This lecture discusses soil fertility management in Tamil Nadu, India. It introduces Dr. B. Ramamoorthy, a prominent soil scientist. It then discusses several approaches to determining soil fertility and fertilizer recommendations, including nutrient budgeting, nutrient indexing, targeted yield approaches, percentage yield sufficiency concepts, and the development of a decision support system (DSS) for integrated fertilizer recommendations. The DSS helps generate site-specific recommendations based on factors like soil type, crop, and water quality. Long-term experiments show that the DSS approach maintains soil nutrients and improves yields compared to blanket recommendations.

1. This lecture is dedicated to the great Soil Scientist
Dr. B. Ramamoorthy

2. Conceptual Framework of Soil Fertility Management in
Tamil Nadu – Developing Stories to Strengthen
Successes and Rectify Failures

3. Natural Ecosystem
Agro Ecosystem

5. Neanderthals may have
cleared a European
forest with fire or tools
Neumark-Nord in Germany, the region had far
fewer trees than surrounding areas, suggesting
they may have cleared the forest on purpose -
about 125,000 years ago

6. New archaeological and
paleoenvironmental findings now date
human activity that transformed our
natural surroundings to more than
80,000 years ago, after early modern
humans settled on the northern shores
of Lake Malawi

7. Sustainability of soil fertility in agricultural
production systems

8. Nutrient Budgeting
NUTMON - Toolbox
(Smaling , 1998)

9.  Proposing possible interventions for adoption
towards mitigating undesirable trends
 Identification of soil fertility related factors
limiting crop production
 Calculation of nutrient balance at different
spatial scales, viz., crop activity, farm &
district
NUTMON - Toolbox (NUTrient MONitoring Toolbox)
Nutrient monitoring at farm level

10. Description of nutrient inflows and outflows in a farm
Murugappan et al. 2006

11. NUTMON Toolbox generated
nutrient balances at farm level
Nutrient
(kg ha-1) Marginal Small Medium Large
N -48.6 5.3 -5.1 - 27.4
P -46.5 -9.6 15.5 25.1
K -126.7 -157.8 -31.3 -74.4

12.  Critically re-looking the existing fertilizer practices
 Recycling of On-farm wastes / crop residues by
composting / vermicomposting
 Proper planning of flow of nutrients from field to
livestock and vice-versa
 Legume in the Cropping system
Proposed management interventions

13. Soil Fertility Management

15. What is a chemical?
• Are not just exotic substances found in a chemistry lab
• Everything is a chemical because everything is made of matter
• Your body is made of chemicals. So is your pet, your desk, the grass, the air, your
phone, and your lunch
• Anything you can taste, smell, or hold consists of matter and is therefore a
chemical
• Examples include the chemical elements, such as zinc, helium, and oxygen;
compounds made from elements including water, carbon dioxide, and salt; and
more complex materials like your computer, air, rain, a chicken, a car, etc.

16. The research output of
Soil Fertility Scientists are easy to tell but
not that easy to sell

17. General / Blanket recommendation
Followed in Tamil Nadu since 1953
• Based on the results of model agronomic experiments on
government farms
• Simple fertilizer trials on cultivator's field
• Economy and efficiency of fertilizer use not known
• Leads to either over or under usage of fertilizer nutrients

18. Soil nutrient as an index of soil fertility
S. No. Soil Nutrients
Soil Fertility Rating
Low Medium High
1 Organic carbon as a measure of available N (%) < 0.5 0.5 - 0.75 > 0.75
2 Available N (Alkaline KMnO4-N) (kgha-1) < 280 280 - 560 > 560
3 Available P (Olsen-P) (kgha-1) < 10 10 - 24.6 > 24.6
4 Available K (Neutral N NH4OAc-K) (kgha-1) < 108 108 - 280 > 280
Soil fertility rating

19. Nutrient indexing and preparation of soil fertility maps
The following equation is used to calculate Nutrient Index Value:-
(Nl X 1) + (Nm X 2) + (Nh X 3)
Nutrient Index (NI) = -----------------------------------------
Nt
Where,
Nt = Total number of samples analyzed for a nutrient in any given area.
Nl = Number of samples falling in low category of nutrient status.
Nm = Number of samples falling in medium category of nutrient status.
Nh = Number of samples falling in high category of nutrient status.
Parker et al (1951)
< 1.5 as indicative of low nutrient status
1.5 to 2.5 as medium
> 2.5 as high nutrient status

20. Nutrient indexing and preparation of soil fertility maps

21. Soil fertility management through targeted yield approach

22. Ramamoorthy et al. (1967)
Nutrient Requirement (NR) Soil (α ) & fertilizer (β ) nutrient
efficiencies
U = f (S, F)
U = α S + β F
NR = U / Y (or) U = NR Y
NR Y = α S + β F
NR = U / Y, α = U0/S0, β = [UF – (α SF)] / F
F = 1/ β [ NR Y - α S ]
Drawbacks
α > 1, β > 1
Does not lend for the estimation of standard errors of α & β

23. Nutrient Requirement (NR) Soil (α ) & fertilizer (β ) nutrient
efficiencies
TNAU model I
U = α S + β F , such that 0 ≤ α ≤ 1 and 0 ≤ β ≤ 1
TNAU model II
U = α S + β F + γ, such that 0 ≤ α ≤ 1, 0 ≤ β ≤ 1 & γ ≥ 0
Drawbacks
α & β sometimes 0 or 1
Do not lend for the estimation of standard errors of α & β
Murugappan et al. (1988a)

24. Nutrient Requirement (NR) Soil (α ) & fertilizer (β ) nutrient
efficiencies
Natarajan (1991)
Bayesian Theorem
U = α S + β F + Є, such that 0 ≤ α ≤ 1, 0 ≤ β ≤ 1 & Є
is a random error component assumed to follow a normal distribution
with mean 0 and unknown variance σ 2
Strength
Mean values of α and β satisfy the prior information on them, viz., 0 ≤ α ≤
1and 0 ≤ β ≤ 1
The variation in α in the presence of F is fully taken into account by the
joint probability density P (α, β / U, S, F).

25. N P K
α β kg ks α β kg ks α β kg ks
Conventional method
0.5755 0.6445 1.12 1.12 1.5065 1.4461 1.61 1.65 0.6316 2.9406 1.64 1.62
TNAU model I
- - - - 1.0000 0.7870 1.36 1.37 0.9232 0.5749 2.23 2.38
TNAU model II
- - - - 0.9704 0.4201 1.50 1.52 0.9232 0.5749 2.23 2.38
(γ = 12.84) (γ = 0)
Bayesian theorem
- - - - 0.9532 0.5224 1.30 1.30 0.4088 0.5865 1.23 1.23
Estimates of α and β by conventional method, TNAU Model I and Bayesian theorem and α, β
and γ by TNAU Model II for rice (IR 20)

26. Soil Test Crop Response based fertilizer prescription under Integrated
Plant Nutrition System (STCR – IPNS) are site and situation specific
technology
STCR-IPNS have been developed for
• 36 agricultural and horticultural crops
• 10 major cropping sequences
• Covering17 soil series in Tamil Nadu
Our Reach in Targeted Yield Concept

27. Location TNAU, Wetland Farm, Coimbatore
Year of start Kharif, 1998 (24 years old)
Cropping sequence Rice-Rice (48 crops)
Soil Typic Haplustalf (Clay loam-Noyyal series)
Seasons Kharif Rabi
Yield targets 6 and 7 t ha-1 5 and 6 t ha-1
After 24 years of cropping with STCR-IPNS technology
• Maintenance of available N (280 to 269 kg ha-1)
• Built up in OC (4.6 to 8.6 g kg-1) and available P (20.2 to 28.9 kg ha-1)
• Lesser magnitude of decline in available K (670 to 585 kg ha-1)
• Increased yield by 26 % (6.85t ha-1) and 24 % (6.05 t ha-1) in kharif and rabi seasons respectively
Long term STCR-IPNS Experiment on rice-rice sequence

28. S. No Treatments
Kharif Rabi
Yield
(t ha-1)
RR
(kg kg-1)
Yield
(t ha-1)
RR
(kg kg-1)
1. Blanket 5.43 12.16 4.88 10.73
2. STCR - NPK alone - 6/5 t ha-1 5.70 14.16 4.99 15.38
3. STCR - NPK alone -7/6 t ha-1 6.61 15.17 5.87 16.25
4. STCR - IPNS- 7/6 t ha-1 6.84 17.10 6.05 18.09
5. Absolute Control 2.67 2.64
26 and 24 %
Yield and FUE (mean of 24 crops)

29. S. No. Treatments
Available nutrients
(kg ha-1)
OC
(mg kg-1)
N P K
1. Blanket 230 21.0 515 6.2
2. STCR –NPK alone -6 tha-1 238 22.2 519 7.2
3. STCR –NPK alone-7 t ha-1 255 25.1 554 7.5
4. STCR-IPNS- 7 t ha-1 269 28.9 585 8.6
5. Absolute Control 162 15.2 435 5.4
Initial status (kharif 1998) 280 20.2 670 4.6
Changes in soil available nutrient status (After 24 years)

30. Available
K
(kgha
-1
)
0
5
10
15
20
25
30
35
1998 2007 2014 2021
BLANKET
STCR 6 t/ha
STCR 7 t/ha
IPNS
CONTROL
Available P (kg ha-1)
Available
P
(kgha
-1
)
100
150
200
250
300
350
400
1998 2007 2014 2021
Available
N
(kgha
-1
)
Available N (kgha-1)
BLANKET
STCR 6 t/ha
STCR 7 t/ha
IPNS
CONTROL
400
450
500
550
600
650
700
1998 2007 2014 2021
BLANKET
STCR 6 t/ha
STCR 7 t/ha
IPNS
CONTROL
Available K (kg ha-1)
0.0
2.0
4.0
6.0
8.0
10.0
1998 2009 2014 2021
BLANKET
STCR 6 t/ha
STCR 7 t/ha
IPNS
CONTROL
Organic carbon (g kg-1)
Organic
carbon
(g
kg
-1
)
Changes in soil available nutrient status over 24 years

31. Soil fertility management through percentage yield sufficiency concept
(Bray, 1954)
The Mitscherlich-Bray equation is:
Y=A (1 - e-C1b – Cx)
This equation can be deduced to
Log (A-Y) = Log A – C1b – Cx
Where,
A = calculated (theoretical) maximum yield,
Y = percentage yield,
C1 = proportionality factor for soil nutrient b = soil test value (kgha-1),
C = proportionality factor for fertilizer nutrient
x = dose of fertilizer added (kgha-1).
The maximum yield (A) is calculated by extrapolation.

32. Decision Support System for Integrated FErtilizer
Recommendation (DSSIFER)

35. DSSIFER
Embedded with
• Targeted yield equations
• Mitcherlich-Bray equations
• Blanket recommendations
• Water quality evaluation parameters
Generates site specific integrated fertilizer recommendation
to crops
• Mineral fertilizers
• Organic manures
• Bio fertilizers
• Water quality for irrigation

36. Farmer’s Practice Vs DSSIFER Based
Marginal Farm
Farmer’s Practice DSSIFER Based
86.4 1.4 -48.6 188.3 7.0 52.5
19.6 0.2 -46.5 60.8 2.2 0.9
30.5 1.5 -126.7 129.3 7.8 6.9
Fertilizers OM Balance
Fertilizers OM Balance
(kg ha-1)
(kg) (kg)
(kg ha-1)
N
P
K

37. Farmer’s Practice Vs DSSIFER Based
Small Farm
Farmer’s Practice DSSIFER Based
228.7 26.9 -47.0 372.1 34.1 22.3
72.8 10.0 2.9 103.2 11.0 13.4
98.9 27.5 -94.6 253.5 31.3 8.0
Fertilizers OM Balance
Fertilizers OM Balance
N
P
K
(kg ha-1)
(kg)
(kg ha-1)
(kg)

38. Farmer’s Practice Vs DSSIFER Based
Medium Farm
Farmer’s Practice DSSIFER Based
419.5 40.7 15.3 564.8 60.2 39.4
72.8 12.7 - 9.6 125.2 20.6 16.9
462.9 33.2 -157.8 621.2 55.8 6.8
Fertilizers OM Balance
(kg ha-1)
Fertilizers OM Balance
N
P
K
(kg)
(kg ha-1)
(kg)

39. Farmer’s Practice Vs DSSIFER Based
Large Farm
Farmer’s Practice DSSIFER Based
379.4 11.3 -27.4 638.8 19.6 27.2
133.7 4.1 25.1 225.9 6.1 46.9
210.9 12.5 -74.4 390.4 18.9 4.5
Fertilizers OM Balance
Fertilizers OM Balance
(kg ha-1)
(kg)
(kg ha-1)
(kg)
N
P
K

40. Construction of Decision Support System

41. Screen Short of DSS www.icnms.cwrdm.org
(Surendran,2022)

42. • Development and Construction of decision support system (DSS) by linking the
model with the GIS (dynamics in soils, plants and atmosphere continuum)
• Deriving site specific fertilizer recommendations for selected crops and assessing
the soil fertility through models
• Formulation / simulation of best possible combination of packages / strategies using
the DSS
• Using the DSS - suitable soil specific fertilizer recommendation package as ready
reckoner for users (successful transfer of technology to assess the economic
benefits )
Reaping the benefits from the DSS

43. Agroecological framework for nutrient management

44. AEZ is based on,
• soil texture (FCC - Buol et al., 1975)
• soil moisture balance
• length of growing period (rainfall and potential evapotranspiration)
Agroecological framework for nutrient management

45. Soil map of Erode and Coimbatore districts

46. FCC, MI, LGP maps of Erode and Coimbatore districts

48. Secondary and Micronutrient Management

49.  Re-fixed the Critical limits for Zn (27) and Cu (23) for soils and plants
 Newly fixed critical limits for Mg (25)
Nutrients Test crops Soils
Critical limits (mg kg-1)
Deficient Medium High
Zinc*
Maize, Sorghum,
Groundnut
All soils < 0.85 0.85 - 1.60 > 1.60
Rice Wetland < 0.83 0.85 - 1.01 > 1.01
Iron* Sorghum
Calcareous < 6.40 6.40 - 8.00 > 8.00
Non calcareous < 3.70 3.70 - 8.00 > 8.00
Manganese* Cumbu All soils < 2.00 2.00 - 4.00 > 4.00
Copper*
Sorghum, Maize, Onion All soils < 0.63 0.63 - 1.00 > 1.00
Rice Wetland < 0.79 0.79 - 0.93 > 0.93
Boron** Groundnut All soils < 0.46 0.46 - 1.00 > 1.00
Magnesium*** Potato Acid & Sandy loam soils < 42.0 42.0 - 83.0 > 83.0
Sulphur**** Onion Sandy loam < 10.0 10.0 - 15.0 > 15.0
Methods used for analysis *0.005M DTPA **Hot Water ***Versenate ****0.15%CaCl2
Secondary and Micronutrient Management

51. Nutrients Crops
Technologies (kg ha-1) % yield
increase
Soil Foliar (Thrice)
Zn (ZnSO4) Rice, Sorghum, Maize, Pulses, Groundnut, Sunflower, Sesame, Beetroot,
Radish, Carrot, Ragi,LabLab, Cowpea, Pumpkin, Chilli, Tomato, Sesame,
Brinjal, Cabbage, Small onion, French bean, Knol khol, Tapioca, Banana
25.0
0.50 %
6.0 - 28.2
Turmeric, Cotton, Tobacco, Grapes 50.0 13.2 - 17.0
Sugarcane, Maize, Cabbage, Cauliflower, Rice 37.5 9.0-25
Fe (FeSO4)* Semi dry and rainfed Rice, Sorghum, Maize, Pulses, Groundnut,
Sunflower, Tobacco, Grapes
50 1% 18.0-22.0
Sugarcane, Tobacco, Turmeric 100 10.0-20.0
B (Borax) Rice, Maize, Pulses, Groundnut , Vegetables, Pulses, Beetroot, Radish,
Carrot, Ragi, Chilli, Tomato, Cabbage, French bean, Knol khol, Grapes
10 0.20 % 17.0 - 25.0
Cu (CuSO4) Rice, Tomato, Cabbage, Bellary onion, Maize, Ragi, Groundnut, Sugarcane,
Cauliflower
5-10 0.20 % 10 – 18.0
Mn (MnSO4) Onion, Sesame, Cumbu 10 0.20 % 16.4-23.8
Mo (Sodium / Amm.
Molybdate )
Pulses, Fodder Cowpea, Cauliflower, Cabbage 0.50/0.25 0.05% 20-30.0
Sulphur (SSP / Gypsum) Rice, Groundnut, Oilseeds, Pulses, Tapioca, Onion, Sugarcane, Sesame 20 - 30 - 20.1-32.0
Magnesium (MgSO4) Potato, Cotton 50 2.0% 15-20
Zn (ZnSO4) +B(Borax) French Bean, Carrot, beetroot, Radish, Onion, Beans, Garlic, Lablab,
Brinjal, Bhendi, Tomato, Tapioca, Ragi, Maize
25 kg ZnSO4
+10 kg Borax
0.50% ZnSO4 +
0.20% boric
acid
8.06 – 36.0
* FYM & 0.10% citric acid need to be added
Soil test based Secondary and Micronutrient recommendations : Agricultural (22) & Horticultural (24) crops

52. For addressing Multi micronutrient deficiencies …. Crop Specific TNAU Micronutrient mixtures
Crops
Dosage* (kg ha-1)
Irrigated Rainfed
Rice 25.0 12.5
Maize 30.0 7.5
Pulses 5.0 5.0
Sugarcane 50.0 -
Cotton 12.5 - 15.0 7.5
Groundnut 12.5 7.5
Gingelly 12.5 7.5
Sunflower 12.5- 15.0 7.5 - 10.0
Castor 12.5-15.0 7.5 - 10.0
Coconut 1 kg / tree / year
Turmeric 15 kg
*Basal soil application as Enriched FYM (1:10 ratio)
Yield increase : 10-20%

53. 15% increase 20% increase 25% increase
17.17 (131.67)* 22.90 (137.40)* 28.62 (143.12)*
Projected increase in food production (lakh tonnes) in TN if fertilizer management is
DSSIFER based
Total food grain production (lakh tonnes)^
* Figures in parentheses indicate projected total production in lakh tonnes
Crop 15% increase 20% increase 25% increase
Rice (3918)* 588 783 979
Maize (6549)* 982 1310 1637
Pulses (689)* 103 138 172
Sunflower (1089)* 163 218 272
Crop wise yield increase (kg/ha)
* Figures in parentheses are average per hectare yield (kg/ha)
^ Current production (2021) is 114.5 lakh tonnes

55. Constrains in Managing Soil Fertility in Tamil Nadu
Infrastructural insufficiency
• There are 36 STL and 16 MSTL in TN
• Soil Testing facility with KVKs & TNAU
• Annual analyzing capacity 6.5 lakh samples as against 81 lakh farm holdings
Insufficiency on the production side
• Decline in organic matter status of soils
• Imbalanced and suboptimal fertilizer use
• Soil fertility depletion
Constraint induced by developments
• Organic manure application

56. Suggestion for improvement
Enhancing manure production
• Go all about it or otherwise sustaining agri - production systems for posterity is ?
• Follow the practices of GR era
Streamlining the soil testing service
• Increasing the analytical capacity and making it target oriented
• Bringing trained staff into soil testing laboratories? (who is training them?)
• Are we to start a PG diploma/certificate course in Soil Testing?