Measurement of Agricultural Productivity and Efficiency

➢ The measurement of production and inputs required for the production of that output

is known as agricultural productivity. In other words, it is an input-output ratio.

➢ In traditional measurement of agricultural productivity, geographers and economists

used to take into account the inputs like labour and capital and see them as costs

which are incurred in the production of agricultural produce.

➢ The traditional approach of measurement of agricultural productivity, however, does

not take into account of social and environmental costs which are also incurred in the

production of crops and raising livestock.

➢ At present, in the measurement of agricultural productivity, the question of

sustainability of soil, health of ecosystem and social acceptability have become

increasingly important. Agricultural productivity of a micro or macro region is closely

influenced by a number of physical (physiography, climate, soil, water),

socioeconomic, political, institutional and organizational fac­tors.

➢ Thus, agricultural productivity is a function of interplay of physical and cultural

variables and it manifests itself through per hectare productivity and the total

production. Agricultural productivity also depends on attitudes of the farmers towards

work and their aspirations for better standard of living.

➢ The measurement of agricultural productivity helps in knowing the areas that are

performing rather less efficiently in comparison to the neighbouring areas. By

delimiting the areas of low, medium and high productivity, agricultural plans may be

formulated to remove and minimize the regional inequalities. It also provides an

opportunity to ascertain the ground reality, the real cause of agricultural backwardness

of a tract/area or region.

➢ In the recent decades geographers and economists have developed sophisticated tools

and techniques to determine the agricultural productivity.

➢ Some of the well-known techniques developed and used for the measurement of

agricultural productivity and agricultural efficiency per unit area/per unit of time

are given below:

1. Output per unit area.

2. Production per unit of farm labour.

3. To assess agricultural production as grain equivalents (Buck, 1967).

4. Input-output ratio (Khusro, 1964).

5. Ranking coefficient method (Kendall, 1939; Stamp, 1960; Shafi, 1990).

6. Carrying capacity of land in terms of population (Stamp, 1958).

7. Giving weight to the ranking order of the output per unit area with the percentage

share under each crop (Sapre and Deshpande, 1964; Bhatia, 1967).

8. Determining an index of productivity (Enyedi, 1964; Shafi, 1972).

9. Computing the crop yield and concentration indices ranking coefficient (Jasbir

Singh, 1976).

10. Involving the area, production and price of each cultivated crop in each of the

constituent areal units of the region, and then relating the out turn in terms of

money of the unit to the corresponding productivity of the region (Husain, 1976).

11. To assess agricultural production in terms of money.

12. Assessing the net income in rupees per hectare of cropped area (Jasbir Singh,

1985).

➢ Each of the techniques advocated and applied for the measure­ment of agricultural

productivity suffers from one weakness or the other. The application of a technique

may give satisfactory results at the micro or meso level but the same technique fails to

deliver the goods at the national or global level.

➢ The input and output ratio tech­nique seems to be a reasonably good one but the

determination of in­puts including environmental and social costs involved in the

pro­duction is not an easy task.

➢ The conversion of production of all crops in terms of money is also a useful technique

but it is constrained by the prevailing prices of agricultural commodities which

fluctuate from one areal unit to another and from one region to another.

Determining Agricultural Productivity in India

➢ The ranking coefficient technique is quite simple and easy to apply. In this technique

the component areal units are ranked according to the per hectare yields of crops and

the arithmetical average rank called the ranking coefficient for each unit is obtained. It

is obvious that a component areal unit with relatively high yields will have low

ranking coefficient, indicating a high agricultural productivity and vice versa.

➢ In other words, if a component areal unit was at the top of every list it would have a

ranking coefficient of one and thus having the highest agricultural productivity and if

it were at the bottom of every list, it would have a ranking coefficient equal to total

number of units considered, showing the lowest agricultural productivity among the

constituent units.

➢ The ranking coefficient method can be illustrated with the help of an example.

Suppose, in a region, there are 80 component areal units. In x component areal unit,

on the basis of average yields, wheat ranks 5, rice 12, gram 20, cotton 21, barley 34,

sugarcane 38, pulses 40 and mustard 54

The average rank, called the ranking coefficient of the areal unit x would be:

Ag. Efficiency = 5 + 12 + 20 + 21 +34 + 38 + 40 + 54/8 = 28

➢ The average ranked position of all the units of the region is thus calculated and then

arranged in an ascending or descending array. The array is divided into five equal

parts to obtain the very low, low, medium, high and very high agricultural

productivity.

➢ With the help of the index scale the agricultural productivity of each unit can be

ascertained and plotted on map. The technique was applied to determine the

agricultural productivity of India and the agricul­tural statistics for the years 1990-95

were taken into consideration. The resultant patterns of productivity have been plotted

in Figure 7.9.

Patterns of Agricultural Productivity:

➢ The patterns of agricultural productivity of India have been deline­ated with the help

of Kendall’s method. The ranking coefficient val­ues of very high, high, medium, low

and very low productivity have been given in Table 7.9, while the resultant patterns of

productivity have been plotted in Figure 7.9.

1. Very High Agricultural Productivity:

➢ It may be observed from Figure 7.9 that the very high agricultural productivity is

found in the upper parts of the Sutlej-Ganga Plain, the Brahmaputra valley, the lower

Gangetic plain, the coastal districts of Orissa, Andhra Pradesh, Tamil Nadu and

Kerala, the valley of Kash­mir, western part of Tripura, and the districts of Kolhapur

and Sangli of Maharashtra. Out of these a significant belt of high agricultural

productivity stretches over the greater parts of Punjab, Haryana and the Meerut and

Rohilkhand divisions of western Uttar Pradesh.

➢ Wheat, rice, sugarcane and cotton are the dominant crops in this re­gion. It is

interesting to note that over 90 per cent of the cultivated area in this tract is irrigated

either by canals or tube wells or by both. The farmers of this region are highly

receptive to new agricultural in­novations.

➢ In the Brahmaputra valley and in some of the districts of West Bengal and coastal

Orissa rice and jute are the dominant crops. The farmers grow two to three paddy

crops in a year in their small hold­ings. The annual floods during the rainy season are

helpful in replen­ishing the soil fertility in the lower Ganga plain. Very high

agricul­tural productivity is also found in coastal Andhra Pradesh, the Kaveri delta

and the coastal parts of Tamil Nadu and Kerala.

➢ Rice is the leading crop in this region where the farmers obtain two to three har­vests

of paddy in a year. In Andhra Pradesh tobacco is an important cash crop. In Sangli,

Satara and Kolhapur districts of Maharashtra, sugarcane, wheat, onion, and grapes are

abundantly grown. In Anantnag, Pulwama and Baramulla districts of Kashmir, rice,

orchards, ap­ple and saffron are the major crops.

2. High Agricultural Productivity:

➢ The areas of high agricultural productivity are generally found in the vicinity of the

very high agricultural productivity regions, especially in the alluvial plains.

➢ The districts in which the agricultural produc­tivity is high are found in Punjab,

Haryana, Uttar Pradesh, northern Bihar, West Bengal, the valley of Manipur, the

Eastern Ghats and Kerala and Tamil Nadu. Isolated patches of high agricultural

produc­tivity are also found in the districts of Jamnagar (Gujarat) and Ganganagar

(Rajasthan).

➢ The dominant crops in these areas include wheat, rice, sugarcane, jute, cotton,

oilseeds and maize.

3. Medium Agricultural Productivity:

➢ Medium agricultural productivity areas cover isolated tracts in the states of Uttar

Pradesh, Bihar, Madhya Pradesh, Gujarat, Orissa and Tamil Nadu (Fig.7.9).

➢ In West Bengal, the districts of Bankura, Birbhum, and West Dinajpur have medium

productivity, while in Tamil Nadu and Karnataka it is confined to the interior parts.

➢ Agri­culture in the areas of medium productivity is highly diversified and the farmers

are growing assorted crops, ranging from the high water requiring (paddy) to less

water requiring (bajra, millets) crops.

➢ The agriculture of these areas is still subsistent and tradition bound. Non­-availability

of irrigation is the major barrier in the intensification and development of agriculture.

4. Low and Very Low Agricultural Productivity:

➢ The regional distribution of agricultural productivity shows that in the greater parts of

the central peninsular India, the productivity of land is low or very low.

➢ The districts of Chotanagpur (Bihar), Barmer and Jaisalmer (Rajasthan), Udhampur

and Doda (J&K), Bhavnagar, Surendernagar, Ahmadabad and Sabarkantha (Gujarat)

have low to very low agricultural productivity.

Merits :

➢ For the delineation of agricultural productivity regions, the ranking coefficient

method is most simple and free from tiring calculations.

➢ The resultant patterns of productivity also seem to be in conformity with the ground

reality.

➢ Nevertheless, the technique of ranking coefficient has the statistical weakness of

ranking the crops.

Weakness

➢ The statistical weakness lies in the fact that if in the three component areal units, i.e.,

A, B, C, wheat occupies 60 per cent in A, 59 per cent in B and 20 per cent in C of the

total cropped area respectively; ac­cording to the ranking coefficient method they will

be ranked as 1, 2, 3, ignoring the range difference in the percentage values. For

exam­ple, B occupies only 1 per cent less than A, and C occupies 39 per cent less than

that of B, but the ranking difference will be one step less in each case which is against

the principle of statistics.

➢ The difference between the various ranks is not maintained which is not uni­form.

Since the ranking coefficient method is based on the arithmetic mean, it suffers from

the weakness of arithmetic average in which the quality weight age is ignored.

➢ Moreover, the method has an inherent weakness which makes it somewhat insensitive

as a measure of agricultural productivity. The weakness arises from the neglect of

areal strength of the crop for which acre yields are taken into account for the

calculation index of ranking coefficient.

➢ The weakness of this method becomes obvious when we consider the contribution of

individual crops in the produc­tivity of component areal unit. For example, in Uttar

Pradesh, the eastern districts of Gonda, Faizabad, Sultanpur, Deoria, Ballia, etc., show

very high productivity of bajra, though only a fraction of 1 per cent is devoted to this

crop.

➢ Probably, the farmers of these districts, through their power of selection, devote only

the well -drained ele­vated fields to this crop and consequently get high agricultural

re­turns which alone should not be taken as the determinant of agricultural

productivity.

➢ Similarly, in Maharashtra, the districts of Ahmadnagar, Sholapur and Pune show very

high agricultural productivity of sugarcane, though it occupies less than 5 per cent of

the cropped land in these districts. Obviously, a crop which occupies a negligible

portion of the cropped land would contribute nothing to the agricultural efficiency of

the areal unit, though it may have a very high yield per acre.

➢ The weakness of the ranking coefficient method becomes more clear when we see

Figure 7.9. The district of Shahjahanpur, which is one of the agriculturally well -

developed districts of India and having intensive cultivation of wheat, sugarcane and

rice, falls into the me­dium productivity category, while many of the districts of

eastern Ut­tar Pradesh and Bihar show high agricultural productivity which is not in

conformity with the prevailing socioeconomic conditions of these districts.

➢ Looking at the prevailing standard of living of the western Uttar Pradesh and western

Bihar it can be said that the for­mer has a much better standard of nutrition and living,

therefore the productivity regions, delineated by the ranking coefficient method, do

not conform to the ground reality.

➢ In the ranking coefficient method weight age is not given to the value of crops in

terms of money. In fact, it is not the average yield per hectare or the area occupied by

a crop in an areal unit but the average output in terms of money per hectare at a given

point of time that in­fluences the farmer’s capacity to apply inputs, the level of

agricul­tural technology and the living standard and all these factors in turn determine

the agricultural productivity of a unit within a given envi­ronmental set-up. This point

can be illustrated with the help of the following example.

➢ Suppose, in district X, barley, bajra and small millets have the ranks of 50, 21 and 10

respectively, resulting into the ranking coeffi­cient of 27, while in the Y district the

dominant crops are wheat, sug­arcane and oilseeds which rank 60, 40 and 10

respectively. In case of Y the ranking coefficient would be 37. Thus, the ranking

coefficient in district X growing inferior coarse varieties of cheap cereals would be

higher to that of Y district in which costly crops of superior quality are grown.

➢ According to this technique, district X will get a higher rank in agricultural

productivity as compared to Y though the farmers of Y district are getting more

agricultural returns in terms of money. It is thus irrational and illogical to ignore the

quality and price of the crop produced in a region.

Agricultural productivity based on Crop Yield Index Method

Yang’s crop yield index method has been used for measuring agricultural productivity

regions of major groups of crop. Yang’s crop yield index method considers yield of different

crops related in a block compared with the average crop yield in the entire district. The

formula of calculating crop yield index is:

Table: Yang’s Crop Yield Index to Calculate Productivity Indices Name

of

crops

Area of

crops in

the block

(in hectare)

Yield Crop yield in the block

as percentage to the

district

Percentage

multiplied by area

in hectare

Crop Yield

Index (CYI) Average

yield in

the block

Average

yield in the

district

1 2 3 4 5=(Col. 3/Col. 4)*100 6= Col. 5* Col. 2 7=T6/T2#

Rice

Wheat

maize

Jowar

Bajra Note: #T= Grand Total in the respective field

Hathras District: Number of blocks under different productivity region of cereals

Category

2000-01 2014-15

Indices No. of

Blocks

Name of the

Blocks Indices

No. of

Blocks

Name of the

Blocks

High Above

102.08

3

(42.86)

Sasni,

Sadabad,

Hasayan

Above

103.40

2

(28.57)

Mursan,

Sahapao

Medium 98.23-

102.08

1

(14.39) Sikandra Rao

99.63-

103.40

2

(28.57)

Sasni,

Sikandra Rao

Low Below

98.23

3

(42.86)

Mursan,

Hathras,

Sahapao

Below

99.63

3

(42.86)

Sadabad,

Hathras,

Hasayan,

Productivity Regions – Composite Crop Yield Index (2000-01 to 2014-15)

The composite Yang’s crop yield index has been calculated by considering all four

indices of agriculture i.e. cereals, pulses, oilseeds and cash crops for the year 2000-01 and

2014-15 which represented in Figure 4.14 and 4.15 respectively. However, all the major

crops come under these major crops category in the study area, but due to dearth of

disaggregated data on vegetables, horticulture, spices and others are not included exclusively

in the study.

High Productivity Regions: (2000-01)

It is evident from Table 4.10 and Figure 4.14 that in 2000-01, the higher productive

region includes Mursan, Sahapao, Sadabad and Hasayan blocks with the indices value of

more than 99.02.

Medium Productivity regions:

There are two blocks namely; Hathras and Sikandra Rao appear as medium productive

regions.

Low Productivity Regions

The low productive region is found in the Sasni block only with the indices value of less than

95.97.

Table 4.11 Hathras District: Productivity Regions, 2000-01

Blocks Cereals Pulses Oilseeds Cash Crops Composite Index

Sasni 102.88 98.19 74.97 95.64 92.92

Hathras 94.39 90.54 103.02 101.32 97.32

Mursan 94.5 102.38 100.84 100.03 99.44

Sadabad 105.78 98.48 98.43 101.85 101.13

Sahapao 96.57 107.54 104.68 99.5 102.07

Sikandra Rao 98.96 89.24 100.57 103.81 98.14

Hasayan 105.93 93.04 103.85 98.67 100.37 Source: Yang’s Crop Yield Index Based on District Statistical Bulletin, Hathras

Table 4.12 Hathras District: Productivity Regions, 2014-15

Blocks Cereals Pulses Oilseeds Cash Crops Composite Index

Sasni 100.11 91.62 90.76 97.01 94.87

Hathras 96.45 95.7 99.84 106.1 99.52

Mursan 106.1 98.95 102.99 98.67 101.68

Sadabad 96.75 112.62 109.67 102.49 105.38

Sahapao 107.17 94.64 102.1 114.62 104.63

Sikandra Rao 100.22 98.67 101.36 100.04 100.07

Hasayan 95.87 99.61 100.97 106.02 100.62 Source: Yang’s Crop Yield Index Based on District Statistical Bulletin, Hathras

Hathras District: Number of blocks under different productivity region based on

composite crop yield index

Category

2000-01 2014-15

Indices No. of

Blocks

Name of the

Block Indices

No. of

Blocks

Name of the

Block

High Above

99.02

4

(57.14)

Mursan,

Sahapao,

Sadabad,

Hasayan

Above

101.88

2

(28.57)

Sadabad,

Sahapao

Medium 95.97-

99.02

2

(28.57)

Hathras,

Sikandra Rao

98.37-

101.88

4

(57.14)

Mursan,

Hathras,

Hasayan

Sikandra Rao

Low Below

95.97

1

(14.29) Sasni

Below

98.37

1

(14.29) Sasni

➢ Source: Based on Table 4.11 & 4.12

Figure 4.15

Source: Based on Table 4.10

Figure 4.16

Source: Based on Table 4.10

High Productivity Regions: (2014-15)

During 2014-15, the composite index of productivity regions affirms that a small proportion

of area about 28.57 per cent to the total block comes under the category of the high

productivity region. It includes Sadabad and Sahapao blocks with an indices value above

101.88.

Medium Productivity Regions

A large number of blocks i.e. Mursan, Hathras, Hasayan and Sikandra Rao accounting 57.14

per cent of the total block have recorded medium productivity,

Low Productivity Regions

The Sasni block with an indices value below 98.37 is found under the low productivity

region.

Conclusions:

The temporal variation of the composite index of productivity denotes that all seven blocks

namely, Sasni, Hathras, Mursan, Sadabad, Sahapao, Hasayan and Sikandra Rao have makes

reasonable improvement in their indices values during the period from 2000-01 to 2014-15.