*2Corresponding author email: velayutham42@yahoo.co.in

Madras Agric. J., 98 (10-12): 295-307, December 2011

The “Law of Optimum” and Soil Test Based Fertiliser Use forTargeted Yield of Crops and Soil Fertility Management for

Sustainable Agriculture

B. Ramamoorthy1 and M. Velayutham2*

1Former Head, Division of Soil Science and Agricultural Chemistry, IARI and Project Co-ordinator,All India Co-ordinated Soil Test Crop Response Correlation (STCR) Project.

2Former Project Co-ordinator, Coordinated STCR Project, Former Assistant Director General (Soils) ICAR,Former Director, National Bureau of Soil Survey and Land use Planning, Nagpur (ICAR) and

Former Executive Director, M.S. Swaminathan Research Foundation, Chennai.

I am honoured to deliver the first Dr.B.Ramamoorthy Memorial lecture under the auspices ofTamil Nadu Agricultural University on 28th Dec 2011. I am most grateful to the Vice-Chancellor,Dr. P. Murugesa Boopathi for instituting Dr. B. Ramamoorthy Memorial lecture and inviting mefor delivering the first memorial lecture.

The “Law of Optimum” is propounded as the unifying concept in Plant Nutrition for realizing“Targeted yield of Crops” through soil test based Nutrient management, as calibrated froma novel factorial field experiment technique, designed and used under the All India CoordinatedSoil Test Crop Response (STCR) project investigations, on a range of soils and crops, in Indiaover four decades and validated through hundreds of demonstration trials in farmers fields.Early studies under the project established that the relationship between yield of wheat,Sonora-64, grain and total uptake of nutrients by the whole plant followed a linear relationship,implying that for obtaining a given yield, a definite quantity of nutrients must be absorbed bythe plant (Ramamoorthy et al., 1967). The “Law of Optimum” is defined as the concept of soiltest based major plant nutrients (N, P and K) application to crops, by taking into account thepercent contribution from the soil available nutrients as estimated by chemical soil tests andthe percent contribution of nutrients from added fertilizers and manures and the nutrientrequirement of the crop as estimated from plant uptake, for obtaining a specific yield target.Over 2000 demonstration trials in Farmers’ fields conducted so far have validated this Law onthe Targeted yield concept by realizing the yield target (s) within 10% deviation by followingsoil test based fertilizer use and adopting all other best Agronomic management practices.Operationally this Law harmonises the much debated approaches namely, ‘Fertilising thesoil’ versus ‘Fertilising the crop’ ensuring for real balance (not apparent balance) betweenthe applied fertilizer nutrients among themselves and with the soil available nutrients. Theprinciples underlying the “Law of Minimum”, “Law of Diminishing returns” and the “Law ofthe Maximum” governing plant nutrition are not only embedded in the “Law of Optimum” butthis Law also provides a basis for soil fertility maintenance consistent with high productivityand efficient nutrient management in “Precision Farming” for sustainable and enduringAgriculture.

Key words: The Law of Optimum, Plant nutrition, Targeted yield of crops and soil testing.

The relationship between crop yield andnutrients : Historical perspectives

Quantitative relationship studies on plant growthfactors and their effect on growth and yield of plantsdates back to Sprengel (1832) and Justus Von Liebig(1843). The well known Liebig’s “Law of Minimum”,says that “every field contains a maximum of one ormore and a minimum of one or more nutrients. Withthis minimum, be it lime, potash, nitrogen,phosphoric acid, magnesia or any other nutrient,

the yields stand in direct relation. It is this factor thatgoverns and controls the yield. With this minimum,the yield will remain the same and not increaseeven though amounts of other nutrients be increaseda hundred fold”. When the most limiting factor atminimum is corrected, yields are then regulated bythe next important limiting factor. In ProductionAgriculture, this process is managed with step-wiseyield increases until there are no remaining growthlimiting factors. Paris (1992) has demonstrated theapplicability of this law in two crop responseexperiments.

Ist Dr. B. Ramamoorthy Memorial Lecture

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Mitscherlich (1909), in his “Law of DiminishingReturns” stated that crop yields are influenced by alllimiting factors simultaneously and the influence ofeach such factor is proportional to the severity of itslimitation. His equation, dy/dx = (A-Y) C provided abasis for optimising fertiliser doses from fertiliserrate trials, where dy is the yield increase fromincrement dx of the growth factor (nutrient) x, A ismaximum possible yield, y is the yield after a givenamount of x has been added and c is a constant,which can be taken as efficiency factor. Mitscherlichyield equation was widely used in the Great GermanSoil Fertility Survey conducted during 1934-38. Infour years a total of 27,069 well replicated field testswith 12 crop plants were made. Willcox (1955) hascommended on the meaning of the Great GermanSoil Fertility Survey. Although Mitscherlich concludedthat the efficiency constants K or C are constant inhis model, it was rejected by Boyd (1956) who foundthat the efficiency constants were decreasing withincreasing levels of applied N and K nutrients asgiven in Table 1.

Y = yield obtained with nutrient ‘b’ in soil, when itis less than adequate

C1= efficiency factor of the nutrient supplied by the

soil

X = quantity of fertiliser added

C = efficiency factor for the method of applyingfertilizer

The exponential function of Mitscherlich - Brayyield curve never reaches an absolute maximum.Regardless of the level of nutrient present in thesoil, the indicated yield never reaches 100%. Thecomputational basis for calculating maximum yieldvital to the percent yield sufficiency concept has thusbeen questioned. The exponential curve will neverindicate yield depression from excess or toxic levelof a nutrient. This method also does not take intoaccount nutrient interactions, their effect on yield andhence on the fertiliser requirement for “balancedfertilisation”.

Colwell (1978) proposed orthogonal polynomialmodel for calculating fertilizer requirement frommultilocation fertilizer rate trials. Similar experimentsand data generated under STCR Project did not meetwith success in optimizing fertilizer requirement. Inthis model, the role of soil test values is reducedand are being used only to refine the fertiliserresponse coefficients as computed in an orthogonalpolynomial model.

Wallace (1993) proposed the “Law of theMaximum”, having two major characteristics.First,the effect of a given input is progressively magnifiedas other limiting factors are corrected. The final resultis greater than the sum of the effects of the individualinputs, because of the way in which they interact.The interaction multiplies the effects of each.Second, yields can be highest or maximum only ifthere are no remaining limiting factors; the fewerlimiting factors that remain, the higher will be theyield. How closely this can be approached andattained, of course, depends on relative economics.When dealing with Mitscherlich-type limiting factors,those most economical to use can be chosen first.

He has shown how the law operates withexamples of multinutrient rate trials. If 100 per centwere the yield attainable (Agronomic yield potential)and all factors except one were optimal, the finalyield would be whatever the one factor represented,whether it be 50 or 80 or 90 per cent. Two suchfactors each at 90 per cent effect would give 81 percent of the yield attainable. For five such factors theyield would be 59% and for 10, it would be only 35%.A farmer may do everything to 90% of perfection andyet get only 35% the maximum yield possible. Thisunderlines the need for best management practicesand precision nutrient management with soil testingfor high yields and sustainable agriculture.

Table 1. The effect of N and K levels on theconstants in the Mitscherlich equation

Boyd (1956)

Level of N KN Level of K KK

0 0.95 0 1.711 0.68 1 1.432 0.15 2 0.73

Alivelu et al. (2003), reported that the fertilizerrecommendations estimated by linear responseplateau (LRP) and quadratic response plateau(QRP) models were considerably lower than thatgiven by modified Mitscherlich equation and alsothe systematic bias with Mitscherlich’s model washigher. Between the two plateau models, the QRPbased recommendations were lower than those ofLRP. Due to its simplicity and applicability to onlysingle nutrient studies, the Mitscherlich model isseldom used for optimisation of fertiliser doses, asrequired in multinutrient studies.

Balmukand (1928) proposed the “resistanceformula”, capable of a direct and physicalinterpretation; for each nutrient there are twoconstants; one represents the importance of thenutrient concerned to the crop concerned, expectedto vary from crop to crop and variety to variety and soto afford a direct comparison between varieties oftheir manurial needs, while the second representsthe amount of nutrient available in the unmanuredsoil.

Percent sufficiency concept: Based on hisnutrient mobility concept, Bray (1945) modifiedMitscherlich equation as follows:

log (A-Y) = log A - C1b - CX where,

A = maximum yield when all nutrients are presentin adequate quantities

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Soil testing research and Advisory service

The usefulness of soil testing service as a vitalpart of the expanding fertiliser use programme wasrecognised by the Government and 24 soil testinglaboratories were first established in 1955-56 withassistance from the USAID. With the initial researchwork carried out at the Indian Agricultural ResearchInstitute (IARI), with the then tall varieties of rice andwheat, the fertiliser doses arrived at for differentcrops on the basis of agronomic experiments in thestates were taken as applicable to the ‘medium’soil fertility status. Those doses were either reducedor increased by 30 to 50% empirically for soils testedas ‘high’ or ‘low’ respectively (Muhr et al.,1965).Ramamoorthy and Velayutham (1971) reported anaverage increase in yield of 11% when the fertiliserswere applied based on such recommendation overthe general recommendation without soil testing.With the introduction of the high yielding varietiesand hybrids of crops during the mid 1960’s, Greenrevolution era, the need for more precise soil test-fertiliser requirement calibration was an urgentrequirement for expanding fertiliser use to increaseFood Production.

Soil Test Crop Response Correlation: A novel fielddesign

Recognising the reported lack of correlationbetween soil test and crop response to fertiliser inmulti-location agronomic trials in the past and theneed for refinements in fertiliser prescriptions forvarying soil test values for economic crop productionin the wake of Green Revolution era, Ramamoorthy(1968) designed a novel field experimentationmethodology for Soil Test Crop Response (STCR)correlation studies and initiated the All IndiaCoordinated Research Project of the Indian Councilof Agricultural Research (ICAR) in 1967 - 1968.

The principle of the methodology is that todevelop a quantitative relationship between differentmeasured levels of any one component (eg. fertiliserN) of a crop production system and the yield obtainedfrom that system, it is necessary to conduct a fieldexperiment with different measured levels of thatfactor and to measure the resultant yield. Whenmore than one factor influences the yield (eg.fertiliser N, P, K etc) and there are interaction betweenthe different variables, a factorial experimentaldesign is necessary to describe the desiredrelationship. If factors other than those included inthe design (eg. climate, soil types, management)also influence the yield, it becomes necessary todevelop the relationship at a ‘standard level’ of theseother factors. Otherwise it becomes necessary toconduct additional studies to determine the effectsof those other factors also.

In soil test crop response studies, it is necessaryto have data covering the appropriate range of valuesfor each controlled variable (fertiliser dose) at

different levels of the uncontrolled variable (eg. soilfertility). Since different levels of the uncontrolledvariable (eg. soil fertility) cannot be expected to occurat one place, normally different sites are selected torepresent the different levels of soil fertility (soil testvalues) and the fertilizer requirement inference isdeduced and applied in general (Deductiveapproach).

In the “Inductive Approach” of STCR fieldexperimentation, all the needed variation in soilfertility level is obtained not by selecting soils atdifferent locations as in earlier Agronomic trials, butby deliberately creating it in one and the same fieldexperiment in order to reduce heterogeneity in thesoil population (types and units) studied,management practices adopted and climaticconditions. Ramamoorthy and Velayutham (1971,1972 & 1974) have elaborated this Inductiveapproach and the STCR field design, which is alsoquoted by Black (1993).

A field, representative of the major soil type inthe region, having low soil fertility level is selectedand divided into four equal strips. While the firststrip receives no fertiliser, the second, third and fourthreceive half, one and two times the standard doseof N, P and K respectively. The standard dose of Pand K are fixed taking into account the P and K fixingcapacities of the soil. A short duration ‘exhaust crop’is grown so that the fertilisers undergotransformations in the soil with plant and microbialactivity. After harvest of this exhaust crop, each of thestrips is divided into sub-plots. Twenty one selectedtreatment combinations from 5x4x3 levels of N, Pand K, in addition to 6-8 controls are randomlyallotted in each of the four strips and the test crop forwhich soil test calibration is required is grown tomaturity, following standard agronomic practices.Before the application of fertilisers, soil samplesare collected from each sub-plot and analysed foravailable nutrients by different soil test methods.After harvest, grain and straw yield and total nutrientuptake are also determined plot wise.

Statistical processing of the STCR field data

Selection of suitable soil test method: The firststep in the processing of data is to establish asignificant relationship between the yield and thechemical soil tests employed to assess theavailable nutrient status in the soil. Using the yieldsfrom the control (unfertilised) plots and thecorresponding soil test values this relation isestablished through multiple regression. Asignificant value of the co-efficient of determination(R2) with high order of predictability (above 66%)and significant partial regression co-efficients (bi)indicate the choice of soil test methods for theirsuitability for advisory purpose. The results fromthe numerous STCR field data indicate that organiccarbon and alkaline KMnO4 method for N, Olsen’smethod for non-acidic soils and Bray I method for

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acidic soils for P and neutral N. Ammonium acetatemethod for K are the most suitable soil test methods.In soils with Illitic clay, and where the contribution toplant from non-exchangeable K is considerable,selection of suitable testing procedure, which couldintegrate the extraction from the different sourcesfor improving the soil test calibration for K in suchsoils has been pointed out by Sekhon andVelayutham (1978).

From significant multiple regressions of thequadratic form established between the soil tests,the added fertiliser levels, the interaction term forthe soil and fertiliser nutrients and crop yield,fertiliser calibration for varying soil test values forobtaining maximum economic yield (MEY) andmaximum profit ha-1 have been derived, where theresponse to added nutrients follow the Law ofDiminishing Returns (Ramamoorthy et al., 1974).

An example of such a soil test calibration forwheat, HD-2204 grown in alluvial soils of Delhi isas follows:

FN = 293 - 0.53 SN- 6.58 R

FP2O

5= 115 - 0.67 SP-16.67 R

FK2O = 386 - 1.00 SK-16.67 R

where, F and S stand for fertiliser and soilnutrient in kg ha-1 and

The high response of 14.8 at 50 kg ha 1 of N wasassociated with 25 kg each of P2 O5 and K2O. Forobtaining a similar response (14.5), at a higher levelof N application viz 90 kg ha-1, the requirement ofthe associated P

2 O

5 and K

2O also has to be

increased to 75 and 50 kg ha 1 respectively. Thistable also shows that response to absorbed N, isvarying within narrow limits than the response toadded N, indicating that the varied response toapplied fertilisers is primarily affected by factorswhich influence the uptake of the nutrient concerned,but when once taken up, the efficiency of appliednutrients is nearly the same. They also showedthat the relationship between yield of grain anduptake of nutrient followed the same linearrelationship, irrespective of the method ofapplication - whether the P and K were placed orbroadcast or applied partially to soil and theremaining as foliar application, although theresponse to fertiliser applied changed with methodof application.

Targeted yield concept

Truog (1960) illustrated the possibility of‘Prescription method’ of fertiliser use for obtaininghigh yields of Maize using empirical values ofnutrient availability from soil and fertiliser. However,Ramamoorthy and his associates establishedduring 1965-67 the theoretical basis and fieldexperimental proof and validation for the fact thatLiebig’s Law of Minimum of Plant nutrition operatesequally well for N, P and K for the high yieldingvarieties of wheat, rice and pearl millet, although itis generally believed that this law is valid for N andnot for P and K which were supposed to follow thepercentage sufficiency concept of Mitscherlich andBaule and Mitscherlich and Bray. Their work,Ramamoorthy et al. (1967), Ramamoorthy et al.(1969a), Ramamoorthy et al. (1969b),Ramamoorthy and Aggarwal (1972) and Aggarwaland Ramamoorthy (1978) showed the importanceof the associated nutrients in determining the valueof response to N and the need for balanced nutritionin making efficient use of fertilisers and laid thebasis for fertiliser recommendation for targeted yieldof crops.

Table 2, taken from their work (Ramamoorthy etal., 1967), shows the effect of balanced nutrition onefficiency and economy in fertiliser use on wheat.

Cost of fertiliser nutrient (Rs./ kg)

Value of economic produce (Rs./ kg)R =

90 P75K50 5,047 14.5 38.7P50K50 4,779 11.8 38.9P

50K

254,760 11.7 40.3

P50

K75

4,588 9.9 42.0P

25K

504,665 10.7 40.2

50 P25

K25

4,330 14.8 43.1P50K50 4,302 14.2 43.5

Control P0K

03,590 - -

Table 2. Effect of balanced nutrition on efficiencyand economy in fertiliser use at Delhi with wheatSonora 64 (1965-1966)

Ramamoorthy et al. (1967)

Responseto absorbedN (kg grainper kg N)

Level ofadded N(Kg ha-1)

Associatedtreatment

Yield inkg ha-1

Responseto added N(kg grainper kg N)

Needed parameters

The essential basic data required from soil testcrop response correlation field experiment forformulating fertiliser recommendation for targetedyield of crops for a given soil type-crop-Agro-climaticconditions are: 1) Nutrient requirement in kg quintal-1

(100 kg) of grain or other economic produce (2) theper cent contribution, the availability of ‘soil availablenutrients’ as measured by soil test method and (3)the per cent contribution from the applied fertilisernutrients.

The linear relationship between yield and uptakeimplies that for obtaining a given yield, a definitequantity of nutrients (both from soil and fertilisers)must be taken up by the plant. This is also borne outby the near constancy when the response isexpressed in the form of units of grain productionper unit of nutrient absorbed by the plant. It is thereciprocal of this form viz response to absorbednutrient which is expressed as nutrient requirement.Once this requirement is known for a given yield,the quantity of fertiliser needed can be estimated

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taking into account the efficiency of contribution fromthe soil available nutrients and that from the fertilisernutrients.

The “Law of Optimum” is propounded as theunifying concept in Plant Nutrition for realizing“Targeted yield of Crops” through soil test basedNutrient management, as calibrated from a novelfactorial field experiment technique, designed andused under the All India Coordinated Soil Test CropResponse (STCR) project investigations, on a rangeof soils and crops in India over four decades andvalidated through hundreds of demonstration trialsin farmers fields.

The “Law of Optimum” is defined as the conceptof soil test based major plant nutrients (N, P and K)application to crops, by taking into account thepercent contribution from the soil available nutrientsas estimated by chemical soil tests and the percentcontribution of nutrients from added fertilizers andmanures and the nutrient requirement of the cropas estimated from plant uptake, for obtaining aspecific yield target.

Operationally this Law harmonises the muchdebated approaches namely, ‘Fertilising the soil’versus ‘Fertilising the crop’ ensuring for real balance(not apparent balance) between the applied fertilizernutrients among themselves and with the soilavailable nutrients. The principles underlying the“Law of Minimum”, “Law of Diminishing returns” andthe “Law of the Maximum” governing plant nutritionare not only embedded in the “Law of Optimum” butthis Law also provides a basis for soil fertilitymaintenance and efficient Nutrient management in“Precision Farming” for sustainable and enduringAgriculture.

It has been shown that the nutrient requirementper quintal of grain production is nearly the samefor a given variety, although variations betweenvarieties of a crop and variations in the same varietygrown in different seasons have also been observedfrom field experiments. The percent contribution fromthe soil is essentially influenced and varied by thetype of soil, the texture, nutrient releasecharacteristics of the soil and rooting pattern of theplant. The per cent contribution from fertiliserdepends on the form, solubility, method and time ofapplication of the fertiliser and other parametersviz. the soil type, plant type, climate and watermanagement.

From the above mentioned three parametersobtained from STCR field experiment, the fertilizerdose required for specific yield target is derived asgiven below.

NR CS

FD = ——— 100 T - ——— STV where,

CF CF

FD = Fertilizer dose of N or P2O

5 or K

2O (kg ha-1)

NR = Nutrient requirement of N or P2O5 or K2O (kg-1)quintal (100 kg))

CF – Per cent contribution from fertilizer N or P2O

5 or

K2O

CS – Per cent contribution from soil N, P, K.

STV = Soil test value of N or P x 2.29 or K x 1.21 (kgha-1).

The fertilizer adjustment equations, in thesimplest form, become,

FN = 4.96 T – 0.63 SN

FP2O

5 = 3.83 T – 4.63 SP

FK2O = 2.66 T – 0.22 SK

for soil test based calibration of wheat var. WH –157 at Hissar, for targeted yield. F and S stand forfertilizer and soil nutrient in kg ha-1 and T is yieldtarget in q ha 1.

Santhi et al. (2010a) documented in a handbooksoil test and yield target based integrated fertilizerprescriptions, for a range of 41 crop situations inTamil Nadu. One such example from this hand book,for integrated nutrient management for Rice onNoyyal Soil Series is given below:

FN = 4.39 T – 0.52 SN – 0.80 ON

FP2O

5 = 2.22 T – 3.63 SP – 0.98 OP

FK2O = 2.44 T – 0.39 SK – 0.72 OK

where ON, OP and OK are the N, P&K nutrientssupplied through organic source.

Subba Rao and Srivastava (2001) havedocumented the soil test based fertilizerrecommendations for targeted yields of crops in theCoordinated STCR project. Such documents foradvisory use by the concerned soil testinglaboratories have been updated by the 17cooperating centres of the STCR project in thecountry. After generating the basic data, theapplicability of these calibrations are tested for theirvalidity, by conducting simple follow-up trials infarmer’s fields on similar soils (taxonomic group).The results from more than 2000 follow-up trials infarmers’ fields over the four decades indicated thatby following the soil test based fertilizer use fortargeted yield and adopting the recommended bestAgronomic management practices, it is possible toachieve the yield targeted within a variation of ± 10per cent, provided the targets are within the range ofyields obtained in the STCR calibration fieldexperiment conducted at the research station.

The dimensions, scope and prospects offertiliser recommendation based on the concept ofyield targeting were documented by Randhawa andVelayutham (1982).

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Yield targeting for a fixed cost of fertiliserinvestment

The relevance and value of soil testing increasesby choosing the yield target at such a level so thatthe cost of fertiliser requirement becomes more orless same as what was being practised by the farmeralready. The results of such demonstration trialsconducted in Delhi villages as given in Table 3 revealthat the response per unit fertiliser is higher thanthat from other practices, when balanced fertilisationis adopted for targeted yield (Velayutham, 1979).

Yield targeting under resource (fertiliser/credit)constraints

When fertiliser availability is limited or theresources of the farmers are also limited, planning

for moderate yield targets, which are at the sametime higher than the yield levels normally obtainedby the farmers or the average yield of the district,provides means for saturating more areas with theavailable fertilisers and ensuring increased totalproduction also. The consequence of this approach- Ramamoorthy and Velayutham, (1973); Velayuthamet al. (1975); Ponnamperuma (1979) andVelayutham (1979) are given in Table 4, which showsthat fertiliser use efficiency and the total productionare higher when the available fertilisers are appliedfor low or moderate yield targets rather than arbitraryreduction in fertiliser dose. The average responseratio obtained in six replicated experimentsconducted in Delhi villages was 10 from the generalfertiliser dose (120-60-40) as against 16.9 for yieldtarget of 4.5 tons ha-1 in case of wheat, Sonalika.

Table 3. Profitability of fertiliser use for targeted yields of bajra on Delhi Territory 1973*

N or OC (%) P2O

5K

2O N P

2O

5K

2O

Pindwala 0.28 20.6 112 i) 23 58 0 1,930 180 420 233 8.5Khurd 0.40 45.3 146 ii) 70 10 10 2,520 178 760 427 14.1Rao Ranjeet HB-3 0.32 34.6 95 iii) 80 60 60 2,460 355 720 203 6.1Shiv Lal HB-3 0.40 16.5 151 i) 23 58 0 1,610 180 200 111 4.1

0.51 36.6 213 ii) 70 10 10 2,360 178 696 391 12.90.36 14.4 146 iii) 80 60 60 2,300 355 610 172 5.1

Hari Ram HB-3 0.32 10.3 168 i) 23 58 0 1,250 180 150 83 3.10.32 20.6 157 ii) 70 10 10 1,550 178 330 185 6.10.32 8.2 168 iii) 80 60 60 2,250 355 750 211 6.3

Goela HB-1 170.00 16.5 140 i) 60 0 0 1,630 130 438 337 12.1Dharam Singh 121.00 26.8 123 ii) 60 0 10 1,800 138 540 391 12.9

160.00 14.4 179 iii) 80 60 60 2,100 355 720 203 6.0Rachpal Singh HB-1 148.00 28.9 241 i) 60 0 0 1,800 130 480 369 13.4

154.00 29.2 201 ii) 50 19 10 2,150 136 690 507 16.5148.00 41.2 321 iii) 80 60 60 2,350 355 810 228 6.8

(i)Farmers practice; (ii) Targeted at the cost of the farmers’ fertiliser practice; (iii) General recommended dose * Work of Dr. J.C. Bajaj

Place andfarmer’s name

Variety Soil test value (Kg ha-1) Fertiliser dose (kg ha-1) Yieldobtained(kg ha-1)

Cost offertiliser(Rs. ha-1)

Netprofit

(Rs.ha-1)

Percentprofit

Responseper unit

o ffertiliser

Yield targeting and maintenance of soil fertility

Fertiliser recommendation for realising greaterfertiliser use efficiency in the short term on the one

hand and for upgradation / maintenance of soil fertilityin the long term on the other seem to have twoopposing dimensions. If soil fertility is to be

Velayutham (1979)

Table 4. Comparison of different strategies of fertiliser application for wheat and rice under conditions offertiliser / credit shortages

A=1 ha having standard fert.dose and other unferilised; B=each ha fertilised with half the standard dose ;C=each ha fertilised on the basis of low yield target; *=calculated values

1.Delhi:Barthal wheat sonalika 1972-73 120 60 40 2,700 70.1 A 422 828 196 7.360 30 20 2,700 84.3 B 422 1,942 460 13.851 75 0 2,700 96.5 C 560 2,722 486 16.9

2. Delhi: Barthal Hira 1972-73 120 60 40 3,484 52.6 A 422 946 224 8.160 30 20 3,484 101.2 B 422 2,005 475 28.629 54 0 3,484 110.0 C 370 2,734 739 24.5

3.Ludhiana farm:wheat kalyansona 1972-73 120 60 30 984 43.7 A 414 1,270 397 11.4*60 30 15 984 53.3 B 414 1,942 469 16.056 37 36 984 57.2 C 464 2,168 467 14.5

4.Delhi IARI farm : rice IR-8, 1972 120 60 60 2,744 87.4 A 438 1,066 243 13.660 30 30 2,744 90.0 B 438 1,466 334 14.664 4 4 2,744 110.0 C 290 2,454 846 38.8

Location/ crop and variety

Fertiliser doses (kg ha-1) ControlYield

(kg ha-1)

Yield obtained(q ha-1)

with fert.distribution

pattern

Costof fert.

(Rs.2 ha-1)

Net profiton

fert.use(Rs./2 ha)

Percentprofit

Responseyardstick

N P2O

5K

2O

Velayutham (1979)

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maintained or even increased, heavier doses offertilisers have to be used to take into account theinevitable losses in the availability due to leaching,volatilization and fixation. Therefore, to get the bestout of fertiliser investment, the turnover from it mustbe very quick. This is ensured when fertilisers areapplied for low / moderate yield targets. Under suchsituations, according to the “Law of Optimum”, theexcess native soil nutrients will make a greatercontribution to increase the yield. This would leadto low doses of application of fertilisers andexhausting of the unutilised excess nutrients fromthe soil. The soil fertility, would, therefore deplete ata faster rate as a result of this exhaustion. Thus,these two approaches seem to be pulling in differentdirections and it will be prudent to adjust the fertilizerpractices over seasons in such a way so as to strikea balance between the two and monitor the soilfertility level (through soil health card) by periodicsoil testing of the farm.

The generation of basic data for targeted yield ofcrops in a crop rotation would hence enableapplication of fertilizer for appropriate yield targetsin multiple cropping for maintenance of soil fertility.Ramamoorthy (1975) showed that the yield targetand the required fertilizer dose for maintenance ofsoil fertility can be calculated from the equation.

Velayutham and Rani Perumal (1976) haveshown the consequences of three types of fertiliseruse viz. for 1) continuously lower yield targets 2)yield targets consistent with the maintenance of soilfertility level and 3) succession of one and two overyears. The results of this field experiment, given inTable 5, shows that when fertiliser is appliedcontinuously for lower yield target of 30 q ha-1 of ricefor three seasons the soil fertility status decreasesto 228-49-107 kg ha-1of available N,P,K from 256-59-170, although the output / input ratio is thehighest. The fertility status is maintained whenrequired fertiliser is applied for an yield target meantfor this purpose, although the output / input ratio isthe lowest. However, greater profit consistent withmaintenance of soil fertility status (256-46-162 kgha-1) is realised when fertiliser is applied forappropriate yield targets in succession over years.Velayutham and Singh (1981) have shown similaradvantage of this approach in Rice-wheat and Pearlmillet – wheat rotation system in the alluvial soil atDelhi.

The yield targeting Block demonstration at a fieldsite from 1998 at Tamil Nadu Agricultural Universityis an eye-opener for demonstrative value and study-ground for further research and refinement in long-term soil fertility management based on thedynamics of yields, soil fertility changes, nutrientmanagement and agronomic practices adopted overyears.

The soil test summaries and Nutrient Indexbased area-wise soil fertility maps at different levels,provide the means for area wise soil fertilityinformation and their interpretation for scientificfertilizer use promotion. Singh et al. (2004, 2005,2007 a & b) have shown the possibilities ofextending soil test information for area wise fertilizerrecommendation and use. Naidu et al. (1999 &2008) have shown the level of economic loss inmisapplication of fertilizers to crops followinggeneral recommendation and the value of soil typeand soil test based fertilizer application.

Table 5. Comparison of types of fertiliser application with rice IR-20 at Kabisthalam (Thanjavur Dt.) havinginitial soil test values, 256-59-170 (kg ha-1)

Yield target q ha-1

I st year II rd year II Ird year

Value of total

produce (Rs.)

Cost of total

fertilizer (Rs.)

Output/ input ratio

(i) Fertiliser doses (kg ha-1)

N P2O5 K2O

(ii) P.H.S.T.V.(kg ha-1) N P K

Yield T.

q ha-1

(i)Fertiliser Doses (kg ha-1)

N P2O5 K2O

(ii)P.H.S.T.V. (kg ha-1) N P K

Yield T.

q ha-1

(i)Fertiliser Doses (kg ha-1)

N P2O5 K2O

(i i)P.H.S.T.V.(kg ha-1) N P K

1.30(L) i)35 0 19 30(L) 44 0 27 30(L) 49 0 31 6750 721 9.36 Ii)242 57 135 234 53 118 228 49 107

2.43(M) i)117 20 44 40(M) 88 21 33 37(M) 68 20 26 9000 1693 5.32 Ii)273 54 194 276 50 198 271 46 188

3.43(M) i)117 20 44 30(L) 25 0 14 37(M) 82 20 35 8250 1358 6.07 Ii)273 54 194 252 50 159 256 46 162

L=low yield target; M=yield target for maintenance of soil fertility; PH.S.T.V.=Post-harvest soil test values Velayutham and Rani Perumal (1976)

T = yield target in q ha-1

n = ratio between the percent contribution from soiland fertiliser nutrient

r = nutrient requirement in kg q-1 of grainproduction

m = ratio between nutrient requirement andcontribution from fertiliser nutrient

s = soil test value in kg ha-1

F.D = fertiliser nutrient dose in kg ha-1

T =n.s

(m-r)

r.n.s

(m-r)and F.D where =

302

Refinements in the estimation of the basic datafor yield targeting

In apportioning the total nutrient uptake (in grainand straw) between the soil nutrient and fertilizernutrient sources by the conventional deductionmethod, the priming effect and interaction effect ofthe fertilizer nutrient enhance the per cent efficiencyof fertiliser nutrient indirectly to values more than100 per cent. Similarly, in case of legumes thebacterial N fixation confounds the fertiliser Ncontribution. Maruthi Sankar et al. (1983) proposeda new method for better estimation of soil andfertiliser efficiencies from the STCR factorial fieldexperiment, by solving a simultaneous equation forthe uptake of nutrient for two plots at a time from the16 selected treated plots out of 84 treated plots, asgiven below.

Un1 = Cs S

1 + Cf F

1

Un2 = Cs S2 + Cf F2

solving the above,

Cs = (Un1F2 – Un1 F1) / (S1F2 – S1 F1) and

Cf = (Un1S

2 – Un

2 S

1) / (F

1S

2 – S

1 F

2)

Ramamoorthy (1993) proposed that the valuesof Cs and Cf may be obtained by regressing theuptake of nutrients from all the 16 plots with the soiltest values S (1-16) and fertiliser dose of theparticular nutrient F (1-16) of the 16 treated plots(chosen for their significant yield response) andidentifying the two regression coefficients, Cs andCf.

Influence of varietal differences on the basic datafor targeted yield of crops and a method forderiving them

With the release of a large number of varietiesfrom time to time, the experimental evaluation of thevarieties will be cumbersome each time.Ramamoorthy (1993) advocated a short-cutmethodology for deriving the basic data for newvariety (ies) by the procedure proposed, as givenbelow:

There are three possibilities of commonnessamongst the varieties: (A) with all the threecomponents of the basic data being the same for agroup of varieties (B) only one of the componentsbeing the same way, b

1 in NR, b

2 in Cs and b

3 in Cf

and (C) with two out of the three being common. Inthe case of (A), all varieties in this group will have thesame fertiliser dose at all targets. In the case of (B),there will be only one target at which any two varietiesindicated by subscript 1 and 2 will have the samefertiliser dose. In the case of (C) there is no possibilityof same fertilizer dose at any target of yield.

Since nutrient uptake from soil is Cs S andfertiliser is C

f F and the targeted yield is total uptake

divided by the nutrient requirement, the general

equation for targetted yield and fertiliser dose for agiven target yield become

T = (Cs S + Cf F) / NR or

F = T x NR / Cf – Cs S/Cf

By conducting 5 or 6 target trials with two varietiesand fitting a regression equation for them, the basicdata can be derived depending on the shape of therelative response of the two varieties as given in Fig1, 2 and 3. In all varieties which have responsecurves like those in Fig 1 or 2, if the yield of anyvariety is plotted on a graph against the fertiliserdose tried in the STCR field trial, the intercept on theY axis is the control yield. Dividing this value by thesoil test value, we get ideal Cs / NR value for thatvariety, which will be better than the value obtainedby the difference method. The regression coefficientfor the fertiliser dose is the response dT / dF = Cf /NR. This method helps in determining not only thebasic data for the unknown variety but also forcorrecting the existing data for even the known variety.Fig 3 is for varieties which do not have any of thecomponents in common but have the ratio of Cf /NR the same. That is Cf and NR of the unknownvariety either increases or decreases simultaneouslybut in each case proportionately. Fig 2 is for varietiesnot covered by 1 and 3.

Screening crop varieties for their nutrientextraction power

Ramamoorthy (1993) showed that by testing anumber of varieties in one and the same soil (reverse

303

procedure adopted by Bray for determiningavailability of a nutrient in the soil) and using twofertiliser treatments, namely, (1) with full and sufficientdoses of all the three nutrients (N+P+K) and (2)same as (1) but omitting the particular nutrient inquestion. The % yield (yield obtained undertreatment (2) as % of that under treatment (1) ofeach variety represents its extracting power orcapacity for utilisation of the soil nutrient. Higher thepercent yield of a variety, higher is its capacity forutilisation of soil available nutrients. In their studies(R.S. Dinesh 1971, Ph.D. thesis, Agra University),the % yield of rice varieties TN-1, IR -8, NP-130, andBC – 5 were respectively 42.5, 33.4, 54.6 and 52.5for Nitrogen; 50.6, 64.5, 95.0 and 85.3 for P and77.3, 77.9, 124.0 and 102.5 for K.

Similarly, Dinesh and Ramamoorthy (1968)showed that properties of plants at flowering stagesuch as equilibrium phosphate potential (EPP)(Ramamoorthy and Subramanian, 1960) andequilibrium potassium adsorption ratio (EKAR)(Ramamoorthy and Paliwal, 1965), which aregenetically controlled and which would be easilydetermined in sand culture provided a goodindication of the relative response of the differentvarieties to these nutrients. The lower theequilibrium phosphate potential of a crop variety,lower the % yield to P and higher the P

2O

5

requirement of that variety for production of onequintal of grain. The analogy is also similar withrespect to EKAR of the variety. Thus there is need forpotassic fertiliser so long as the equilibrium KAR ofthe soil is less than that of the plant variety.

Field experimentation design for determining thebasic data for targeted yield of crops underIntegrated Nutrient Management (INM)

Ramamoorthy devised the field design forcreating simultaneously soil fertility gradient andorganic manure gradient. As in the case of the twocomponent system (Soil and fertiliser nutrient), asoil fertility variation (gradient) is created in a field inone direction by increasing the four levels offertilisers as L0, L½, L1 and L2, with the levelsrandomised in that direction. The exhaust crop isthen sown and harvested. The manure variation(organic sources) is then created in a perpendicular

direction to the previous one and again randomisedby applying M0, M1, M2 and M3 levels of manure (FYMor slurry or compost) about one month before sowingof the test crop. These manure levels are so chosenas to contain 0, 25, 50 and 75 kg N ha-1. Thus thefield (60 x 40 metres) is divided into 16 sub-blocksas shown in Fig 4 (Ramamoorthy,1994).

Each of these sub-blocks is divided into six plots,three in one direction and two in the other, with theusual irrigation channels and soil samples aretaken for analysis from all the 96 plots before sowing

of the crop. The 24 fertiliser treatments are given,with nitrogen in the usual splits and all P placedand all K applied before sowing of the test crop insuch a manner that the full set of 24 fertilisertreatments occur in the four consecutive sub-blockswhether taken in the north to south or west to eastdirection.

The plant and grain samples are taken fornutrient uptake studies from only 16 plots selectedas follows: One plot for each of the strips of themanure gradient giving the highest yield as well asanother which gives sufficient response to theadditional increment in the nutrient level. Thus withtwo plots for each of the four manure levels, eightplots are chosen for plant analysis. Similarly anothereight plots are chosen with two for each soil of thefour fertility gradient strips. NR for each nutrient isdetermined by averaging the quotient obtained bydividing the uptake of the nutrient by the crop from

Fig. 4. Creation of Soil Fertility and ManureGradients

1.N0 P

0 K

05. N

1 P

0 K

08. N

2 P

0 K

015. N

3 P

0 K

021. N

4 P

2 K

1

2. N0 P

0 K

06. N

1 P

1 K

09. N

2 P

0 K

116. N

3 P

1 K

122. N

4 P

2 K

2

3. N0 P

0 K

07. N

1 P

1 K

110. N

2 P

1 K

017. N

3 P

2 K

223. N

4 P

3 K

1

4. N0 P0 K0 11. N2 P1 K1 18. N3 P3 K0 24. N4 P3 K2

12. N2 P2 K0 19. N3 P3 K1

13. N2 P

2 K

120. N

3 P

3K

2

14. N2 P

2 K

2

24 Fertiliser treatments (till 2004)

304

each of the 16 selected plots by the yield of the cropin the plot concerned. The other three parametersCs, Cf and Cm are obtained as the regressioncoefficients of soil (S), fertiliser (F) and manure (M),when the uptake of the nutrient is regressed withthe following model equation.

U = S (Cs) + F (Cf) + M (Cm) + R, where U is theuptake of the nutrient and R is the absolute constantequal to the nutrient content of the seed.

The yields from the 96 plots are regressed withthe sum of the rates of nitrogen applied and similarvalues for P and K in each plot both from the fertiliserand manure (N1), (P1) and K1, (C1 / N1) and the C / Nwhich are respectively the C / N ratios of thecombination of the manure and fertiliser appliedand the C / N ratio of the soil of each plot before theapplication of the fertiliser and manure, on thefollowing regression model.

Y = R + a N1 + b N12 + d SN x N + g (C1 / N1) + h (C1 / N1)

2

+ F (C1N1) (C/N) + i P1 + m(P1)2 + q K1 + r (K1)

2

+ t (P1) SP + V (K1) SK

From this equation, the optimum levels of N1, P

1

and K1 and (C1 / N1) ratio can be calculated. Theoptimum value of C1/N1 value can be estimatedusing a quadratic regression model, with theincrease in yield due to the addition of organicmanure to a fertiliser treatment regressed withC

1 / N

1.

Δ Y = R + a (C1 / N1) – b (C1 / N1)2 + d (C1 / N1) .

(C / N), where

Δ Y is the increase in yield (with the appropriatealgebraic sign of the yield (Ym+) with manure over(Ym-) the yield of the corresponding fertilisertreatment without the manure (Ramamoorthy,1994).

The integrated nutrient management for a givenyield target (T) is calculated from the equation.

gives the efficiency of the manure in terms of thefertiliser, Cs / Cf is the efficiency of the soil nutrient interms of the fertiliser.

From 2005 the revised treatment scheduledeveloped by AICRP-STCR project in consultationwith Indian Agricultural Statistical Research Institute,New Delhi is being followed by all the cooperatingcentres of AICRP-STCR. In the new design, thegradient L1/2 is omitted and L0, L1 and L2 areretained with OM1, OM2 and OM3 blocks for the testcrop experiment (Fig 5).

Ramamoorthy et al. (1975) showed from suchintegrated INM studies that if a suitable C/N ratio ismaintained at the time of application of fertilisersand manure, the effect of the latter can be positivewithout any ‘nitrogen effect’ as occurs when the Nmineralised from manure is immobilised by the

microbes. This value is about 9.9 for cotton (H14) atHissar, 9.3 for potato (C3805) at Pantnagar and lessthan 10.7 for rice (Jaya) at Nalhatti (W.B.).

24 Fertiliser treatments (since 2005)

1. N0 P

0 K

05. N

1 P

1K

19. N

2 P

1 K

118. N

3 P

1 K

1

2. N0 P0 K0 6. N1 P1 K2 10. N2 P0 K2 19. N3 P2K1

3. N0 P0 K0 7. N1 P2 K1 11. N2 P1 K2 20. N3 P2 K2

4. N0 P

2 K

28. N

1 P

2 K

212. N

2 P

2 K

021. N

3 P

3 K

1

13. N2 P

2 K

122. N

3 P

3 K

2

14. N2 P

2 K

223. N

3 P

2K

3

15. N2 P

2 K

324. N

3 P

3K

3

16. N2 P

3 K

2

17. N2 P

3 K

3

Reddy et al. (1989) have documented theintegrated nutrient management prescriptionsderived from the above mentioned approach forwheat, sunflower, rapeseed, cotton and jute grownin different soils.

Epilogue

Soil testing is the first entry point to the farmers’field for extending Agro-technology transfer to thefarming community, including long-term soil fertilitymanagement. The soil testing service through nearly600 soil testing laboratories in the country, to be ofpotential value for the farming community needs tobe continuously backed up by soil test cropresponse research at all research stations. Dr. M.S. Swaminathan, Chairman, National Commissionon Farmers in his third report (2005) has rankedsoil fertility upgradation and soil health maintenanceas the first strategy for Ever - Green Revolution. Thecountry is delineated into 265 Agricultural zones,each zone being endowed with a RegionalResearch Station. Soil fertility (Physical, chemicalmicrobiological aspects) and integrated nutrientmanagement should form the core researchprogramme at all research stations on a continuingbasis, with bench mark sites chosen at the researchstations and representative farmers’ holdings(small, medium and big) in the zone for monitoringof ‘soil health’ in the context of intensive andsustainable agriculture.

Fig. 5. Creation of Soil Fertility and ManureGradients

where the term T. NR M. Cm

F =Cf Cf

Cs.S

Cf

Cm

Cf

305

From static soil testing laboratories, the adviceon fertilizer use by post in earlier times has comenow ‘on line’, thanks to the advances in InformationCommunication Technology. The DSSIFER softwaredeveloped by Tamil Nadu Agricultural University andupdated as DSSIFER 2010 (Santhi et al., 2010b) andthe web based (http://www.stcr.gov.in) online fertilizerrecommendations developed by the STCR projectand National Informatics Centre (NIC), Pune arepotent examples of such advances in ‘knowledgedelivery’ to the farmers.

All the stakeholders have a role to saturate theconcept of soil based and soil test based fertiliseruse just as the very saturation of the cultivated areasunder high yielding varieties and hybrids of crops isnecessary for increased agricultural production sothat agrotechnology transfer in the future will be notonly ‘seed centered’ but also ‘soil driven’.

Karamanos and Cannon (2002) have shownthat it is even possible for ‘virtual soil testing’ throughmechanistic model predicted soil test levels, forwestern Canadian soil testing laboratories to offersupplemental information for those fields that arenot soil tested on a yearly basis.

Ramamoorthy envisaged the creation of a“National Soil Datamatics Centre” for storing,processing and retrieving soil test information fromall soil testing laboratories and AgriculturalUniversities in the country for assessing, monitoringand upgrading the soil health of the Nation. TheIndian Institute of Soil Science, Bhopal, is well placedfor undertaking this challenging operational andcoordinating task. This will be a tribute and homageto the memory of Dr. B. Ramamoorthy, who oncetold me that “Soil Testing is Testing Soil Science”.

Acknowledgement

Grateful acknowledgement is made for thesupport given to the All India Coordinated STCRProject by the ICAR, by Dr.B.P.Pal, Director-Generaland the successive Director Generals; by Dr.J.S.Kanwar, Deputy Director-General and thesuccessive Deputy Director Generals of the ICAR.

In the early phase of the ProjectDr.Ramamoorthy’s students and colleaguescontributed immensely to the technical content ofthe Project, in particular the Ph.D thesis ofDrs.T.R. Subramanian, K.V. Patwal, R.S.Dinesh,R.L.Narasimham,N.Rajendran,K.D.Singh, V.N.Pathak,J.C.Bajaj, G.Narayanasamy, R.K.Aggar wal, J.S.Samra,Rani Perumal, A.C.S.Rao, R.Hasan,K.M.Varadan andB.M.Sharma.

The statistical context and content andprocessing of the data of the Project was greatlyenriched by Dr. Daroga Singh, Shri. S.K.Raheja,Dr.S.S.Pillai, Dr.R.C. Garg, Dr. N. Saxena, Dr. V.K.Mahajan, Dr.K.N.Mathur, Dr.G.R. Maruthi Sankar andDr.Alok Dey.

Scores of Scientists at different cooperatingcentres and the Project Coordinators of the Projectover years, have put in their devotion and dedicationto this project.

I thank Dr. R. Santhi, Professor, STCR project,Tamil Nadu Agricultural University and her staff forhelp in the preparation of the lecture and the article.

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Received: December 14, 2011; Accepted: December 28, 2011

About the Memorial Lecture

This memorial lecture was instituted in 2011 by theTamil Nadu Agricultural University and the CoimbatoreChapter of Indian Society of Soil Science in memory of Dr.B. Ramamoorthy who passed away on February 1, 2006.

Dr. Bharatula Ramamoorthy was born on 22nd October,1913 in Ongole, Andhra Pradesh. He completed hispostgraduate and doctoral studies specializing in PhysicalChemistry from Allahabad University. He has also earnedthe Assoc. IARI (Soil Science and Agricultural Chemistry)and ARIC (London) associateships and Fellow of theNational Academy of Sciences. Dr. B. Ramamoorthy joinedthe Indian Agricultural Research Institute as a ResearchAssistant in the year 1939 and rose to Head of the Divisionof Soil Science and Agricultural Chemistry in which capacityhe retired on 20th October, 1972. During 1954, he wasdeputed to the MaCaulay Institute for Soil Research,Aberdeen as Colombo Plan Scholar. He initiated and guidedthe All India Coordinated Research Project on Soil TestCrop Response Correlation (AICRP-STCR) as ProjectCoordinator from 1967 till 1975. Subsequently he workedas Emeritus Scientist of ICAR till 1977. During his academiccareer spanning over 39 years, Dr. B. Ramamoorthy wasactively involved in Teaching and Research and has heldseveral important assignments. He visited USA and Canadaas an Indian delegate to attend International ScientificConferences. He presided over the session VI at theInternational Symposium on Soil Fertility Evaluation held atNew Delhi in 1971 and participated in the FAO SubCommittee Meeting on Plant Nutrients held at Frankfurt in1974. He was a Member of several Scientific and ExpertCommittees of ICAR, Ministry of Food and Agriculture, IndianStandards Institution, Planning Commission and others. Hedid pioneering research on Soil Chemistry and introducedseveral concepts like equilibrium phosphate potential andequilibrium potassium adsorption ratio of soils. He evolveda new energy concept for determining the gypsumrequirement of soils and evaluated the role of soil factorsin determining the quality of irrigation water.

In Soil Fertility and Fertiliser use research,Dr. B. Ramamoorthy has evolved the Inductive cum Targetedyield Concept suited to our Indian soil and climaticconditions. This methodology of research took the shapeof the All India Coordinated Research Project on Soil TestCrop Response Correlation by ICAR in 1967 with Dr. B.Ramamoorthy as its first Project Coordinator. The firstpublication in the May issue of Indian Farming in 1967broughtout the theoretical basis and sound experimentalproof for balanced fertilization. This has revolutionised theconcepts relating to economic and efficient fertiliser use.These studies have also made it possible to rationalisefertilization practices for targeted yield of crops through

Integrated Plant Nutrition System, for cropping sequencesbased on initial soil test values and also for farmers’ resourceconstraints. His research has demonstrated that fertilizeruse in rainfed agriculture can be made profitable by adjustingthe rates of application of fertilizers to availability of moisturein soils and other soil physical properties. He has guided anumber of Doctoral and Masters Degree students in theiracademic programmes and research work. He has published85 papers in both National and International Journals. Inrecognition of his meritorious work and service to the country,the prestigious Rafi Ahmed Kidwai Memorial Award waspresented to him by the Government of India in the year 1975.One of his students received the Jawaharlal Nehru Awardfor outstanding research contributions for his doctoral thesis.

About the Speaker

Dr.M. Velayutham was born on 7th April 1942 at Sivagiri,Tirunelveli District, Tamil Nadu.He graduated from theAgricultural College and Research Institute, Coimbatore in1962 and M.Sc (Ag) in Soil Science in 1964. He was arecipient of many gold medals for his academic distinctionin the College. He worked as a Research Assistant at AC& RI during 1964 - 65.

As a Commonwealth Scholar, he obtained Ph.D degreein Soil Science from the University of Aberdeen, U.K, during1965 - 68. He joined the Indian Agricultural ResearchInstitute, New Delhi in 1969 and worked closely with Dr.B.Ramamoorthy in the Coordinated Soil Test Crop ResponseProject. In his career with the Indian Council of AgriculturalResearch, he rose to the positions of Project Coordinator(STCR Project), Assistant Director General (Soils), ActingDeputy Director General (NRM) and Director, NationalBureau of Soil Survey and Land Use Planning, Nagpur. Heworked as a consultant at the Regional office of FAO,Bangkok during 2001. From 2002 - 07, he worked at the M.S. Swaminathan Research Foundation, Chennai as NationalCoordinator and Executive Director.

He was the President of the Indian Society of SoilScience and Indian Society of Soil Survey and Land UsePlanning, both during 1999 and 2000. Widely traveled, hehas served as member / Chairman in several National andInternational Committees and technical sessions ofconferences. He is the Chairman of Bhoovigyan VikasFoundation (Foundation for Earth Sciences Development),New Delhi.

Dr. Velayutham has Published 143 titles in the form ofpapers, chapters and coauthor of 32 books and presented122 papers in National / International Seminars andConferences. The Indian Society of Soil Science conferredon him the highest recognition of “Honorary Membership”of the Council of the Society in 2008 for his significantcontributions and advancement of Soil Science.