The output per unit of input may be termed as productivity. In agricultural context, productivity is defined as yield per unit area where input includes arable land, labour, capital and more. Productivity however masks the socio-economic or value dimension as it is quantitative and a physical measure. In fact increase in agricultural productivity is related to choice of inputs, their relative quantities, the agricultural techniques used and skill of the labour. Normally various crops are grown in an agricultural area, sometimes at the same time, which differ in nature, quantity and quality over regions. Crops are thus quantified on the same level to measure the contemporary level of farm production as conventional units, calories, monetary values, etc. (Box 1).
The states of Punjab followed by Himachal and Haryana show a high intensity of cropping. As per the Food and Agricultural Organisation (FAO), the global agricultural cropping intensity stood at 93 in 1997-99 and is projected to rise to 99 by 2030. Cropping intensity is derived by dividing the total cropped area of the state by the net sown area of the same state. An intensity of 100 percent indicates that all of the state’s cultivable area is under the plough. If however the intensity is more than 100 it means that the state’s land is sown more than once. (Table 1)
Apart from calculations of output and their basis of computations to allow broad based comparisons, several theories were also propounded to understand and assess cropping patterns and success parameters.
Introduced by J C Weaver (1954) during the study of crop combinations in the American mid-west – a model situation was created wherein the total cropped area was computed to be 100. It was supposed that if the farmer wanted to plant just one crop he would use all the area. But, in case he wanted to plant two or three crops he would logically divide the cropped area. In case of two crops, each crop would theoretically be assigned 50 per cent of the area and in the case of three crops each crop should be devoted at least 33.3 percent of the cropped area. The field or actual data of area cropped is then fed into the analysis and deviation from the theoretical value calculated. The crop/s that deviates the least from the theoretically assigned value is ascertained to be the true character of the region. In this way boundaries can be drawn for regions that support mono, two, three or more crops (Table. 2). The theory underwent modifications at a later stage and was used to study livestock combinations too by J T Coppock in the 1960s. He published a work titled Agricultural Atlas of England and Wales.
Source: J C Weaver, Crop-Combination Regions in the Middle West, Geographical Review, Vol. 44, 1954.
C : corn, O : oats, H : hay, S : soybeans, and W : wheat
The above is the original table computed by Weaver. The first line of data showing percentage of cropland occupied refers to the amount of land available for different crops grown in the region. The following row indicates the theoretical percentage – the ideal situation. In the next line the deviation from the ideal situation is calculated. The lowest value which in this case is 309, shows the true character of the area – this is a three crop region growing corn, oats and hay.
The Game theory
Intrinsic to the game theory is the pay-off matrix, the least-risk – maximum-returns position. P R Gould in 1963 and M H Yeats in 1968 investigated the pay-off matrix. The game theory thus provides a number of abstract solutions in the face of environmental crisis especially for India which is experiencing the changing climate scenario at present (Fig 1).
Crop Sequence Analysis
This method allows interpretation of agricultural practices at grass root level. Crop sequence indicates the growing of crops one after another on the same field in an orderly manner. When one sequentially raises crops, enrichment of soil with the fixation of nitrogen, adding of organic matter and green manures, etc. must be taken care of to ensure adequate yield in every sequence. Such interpretations at grass root level will provide guidelines for planning which can restore the lost soil nutrients besides aiding agricultural regionalisation.
Measuring commercialisation in agriculture
Agriculture intensiveness and marketing of produce indicates whether an area practices cash, commercial or subsistence farming and is an important indicator of the agricultural development of an area. Commercial and cash crops are characterised by production for sale – capital being used to purchase of tractors and machinery, fertilisers, insecticides, pesticides, herbicides, improved plants, better breeds of animals, and many other technological innovations. In fact the commercial farmer is aware of his surroundings and is constantly associated with the waves of change.
The pay-off matrix showing two main crops a farmer may choose for the least risk solution. The good monsoon year produce of a crop may be measured on one axis and the dry year produce of the same crop may be measured on the other and a line drawn to join the two. Such lines may be drawn for all the crops grown by the farmer. The lines drawn for different crops will intersect at a point. The theory states that the least risk combination lies in lowest point in the topmost line (a b in the graph alongside and the point is c). P Gould studied the Middle Zone of Ghana and estimated that 33.9 per cent hill rice should be grown and 66.1 per cent maize should be grown in the region. There are however non-graphical methods of computing the game theory too.
In the Indian context, commercialisation would bring transformation in the peasant’s way of life. It would imply changing our traditional subsistence economy into a market economy. To measure the degree to which different units have been commercialised, techniques have been devised based on the amount of arable land devoted to cash crops, utilisation of man-hours for cash crops, and gross-output sold in rupee terms. Jhujjar Singh (1979) while studying in Punjab converted farm produce into monetary terms on the basis of the current market price of each commodity. The computation can be applied to livestock and dairy farming as well. The results obtained show that a high degree of commercialisation in areas of large operational holdings, especially for regions that practice commercial grain farming, dairy farming and mixed farming systems. However, the purpose of the method is to allow us to understand overall rural upliftment through farm surpluses. In fact the information generated can help develop new strategies for near-subsistence and subsistence farming areas, which may possess a rich agro-climatic potential.
Primary stage: This is the onset of the diffusion process. The stage shows a great contrast between the areas which lie close to the innovation centres and those that are far away.
Diffusion stage: Here the actual process of diffusion takes place. Ideas are spread faster, resulting in strong centrifugal forces. Thus secondary innovation centres are created at distant locations and initial variation is reduced.
Condensing stage: The relative increase is equal in all locations
Saturation stage: The innovation has more or less reached everywhere and increase is thus slow.
Diffusion of Innovation
T Hagerstrand in 1952-53 developed a model to describe diffusion of innovation over space – wherein he developed a four-stage model explaining the diffusion process (Fig 2). In 1953 Hagerstrand developed a model which could comprehend the neighbourhood effect – and made it effective by developing the Monte Carlo method. J Wolpert in 1964 undertook further research in this field.