990; Dampney 1992; Marschner
1995), thus giving another proof of credibility both to the
database and to the DRIS model. Potassium is known to have
a key role in N uptake and translocation (Minotti et al. 1968;
Cushnahan et al. 1995), and therefore both N and K need to be
present in quite specific proportions whether N accumulation
and subsequent assimilation into proteins is to take place at
optimal rates. Furthermore, Ca and Zn are in a good balance
(Ca/Zn = 500, derived from Zn/Ca = 0.002) as reported that
Ca/Zn ratio less than 45-50 indicated zinc toxicity which is a
significant problem and occurs when Zn concentration
approaches 200 ppm (Campwell, 2000). The DRIS model for
groundnut, developed in this study, is then a diagnostic tool
that may be used to predict if insufficiencies or imbalances in
N, P, K, Ca, Mg, S and Zn supplies are occurring in groundnut
crops in Benin. Data from future field and surveys
experiments may subsequently be used to enlarge the model
database and allow the refinement of DRIS parameters and
hopefully an expansion of diagnostic scope to include other
micronutrients. As it stands, though, this preliminary DRIS
model for groundnut is one of the best diagnostic tools
currently available for simultaneously evaluating the N, P, K,
Ca, Mg, S and Zn status of groundnut crops in Benin.
Acknowledgements
Authors are very grateful to the government of the people of
the Federal Republic of Germany through German Ministry of
Education and Science (BMBF) for financing this study and
the authorities of Integratives Management Projekt für einen
Effizienten und Tragfähigen Umgang mit Süßwasser
(IMPETUS) especially Professor Dr. P. Speth, Dr. M.
Christoph and Dr. E. van den Akker for giving material and
administrative supports during all phases of this work. They
thank also Prof. Dr. H. Goldbach for his scientific support.
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