Plant need water, air, light, suitable temperature and 17 essential nutrients for growth and development in the right combination. When plant suffers from malnutrition, exhibits symptoms of being unhealthy reliable nutrient recommendations are dependent upon accurate soil tests and crop nutrient calibrations based on extensive field research. An important part of crop production is being able to identify and prevent plant nutrient deficiencies. Optimization of pistachio productivity and quality requires an understanding of the nutrient requirements of the tree, the factors that influence nutrient availability and the methods used to diagnose and correct deficiencies. Several methods for nutritional diagnosis using leaf tissue analysis have been proposed and used, including the critical value (CV), the sufficiency range approach (SRA), and the diagnosis and recommendation integrated system (DRIS). de both soil and tissues analysis. Renewed and intensified efforts are in progress to identify nutrient constraints using latest diagnostic tools and managing them more precisely through intervention of geospatial technologies (GPS, GIS etc.). There have been consistent concerns about the relegated fertilizer use efficiency, warranting further the revision of ongoing practices, and adoption of some alternative strategies. Diagnosis of nutrient constraints and their effective management has, therefore, now shifted in favour of INM.

1. 1

2. Seminar on
“RECENT DIGNOSTIC TECHNIQUES AND AMELIORATIVE MEASURES OF NUTRIENTS
DEFICIENCIES IN FRUIT CROPS”
Submitted By,
SIRSATH SATISH SHIVAJI
Reg. No: 2017A/117M
Research Guide
Dr . A. L. DHAMAK
Associate Professor,
Dept. of Soil Science and Agril. Chemistry
V.N.M.K.V, Parbhani (M.S.)
Submitted To,
Dr . SYED ISMAIL.
HEAD,
Dept. of Soil Science and Argil. Chemistry
V.N.M.K.V, Parbhani (M.S.)
DEPARTMENT OF SOIL SCIENCE AND AGRIL. CHEMISTRY
COLLEGE OFAGRICULTURE
VASANTRAO NAIK MARATHWADA KRISHI VIDYAPEETH 2

3. Introduction
 Plant need water, air, light, suitable temperature and 17
essential nutrients for growth and development in the right
combination.
 When plant suffers from malnutrition, exhibits symptoms of
being unhealthy reliable nutrient recommendations are dependent
upon accurate soil tests and crop nutrient calibrations based on
extensive field research.
 An important part of crop production is being able to
identify and prevent plant nutrient deficiencies.
 Optimization of pistachio productivity and quality requires
an understanding of the nutrient requirements of the tree, the
factors that influence nutrient availability and the methods used to
diagnose and correct deficiencies
3

4.  Several methods for nutritional diagnosis using leaf tissue
analysis have been proposed and used, including the critical value
(CV), the sufficiency range approach (SRA), and the diagnosis and
recommendation integrated system (DRIS). de both soil and tissues
analysis.
 Renewed and intensified efforts are in progress to identify
nutrient constraints using latest diagnostic tools and managing them
more precisely through intervention of geospatial technologies (GPS,
GIS etc.).
 There have been consistent concerns about the relegated
fertilizer use efficiency, warranting further the revision of ongoing
practices, and adoption of some alternative strategies.
 Diagnosis of nutrient constraints and their effective
management has, therefore, now shifted in favour of INM 4

5.  Biofertlizer is a cost effective renewable energy source and
plays a crucial role in reducing the inorganic fertilizer application
and at the same time increasing the crop yield besides maintaining
soil fertility.
5

6. Soil Order
U. S. Taxonomy
Soil Group
FAO
Deficiency Toxicity
Entisols(fluvents) Fluvisol K, Zn, Fe, Cu, Mn Al
Mollisols (aqu), inceptisols,
entisols, etc. (poorly drained)
Gleysol Mn Fe, Mo
Mollisols (borolls) Chernozem Zn Mn, Fe
Mollisols (ustolls) Kastanozem K, P, Mn, Cu, Zn Na
Vertisols Vertisol N, P, Fe S
Aridisols Xerosol Mg, K, P, Fe, Zn Na
Source - Baligar et.al,(2001) COMMUN. SOIL SCI. PLANT ANAL., 32(7&8), 921–950
Table 1. Potential Element Deficiencies and Toxicities Associated with Major Soil Orders
6

7. DIGNOSTIC TECHNIQUES OF NUTRIENT
DEFICIENCIES
Fruit growers have following main tools to use in evaluating the mineral
nutrition status of their plantings.
 Visual methods of dignosis based on deficiency symptoms shown by the
plants
Soil analysis to ascertain the supply of nutrients in the soil.
The chemical analysis of whole plants and part of plants
 interpretation is based on the total contents of nutrients or other suitable plant
parts , in comparison with critical nutrient concentrations but there are more
sophisticated methods which either consider mobile content (Ca and Fe)
DRIS (Diagnosis and recommendation integrated system)
Biochemical analysis
7

8. Visual Symptoms Diagnosis tools of
Nutrient Deficiencies
8

9. Nutrient deficiency visual symptoms of plants
• Complete crop failure at seedling stage
• Sever stunting of plants
• Specific leaf symptoms appearing at varying times during the season
• Internal abnormalities, such as clogged conductive tissues.
• Delayed or abnormal maturity.
• Obvious yield differences, with or without leaf symptoms.
• Poor quality of crops, including unseen chemical composition
differences
• Yield differences detected only by careful experimental work.
9

10. Source-https://www.google.com/url?sa=i&source=images&cd=&cad 10

11. Source -https://i.pinimg.com/474x/a8/97/c5/a897c5f5484f7609f44e22f7edca0ad4--plant-problems-leaves-leaf-problems.jpg
11

12. A . Nutrient deficiency :
Nitrogen deficiency : Yellow leaves of citrus
Phosphorus deficiency : Necrosis of leaves ( Litchi &
Peach)
Potassium deficiency : Improper finger filling in banana.
Sulphur deficiency : Thick and leathery leaves of citrus.
Boron deficiency : Hen and chicken of grape, necrosis
of Aonla and Mango, water core of
pear, fruit cracking in
pomegranate.
Calcium deficiency : Bitter pit of Apple.
Copper deficiency : Die back in citrus
Zinc deficiency : Little leaf in mango.
Magnessium deficiency : Interveinal chlorosis in Apple.
12

13. P-
Deficiencies
source-https://www.google.com/url?sa=i&=image
13

14. K-
Deficiencies
Source – www.google.com
14

15. Cu-
Deficiencies
Ca-
Deficiencies
15

16. Mg-
Deficiencies
Zn-
Deficiencies
16

17. Fe-
Deficiencies
17

18. Bo-
Deficiencies
18
source-https://www.google.com/url?sa=i&=image

19. Critical limits in soils of Micronutrients
Micronutrients Critical concentration (ppm)
Cu 0.2
Zn 1.0
Fe 4.5
Table 2. Critical limits of some micronutrients
Tablee 3. General sufficiency or optimum range of macro and micronutrients
elements in pants
Macronutrients (%) Sufficiency or
optimum range
Micronutrients
(µgg-1)
Sufficiency or
optimum range
N 2.0-5.0 Zn 20-100
P 0.2-0.5 Fe 50-250
K 1.0-5.0 Mn 20-300
Ca 0.1-1.0 Cu 5-20
Mg 0.1-0.4 B 10-100
S 0.1-0.3 Mo 0.1-0.5
Cl 2000 to20000
Source - Singh et. al. (2005) Mannual of soil plant and water analysis
19

20. Soil testing Diagnosis tools of
Nutrient Deficiencies
20

21.  A soil analysis should always be a part of preparing the site
before planting. Because fruit plants are relatively long lived it
makes sense to amend the soil prior to planting.
 A soil test is practical means using for the dignosis nutrient
status in fruit orchards.
 Take soil samples from the site a year before planting and
apply and deeply incorporate any fertilizer or lime
recommended.
 A soil test is the only practical means to determine which
and how much fertilizer are needed for best growth.
Evaluating soil fertility is a good practice to establish in order
to make most efficient use of applied fertilizer.
21

22. S.N. Soil groups Totle
no.
of
samp
les
Depth of
sampling
(cm)
Available
N
(mgkg-1)
Olsens
P
(mgkg-1)
Exchangeable
K
(mgkg-1)
Exchangeable
Ca
(Cmol (p+ kg1
Exchangeable
Mg
(Cmol(p+)kg-1
1 Typic
Haplustert
35 0-30
30-60
62.05-179.46
(108.03)
53.57-136.6
(93.32)
5.34-27.77
(11.94)
3.19-19.62
(7.30)
214.5-647.4
(425.10)
198.0-565.5
(366.90)
14.50-41.70
(21.22)
13.20-40.50
(20.05)
5.99-11.20
(8.20)
5.30-9.20
(7.36)
2 Vertic
Ustochrept
26 0-30
30-60
88.39-179.01
(129.26)
64.73-145.98
(101.90)
6.54-32.50
(13.96)
3.39-13.28
(7.26)
312.0-551.7
(374.40)
218.4-514.8
(308.10)
12.21-25.20
(16.81)
11.11-21.75
(14.50)
5.79-9.21
(7.09)
4.58-8.16
(6.30)
3 Typic
Ustorthent
15 0-30
30-60
75.00-158.92
(119.57)
61.41-135.58
(102.15)
7.14-26.33
(12.38)
3.04-21.79
(11.19)
241.8-627.9
(390.5)
163.8-331.5
(284.70)
14.85-24.75
(20.09)
10.24-19.55
(16.24)
5.78-8.56
(7.00)
5.21-7.26
(6.53)
4
Lithic
Ustorthent
4 0-30
30-60
75.89-96.87
(83.92)
67.85-82.88
(74.99)
6.96-9.91
(8.37)
4.28-9.07
(6.91)
234.0-276.9
(257.40)
163.8-218.4
(191.10)
12.30-14.45
(13.58)
10.29-12.80
(12.01)
5.21-7.15
(6.06)
5.16-6.03
(5.79)
Table 4. Macronutrient staus of different soil groups of sweet orange Orchards
Source : Patil V.D. and Malewar G.U. (1998) J. Indian Soc. Soil. Sci. 46 (1):151-152
Location- central –eastwest region of marathwada

23. Location Available nutrent (kg ha-1) Location Available nutrent (kg ha-1)
N P2O5 K2O S N P2O5 K2O S (ppm)
Healthy orange orchards Declined orange orchards
Loni (14yr) 222.9 37.6 392.0 11.0 Loni (7 yr) 206.8 22.0 420.0 9.0
Dhanodi
1(12yr)
234.2 38.4 672.0 11.3 Jamalpur(15yr) 208.8 19.8 368.4 9.7
Bahda (12yr) 206.0 25.0 324.0 11.0 Jamalpur(9yr) 177.8 20.2 750.4 10.8
Temburkheda
(8yr)
241.6 31.4 492.8 11.2 Pusla (13yr) 197.5 23.6 364.0 11.5
Dhanodi (9yr) 245.5 26.8 560.0 11.6 Wathoda (16yr) 149.5 23.3 554.4 11.4
Warud (15yr) 273.7 38.3 655.2 10.4 Dhanodi (9yr) 135.4 19.4 571.2 10.6
Table. 5. Nutrients status of orange orchards
23

24. Depth
(cm)
pH EC(dSm-1) Oraganic
carbon
(mgkg-1)
CaCO3
(g/mol)
Zn Cu Fe Mn
mgkg-1
Pedon 1 Dorali
0-20 7.6 0.14 9.3 2.75 1.16 3.87 24.48 8.12
20-35 7.7 0.12 2.5 2.37 0.38 2.11 12.10 4.95
35-70 7.8 0.13 0.4 1.87 0.32 2.29 21.55 6.32
Pedon -2 Chakodh
0-13 7 0.1 9.6 1.8 1.31 3.83 26.48 12.23
13-30 7.2 0.11 3.7 2.3 0.26 2.46 20.14 8.90
30-45 7.4 0.31 4.2 4.8 0.23 0.94 12.33 6.54
45-72 8.1 0.11 1.7 20.2 0.19 1.0 12.33 2.89
Pedon -3 Sonkhamb
0-20 7.2 0.15 13.2 3.8 0.81 2.98 24.89 5.54
20-35 7.1 0.12 5.6 3.7 0.33 0.70 28.49 3.36
35-70 7.1 0.11 2.5 1.8 0.30 1.51 27.16 2.79
Table.6 Soil chemical analysis of Nagpur mandarin orchard soils of
villages in Katol Tahsil.
Source: (Patil et.al 2014) Bioinfolet 11(c)
24

25. Leaf and Tissue testing Dignosis
tools of Nutrient Deficiencies
25

26. soil testing alone may not provide enough information to make
accurate fertilizer decisions for perennial fruit crops. Reliable
commercial soil tests have not been developed for nitrogen, copper
or iron. The need for these elements can best be evaluated by plant
analysis.
Tissue analysis is a powerful tool in assessing mineral nutrition
of crops. Chemically analyzing the concentration of nutrients in the
leaves of growing crops can more precisely define the nutrient
status than an examination of deficiency symptoms. This is
particularly true for perennial fruit crops.
26

27. States Leaf-nutrient concentration (%)
Macronutrients (%) Micronutrients (ppm)
N P K Ca Mg Fe Mn Cu Zn
1.Meghalaya (13) 2.35 0.12 1.28 2.14 0.24 148.2 51.6 5.8 20.4
2. Assam (4) 2.31 0.13 1.12 2.22 0.28 155.0 62.0 5.1 22.2
3.Arunachal Pradesh (6) 2.17 0.09 1.42 2.01 0.22 156.3 51.6 7.0 22.8
4. West Bengal (6) 2.28 0.10 1.45 1.92 0.32 144.2 58.5 8.1 24.3
5. Sikkim(3) 2.05 0.09 2.13 2.38 0.42 133.5 100.3 7.4 24. 2
6. Manipur (4) 2.17 0.09 1.97 2.42 0.38 153.3 70.9 5.7 14.5
7. Mizoram (7) 2.16 0.10 1.73 1.82 0.32 150.1 72.9 22.4 25.6
8. Tripura (9) 2.11 0.09 2.03 1.94 0.29 281.2 83.5 15.3 22.2
Mean 2.20 0.09 1.64 2.11 0.31 165.2 68.9 9.6 22.0
Median 2.47 0.11 1.43 1.98 0.34 138.9 60.2 8.5 25.5
Source : (Shrivastava and Singh, 2006) Journal of Plant Nutrition, 29: 1061–1076
Figures in parenthesis indicate the number of locations in each state.(Pooled values for
Table 7. Leaf nutrient status of citrus orchards across eight states of east-northeast India
27

28. Critical reproductive
growth stages
Leaf K content (%) Soil available
(mg K kg-1)
Correlation
coefficient (r)
values
Flowering 1.04
(0.96-1.12)
221.2
(201.4-241.3)
0.312
Fruit set 1.25
(1.18-1.33)
200.8
(190.0-221.7)
0.176
Fruit development 0.98
(0.92-1.07)
272.4
(233.4-329.3) 0.346
Colour break 0.87
(0.83-0.92)
227.7
(214.2-241.3) 0.884
Maturity 0.65
(0.58-0.73)
302.0
(245.1-359.1) 0.112
Table 8. Leaf K accumulation, soil available K and their relationship at critical
reproductive growth stages of Nagpur mandarin
Source : Srivastava (2011) Karnataka J. Agric. Sci., 24 (1) : (60-66)
Figures in parenthesis indicate range
. 28

29. Sr.
no.
Macronutrient (% of oven dry basis) Micronutrient (mg kg-1 of oven dry basis)
Location N P K Fe Mn Cu Zn
1 Nokha 1 1.87 0.76 0.36 190.60 23.33 19.50 13.77
2 Nokha 2 0.79 0.43 0.57 133.45 7.10 7.67 6.00
3 Khichiya1 0.88 0.62 0.76 173.87 10.25 9.29 4.25
4 10 JMD 1 0.82 0.43 0.74 209.67 14.60 10.60 9.86
5 12 JMD 2 1.17 0.21 0.37 185.75 4.07 9.25 4.91
Overall mean 1.10 0.49 0.56 178.66 11.87 11.26 7.75
range 0.79-1.87 0.21-
0.76
0.36-0.76 133.45-
209.64
4.07-23.33 7.67-
19.50
4.25-
13.77
Low 60 - 100 - 100 - 100
Sufficient 40 20 - 100 - 100 -
High - 80 - - - - -
Table 9. Content of macro and micronutrients in leaf samples of Mosambi
collected from orchards in Bikaner District
Source: Bhatnagar and Chandra (2003) Joumal of Eco-Physiology 6(1-2) 69-72.
29

30. Table 10. Soil and leaf zinc concentrations and relative dry matter yield under various
treatments
S.N. Soil Zn conc.
(mg kg-1)
Leaf Zn conc.
(mg kg-1)
Relative* yield
(%)
1 0.42 20.30 81.73
2 0.62 24.60 83.24
3 0.74 26.50 83.29
4 0.80 28.30 83.41
5 0.88 29.21 84.38
6 0.95 30.20 83.33
7 1.00 31.60 84.95
8 1.12 32.26 89.78
9 1.17 34.60 92.90
10 1.26 36.60 93.53
11 1.49 38.80 91.95
12 1.66 40.20 87.84
13 1.80 41.20 88.37
14 2.26 43.40 87.77
15 2.62 44.90 87.19
Source: Patil V.D (1997) Ph.D Thesis, VNMKV, Parbhani, MH., India
30

31. Fig. 1 DTPA Extractable Zn in relation to relative yield (biological) of
sweet orange
80
82
84
86
88
90
92
94
96
0 0.5 1 1.5 2 2.5 3
Relative* yield (%)
Relative* yield (%)
1.05 mg Zn kg-1 Critical limit

32. DRIS Diagnosis tools of Nutrient
Deficiencies
32

33. The usual methods for soil and leaf chemical analysis interpretation presuppose the nutrient
concentration comparison with reference values (critical concentrations or sufficiency ranges).
The DRIS method expresses results of plant nutritional diagnosis through indices, which
represent, in a continuous numeric scale, the effect of each nutrient in the nutritional balance of
the plant
The working premises for DRIS are based on:
(a) the ratios among nutrients are frequently better indicators of nutrient deficiencies than
isolated concentrations values;
(b) some nutrient ratios are more important or significant than others;
(c) maximum yields are only reached when important nutrient ratios are near the ideal or
optimum values, which are obtained from high yielding-selected populations;
33

34. The working premises for DRIS are based on:
• as a consequence of the stated in (c), the variance of an important nutrient ratio is smaller in
a high yielding (reference population) than in a low yielding population, and to the relations
between variances of high and low yielding populations can be used in the selection of significant
nutrient ratios;
• (e) the DRIS indices can be calculated individually, for each nutrient, using the average
nutrient ratio deviation obtained from the comparison with the optimum value of a given nutrient
ratio, hence, the ideal value of the DRIS index for each nutrient should be zero.
34

35. Table 11. Leaf analysis based DRIS norms in relation to fruit yield of
mandarin orchards Norms
Nutrients Deficient Low Optimum High Excess
N (%) <1.12 1.12–1.69 1.70–2.81 2.82–3.38 >3.38
P (%) <0.06 0.06–0.08 0.09–0.15 0.16–0.19 >0.19
K (%) <0.22 0.22–1.01 1.02–2.59 2.60–3.38 >3.38
Ca (%) <1.1 1.1–1.79 1.80–3.28 3.29–4.02 >4.02
Mg (%) <0.31 0.31–0.42 0.43–0.92 0.93–1.38 >1.38
Fe (ppm) <55.6 55.6–74.8 74.9–113.4 113.5–132.7 >132.7
Mn (ppm) <40.2 40.2–54.7 54.8–84.6 84.7–98.7 >98.7
Cu (ppm) <5.9 5.9–9.7 9.8–17.6 17.7–21.5 >21.5
Zn (ppm) <5.5 5.5–13.5 13.6–29.6 29.7–37.7 >37.7
Yield (kg tree−1) <12.9 12.9–47.6 47.7–117.2 117.3–152.1 >152.1
Source: Shrivastava and singh (2008) Journal of Plant Nutrition, 31: 1091–1107, 2008
35

36. Nutrients Deficient Low Optimum High Excess
N (%) <0.96 0.96–1.20 1.21–1.85 1.86–2.10 >2.10
P (%) <0.09 0.10–0.12 0.13–0.18 0.19–0.22 >0.22
K (%) <0.82 0.82–1.18 1.19–1.62 1.63–1.82 >1.82
Ca (%) <0.18 0.18–0.26 0.27–0.35 0.36–0.42 >0.42
Mg (%) <0.24 0.24–0.42 0.43–0.56 0.57–0.70 >0.70
Fe (ppm) <61.1 61.1–78.3 78.4–102.5 102.6–168.1 >168.1
Mn (ppm) <30.2 30.2–41.4 41.5–58.3 58.4–61.6 >61.6
Cu (ppm) <5.8 5.8–7.3 7.4–10.2 10.3–12.3 >12.3
Zn (ppm) <9.6 9.6–12.1 12.2–15.8 15.9–19.6 >19.6
Yield (kg tree−1) <38 38–55 55–72 72–88 >88
Table 12. Leaf nutrient norms determined from DRIS based analysis
for pineapple grown in tropical India
(Source: Akali Sema et.al, (2010 )Journal of Plant Nutrition, 33:1384–1399.
36

37. Nutrients
mgkg-1
Low yielding
orchards(A) (n = 27)
High yielding
orchards (B) (n = 30)
X− CV(%) SA X− CV(%) SB (SA / SB)*
N 101.23 11.33 12.38 124.75 18.07 22.54 0.549
P 9.11 22.98 1.82 11.23 31.75 3.56 0.511
K 199.37 31.19 28.96 229.35 27.03 61.98 0.434
Ca 318.96 10.11 1.30 511.60 15.18 3.88 0.335
Mg 82.39 31.28 1.18 123.48 24.19 2.48 0.475
Fe 10.11 29.23 6.19 19.85 58.05 11.53 0.536
Mn 8.23 21.20 3.81 15.31 38.64 5.92 0.643
Cu 2.82 30.72 0.79 3.73 27.76 1.02 0.774
Zn 0.58 52.12 0.18 0.79 44.32 0.35 0.514
Yield
(kg/tree)
38.29 21.27 18.16 82.44 31.67 26.11 0.695
Table 13. Mean soil nutrient concentration between low and high-yielding
orchards of Nagpur mandarin in vidharbha region
∗Variance of low - and high- yielding orchards’ population was significantly different (p = 0.01).
X− and CV stand for mean and coefficient of variation, respectively.
SA and SB stands for variance of low and high yielding orchards.
Source: Shrivastava and singh (2008) Journal of Plant Nutrition, 31: 1091–1107.
37

38. Parameters Deficient Low Optimum High Excess
pH < 7.2∗ 7.2–7.5 7.6–8.2 8.3–8.6 >8.6∗∗
OC (%) <0.26 0.26–0.37 0.38–0.62 0.63–0.74 >0.74
N (mgkg-1) <64.7 64.7–94.7 94.8–154.8 154.9–184.9 >184.9
P (mgkg-1) <4.8 4.8–6.5 6.6–15.9 16.0–20.7 >20.7
K (mgkg-1) <64.1 64.1–146.7 146.8–311.9 312.0–394.6 >394.6
Ca (mgkg-1) <306.1 306.1–408.0 408.1–616.0 616.1–718.0 >718.0
Mg (mgkg-1) <43.3 163.3–202.8 85.2–163.2 163.3–202.8 >202.8
Fe (mgkg-1) <4.6 4.6–10.9 10.9–25.2 25.3–40.6 >40.6
Mn (mgkg-1) <4.7 4.7–7.4 7.5–23.2 23.3–31.1 >31.1
Cu (mgkg-1) <1.1 1.1–2.4 2.5–5.1 5.2–6.5 > 6.5
Zn (mgkg-1) <0.33 0.33–0.58 0.59–1.26 1.27–1.73 >152.1
Yield (kg tree−1) <12.9 12.9–47.6 47.7–117.2 117.3–152.1 152.1 >152.1
Table 14. Soil analysis based DRIS norms in relation to fruit yield of
mandarin orchards Norms
Source: Shrivastava and singh (2008) Journal of Plant Nutrition, 31: 1091–1107, 2008.
38

39. Ameliorative measures
39

40. 40

41. Treatment No. of
fruits/tree
No. of fruits
(kg/tree)
fruits yield
(t/ha)
T1–Control 866 78.72 8.66
T2–Pot. silicate @ 4 ml/l (Foliar spray) 1225 122.50 12.25
T3–Pot. silicate @ 6 ml/l (Foliar spray) 1131 113.48 11.30
T4–Pot. silicate @ 4 ml/l+1/2 dose of pesticide
(Foliar spray)
1447 144.75 14.47
T5–Pot. silicate @ 6 ml/l+1/2 dose of pesticide (Foliar
spray)
1157 116.0 11.60
T6–Cal. silicate–1 kg/tree (Soil application) 977 97.5 9.73
T7–Cal. silicate–1.5 kg/tree (Soil application) 1050 105.50 10.50
T8–Cal. silicate–2.0 kg/tree (Soil application) 1140 114.00 11.40
T9–Cal. silicate–2.5 kg/tree (Soil application) 1270 127.0 12.70
C. D. (P=0.05) 54.17 7.52 0.75
S. Em± 18.07 2.51 0.25
Table 15. Influence of soil and foliar application of silicon on fruit yield
Source :Thippeshappa, et.al.2014 reaserch on crops 15(3), 626-630
41

42. Treatments Macro- Nutrient (per cent dry weight basis)
N P K Ca Mg S
T1- Full dose of NPK 2.56 0.14 0.16 2.35 0.35 0.25
T2- ¾ NPK + AMF (Arbuscular mycorrihzal fungi mixed strains –
Nutrilink of IARI) T3- ¼ NPK + AMF + Azospirillum
2.41 0.16 1.52 2.58 0.36 0.24
T3- ¼ NPK + AMF + Azospirillum 2.19 0.12 1.36 2.42 0.30 0.22
T4- ½ NPK + Azospirillum+ AMF 2.25 0.15 1.45 2.65 0.33 0.29
T5- ¾ NPK + Azospirillum+ AMF 2.52 0.14 1.51 2.39 0.35 0.31
T6- ¾ NPK + Azospirillum+ AMF + micronutrient (Cu + Fe + Zn + B,
0.4% each)
2.65 0.16 1.58 2.78 0.38 0.35
T7- ½ NPK + Azospirillum+ AMF + micronutrient (Cu + Fe + Zn + B,
0.4% each)
2.31 0.13 1.55 2.45 0.38 0.33
T8- Control 2.05 0.11 1.31 2.12 0.27 0.19
CD at 5% 0.26 0.04 0.32 0.12 0.06 0.04
Table 17. Response of microbial and inorganic fertilizers on leaf macro-nutrient
contents of sweet orange cv. Mosambi.
Source: Patel et.al 2009, Indian J. Hort 66 (2) June 2009:
163-168 42

43. •Leaf nutrient composition as affected by different INM treatments
T1= (RDF) (1000 g N+400 g P2O5+400 g K2O/ tree/yr)
T2= FYM (to supply 100% N)
T3= VERMICOMPOST (to supply 100% N)
T4= FYM ((to supply 50% N)+ 50% RDF
T5= VERMICOMPOST (to supply 50% N)+ 50% RDF
T6= GREEN MANURING WITH SUNHEMP (to supply 50% N)+50% RDF
T7= WHEAT STRAW (to supply 50% N)+50%RDF
T8= FYM (to supply 25% N)+50%RDF+AZOTOBACTER+PHOSPHATE
SOLUBILIZING BACTERIA (PSB)
T9= VERMICOMPOST (to supply 25% N)+50% RDF+ AZOTOBACTER+PSB
T10= 75% RDF + AZOTOBACTER+PSB
T11= FYM (to supply 75% N) + AZOTOBACTER+PSB
T12= VERMICOMPOST (to supply 75% N) + AZOTOBACTER+PSB
T13= CONTROL
43

44. Table 18. Leaf nutrient composition as affected by different INM
treatments (pooled mean of four seasons)
Treatment N(%) P(%) K(%) Ca(%) Mg(%) Zn(ppm) Mn(ppm) Iron(ppm) Copper
(ppm)
T1 2.07 0.096 1.15 2.25 0.275 20.9 67.5 121.5 14.3
T2 1.99 0.094 1.27 2.25 0.287 23.1 74.8 154.6 14.2
T3 2.05 0.092 1.18 2.19 0.277 22.6 71.3 144.4 14.4
T4 2.19 0.111 1.38 2.43 0.302 26.3 70.7 145.5 16.2
T5 2.11 0.097 1.19 2.39 0.289 24.0 68.9 134.2 14.3
T6 2.05 0.096 1.34 2.51 0.294 23.2 68.4 137.8 15.8
T7 2.02 0.093 1.14 2.21 0.273 23.5 69.5 131.2 13.1
T8 2.05 0.096 1.19 2.29 0.279 22.8 69.2 132.4 15.1
T9 1.98 0.098 1.20 2.22 0.281 21.6 69.6 131.0 13.7
T10 1.94 0.094 1.10 2.14 0.258 20.3 66.4 117.4 13.5
T11 1.98 0.101 1.20 2.15 0.289 22.5 71.6 145.0 13.9
T12 1.98 0.100 1.11 2.03 0.272 21.2 68.8 132.8 14.5
T13 1.83 0.089 0.99 1.90 0.251 18.0 63.7 108.0 12.5
CD at 5% 0.13 0.005 0.09 0.15 0.013 1.52 4.19 10.4 1.96
Source: Marathe et.al.(2012) Indian J. Hort 69 (2): 163-168
44

45. Conclusion
• Diagnosis techniques of nutrient deficiency are mainly visual diagnosis, soil
analysis ,leaf analysis, biochemical analysis, tissue analysis and DRIS techniques
• Soil and leaf analysis such as this would improve the very often inconsistent
response of orchards to fertilization due to discrepancies in soil fertility and plant
nutritional problems at the regional level.
• You can monitor the nutrient status of your fruit crop by routine soil and tissue
analysis. By doing so, you can prevent deficiencies before they occur and
minimize inefficient use of applied nutrients
• Diagnosis of nutrient constraints based on DRIS analysis showed a good
agreement between leaf and soil analysis data. All the nutrient constraints
identified through original orchard data analysis further 45

46. •indicated a significant field response on fruit yield and improvement in respective
nutrient concentration in leaves These observations lend strong support for utility of
DRIS in identification and management of nutrient constraints in fruit orchards.
• In general organic manures treated fruits have higher storage life with lower physical
loss weight as compared to inorganic fertilizer treatments.
•Higher dose of FYM can be lowered for sustainable yield and high return through
scientifically planned integrated nutrient management supply.
46

47. “ Feed the soil rather than feeding
the plant….”
Thank you….
47