the analysis of net farm income: an examination of farm management survey data

35 1

THE ANALYSIS OF NET FARM INCOME: AN EXAMINATION OF FARM MANAGEMENT SURVEY DATA D. K. Britton University of Nottinghani

The practical significance of the conventional “net farm income” is elusive. It would be useful to have an accepted method of dividing “management and investment income” into its recognised C O M - ponents-managerial salary and return on tenant’s capital. One is a residual if the other can be calculated, and both alternatives are considered. An empirical formula is suggested for estimating the managerial salary which may be imputed to the farmer, taking account of his total turnover, his labour bill and his net farm income. This formula is then applied to Farm Management Survey data, for individual farms and for groups of average, high and low performance (output per € input). The method may permit closer analysis of relative profitability.

I The problem of estimating “rate of return on capital” Farmers and other users of farm accounting data have become increasingly critical of the economist’s use of “net farm income” as an indicator of the profitability of a farm business. They complain that in the eyes of the un- initiated it is confused with final profit or disposal income when all business expenses have been met, and that this leads to a double error: there is a tendency to overlook the fact that much of the net farm income has to be set aside for repayment of interest on capital which has often been borrowed at the high rates recently prevailing; and on the other hand there is a tendency for net farm income to be mistakenly regarded as the return on the farmer’s investment, without regard to the fact that nothing has been charged by way of a managerial salary. This can sometimes lead to ill-considered

Net farm income Value of tenant’s capital calculations of “rate of return on capital” (i.e. as x 100)

which exaggerate the true position and are clearly not comparable with rates calculated for other industries after managerial salaries have been charged.

I t seems important to proceed as best we can with an analysis of “management and investment income” (i.e. of net farm income minus the value of the farmer’s and wife’s manual labour) into its components, namely managerial salary and return on tenant’s capital.* This would not only help to avoid the misinterpretations just mentioned. It would also give recognition to the professional standing of the modern commercial farmer as a business manager

* The equally important question of return on ford capital (landlord‘s and tenant’s) is not considered here, accepting the F.M.S. convention that all farms are treated as tenant- operated by charging an imputed rent on owner-occupied farms.

352 D. K . BRITTON

using all the skills required of managers in other walks of life; and it would at the same time provide an answer-however circumspect-to the question which is being asked with more and more insistence now that agriculture has become such a highly-capitalised industry: what rate of return is the farmer obtaining, or what rate can the reasonably efficient farmer expect to obtain, on the capital which he has invested in his farm business?

We can find no answer to this question in the Farm Income “Blue Books”. It may be impressive to find, for instance, that General Cropping farms of 1,800 smd. or over have an average net farm income of over €7,000 (1967 Blue Book, p. 55); but when we also see that the tenant’s capital valuation on such farms amounts to nearly f27,000, and that the farmer manages a labour force costing €7,400 a year with a turnover of f34,000, we are entitled to wonder whether the net farm income represents a “fair return” or not. In itself the figure of f7,000 is elusive of meaning.

In Denmark it has been the practice for many years to tackle this problem by making an allowance in the accounts for the farmer’s managerial work “on the basis of salaries paid to hired managers”. * This permits the calculation of the “net returns from the unencumbered (owner-occupied) holding”, which is “the amount available for remuneration of the total capital invested in the farm, whether by the farmer himself or in the form of mortgages and other loans”. These net returns are then expressed as a percentage of invested capital, valued either at purchase price or at current market value. Over the forty years 1917 to 1957 net returns on total farm capital at current market values averaged about 4 per cent, with annual fluctuations (for the whole sample) from 8 per cent to just below zero. An analysis by size of farm (op. cit., p. 65) showed that both in prosperous and in difficult periods the rate of return on investment was consistently higher with increase in farm size. When economic conditions were adverse, the rate of return was particularly low on the smallest farms (under 20 acres).

In “Report on Farming, 1962/63: A Study of Production and Profits in the Eastern Counties” (Farm Economics Branch, University of Cambridge), Camm went some way to deal with the question of return on tenant’s capital. In the sample of farm accounts for 1962 he charged El0 a week for manual work done by a farmer or €20 a week for managerial work. The farmer’s total remuneration for labour and management was then calculated according to the amount of time he spent on each kind of work, due allowance being made where farmers employed foremen or managers “to do their work for them” and had already charged for this in the accounts. Camm’s purpose was “to make fair comparisons in the return on capital”, and he proceeded to deduct the farmer’s total remuneration (as described above) from the net farm income and express the result as a percentage of the tenant’s capital.

Naturally enough, Camm found a wide variation between farms, with over 18 per cent having a negative return on capital, while 17 per cent had returns of over 35 per cent. The weighted average was 16.4 per cent. He also observed that between 1948 and 1962 the average return had been falling at an average rate of 4 per cent a year.

In the Cambridge sample there was a significant positive correlation between size of farm (in acres) and return on capital, as in Denmark. To some extent this relationship, however, seems likely to have been exaggerated by the method used, assuming as it apparently did that managerial salary could not be more than €20 a week, however large the farmer’s responsibility. Nevertheless, as I

Technical and Economic Changes in Danish Farming: 40 Years of Farm Recordr, 1917-1957. Institute of Farm Management and Agricultural Economics, p. 22.

NET FARM INCOME: EXAMINATION OF FARM MANAGEMENT SURVEY DATA 353

hope to demonstrate later, rate of return on tenant’s capital does tend to increase with size of farm-at least up to a point-even when salary is allowed to ipcrease as the farm business becomes larger.

In this connection Raeburn* has observed that management “becomes more important, more difficult and more costly as size of farm business expands”. Approaching the problem rather differently from Camm, he first calculated a “basic” interest charge on tenant’s capital (4 per cent on the current market value, or 6.8 per cent on the written-down values given in the Farm Incomes “Blue Books”), added an allowance for farmer’s and wife’s manual labour? and, deducting these two items from net farm income, arrived at his estimate of the farmer’s net return for management and risk-bearing. He concluded that the difference between this reward on large and small farms respectively did not appear to be “much more than might be explained as reasonable charges for the additional management skills and energies required”. Raeburn’s results for 1952-3 in England and Wales are summarised in Table 1. I have added figures which express his estimated reward for management and risk-bearing as a percentage of net farm income; they show a very marked increase with size of farm.

Table 1 Relationship between size of farm and reward for management and risk-bearing, England and Wales, 1952-3

TYPE OF FARMING

AREA

SIZE OF FARM

(ACRES)

REWARD FOR MANAGEMENT AND RISK-BEARING

NUMBER OF

IN SURVEY E OF NET FARM FARMS AS PERCENTAGE

INCOME

Dairying 50- 99 145 58 9.7 100-149 83 238 27.2 150-299 91 550 42.0

Livestock 50- 99 134 78 12.6 100-149 106 I59 19.8 150-299 183 333 30.6 300-499 59 557 39.4 500 and over 43 637 40.2

Arable 50- 99 100-149 150-299 300-499 500 and over

143 101 I 50 90 30

222 30.3 602 49.5

1,201 60.7 2,059 68.3 3,554 73.0

Source: J. R. Raeburn, op. cit., p. 24.

I have made a rather similar calculation for 1952-3 and for 1967, but allowing 10 per cent “basic” interest charge on the “Blue Book” tenant’s capital figures instead of the 6.8 per cent used by Raeburn (Table 2). I have also calculated for illustrative purposes the rate of return on tenant’s capital if no management salary is drawn, i.e. if the farmer is content to allow himself only a payment for his manual labour (Table 3).

* J. R. Raeburn, Chzpter on “AgricuItural Production and Marketing”, in The Structure ofBritish Industry, Vol. 1, edited by Duncan Burn, N.I.E.S.R. 1961.

t Actually Raeburn made a minor miscalculation in that the figures which he extracted from the 1952-3 “Blue Book” for farmer’s and wife’s manual labour included a certain element of other unpaid family labour-see footnote to Table 27 of that publication.

3 54 D. K. BRITTON

Table 2 Management salary which could be drawn after allowing 10 per cent interest on tenant’s capital, 1952-3 and 1967, England and Wales

DAIRYlNG AREAS, 1952-3 SPECIALIST DAIRY, 1967 SIZE OF FARM PERCENTAGEOFNET PERCENTAGE OF NET

(ACRES) E FARM INCOME E FARM INCOME

50.1-100 70 11.7 253 17.5 100.1-1 50 I50 1-300 300.1-500

193 22.0 464 35.5

1,288 52.9

432 22-2 1,086 37.7 2,799 50.7

~ ~~~

CROPS WlTH SUPPLEMENTARY CROPPING, MOSTLY CEREAW, LIVESTOCK, 1952-3 1967

PERCENTAGE OF NET PERCENTAGE OF NET E FARM INCOME E FARM INCOME

501-100 100*1-150 . . . . .

150.1-300 300.1-500 Over 500

~ ~

- ( -9) 45 1 40.4

1,132 54.2 2,167 63.2 2,588 60.6

(-217) - 1-204) - . -

648. 32.2

3,102 49.2 1,898 49.9

Source: Farm Incomes in England and Wales. Ministry of Agriculture, Fisheries and Food. H.M.S.O. (1955 and 1969).

Table 3 Rate of return on tenant’s capital if no management salary was drawn, 1952-3 and 1967, England and Wales

CROPPING, WITH CROPPING, SIZE OF DAIRYING SPECIALIST SUPPLEMENTARY MOSTLY

FARM AREAS, DAIRY, LIVESTOCK, CEREALS, (ACRES) 1952-3 1967 1952-3 1967

% 1 *3

100*1-150 15.5 15.5 21.6 63 150.1-300 17.8 18.8 26.2 17.0 300.1-500 23.9 22.6 31.4 21.4 over 500 .. .. 26.9 205

% % % 50.1-100 13.2 150 9.6

Although the types of farming are not strictly comparable between the two dates, the figures do strongly suggest that whereas rates of return (calculated on this rather special assumption, which will be modified later) have remained fairly stable in dairy farming, they have declined very markedly on cropping farms.

11 Alternative allocations of income, using Farm Management Survey data It is possible to elaborate the approach described in the previous section by postulating various rates of return on tenant’s capital and calculating management salary as a varying residual; or by postulating various managerial salary allowances and calculating return on capital as a residual. Examples are given for “Specialist Dairy” farms and for “Cropping, mostly cereals” farms in Tables 4 and 5. The same information is given in Figures 1 and 2. In this way it is possible to present the farmer with various alternative ways of allocating his “management and investment income” according to his personal requirements. All the data are averages for the various size-groups which are distinguished in the Farm Income “Blue Books”. The meaning of the dotted lines in Figures 1 and 2 is explained in the next section.


Table 4 Managerial salary and rate of return on tenant’s capital on various assumptions: Specialist 1st Dairy farms, 1967

1,800 SIZE OF BUSINESS (SMD.) 275- 450- 600- 1,200- AND ALL

449 599 1,199 1,799 OVER SUES

Average size (smd.) 365 525 873 1,457 2,652 758 Averaee size [acres) 51 68 117 202 362 102 _ . ~ - ._ - _- Net income ( k ) ‘ 816 1,220 1,881 3,070 5,226 1,639 Farmer’s and wife’s labour (€) 675 690 730 554 484 68 1 Tenant’s capital (€)* 2,721 4,042 7,416 13,236 25,034 6,399 Residual salary at following

rates of return on tenant’s caDital:

141 530 1,151 2,516 4,742 958 5 328 780 1,854 3,490 638 - 126 409 1,192 2,239 318 - - 48 530 987 - - - - - - -

Residual rate of return on tenant’s capital at following salary allowances: € 0 f. 500 €1,000 €1,500 €2,000 €2.500 f3,000

5.2 13.1 15.5 19.0 18.9 15.0 - 8.8 15.2 16.9 7.2 0‘7

2.0 11.4 14.9 - - 7.7 13-0

- - - 3-9 11.0 - - - - 0.1 9.0 - - - - - 7.0 -

- - - -

~ ~~

* Average of opening and closing valuations of tenant’s physical assets. See Table 72 of

t The figures in this line correspond to “management and investment income”, i.e. net income Farm Incomes in England and Wales, 1967.

minus farmer’s and wife’s labour.

Table 5 Managerial salary and rate of return on tenant’s capital on various assumptions: Cropping, mostly cereals farms, 1967, England and Wales

1,800 SIZEOFBUSINESS(SMD.) 275- 450- 600- 1,200- AND ALL

449 599 1,199 1,799 OVER SIZES

Average size (smd.) 371 523 857 1,448 2,507 1,001 Averaee size (acres) 146 195 302 482 794 342 Net income (€) ‘ 1,036 1,530 2,809 4,375 6,808 2,994 Farmer’s and wife’s labour (€) 579 563 469 437 249 472 Tenant’s capital (€)* 5,674 6.604 12.347 20,372 32.519 13,820 Residual salary at following

rates of return on tenant’s capital :

10 A 20 A

O % t 457 967 2,340 3,938 6,559 2,522 173 637 1,723 2,919 4,933 1,831 - 307 1,105 1,901 3,307 1,140 - 488 882 1,681 449

5 F

1 5 F 55 - - - - - -

Residual rate of return on tenant’s capital at following salary allowances: € 0 8.1 14.6 18.9 19.4 20.2 18.2 € 500 - 7.1 14.9 16.9 18.6 14.6 €1 ,000 - - 10.9 14.4 17.1 11.0 €1,500 - - 6.8 12.0 15.6 7.4 €2,000 - - 2.8 9.5 14.0 3.8

- - - 7.1 12.5 0.2 €2,500 €3,000 - - - 4.6 109 -

For footnotes, see Table 4.

356 D. K. BRITTON

Fig. 1 Specialist dairy Fig. 2 Cropping, mostly cereals farms, 1967

.I,. d hurm., lnoni\ Capld

R r.m b l u n M m n.wn farms, 1967 l.“.”,’, topod.

” 1

Il’I A new approach to the estimation of managerial salary I should like to put forward for discussion a possible method of estimating an allowance for managerial salary without treating it as a residual after calculating return on capital, but having regard to other features of the farm in question.

I have already suggested that Camm’s approach, using €20 a week as a rate of managerial salary regardless of the size of farm business, is not entirely acceptable. Nor would it be satisfactory to calculate management salary as being simply and directly proportionate to acreage or to standard labour- requirement; twice as much land or labour-input does not necessarily require twice as much managerial attention and skill.

I propose a calculation of imputed salary which takes account of three factors:-

(i) the value of gross output (or “turnover”) of the farm business-on the grounds that the greater the turnover, the greater the degree of managerial responsibility and risk-bearing ;

(ii) the annual amount of the paid labour bill-on the grounds that a farmer who has to supervise several workers has a larger managerial role than that of the farmer without paid workers; and

(iii) the net farm income-on the grounds that the farmer whose management is profitable in the broad sense should receive a higher salary than the farmer who makes only a small profit or even a loss.

Looking at the first and second factors in turn and separately, and bearing in mind average earnings in certain other occupations, it seemed that as a first approximation salary might be taken as 10 per cent of gross output or 50 per cent of the paid wage bill. For example, on the average Mixed farm of 1,200 to 1,799 smds. in 1967 these percentages would indicate a managerial salary of €1,626 or €1,298 respectively,* (this being over and above the * This result did not seem unreasonable in the light of the following earnings in certain other

Average earnings of male agricultural workers, 1968/69 . . . . . . . . . E837 Average earnings of male salaried employees in national and local govern-

ment (including teachers and N.H.S.), 1968 . . . . . . . . . . . . €1,542 Ditto in nationalised industries, 1968 . . . . . . . . . . . . . . . . . . €1,505 Ditto in banking and insurance, 1968 ... ... €1,514 Average earnings of monthly-paid male ad;niniscative; ’techdical and

clerical employees in all manufacturing industries, October 1967 ... €1,671 It is also relevant to recall Bessell’s comment (in an unpublished paper) that since the scale of most farm businesses is small, a farmer is generally only a part-time manager and “the argument that he is constantly thinking of the problems of the management of his farm as he goes about his non-managerial tasks does not constitute a case for a full managerial salary”.

fields of employment:-


allowance for the farmer’s and wife’s manual labour). Combining these two factors in equal importance would give: Imputed Salary = 5 per cent of gross output + 25 per cent of wage bill.

To allow some influence to the third factor, namely the profitability of management, I decided to reduce the above to 4 per cent of gross output and 20 per cent of the paid wage bill respectively, allowing a proportion of net farm income to make up the balance brought about by this downward adjust- ment. On making a number of trials I found that the necessary proportion was, on average, about 7 per cent.

By these admittedly arbitrarily-chosen steps the following formula for the estimation of a managerial salary allowance was reached:-

Imputed Salary = 4 per cent of gross outputS20 per cent of wage bill + This was then tried out on the various types and sizes of farms for which data are given in “Farm Incomes in England and Wales, 1967”. The results per farin are given in Tables 6 and 7, and for Specialist Dairy and Cropping, mostly cereals farms they are shown by the dotted lines on Figures 1 and 2.

7 per cent of net income.

Table 6 Average imputed management salary, 1967, by size of business f per farm

SIZE OF BUSINESS (STANDARD MAN-DAYS)

TYPE OF FARMING ALL ~~

1,800 SIZES 275- 450- 600- 1,200- A N D 449 599 1.199 1.799 OVER

Specialist Dairy 218 377 637 1,168 2,263 558 Mainly Dairy 242 336 678 1,309 2,633 196 Livestock, mostly Sheep 180 . . 379 625 1,233 3 80 Livestock, Cattle and Sheep 236 393 679 1,272 1,336 531 Cropping, mostly Cereals 457 553 1,012 1,678 2,877 1,160 General Cropping 353 48 1 904 1,563 3,332 1,485 Mixed 476 753 1,449 3,129 1,152 Pigs and Poultry 357 . . 812 1,446 2,497 1,029 Horticulture .. . . 531 861 2,207 1,006

Table 7 Average imputed management salary, 1967, by size of farm € per farm

SIZE OF FARM (ACRES)

N P E OF FARMING SOAND 50.1- 100.1- 150.1- 300.1- OVER UNDER 100 150 300 500 500

Specialist Dairy 275 418 688 1,099 2,055 Mainly Dairy 323 425 638 1,152 1,897 3,058 Livestock, mostly Sheep .. 113 402 410 630 Livestock, Cattle and Sheep 88 183 3’12 60 1 902 1,002 Cromine. mostlv Cereals . . 212 406 788 1.372 2.577 General IdroppiGg 302 516 842 1,489 21463 41216 Mixed . . 506 597 1,205 2,248 3,905

These imputed salaries represent about €5 an acre on Dairy farms and Mixed farms, € 1 4 2 an acre on Livestock (rearing and fattening) farms, f3.4 an acre on Cropping, mostly cereals farms and €6.4 an acre on General

358 D. K. BRITTON

Cropping farms. These rates Der acre tend to rise with size of business measired in smds. (Table 8) b i t to fall with size of farm measured in acres (Table 9).

Table 8 Average imputed management salary per acre, 1967, by size of business E per acre


TYPE OF FARMING ALL . __ 1,800 SIZES

215- 450- 600- 1,200- AND 449 599 1,199 1,799 OVER

Specialist Dairy Mainly Dairy Livestock, mostly Sheep Livestock, Cattle and Sheep Cropping, mostly Cereals General Cropping Mixed Pigs and Poultry Horticulture

4.3 5.5 54 58 6.2 5.5 3.4 3.6 4.4 5.0 5.4 4.6 1.3 .. 0.8 0.8 0.7 0.8 1.9 2-0 2.3 2.6 1 .o 2.0 3.1 2.8 3.4 3-5 3.6 3.4 5.4 5.6 5.9 6.1 6.8 6.4 .. 4.3 4.1 5.3 6.2 5.1 7.0 .. 9.9 10.7 141 11.4 .. . . 33.2 21.0 30.2 305

Table 9 Average imputed management salary per acre, 1967, by size of farm f per acre

SIZE OF FARM (ACRES)

TYPE OF FARMING S O A N D 50.1- 100.1- 150.1- 300.1- OVER UNDER 100 150 300 500 500

Specialist Dairy 6.9 5.8 5.5 5.5 5.5 .. Mainly Dairy 9-0 5.7 5.0 5.1 4.9 4.3 Livestock, mostly Sheep .. I .6 .. 2.0 1.1 0.5

Cropping, mostly Cereals .. 2.8 3.1 3.5 3.6 3.5 Livestock, Cattle and Sheep 24 2.4 2.5 2.7 2.3 1.4

General Cropping 8.4 6.8 6.8 6.8 6-4 6.0 Mixed . . 6.2 4.8 5.7 5.9 5.2

As a proportion of net farm income, the imputed salary tends to rise with size of business, so that while it is only about 30 per cent on small Dairy farms it is almost 50 per cent on large Mixed or General Cropping farms and on large Horticultural holdings (Table 10).

Table 10 Imputed management salary as per cent of net farm income, 1967, by size of business per cent


TYPE OF FARMING ALL 1,800 SIZES

275- 450- 600- 1.200- AND 449 599 1,199 i,799 OVER

Specialist Dairy 26.7 30.9 33.9 38.0 43.3 34.0 Mainly Dairy 30.5 27-4 33.2 41.1 43.1 36.5 Livestock, mainly Sheep 32.3 40.1 33.7 36.1 35.4 Livestock, Cattle and Sheep 30.6 29-? 32.4 32-9 36.8 32-1 Cropping, mostly Cereals 44.1 36.1 36.0 38.4 42.3 38.7 General Cropping 31.9 35.7 33.5 37.7 467 41.0 Mixed 30.0 41.3 36.2 474 41.1

Horticulture . . . . 31.3 355 49.6 424 Pigs and Poultry 34*’1 . . ’ 36.4 35.2 43.1 38.9

NET FARM INCOME: EXAMINATION OF FARM MANAGEMENT SURVEY DATA 3 59

These results suggest that if it is desired to separate management and investment income into its components by calculating an imputed managerial salary for each farm, it would be inappropriate to use a fixed rate per acre or a fixed proportion of the net farm income.

If it is accepted that the imputed salaries shown in Tables 6 and 7 are generally reasonable in terms of the opportunity cost of the managerial time and skill utilised, we may then proceed to estimate the average residual return on capital on the various types and sizes of farms in 1967. These results are shown in Table 11. They indicate that while there was generally a negative return on capital in small farm businesses (and especially on small farms dependent mostly on sheep), farms of 1,200 smds. and above generally returned about 10-15 per cent on tenant’s capital. The horticultural holdings in the Survey appear to have done substantially better than this.

Table 11 Average investment income as per cent of tenant’s capital vahtion, 1967, by size of business

per cent


TYPE OF FARMING ALL AND

275- 450- 600- 1,200- 1,800 SIZES 449 599 1,199 1,799 OVER

Specialist Dairy - 2 8 3.8 6.9 10.2 9.9 6.2 Mainly Dairy -2.2 4 0 8.8 9.0 10.9 8.2 Livestock, mostly Sheep -12.8 . . -1.0 5.1 9.0 0.5 Livestock, Cattleand Sheep -3.0 4.0 7,3 12.3 7.4 5.7 Cropping, mostly Cereals 0 6.3 108 11-1 11.3 9-9 General Cropping 3.8 6.4 13.5 15.0 129 12.7 Mixed . . 8.2 4.9 13.3 9.9 8.5 Pigs and Poultry 2.0 .. 7.7 15.4 14.4 10.9 Horticulture .. . . 21.4 26.5 22.2 22.4

So far this analysis has been concerned with average results on groups of farms. To examine further the validity of the proposed formula for an imputed managerial salary, it was applied to each of 234 farms in the 1967 Farm Management Survey in the East Midlands province.

The lowest imputed salary, on a farm operated entirely by the farmer and his wife and with a gross output of only €2,645, came to €79. This farm made a loss in 1967 (net farm income -€390), so that if allowances had to be made of €680 for farmer’s and wife’s labour and €79 for managerial salary, there was a negative return on capital of €1,149.

At the other extreme was a farm with a gross output of €91,491, a net farm income of €24,887 and a wage bill of €24,024. In this case the salary calculation came to €10,207, leaving €14,405 as the return on tenant’s capital. It should be emphasised that imputed salaries of this magnitude are quite exceptional; only four farms out of 234 had results which justified-according to the formula-a salary of over €5,000. It should also be mentioned that if a farmer paid a salary to a manager and this figure was included in the wage bill, the formula would need to be adjusted, since it would be inappropriate for a farmer to draw a salary in respect of certain responsibilities which he has delegated to a paid manager. In the East Midlands sample, however, the number of farms with paid managers is very few indeed, so that this consideration would not constitute a serious objection to the application of the formula in the great majority of cases.

360 D. K. BRIlTON

The frequency distribution of imputed salaries in the East Midlands sample of farms in 1967 is shown in Table 12. The majority of the farms of under 75 acres did not in fact make enough net income to allow any salary to be drawn if due allowance for farmer’s and wife’s labour (usually between €800 and f900) was made. However, on medium-sized farms of 150 to 300 acres the majority of farmers would have drawn a salary of over f900, as well as a labour allowance of about f700 for themselves, and still have left over €800 as return on tenant’s capital, i.e. a return of 6-7 per cent (before tax) on a total tenant’s capital valuation of about €12,000 on farms of this size.

Table 12 Imputed managerial salaries on 234 farms in the 1967 Farm iManagement Survey in the East Midlands

SALARY (El

DISTRIBUTION IN ACREAGE SIZE-GROUPS NUMBER OF

FARMS* 75AND 150AND UNDER UNDER UNDER 3 0 0 ~ ~ ~

75 150 300 OVER

Under 200 € 200- 399 € 400- 599 E m 799 E 800- 999 €1,000-1,199 €l,200-1,399 E l ,4004,599 El -600-1 -799 E i :800-i :999 €2,000-2.499 €2,500-2,999 €3.000-4.999

- -

5 6 7 6 8

11 I8 10 15 4

~ ~~

Total 234 (54) 34 39 71 90

AVERAGE PER FARM (E)

Salary 1,376 307 628 966 2,434 Farmer’s and wife’s labour 722 816 84 I 142 588 Return on capital 1,440 -263 425 805 3,024

Net farm income 3,538 920 1,894 2,513 6,046

* The numbers in parentheses represent the farms on which the management and investment income was insufficient to pay the imputed salary, i.e. the farms on which the return on tenant’s capital would be negative if this salary were charged. The total of 54 in this category, or 23 per cent, may be compared with Camm’s figure of 18 per cent in the Eastern Province in 1962.

IV “High Performance” and “Low Performance” farms Since 1965 the Farm Incomes “Blue Books” have included separate information about High Performance farms and Low Performance farms in each type of farming and size of business group, defining these respectively as the “top quarter” and the “bottom quarter” of the farms in each group when they are ranked in order of their performance (output per E input) in the two most recent years taken together.


Fig. 3 Output. efficiency and income on High Performance and average farms, 1965-67 f

f "J'"-.. . . .___ N l 1 1HCOUt L.IOIS WIN1 . P ' I S .

P..tlOO I N N 1 jQ GROSS WIN1

c( -9.

average

I , * - -__ "1

Key: High Performance farms - - - - - Input includes farmer's and wife's labour.

Figure 3 summarises some of the data relating to High Performance farms in 1965-67 and compares them with average data for the corresponding groups of farms. The following points may be noted:-

(i) Taking all size-groups together, net farm incomes per acre on High Performance farms are about 50 per cent above average on Dairying and Cropping farms, and about 75 per cent above average on Livestock (rearing and fattening) farms.

(ii) On Dairy and Livestock farms, net income per acre tends to be higher in the small farm businesses than in the large; but this is not the case on Cropping farms. This appears to be a reflection of the fact that gross output per acre is-in contrast with earlier years*-actually greater (more intensive) on large Cropping farms than on small, at least when these are defined in terms of smd.

(iii) In small farm businesses of all three types, gross output per acre tends to be higher than average on the High Performance farms, but in the larger businesses "High Performance" is not associated with high output per acre. One possible inference from this, which deserves further examination, is that if all farms were organised on the lines of the High Performance farms there might be no significant increase in total output, though the composition of output might be changed in important respects. The validity of this conclusion, however, may be questioned on the grounds that the fact of being in the High Performance category

Average farms

See for instance, Scale of Enterprise in Farming, p. 18. H.M.S.O. 1961.

362 D. K, BRITTON

may have itself resulted in a farm moving further up the smd. scale than it would have been on average performance and intensity of production. For a better comparison of efficiency with intensity of production per acre it would be necessary to distinguish High Per- formance and Low Performance farms within given acreage size-groups, Data of this kind are not at present available in the Farm Incomes “Blue Books”, but figures in “Farming in the East Midlands” (University of Nottingham, January 1970) suggest that high performance dairy farms of about 200 acres keep more than the average number of livestock to the acre for farms of that size, buy more feedingstuffs and have a greater investment in equipment. On the other hand, the most profitable cash cropping farms in the group “500 acres and over” are not noticeably more intensive in their systems than the average for that group nor do they have more equipment per acre; their cereal yields per acre, however, are appreciably higher.

Table 13 Imputed management salary, investment income and investment income as a per cent of tenant’s capital valuation, on High Performance and Low Performance farms, 1967

MANAGEMENT INVESTMENT INVESTMENT INCOME SALARY INCOME AS % OF TENANT’S

TYPE OF FARMING (f) (f) CAPITAL

HIGH LOW HIGH LOW HIGH LOW

All Dairy Farms 742 536 1,571 - 4 6 1 20.5 -6.7 All Livestock Farms 580 372 1,385 -593 16.4 -8.6 All Cropping Farms 1,489 1,173 3,497 -518 25.2 -4.2 Mixed 1,443 1,288 3,026 -663 18.9 - 4 . 4 Pigs and Poul try 1,705 985 4,386 271 28.7 2.6 Horticulture 1,520 1,029 3,460 -803 . . ..

Table 14 Investment income as per cent of tenant’s capital valuation on High Performance and Low Performance farms, 1967, by size of business

1,800 TYPE OF FARMING 275- 450- 600- 1,200- AND ALL

449 599 1,199 1,799 OVER SIZES ~~ ~~

All Dairy Farms High 13.0 23.7 19.9 21.6 22.3 20.5 LOW -19.4 -13.2 -6.3 -2.5 -1.8 -6.7

~~ ~~

All Livestock Farms High 9.1 19.9 17.0 18.4 19.6 16-4 LOW -21.6 -12.7 -6.2 0.3 4 . 6 -8.6

All Cropping Farms High 19.3 30-8 23.9 25.9 25.8 25.2 LOW -19.1 -16.8 -3.3 4 . 6 -3.1 4 . 2

Mixed High .. .. 14.3 28.1 21.6 18.9 Low .. .. --I 1.4 0.6 -2.5 4 . 4

Pigs and Poultry High .. . . 28.9 .. 28.5 28.7 Low . . .. - 0 . 7 .. 5.2 2.6

(iv) The greatest difference between High Performance and average farms ‘ is in their economy in the use of resources (including the farmer’s own

labour). The figures suggest that if the average level of efficiency could


be brought up to that of the top 25 per cent, there might be a saving of resources of as much as 15-20 per cent. If prices and factor unit costs remained the same-a rather doubtful supposition in such circumstances-total net farm income might then increase by some 60 per cent, without any improvement in the present size-structure of farms.

If the imputed managerial salary formula described in section 111 is applied to the 1967 High Performance and Low Performance farms respectively, the results shown in Tables 13 and 14 are obtained. They show that, after making the salary allowance, it is quite possible for a return of over 20 per cent on tenant's capital to be obtained, even on relatively small farms of 450-600 smds. Indeed, on the 13 High Performance Cropping farms of this size, a return of over 30 per cent was obtained. On the other hand, among Livestock farms of 2 7 5 4 9 smds. even the 15 High Performance farms obtained only a 9-1 per cent return.

The Low Performance farms of all types, except Pigs and Poultry farms, were not able to provide any positive return on tenant's capital after charging a salary-even though this salary was generally only about 70 per cent of the salary payable to High Performance farms of the same type and size.

Fig. 4 High Performance dairy farms, 1967 Fig. 5 High Performance cropping farms, 1967

Figures 4 and 5 correspond to Figures 1 and 2 except that they relate to the High Performance farms only. They illustrate that the efficient small businesses gave about the same return on tenant's capital as the efficient larger businesses.

V Conclusion In recent years much of the public dicussion about the level of net farm incomes has been focused upon the estimates of the total national farm income which are prepared and published annually as an essential part of the Price Review. Less attention has been given to the distribution of that income between farms or to its interpretation at the individual farm level in terms of the adequacy of the reward which it provides to the farmer's labour, management and capital. An analytical approach along these lines has been hampered by the absence of an accepted convention according to which the remuneration of these respective elements can be calculated. Political state- ments are made about "providing the farmer with a full and sufficient return", yet there is no agreed basis of comparison between farms, or between farmers and non-farmers, by which to form a judgement on the sufficiency of the net income as traditionally calculated.

D

364 D. R. BRITTON

In this paper an attempt has been made to construct such a basis. It suggests a method which may be applied to all farms for which the value of output, the wage bill and the net income are known. If this method, or a modification of it, could be generally adapted it would open the way to a closer analysis of relative profitability than has hitherto been carried out on Farm Management Survey data.


APPENDIX I Farmer’s income and return on capita1 on farms of 1200 to 1799 smd’s

AVERAGE LABOUR AND MANAGEMENT INCOME (E‘S) TYPE OF FARMING

1964 1965 1966 1967 1968

Specialist dairy Mainly dairy

1,550 16,50 1,600 1,800 2,000 1,600 1,650 1,700 1,900 1,950

Livestock, mostly sheep 1,150 1,200 1,200 1,350 1;450 Livestock, cattle and sheep 1,600 1,650 1,650 1,800 1,850 Cropping, mostly cereals 1,900 1,950 2,000 2.150 2.050 General croooine 1.900 1.900 2.050 2.100 2.100 r . I

Mixed Pigs and poultry Horticulture

-,___ 1:750 1;750 1;750 2;OSO 2,000 1,800 1,850 2,000 2,050 2,200 1,400 1,350 1,450 1,700 2,000

ESTIMATED RETURN ON CAPITAL (%)

FNF.

AVERAGE 1964 1965 1966 1967 1968 YEAR

Specialist dairy 10 I 1 7 1 1 8 9.4 Mainly dairy 9 7 5 9 4 6.8 Livestock, mostly sheep 14 10 4 10 8 9.2 Livestock, cattle and sheep 10 6 5 11 5 7-4 Cropping, mostly cereals 10 9 9 1 1 5 8.8 General cropping 16 14 16 1s 9 14-0 Mixed 9 8 7 14 3 8.2 Pigs and poultry -3 1 12 14 15 7.8 Horticulture 3 16 23 28 12 16.4

366 DISCUSSION ON PAPER BY D. K. BRITTON

DISCUSSION ON PAPER BY D. K. BRITTON

DR. KEITH DEXTER We are grateful to Professor Britton for submitting for discussion the general subject of the farm accounts scheme and what further analytical use can be made of it. Quite rightly he has concentrated on the focal points of measuring net income and return on capital. His diagram- matic presentation of the management and investment income as a salary and/or a return on capital is a demonstration that i t is one reward which the farmer or his adviser can regard as either a return to capital or a return to management or any mixture he cares to choose in between.

Professor Britton then makes his choice with his formula of allocating the joint return, some to management and some to capital. Other arbitrary allocations could be made: for instance, I believe the O.E.C.D. have recently considered the idea of apportioning a wage to the farmer and his wife of some €2 10s. Od. per standard man day up to 400 standard man days and thereafter apportioning a management salary of €1 per standard man day. Professor Britton himself also mentioned the views of others that farmers should earn an average of €1,500 as a salary and, in addition, should receive a return of 15 per cent on their capital. An alternative approach would be to use as a norm the level of earnings of managers in agriculture; but there are not many of them and the sample would not be very representative of farmers as managers. Again, one could look at equivalent earnings elsewhere, but that raises the question "what is equivalent?' Are farmers to be equated with manual workers and, if so, with farm workers at €16 a week or dockers at €40 a week? Are they to be equated with professional N.A.A.S. officers, as Professor Britton suggested, or with more affluent academics?

What this analysis does not do, however, and what the farm management survey itself itself cannot do, is to assess the adequacy of the return on capital. It can describe what the return to capital and labour is. It can never show what farmers should earn. The adequacy of earnings can never be resolved in any absolute manner, but must be judged against generally acceptable criteria. It may be argued that the adequacy is determined by the long-term supply price of farm labour, and since small farmers are leaving the land, current incomes earned by such farmers are deemed by them to be inadequate. On the othef hand there is a strong demand for the larger farm notwithstanding a high capital requirement, and therefore prospective large-scale farmers deem their prospective earnings to be adequate to encourage them to come into the industry. Another means of defining adequacy would be to consider the economic condition of agriculture in relation to the national interest (however that might be defined). We know that over time the number of workers in agriculture is declining as technology develops and as standards of living rise. Is this decline in numbers taking place sufficiently rapidly in agriculture whilst producing that quantity of food which is deemed to be in the national interest to produce at home? No one can answer in absolute terms. It is decided by pressure groups, by political decisions and, if the populace disagrees with too many of these political decisions, it is decided in the polling booths. It will not be decided by further analysis of farm accounts.

A similar point may be made when attempting to use the Farm Management Survey to measure the causal relationship responsible for the changes in productivity on different types of farm. Professor Britton referred to this when he suggested that the most significant feature of the high performance farms was the greater economy in the use of resources. But he did not say how the resources were used differently in order to earn the higher profit. For SO years our colleagues and predecessors have been analysing farm accounts in this and many other countries to find the answer but they have not found it and I doubt very much whether they will find it. Dr. Rasmussen, Professor Britton's predecessor at Nottingham, pointed to the inadequacy of this approach in his penetrating study of English and Irish Farm Accounts some years ago when he showed that until the capital and labour inputs can be disaggregated into much more detailed items than at present we will not be able to establish casual relationships from this type of farm account analysis., As a descriptive, rather than analytical, tool the farm accounts scheme is probably as good

as anything we have in this country and I think we are in the habit of not giving sufficient weight to its value. This type of material is not available in other sectors of the economy. If it were available we would be much better informed about the relative efficiency of resource use in different sectors. The farm accounts scheme is useful to Government for Annual Review purposes and for answering Parliamentary queries. It has useful purposes in the academic world for teaching students, and much of the farm management advisory work was built on the F.M.S., and still depends very heavily on the F.M.S. There are nevertheless improvements which could be made. Professor Britton referred to the need to have more information on the capital aspects of the f m account and to have more balance sheet information. Land- lord's capital should perhaps be included in the farm account although the treatment of the capital value of land itself poses some thorny problems. Perhaps two parallel series of farm

DISCUSSION ON PAPER BY D. K. BRITTON 367

accounts should be prepared describing separately, but concurrently, tenant farms and owner- occupied farms. The total capital invested in each might give an even better description of the agricultural industry which, after all, is what the farm accounts schem: is there for.

Finally, we have recently looked at an identical sample of high performance farms in 1963/64, and found that 60 per cent of them were still high performance farms in 1968. In other words the high performance farms tend to stay at the top. Furthermore. in years which were generally unfavourable for farm profitability the divergence between the high performance and the low performance farms was greater, thus revealing the real abilities of the good manager under adversity. What he does in adversity is not known. Perhaps further study by academic economists of what happens on farms will give the answer. In the meantime we are grateful to Professor Britton in giving his further information which better describes the farming industry as we know it.

Professor Britton has given us a very interesting paper which should help to reduce the confusion which has occurred with the use of net farm income but I would like to make a plea that we don’t add to the confusion. My objection is to the use of the word “salary”. I think we have got to remember that the farmer is an entrepreneur and not an employee and that if we make comparisons between the earnings of an entrepreneur and a salary we can cause confusion. A salary doesn’t fluctuate from year to year though profits do and this has taxation implications, similarly the perquisites of a salaried employee and a farmer are very different. I think the comparison, if you want to make one, is with other small scale business men. I think the footnote on page 356 referring to Mr. Bessell’s comment is very relevant. I suspect that in this country there are very few farms which would justify the employment of a full-time manager.

My own feeling is that we are always going to have this question of the residual and I think that we have got to look more in the future to a return on tenants’ capital as our measure. I think that on a lot of farms we have to recognise that the management reward, I deliberately won’t use the word salary, would be small.

R. ANDERSON

J. S. NIX I do not remember seeing before the calculation of return on capital given on the first page of this paper, taking net farm income as the return. 1 would indeed agree this to be “ill- considered”, but the comments immediately following this calculation apply equally to the usual calculation, which uses management and investment income as the return.

I have been becoming increasingly disillusioned with the concept of net farm income over the last few years. It is a poorish measure of anything-it is neither fish nor fowl. At least management and investment income does have some use as a comparative measure of efficiency, but net farm income simply omits one of the adjustments made in calculating management and investment income, namely the value of unpaid family manual labour, and yet still makes all the other adjustments. Thus it is neither an actual profit figure that means anything to the farmer nor is it a comparative measure of any value. Because of the omission of the value of unpaid family manual labour I do not think the final columns in Tables 1 and 2 really mean anything.

In practice the management and investment income concept is becoming less and less meaningful as a comparative measure as time goes on because of the land factor, since rents vary so enormously now from a few € per acre for farms with a long-standing tenant and a benevolent landlord, to-for the very same size and type of farm-410 or €12 or even more per acre if recently let by competitive tender; furthermore, over half the farms now are owner-occupied, which means the rental value is only somebody’s estimate anyway. Thus I feel that for comparative efficiency purposes we may be better advised to use a measure not narrower than management and investment income or net farm income but something broader, i.e. management and investment income plus the rent or rental value, which would include the return to land. Of course, Professor Britton’s objective is quite different; he is trying to arrive at an estimate of a “pure” profit as a return for risk bearing, after allowing for a given rate of interest and a managerial salary. I was most interested in Tables 4 and 5 , but would have liked to have seen the calculations taken still further and figures of “pure” profit (or “pure” loss) explicitly stated.

I myself made some calculations of managerial salaries recently. I agree that it is quite pointless to give a given sum per farm and also inadequate to take a given amount per acre. I suggested several possible sliding scales, with reducing proportional increases to successive additions to acreage, standard output and standard gross margin. Standard man-days or tenant’s capital might also have been used. I tend to favour the standard gross margin calculation. It is true that my purpose was different from that of Professor Britton-I was trying to suggest suitable levels of salaries for employed managers. I would certainly not use gross output for such a calculation because the figure is so easily inflated by, for instance,

368 DISCUSSION ON PAPER BY D. K. BRITTON

having lots of pig and poultry consuming purchased food; furthermore the manager gets the benefit, for example, of farming on soil of exceptionally good innate fertility. Although I suggested possible profit-based bonuses in my calculations I would not, for the purposes of Professor Britton’s analysis, have included a percentage of net farm income, because it seems to me that the object of the exercise here is to calculate the net reward obtained for good, or bad, management. If I hod wanted to include a bonus for better management in such calculations I would not have made it a share of net farm income, because this means including a portion of the unpaid manual labour element; I would have t e e n a percentage of management and investment income. I also doubt the validity, of including a percentage of the labour bill, since some farmers have higher labour bills either because they are less efficient or because they are obliged to retain some workers for social reasons. This is no reason then to give them more salary, because the more labour that is available the easier the manager’s job, generally speaking.

Finally, I am always extremely dubious about conclusions based on calculations of a single year’s financial results. I should prefer to take, say, a 3-year average of the individual farm figures in order to rule o u t - o r at least reduce considerably-the chance element and the accountancy error element. Obviously there will still be a wide range between levels of efficiency but it will be less exaggerated than the single year figures suggest.

DR. R. J. COLLEY

Dr. Dexter has mentioned an O.E.C.D. report on which we have been working in Paris SO, even though it is not due to be published till late 1970, I feel that a short comment about this work might be appropriate.

Like Professor Britton we were faced with the task of finding a method of allocating the farmer’s income between labour, management and return on capital. We both used the same basic approach of estimating the farmer’s “worth” and calculating return on capital as a residual, but in Paris we were able to adopt a more rule of thumb method as we were interested in the national figures from the policy viewpoint while Professor Britton was preparing a farm management tool. We paid the farmer and his wife for their labour at the rate of €2 10s. Od. for each of the first 400 smds (i.e. a farm worker’s wage) and thereafter allowed one-fifth of a worker’s wage for each smd as payment for management. In general the results of this calculation were similar to those obtained by Professor Britton except for the “Live- stock, mostly sheep” and “Livestock, cattle and sheep” farming types, and I would like to ask him if he feels, as his calculation implies, that a farmer of farm size 1,500 smds and specialising in sheep is worth €600 less than one running a “mostly cereals” farm? This result is rather critical as, with return on capital being a residual, the conclusions are rather different in the two calculations.

It is very encouraging to see that workers at Nottingham and Paris are in fairly close agreement about the social standing in terms of income of farmers on different sizes of farm: it was quite a problem to decide if a 500 acre “mixed cropping” farmer should be paid the same as a doctor, university professor or a solicitor with the resultant return on capital varying from 8 to 16 per cent according to the choice made.

Professor Britton has given us a very interesting paper but I am not sure that I can accept his formula. I don’t think we have a clear conception of reward to the various factors and I agree with John Nix that we have to aggregate the reward to the farmer for his manual work, management, return on capital and to land to have a common basis for combining or comparing the many farms for which we have accounts. 1 would like to comment on some specific points. First, I don’t entirely accept the procedure because it seems unreasonable when dealing with low performance or small farms, to show a figure for a negative return on capital which has been depressed by a negative management income. If these people have not made sufficient money they haven’t earned a reward for management. Next, Professor Britton tries, in the footnote on page 356, to justify the level of management reward, or salary, he has included by comparison with the earnings of other groups of salaried employees. But surely the farmer’s managerial salary is in addition to his reward for manual work .and for the group discussed the combined earnings are €2.000 or more, whereas the companson IS made with the total earnings of other groups of €1,500-€1,600. If these groups are comparable we should be dealing-in this context-with a management salary of €1,000 or less rather than €1,3CQ-€1,600 because of the additional reward for manual work.

On the specific elements in the formula, I can’t see how the payment of high wages to responsible workers who are taking some of the managerial load from the farmer justifies a higher contribution to managerial salary. Some of the higher paid workers are in fact managers of enterprises and their presence should reduce the salary that needs to be paid to the farmer as manager of the whole business, particularly when another part of the formula is based on total output. One further small point concerns the Appendix table. I don’t think

G. W. FURNESS


the 1968 figures are comparable with the earlier years because the 1968 size grouping is based on a revised set of standard man-days and the farms which happen to fall into the 1,200-1,799 smd group would on average have been rather larger businesses than those in the same category in the previous years. This probably explains why the labour and management incomes jumped in the last year for most farm types in the top half of the table.

I am sorry to be critical and not very constructive but, basically, I think in trying to evaluate the managerial input we should consider how low the reward needs to be before these people would withdraw their services as farm operators or, alternatively, we might try to deduce the opportunity cost of these farmers’ contributions in some other function both manual and managerial. There would then be the problem of what kinds of employment to compare with farming.

1. G. REID

It seems to me that one of the things we are just beginning to do is to try and discover what managers do when they manage, and may I pay tribute here to the study that Giles did-“The Farmer and his Time”-which showed there was quite a considerable difference in the amount of manual work and various kinds of managerial work that farmers did according to the size and type of the farm that they were operating. May I suggest also that there is a whole range of technique coming in under the name of Job Analysis and Job Evaluation, which does try to produce a framework for obtaining comparability between a whole range of jobs. These techniques are commonly used when trying to get comparability of management payment, between one kind of business and another, and is it not time that we in farming and in farm management started looking at some of these techniques?

A. I. W Y N N E I am very glad Professor Britton has written this paper. We do not value suficiently the large amount of economic data about agriculture that we collect, and which is probably better than is available for any other industry. However, before we try to take the analysis of the surplus further we should look rather carefully at the way we calculate some of the items.

Return on capital has little meaning unless capital is clearly defined. Professor Britton has used average valuation which is much too crude. Dairy farms, with a monthly cheque coming in, may manage with little liquid capital but other types of farm need capital to cover up to a year’s expenses. We also need to know the indebtedness of farmers to merchants. This affects not only capital but also expenses and profits as the loss of discounts may be equivalent to a high interest payment. On the other side, we should take into account the perquisites that come from living on a

farm. The supply price of farmers may be low but it is not as low as it appears to be in some of our accounts. Furthermore, if we are to estimate the reward for management perhaps we ought to estimate the time devoted to this function. If a farmer devotes half his time to manual labour, he cannot be more than a half-time manager.

If we can make this sort of analysis more realistic, it should be a great help to farmers in assessing their current level of resource use.

J. H. SMITH I would like to ask why we need to apportion farm income between farmer’s capital, his manual labour and his managerial skill. An intelligent farmer knows whether he is, in the long run, getting an adequate reward for his capital and himself. He will naturally consider the market worth of himself and his capital in other fields of economic activity. This is a personal judgement and I cannot see how any general rule of thumb procedure for apportioning some part of farm income to management would have any validity or prove satisfactory to farmers.

PROESSOR D. K. BRITTON

Many thanks to Dr. Dexter for his comments. I do not think he put any questions to me and I was very glad for the amplifications he gave, and the opening up of further fields of exploration to which he drew attention. I hope we shall be able to follow up some of those ideas.

Mr. Anderson objected to the use of the word salary, and I must say I am not particularly wedded to it. I would be quite happy to take Raeburn’s phrase as given on page 353- “Reward for management and risk bearing”-if that is preferred; but I do not think I would go with Mr. Anderson when he said the proper comparison is with small retailers. I am really talking about farm businesses which are much larger than that, farm businesses where the capital investment is getting so large that they need to know what return they are getting on their capital, and I want to come back to this question in a moment when we deal with the point “Why do we need to make this split?’

3 70 DISCUSSION ON PAPER BY D. K. BRITTON

Usually when people quote a return on capital in a non-agricultural industry, they have already charged salary in the accounts; in agriculture we do not. This needs to be done if we are going to enter into any kind of comparisons. and they are bound to be made by people who are concerned with the question, “Is capital being used more effectively in agriculture than in other sectors?”

I agree that not many farms could justify full-time management, and certainly some of the salary figures I have shown in the tables are well below a full-time salary, in the region of f4004500, indicating that on such small farms there is no full-time management job to do.

Coming to Mr. Nix’s comments, I quite agree that the actual rent paid on a farm can distort this kind of comparison. Inter-farm comparisons should not be undertaken until an examination of the realism of the rental charges has been made. If our rents were all really fluid and really up to date such an examination would not be necessary. But I do not like the idea that we can do nothing but aggregate these items of management and labour income, return on capital and rent. I am here replying to those who suggested that rather than analyse the net income into its components they even want to add other things to it. This to my mind means turning one’s back on the question which I feel farmers do ask, “What return do I get on my capital?” I maintain that it is not right to include the management reward as though it were indistinguishable from the return on capital. Possibly for comparisons within agriculture-if we are going to eschew any kind of comparisons across sector boundaries-I might be quite willing to pool the two elements again and not to split them; but I feel that concurrently with the greater emphasis on capital in farming as distinct from labour, more and more farm businesses need to be analysed in terms of answering the question, “What return is being earned on capital?” If we dodge this point we shall never be able to answer that question.

Table 4 was referred to by Mr. Nix. He said he would like to have seen me go a step further and charge a standard rate of interest on capital and a standard salary and see what is left for pure profit. Well, this can certainly be done straight from the Table. You are able to take whatever rate of interest you think is valid, and no doubt you would want to change this from time to time. When Raeburn was doing his paper he was using 4 per cent on capital; obviously we would not use that today, but I do not say that we should use 10 per cent for ever. We can operate on a sliding scale as we wish. To use standard gross margins rather than gross output-yes, I would certainly go along with that. I would like to see whether such results came out significantly different from mine, and if they did I would be certainly willing to abandon my cruder use of gross output, provided standard gross margins can easily be obtained from the Farm Management Survey data. I certainly accept the point that to use only one year’s figures tends to exaggerate the range between high performance and low.

I think Mr. Colley’s alternative is intriguing. Presumably it has the intention of doing the same analysis as mine but in a different way. I noticed that Mr. Colley rejected the idea- as did some others-that the farmer’s managerial reward should vary with his profits; they do not accept my idea that a profitable farmer deserves to draw more salary than an un- profitable one. I agree that you are here getting near the margin of argument as to which is return on capital and which is a managerial reward, but I would still feel there is something in my argument. After all, in a non-agricultural business I would expect that initiative and success would eventually be rewarded by higher managerial salaries, not just by higher profits for the whole firm and the shareholders and so on.

Mr. Furness mentioned that he cannot accept the idea of a negative managerial reward. Nor do I. and that is precisely why I put a dash in the Tables as soon as the figures became negative. What it really means is that the return on investment asked for cannot be achieved without salary being a minus quantity. I am not suggesting at all that it is of any value to say to a farmer, “Your salary is negative”; it just would not mean a thing to him. But it might mean something to him to say “You are getting no return on your capital”. Indeed, if the net farm income is negative-which the Farm Management Survey shows it to be on many farms every year-then we have got to say that one of the components is negative, either the managerial reward, or the return on the investment.

IS it justifiable to relate salary to the labour bill? I agree this is not a self-evident relationship, but I feel that even the act of delegation of work to other people is a managerial responsibility. Many people in other walks of life are paid very high salaries for delegating skilfully and therefore I do not think it is a necessary criticism here, that because a farmer is able to load off subdecisions on to other people, leaving himself with only the major decisions, he deserves leu reward for doing that.

I entirely agree that the revision to the smd coefficients themselves in 1968 may have affected the last column, indeed not only in that year. Every time they are changed it must disturb the series to some extent; that would have to be a subject for further research.


I agree with Mr. Reid that we ought to look more at the analysis of the function of management, and perhaps if we had a closer knowledge of that it would quite vitally affect Some of my definitions here.

Mr. Wynne backed up Mr. Nix in asking for a figure of the residual profit, and I would repeat that I think Tables 4 and 5 permit you to calculate a residual profit, provided you would stipulate what rate of interest you are going to charge on the capital in the business. I acknowledge the crudeness of the present valuations as a means of representing capital. I would point out that the Ministry, through the Universities, has had a go at this by making monthly estimates of capital requirements so as to build up to an annual average based on these monthly estimates and to compare this with the average of opening and closing valuations. There was not a very wide margin generally, though on some types of farm there was quite a perceptible difference. I agree that ideally we should take not simply an opening and closing average, but a more sensitive indicator.

Coming to perquisites this is, I think, quite another subject. There is not only the question of housing, cars, and so on, but what about non-farm income on the farm? If you are thinking of a farm’s total prosperity then I think it is wrong to stop at the farm income; you have to think of other income flowing into the farm family. This is outside the scope of the Price Review, but if you are trying to make a comparison of standards of living and particularly to explain why people continue in farming when they apparently have a negative income, then I agree the true income may not be as negative as it looks. As to finding out how much manual work the farmer does, I agree these figures are often very approximate and I do not think some of them would bear much examination. But it would be a pretty big effort, and probably one that would be rather ridiculed by the farmer, to ask him for a more detailed diary than he now gives.

Mr. Smith came back to the theme, “Why do we need to know these figures?’ I think it is a failure professionally on our part to say to a farmer, “Well, you have given us all your figures, you have given us your valuations, but we still cannot tell you what return you are getting on your capital”. I think we ought not to turn our backs on that question, but make the best shot we can at it, telling him what assumptions we have made. I think this is better than not answering at all.

3 73

SIMULATION OF AGRICULTURAL SYSTEMS P. J. Cbarlton and S. C. Thompson University of Reading

Agricultural systems are, as a rule, dynamic rather than static, and computer simulation is a technique well stiited to the modelling of such time-dependent systems. The elements of simulation are introduced, and the advantages and limitations of the approach are discussed. This is followed by a broad review of its principal Agri- cultural applications, including a more detailed description of a model o f j r t n growth. Finally, an attempt is made to outline the probable future uses of simulation in Agriculture.

Introduction Computer simulation is a technique for the study and analysis of time-dependent systems and, in Agriculture, can be used to model the behaviour of a wide range of economic, production or biological processes. To evaluate whether the simulation approach is likely to be necessary or practicable for a particular study it is essential to appreciate the basic principles underlying the approach and to understand the practical and theoretical problems involved. I t is felt that with the increasing use of simulation in the Agricultural field, i t is necessary to present a general coverage of the subject in order to give the research worker an objective appreciation of its present and future roles.

1. Computer sirnulation Dynamic systems The majority of economic and agricultural systems are time-dependent and dynamic in the sense that they are in a constant state of change and evolution, whereby changes which occur at the present time affect the way in which the system develops in the future.

The finances of a farm are dynamic in this way, with future profitability being affected by current investments, by the weather, or by the incidence of disease. Similarly, biological systems are mostly dynamic with, for example, the rate of growth of a plant being related to factors such as its current size, the soil moisture deficit and the rate of application of fertiliser.

These instances demonstrate the truly time-dependent nature of agricultural systems, with their behaviour being continuously affected by their previous development and by the environment around them. The timing of events and decisions within these systems clearly has a very significant effect upon their behaviour, both from a physical and financial point of view. The problem of investigating time-dependent systems It may be possible to investigate empirically the behaviour over time of a single aspect of an enterprise, provided that this aspect can be considered in isolation from all others. If one is considering even a very simple system, however, the introduction of the time element makes any investigation extremely difficult,

3 74 P. J . CHARLTON AND S. C. THOMPSON

the inter-relationships between all the different factors becoming very complicated. On the grounds of simplicity, therefore, most techniques for farm or enterprise planning such as linear programming largely ignore any consideration of the timing of events and assume that decisions are being made in an environment which is not changing. In certain situations such an assumption is justified, but very often the most interesting aspect of a system’s behaviour is precisely this time dependence, the way in which the system’s growth and development is affected by changes in its structure or environment.

It is possible, when investigating essentially simple systems, for mathematical techniques to be used. For example a sequence of linear programmes can be run, one after the other, each of which provides information for the next stage and this then enables consequences over time to be investigated. Similarly, multi-period linear and Monte Carlo(so) programmes can be constructed, but normally only at the expense of making considerable simplifications and the introduction of artificial restrictions.

The technique of simulation For most systems, however, it is either not possible or not practical to use any mathematical technique, however sophisticated, to adequately represent, or model, a system’s behaviour over time. In fact it is necessary to get away from any attempt at looking for “best” or “optimal” solutions and to try instead to follow, in a rather naive way, the future consequences of present decisions and external changes.

A set of forward budgets is an example of the use of a set of systematic calculations to model, or “simulate”, the expected behaviour of a financial system. The time that it takes to simulate say five years is only as long as it takes to carry out the necessary calculations. However, the complexity of many real-life systems, and the consequent vast increase in the amount of tedious calculation involved, have in the past acted as an effective deterrent in the building of models. Computers, however, have made it possible to carry out such studies relatively easily and can simply be used as very fast, powerful calculating machines. This method of studying the behaviour of a system over time is referred to as “simulation” because it simulates or models the way in which the real-life system behaves. It is an approach that only recently has become practical as the availability of computing facilities has increased, and is only of use if it is applied to clearly defined situations to investigate specific objectives. In such cases, however, it has the advantage of enabling one to “speed up’’ time-to investigate in a few minutes the consequences of changes over periods of months or years.

2. The development of a simulation model Identification of a problem and of the need for simulation Before undertaking any simulation study it is extremely important to have a clear appreciation of the particular aspect of a system that is to be investigated and the type of analysis that is required. Without a clearly defined aim behind the construction of the model it is unlikely that it will develop to the stage where it can produce useful and informative results. A simulation study is also likely to take a relatively long time, the resulting model will have been expensive to develop and run on the computer, and it will inevitably be relatively specialised in its application. One must therefore be satisfied, before starting to construct a simulation model, that the problem cannot in fact be studied more directly and simply by using some standard method of mathematical analysis, and that the potential benefits to be derived from the study can justify its cost.

SIMULATION OF AGRICULTURAL SYSTEMS 375

Simulation can have very considerable uses and advantages as a tool for investigating the time-dependence of systems. However, it also has its limitations and it is as important to understand and appreciate these as it is to be aware of its capabilities.

The system nnalysis I n order to build a model of a system it is necessary to determine all the significant factors and relationships that go together to produce the overall system behaviour or response. This is the system analysis, and it is perhaps the most difficult, as well as being the most important, part of the whole study.

In order to express a system as a set of mathematical relationships it is necessary to completely enumerate the way in which the individual parts of the system interact with each other. Very often, however, it is found that, although some of the inter-relationships can be extracted from previous research studies or from experience, many others in fact need to be determined. Experimentation or survey analysis for this purpose may only rarely be feasible and so the model will have to be simplified to overcome this lack of knowledge. The result of this will be to put a limit or boundary upon the scope of the model, and the effect of this is equivalent to specifying that only certain variables and inter- relationships will be considered as having any effect upon the system’s response. The chief problem in carrying out the system analysis is defining this boundary, that is having confidence that all the important factors have been included, and that the eventual behaviour of the model will be an adequate representation of reality. A failure to identify a factor as being significant could radically change the way in which the model behaves over time and invalidate any results produced.

This detailed initial analysis of the problem is the most important part of the study because the ultimate potential ability of the model to produce realistic and useful results depends entirely upon the adequacy of this analysis. It is also, in many cases, the most useful part of the whole simulation study. This is because the necessity of having to define the system so precisely, and the discipline of determining the inter-relationships, may result in a considerably better understanding of the system’s overall structure and behaviour, and hence be of considerable benefit.

Different types of system Although simulation can be used to study widely different types of system and so can be considered as a general technique, there is in practice considerable variation in the approach needed to study particular systems, and the more significant differences are outlined below.

(a) Feedback systems. These are systems which contain relationships such as those involved in the growth of a plant where the current rate at which some quantity is changing depends upon its existing state. The variables are related by a number of differential equations and the behaviour of the system over time is the set of solutions of these equations. In general, such sets of simul- taneous equations cannot be integrated analytically and it is impossible to obtain solutions which are explicit functions of time. The only way of investigating the behaviour of such a system is to use some indirect method of integration.

One possibility is to use an analogue or hybrid computer to construct an electric network which will represent the equations and behave in a way analogous to the real system. There may, however, be considerable problems in representing a system in this way. Analogue machines are relatively difficult

376 P. J . CtIARLTON AND S. C. THOMPSON

to programme and to use, and it is also very difficult to represent decisions or sequential logical processes. Although hybrid machines overcome many of these limitations by combining the features of both analogue and digital computers, considerable problems arise in combining, or interfacing, the two essentially different types of machine. Hybrid computers are therefore, at present, extremely costly and their availability very limited.

The alternative to using any type of analogue machine to carry out the integration of the equations is to use methods of numerical analysis to derive approximate solutions. The calculations involved in this process can be extremely lengthy and repetitive, but can easily be carried out on a digital computer. The greater flexibility of the digital machine means that more complicated relationships can be included in the model and it is relatively easy to represent logical and decision processes.

(b) Sequential systems. Many systems are not concerned in any way with feedback processes, but with the sequencing or scheduling of jobs or the movement of materials. The processes involved in the harvesting of a grain crop, for example, are essentially of this type, where the grain after combining is taken to temporary storage before passing through the drier and finally into the storage bins.

The movement of grain through the various processes will be affected by bottlenecks occurring through inadequate capacity of the carting facilities or drier, and also as a result of other factors such as the moisture content of the grain and the size of the temporary storage facilities.

Not all sequential systems are concerned, however, with the development of queues and production bottlenecks of the type illustrated by the grain-harvesting example. A simulation of the financial changes taking place within a business, in which the calculations carried out are precisely equivalent to those made in producing forward budgets, is a sequential process which requires a rather different approach to that necessary for simulating a harvesting system. To simulate any of these different sequential systems on a digital computer, though no integration processes are involved, and it is only necessary for the model to advance through simulated time recording the occurrence of events, con- trolling the movement of materials, and carrying out decisions and allocations in the appropriate chronological order.

These are extreme examples of the different type of behaviour that can occur and most systems in fact combine some aspects which behave as feedback processes and others which are sequential. Combining these essentially different modes of behaviour in the same model can, in practice, be very difficult.@) Computer languages Any type of system can, in theory, be simulated using one of the general- purpose languages such as FORTRAN or ALGOL. However, many of the facilities that are required in a simulation model, such as mechanisms for advancing time, for producing output statistics, or for easily re-running the model with changed values of the parameters, are common to all simulation studies. A large number of different languages, specially designed for simulation purposes, have therefore been created so as to take over the responsibility for carrying out routine tasks such as these.(26) The differences between the various languages reflect those between the different types of system, as the requirements for simulating feedback systems are quite different from those for modelling sequential processes.

The complexities of designing a language that would be capable of representing both modes of behaviour equally well, and the fact that the size of the


resulting compiler would preclude its use on any but the largest machines has so far meant that all languages are designed essentially to model either one type of system, or the other. The specialised simulation languages, therefore, can be classified either as “integrating” or “continuous” languages for modelling feedback systems, or as “non-integrating” or “discrete” languages if they are basically designed to model sequential processes.

Even these existing compilers cannot be considered as being generally available as each one is usually designed for only a single, or very limited number of machines, and even then may not be implemented at a particular installation.

In selecting a language for simulating a system one should be fully aware of the disadvantages as well as the advantages of trying to use these special- purpose languages. For example, if the compiler is not already implemented at a conveniently accessible computer installation, then experience has shown that there may be considerable practical difficulties in obtaining it and making it compatible with the computer’s operating system. It is also easy to under- estimate the time that it will take to become familiar with the detailed features of a new language. In using these specialised languages, it is unlikely, in practice, that the requirements needed to model a particular situation will be precisely met by the basic features of the language. There will therefore be a considerable danger of one’s conception of the system being governed by the features known to be available in the language, and therefore of constraining the model to such an extent that its behaviour is governed more by the language than by its own inherent structure.

This is not so likely to be a limitation, however, with simulation languages which enable FORTRAN or ALGOL routines to be included for carrying out operations that are not explicitly provided. However, with these more powerful languages, the increase in flexibility is also unfortunately accompanied by an increase in the difficulty of learning their complexities so as to make full use of their potential advantages.

The implications of using any special-purpose language should therefore be considered very carefully before one is chosen in preference to the more readily available and familiar languages such as FORTRAN or ALGOL. It is probable, i n fact, that their use will only really be justified if a convenient compiler exists, if the study, or studies, are sufficiently extensive to justify learning the details of the language, and if the problems to be investigated can be easily fitted into the language facilities. The validation of models and the analysis ofthe results The validation of models and the analysis of their results raises a great many practical and philosophical problems which cannot be discussed here in detail. However, some of the more basic underlying difficulties will be briefly discussed.

A typical simulation model will contain a very large number of relationships and parameters, each of which affects in some way the model’s overall behaviour. The form of these relationships and the values of the parameters will in turn have been determined from one’s assumptions about the system, and from available data. The results from the model will be basically in the form of time- series for each of the system’s dependent variables, and any changes made to input parameters, or to the basic model relationships, will produce changes in the pattern of these time-series.

It is clear that it is not a straightforward process to validate such a model against reality because of the infinite variety of behaviour which it can exhibit. For the same reason it is also extremely difficult to know how to set about analysing the results.

3 78 P. J. CHARLTON AND S. C. THOMPSON

If it were possible to precisely define a system and accurately quantify its relationships, then any errors made in constructing the model would probably be relatively obvious and there would not be any great difficulty in validating its behaviour against reality. Even in this ideal case, however, it may not always be possible to interpret the behaviour of the model easily or to use it to give precise numerical results. It may then only be possible to derive more general, qualitative, results about the nature of the system’s response. It may be possible, for example, to determine that certain factors are relatively unimportant or that one type of investment is more sensitive to small price changes than another.

Systems in general, however, cannot be so precisely and accurately defined that exact numerical values can be given to all parameters, or that one can have complete confidence that all circumstances have been allowed for and that all significant variables have been included. In such cases it is clearly never possible to assume that the results can be regarded as more than representative of the behaviour of the system, exhibiting the same basic responses and sensi- tivities.(6) This after all is the most that one should expect of any other technique of economic or scientific analysis, but it is important to reiterate it in the context of simulation as it is easy to forget its validity, especially when the mathematical technique is complicated, unfamiliar, and superficially impressive. The acceptance of this fact, therefore, need not be a very significant limitation on the technique’s use, and it is very often this more general information about a system’s likely behaviour that is required.

It has so far been implicitly assumed that the relationship and parameters involved i n most models are fixed and that their behaviour is therefore rigidly determined-in other words the models have been assumed to be deterministic. It is possible, however, to include variables which change in a random manner, following some pre-specified statistical distribution. However, very considerable care has to be exercised when including stochastic elements of this type as the problem of validation and interpretation of the results can then become even more difficult than when the model is entirely deterministic. This is because the randomly changing variables can interact with each other to produce radically different results for each independent run of the model. The problem is then to distinguish whether any observed behaviour is merely a consequence of those particular random values, or whether it is a true response produced by the basis structure of the system. It can be shown statistically, in fact, that many hundreds of thousands of runs of a stochastic model may be necessary, in many circumstances, to determine this with a sufficient degree of confidence.(2) This fact has only recently been adequately appreciated and it is likely that in future far greater care will be exercised when using stochastic elements in simulation models.

3. Simulation models in agriculture It is not intended in this section to give a comprehensive description of all current and past applications of simulation to agricultural systems, as these are too numerous, but to illustrate the variety of approach and the range of application that exists within the general description “computer simulation”.

The simulation approach, as has been mentioned, has only been feasible since fast, powerful computers have become readily available. Although the approach has been widely used in industrial and military applications for nearly fifteen years, published work on the simulation of agricultural systems has mostly appeared within the last six years. Since that time a large number of studies have been carried out, and these have been concerned with a wide range of biological, economic and production aspects of different types of agricultural system. Each type of model reflects the difficulties of defining systems and


obtaining data to determine their internal relationships, and considerations such as these have always imposed restrictions on the scope of any model.

At the lower end of the scale, models have been built of individual machine operations or growth processes which were relatively self-contained and where the relationships involved were known or could be easily determined. Other studies have looked at slightly larger systems but have modelled them in less detail, aggregating parts of the system whose internal behaviour either was too complicated to determine or else was irrelevant to the principal responses that were being studied. This process of aggregation has been similarly carried out for larger and larger sub-systems until some studies have looked at national economic behaviour where the reactions of individual producing units are combined into overall responses.

The ability of a study to produce accurate results depends largely upon the validity of carrying out this aggregation process for particular sub-systems, and the difficulty of doing this has been widely recognised. One possible approach to the problem has been adopted by E i s g r ~ b e r ( ’ ~ ’ ~ ~ ~ ~ ) at Purdue, and others, who have created models which can be used as business games to simply instruct users in the type of decisions that have to be taken in running a firm. It is accepted that these models are considerable simplifications of reality and could not be used to give explicit advice on running a particular farm, but by exhibiting the same type of response as the real-life system they can serve an extremely useful education role.

The number of genera1 models which have attempted to study national agricultural responses or even whole farm behaviour is quite limited, largely because of one of the principal problems of aggregation-modelling the system in sufficient detail to produce useful results while overcoming the difficulties of obtaining data in a suitable form. Low and AgarwalacZ1) studied the national egg supply industry using an econometric model which they then used to simulate the effects of support buying. Stojkovi~(~’) used similar models to look at the national supplies of all the major agricultural products and to predict future behaviour of the industry. The Milk Marketing Board has also used models to simulate the effect of imposed seasonal price variations on national milk supplies.

On the farm as opposed to industry scale, Hesselbach and Eisgruberc”) and Patrick and Eisgr~ber(~*) have attempted to simulate whole farm growth, looking at the inter-relationships between the various biological, technical and financial aspects. However, these studies demonstrated the inevitable conflict between constructing a model which is at once sufficiently general to justify its construction and sufficiently detailed to enable it to be simply applied to specific situations. Because of the difficulty in resolving this conflict the majority of models have been restricted in scope to specific enterprises, or to sub-systems within enterprises, in an attempt to produce realistic and useful results. One of the earliest examples of a specific model of this kind was Halter and Dean’s simulation of a Californian Range-Feedlot operation.(13) This was based upon the Industrial Dynamics philosophy of J. W. Forrester(’O) and was designed to investigate the economic implications of different policies of range grazing combined with controlled concentrate feeding on a “feedlot”. A study by Hutton(15) also looked at a specific enterprise, dairying, and in particular at the replacement problem-when cows should be replaced bearing in mind the inter-related financial aspects of milk and calf sales.

Further work on the dairy enterprise has been carried out by Street(2e) who is using a model which concentrates chiefly upon the economic aspects of the system. This model, amongst other applications, is currently being used to

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380 P. I. CHARLTON AND S. C. THOMPSON

investigate the effect on profitability of different calving policies applied to complete herds, as a consequence of the seasonal differences in milk and feed prices and herbage availability.

Crabtree(’) has developed a model which concentrates upon the nutritional aspects of dairying and the consequences of using different feeding regimes. Both of these models, like Halter and Dean’s, are based upon the Industrial Dynamics approach to modelling feedback systems and are written in the simulation language DYNAMO.

A nutritional model, in some ways similar to Crabtree’s work, has been developed by Johansson(l8) at Uppsala to study the behaviour of beef fattening enterprises. This model is unusual in that although it uses the same approaches as those embodied in the DYNAMO language,(2s) the relevant routines are all programmed in FORTRAN. This has resulted in considerable flexibility and has, for example, enabled a modified linear programme to be incorporated within the simulation model to provide a least cost ration for each successive set of nutrient requirements.

One of the major problems on any arable or mixed farm is to select combinations of machinery that are compatible, adequate for coping with any likely workload and yet involve an acceptably low cost to the farmer. Dalton(8) has simulated the grain-harvesting operation for different acreages and machinery capacities, considering the different complements of machines and storage facilities and their total associated costs. Donaldson(u) has used Monte Carlo methods in a simulation of the costs involved in different cereal harvesting systems and their relation to grain moisture content in the field under varying weather conditions. A further study of grain-harvesting systems has been carried out by Van Kampen(20) who has looked at the selection of low-cost systems under the special conditions that exist on the very large Dutch polder farms.

Machinery systems, other than those for grain harvesting, have also been studied. Gemrnill,(ll) for example, has looked in particular at the effects on timeliness of different combinations of equipment. By simulating the cultivation and harvesting processes on a hypothetical arable farm under different weather conditions he was able to derive conclusions about the suitability of various combinations of equipment. An essentially similar approach, involving the systematic evaluation of a large range of alternative systems has been used by G6ransson(l2) who has recently completed a study of the economic aspects of alternative forage handling systems.

All of the models that have been discussed so far have been related to complete enterprises or to larger systems. However, considerable research using simulation techniques has been carried out into the behaviour of some of the basic biological systems, thus restricting the bounds of the models even further. In general, these studies, many of them carried out at the Grassland Research Institute, Hurley, and at Wageningen, Holland, have not been concerned with any economic implications but solely with processes such as physiological growth, energy balance or chemical diffusion. All of the systems are of a feedback nature and simulation languages such as DYNAMO, C S M P / ~ ~ ~ and D S L / ~ 130 have therefore been used to facilitate the integration of the differential equations that govern the basic behaviour of these system models. Perhaps the principal models of this type that have been reported to date are De Wit’s highly sophisticated model of the growth of a crop used by Brouwed4) and others, Watkin-Jones’ model of lamb growth(31) and Brockington’s of herbage g r0~ th . c~)

It is important to emphasise that most of the models that have been described were designed to look a t a number of inter-related aspects of the various systems, but that it has not been possible, in these necessarily short descriptions,

SIMULATION OF AGRICULTURAL SYSTEMS 38 I

to mention more than the models’ principal purposes. To illustrate the way in which such a simulation approach is in fact applied to a system and the type of results that can be produced, a model of firm growth that has been constructed by Charlton will be described in rather more detail.

A simulation model offirm growth A model has been constructed which simulates the physical and financial growth of large pig farms and enables desirable and feasible growth policies to be determined. The model is highly specific in that it only provides for certain, limited, types of pig production. However, by restricting its scope in this way, it is believed that the production and financial processes have been modelled with sufficient accuracy for it to be used as a practical management aid for investigating the likely future growth of similar pig farms. The alternative of producing a more general model would have been less likely to have been successful because the growth of any particular type of farm is highly dependent upon its own detailed individual structure.

The model is essentially a financial one, following in a deterministic way the flows of cash which result from decisions to expand production and then using the resulting financial position as a basis for budgeting ahead to determine whether further expansion is possible or desirable.

The model can be applied to existing farm situations by specifiying existing production and financial figures in considerable detail-type and age of buildings, scale of production, overdraft repayment commitments, initial cash position, etc. The chief interest in using such a model is to investigate the ways in which money is accumulated and the effect that different borrowing policies, rates of interest, repayment periods, etc., have on the type of expansion that occurs. One can also investigate the sensitivity of the firm’s finances to changes in performance levels and to sudden or cyclical changes in prices. The model can also be used to study the effect of different building policies, e.g. the consequences, particularly during the early stages of expansion, of investing in expensive, efficient and long lasting buildings, as opposed to cheap, short-life buildings which minimise the immediate cash requirements. By including different sets of rules for expansion it is possible to experiment with different courses of action and to follow their physical and financial consequences through time.

The preliminary results from the model have already clearly demonstrated that the considerations affecting investment in a growth situation are essentially different from those in a stable or static state. In the growth situation it is not possible to consider investments in isolation from the rest of the system, and without an adequate consideration of their timing, the sources of finance, existing financial commitments and valuation and taxation effects. It has shown that the use of simple techniques in these circumstances can often be misleading with, for example, investments desirable on a D.C.F. basis not being necessarily suitable for rapid expansion. It has also demonstrated that expansion, even if financially possible, need not necessarily be of any real economic advantage to the farmer. The effect of loan principal repayments on the cash flows has also been shown to be often far more important than that of the interest charges, and small changes in interest rates will, therefore, in many cases be relatively insignificant.

It is only by using a simulation approach which models in detail the dynamic changes which take place in the course of the growth and evolution of the particular real-life business, that it is possible to obtain information about these highly inter-related and time-dependent aspects. Their behaviour is not simple or self-obvious and it cannot be fitted into any convenient mathematical

382 P. I. CHARLTON AND S. C. THOMPSON

formulation. There are consequently no simple once-for-all solutions for such systems. This particular simulation model, however, has shown how different expansion

courses can be rapidly investigated and how particular performance levels and financial conditions, that are necessary for a stable and feasible financial growth, can be determined. This approach is typical of the use of simulation to obtain a better understanding of the behaviour of a system and has demonstrated the type of results that can be produced by a model designed for application to a highly specific situation.

4. The future development of simulation The real problems in carrying out a simulation study are the difficulties in identifying and quantifying relationships, in defining the bounds of the system to be studied, in collecting data in the correct form and in validating, analysing and interpreting the results. These problems are all largely independent of any developments in computing power or in language availability and sophistication.

Increasing familiarity with programming techniques will result in the more widespread incorporation of facilities for automatically changing parameters and re-running the model, on the basis of information extracted from an auto- matic analysis of previous runs. Such heuristic methods will be of assistance in discarding unsatisfactory areas of exploration, though the efficiency of the seeking process used will be markedly influenced by the type and number of objectives embraced by the study.

As agricultural studies become more sophisticated there will be an increasing use of mathematical optimising techniques within the simulation framework, in cases where sub-systems which can be satisfactorily solved in this way exist within the overall system. For example, the computation of least cost resource combinations or minimum span networks can be included as short run criteria within a long run model, and their efficiency compared with alternative decision rules.

The increasing availability of on-line computer facilities may reduce the period necessary for designing, modifying and implementing the programme, and will allow greater flexibility in re-running the model with changed parameters. It is unlikely, however, that any substantial decrease in the very considerable cost of developing models will result, and no major change appears imminent with regard to the way in which simulation studies are carried out. While a great many of the difficulties experienced at present will remain, the technique of simulation will become a more familiar tool in agricultural research and will find application over a wide range of problems.

I t seems probable, therefore, that developments will continue along three principal lines.

Firstly, simulation models are an excellent educational tool for imparting an understanding of the way in which dynamically changing systems behave through time. Models in this field will be either of a demonstrative kind, illus- trating different types of economic and biological behaviour, or they may involve participation in the form of a business “game”, so as to educate users in the actual process of taking decisions.

Secondly, there will be a further development of models for specific research projects which are designed to provide results of a qualitative nature. These results will generally be used in the comparison of alternative broad policies and in the production of management advisory aids. These aids might be reference data on combinations of buildings and machinery suitable for different


scales or combinations of enterprises, or perhaps tables for machinery replacement.

Thirdly, there is likely to be an increased use of highly specific models, such as the firm growth model, which can themselves be directly used as management advisory tools for application to different types of investment problem. The cost of simulation studies is likely to continue to preclude the construction of one-off models to solve a problem on an individual farm. This high cost may, however, be justified either when performing studies of large scale systems within agricultural industries, or through the application of a single model to four or five similar farm scale situations.

5. Conclusions Simulation is a highly flexible tool which can be applied to a wide range of problems in order to investigate their behaviour over time. Simulation studies, however, can be time-consuming and costly, and it is important that, before embarking upon any project, the objectives are both clearly defined and realistic, and also that one has an appreciation of the limitations of the approach, as well as of its advantages. Simulation has already been applied quite widely to agricultural systems, and it is likely that use of the technique in this field will continue to increase. The principal reason for this is that, in spite of the limitations of the approach, it does at least attempt, unlike most other techniques, to realistically reproduce the essentially time-dependent structure of real-life systems and to follow their behaviour in a logical and straightforward manner.

The relative simplicity and flexibility of this basic approach is perhaps simulation’s most important advantage in its role as a research technique and management aid.

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Gothenburg Business School. May 1968. 3. Brockington, N. R.: “Herbage Growth”. Proc. of Agric. Res. Council Symp. on the

Use of Models in Agricultural and Biological Research, G.R.I. Hurley, Berks, 1969. 4. Brouwer, R. and De Wit, C. T.: “A Simulation Model of Plant Growth with Special

AttentiontoRoot Growth and itsconsequences”. In: Roor Growth, Ed. W. J. Whittington, Univ. of Nottingham Fifteenth Easter School in Agricultural Science, 1968. Buttenvorths, 1969.

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Halter, A. N. and Dean, G. W.: “Simulation of a Californian Range Feedlot Operation”. California Agric. Expt. Sta., Giannini Foundation Res. Rep. NO. 282, 1965. Hesselbach, J. and Esgruber, L. M. : Befriebliche Entscheidungen Mittels Simulation. Paul Parey, Hamburg and Berlin, 1967 (in German). Hutton, R. F.: “A Simulation Technique for making Management Decisions in Dairy Farming”. Agric. Econ Rep. No. 87, U.S.D.A. Econ. Res. Service, 1966. Hutton, R. F. and Hinman, H. R.: “A General Agricultural Firm Simulator”. Dept. Agric. Econ. and Rural Sociology, Agric, Expt. Sta. Pennsylvania Univ. A.E. and R.S. No. 72, 1968. Irwin, G. D.: “A Comparative Review of some Firm Growth Models”. Agric. Econ. Research, Vol. 20, No. 3, July 1968. Johansson, V.: “Simulation of Biological Growth”. Scandinavian G.S.I.A. Joint Faculty Seminar, Lerum, Sweden, 1969. Johnsson, B. : “Simulation of Environments for Agriculture Production Units-An Approach to a Farm Business Laboratory”. Scandinavian G.S.I.A. Joint Faculty Seminar, Lerum, Sweden, 1969. Kampen, J. H. van: “Optimising Harvesting Operations on a Large-scale Grain Farm”. Ph.D. thesis, Univ. of Wagcningen, Netherlands, 1969. Low, E. M. and Agamala, R.: “An Econometric Analysis of Support Buying in the U.K. Egg Market 1958-68”. Proc. of Agric. Res. Council Symp. on the Use of Models in Agricultural and Biological Research, G.R.I. Hurley, Berks, 1969. Meier, R. C., Newell, W. T. and Pazer, H. L.: Simulation in Business and Economics. Prentice-Hall, New York, 1969. Naylor, T. H., Balintfy, J. L., Burdick, D. S. and Kong Chu: Computer Simulation Tech- niques. Wiley and Sons, New York, 1966. Patrick, G, F. and Eisgruber, L. M.: “The Impact of Managerial Ability and Capital Structure on Growth of the Farm Firm”. Amer. J . Agric. Econ., Vol. 50, No. 3, 1968. Pugh, A. L.: Dynamo User’s Manual, 2nd Edn. M.I.T. Press, 1963. Radford, P. J.: “Some Considerations governing the Choice of a Suitable Simulation Language”. Proc. of Agric. Res. Council Symp. on the Use of Models in Agricultural and Biological Research, G.R.I. Hurley, Berks, 1969. Stojkovic, G.: “A Prediction Model of Agricultural Products for Sweden”. Simulation Symp., Gothenburg Business School, 1968. Street, P. R. and Dent, J. B.: “Industrial Dynamics and an Approach to its Use in Farm Management Research”. Farm Economist, Vol. 11, No1 8, 1968. Street, P. R.: “An Outline of a Continuous Computer Simulation Model of theDynamics of Grazing Utilization with Dairy Cattle”. Agric. Econ. SOC. Discussion Paper, Univ. Reading, Dept. Agric., 1969. Thompson, S. C.: “An Approach to Monte Carlo Programming”. Study No. 3, Univ Reading, Dept. Agric., 1967. Watkin-Jones, J. G.: “Lamb Production”. Proc. of Agric. Res. Council Symp. on the Use of Models in Agricultural and Biological Research, G.R.I. Hurley, Berks, 1969.

DISCUSSION ON PAPER BY P. J. CHARLTON AND S. C. THOMPSON

J. P. 0. WEBSTER

I would like to thank Messrs. Charlton and Thompson for a thoughtful exposition of some of the strengths and problems associated with simulation.

The difficulty with any discussion on simulation is that it must refer to an area of activity rather than a definable technique. As the authors have pointed out, the form of a simulation model will depend upon the objectives of the study, the data available, and, not least, the experience, skills, and preferences of the analyst himself.

My general criticism of the paper was that the first two sections gave too restricted a view of simulation, and that later on, insufficient detail was given of Charlton’s model to allow constructive discussion.

I wish to make five specific points. Firstly, simulation was introduced and justified as a technique for analysing time-dependent systems. I t seems to me that simulation should be conceived of much more broadly. Computer simulation is simply the application of computing power to the problem of abstraction and prediction-in other words computerised scientific

D~SCUSSION ON PAPER BY P. J. CHARLTON AND S. C. THOMPSON 385

method. One use, as the authors point out, is in handling dynamic systems; another, which they mentioned only briefly, is in handling uncertainty. Previously, agricultural economists have tended to do sensitivity analyses on their recommendations only as an ofter thought. The sheer volume of calculations put them off. Now, with the power of simulation, they can very rapidly appraise the effects of price and weather uncertainty. I see this as being an important area for agricultural economists.

Secondly and more specifically, Charlton and Thompson suggest that the number of runs required by a stochastic model precludes useful application. On the other hand Conway* has shown that in cases where a few strategies need to be compared (and this is so often the case in agricultural economics), the fact that we can use identical streams of pseudo- random numbers, means that far fewer runs are needed. In other words if all we need is an ordinal ranking of some strategies, then we can make do with fewer runs using identical starting points for the random number generator.

Thirdly, I would suggest that the modification of the objective of a study to exploration rather than optimisarion is a direct result of the uncertainty of specification of a model rather than its complexity alone, In simulation we are not given a framework of relationships to build upon. This forces us to realise how uncertain our model is, and in turn makes us realise that an optimisation procedure may only give us the correct answer (if there is such a thing) by chance. The authors have hinted at this, but I think it needs stressing.

My fourth point concerns the validation of models. Although the authors discussed some of the difficulties, very little emerged here and few references to this subject (e.g. t) were given in the bibliography. Once a model has been built it is generally simple to obtain results and so once again we are reminded of the importance of asking the right questions. I felt that insufficient emphasis was given here.

My final point concerns the example. At the top of page 381 we were given to understand that Charlton’s model would be discussed in some detail. I was disappointed to find in fact that very little detail was given. As we have seen, a simulation model can only be discussed in relation to its own particular circumstances. Unfortunately we were given insufficient material to work on. This is a pity because the authors claim to have modelled a real situation; a discussion of some of the detail could have brought out many of the earlier points. So often one is faced with hypothetical models of hypothetical situations using hypothetical data.

P. J. CHARLTON

The principal points that Mr. Webster has raised are, I think, the questians of the definition of “simulation”, the introduction of stochastic variation and the failure to include sufficient detail of the growth model.

‘ I appreciate that our definition of simulation is different from his in that it is essentially more specific. The definition that we have given is, however, the now generally accepted use in industrial applications of the technique. The more general use of the term “simulation” that he has suggested includes, for example, Monte Carlo simulation which is essentially different from the time-dependent definition used in Operations Research and Management Science nowadays. I think it is important to distinguish between these different types of simulation if considerable confusion is to be avoided.

The second point concerned the number of runs required when one introduces stochastic variability. I am not familiar with the work that Conway has done, so I cannot comment specifically on that paper. Our comments, however, were based on a number of more recent sources including, particularly, the paper by Blomquist in which he showed that if one has more than a few random variables which interact with each other In a dynamic way then many thousands of runs may be necessary to statistically validate the model. The use of identical streams of random numbers can only partially help to reduce the number of runs required. It should be emphasised that it is this interaction between the random variables over time which results in the generation of widely differing time-series and hence produces these problems of validation and of the analysis of the results.

As far as the comments on the growth model are concerned I apologise for it not being possible to include more detail within this paper. In the planning of the paper we were requested to give a general coverage of the subject, with a review of the principal agricultural applications, and not to dwell in detail on my own model. The model is, however, far from hypothetical, and is currently being used to give specific guidance on the capital problems of setting up a large intensive pig unit on one of the University of Reading farms.

* R. W. Conway, “Some Tactical Problems in Simulation Method”. Memorandum RM-

* T. H. Naylor and J. M. Finger, “Verification of Computer Simulation Models”. Marrag. 3244-PR, Rand Corp. Santa Monica, California, U.S.A., 1962.

Sci. 14, 2, B92, 1967.

386 DISCUSSION ON PAPER BY P. 1. CHARLTON AND S. C. THOMPSON

S. C. THOMPSON

I have a couple of philosophical comments on two of the points that Mr. Webster mentioned. The first was the definition of simulation. It is very difficult to disagree with wide definitions

of a word. I think the classic example is the definition of the word "utility" which can have virtually no bound on its meaning. The only comment I have on wide definitions is the wider they become the less useful they are.

Mr. Webster's fourth point concerned validation of the model. The true validation of a model must lie firstly in the mind of the builder of the model, and secondly, he must be able to project this validation into the minds of those people who want to use the results of the model. The only way he can do this, I think, is by an appreciation of the relationships which went into the model and an acceptance of the fact that although they are not entirely accurate, they are good enough for the purposes for which it is. to be Fed. Validation really comes right at the beginning in the building of the model. If it is not valid then, then it never will be.

J. 0. 5. KENNEDY

Farm management entails the study of a number of interacting systems-biological, meteorological and economic-so that their interaction may be manipulated to give results the farm manager feels desirable.

The study of systems may perform two functions. It may enable the behaviour of a system to be predicted, and it may enable a more desirable system to be designed.

In the case of the relatively fixed biological and meteorological systems simulation is a valuable tool for understanding their behaviour. However in the case of economic systems at the management integration level the economic inter-relationships are much more amenable to direct analysis, and the objective shifts from just understanding to optimising.

There is much emphasis in the paper on the extent to which optimisation is possible using analytical techniques. and the authors are fairly pessimistic about further extension without using simulation. I feel that dmulation may more efficiently and usefully be applied to gaining a better understanding of the technical systems, and applying the results in programming methods in the management system.

S. C. THOMPSON

Probably the best answer is given in our paragraph on the future development of simulation. We do say that with increasing familiarity with the technique of simulation there is no reason why within a simulation model one shouldn't incorporate an analytical optimising technique; provided that, by doing so, one is actively and accurately modelling reality. If at some stage during planning the development of a farm, for instance, a farm manager has reason to use such an analytical technique, sub-optimising though it may be at that particular stage, then there is no reason why a sub-routine shouldn't be built into the simulation model to carry out the calculation at that point. Johansson, working on the raising of beef animals to maturity, has built a simulation model which incorporates a linear programme as a sub- routine for solving particular questions which L.P. is well fitted to solve at particular stages of the simulation model. But we can't, unless we are prepared to embark upon a discussion of dynamic programming, visualise an all-embracing model which is wholly analytical and contains no simulation element. But it is possible to incorporate analytical techniques at certain stages in a simulation model. There is, however, no n priori reason why these techniques should produce a better answer than any other decision rule built in to a simulation programme. A linear programme is merely one type of decision rule which can be included in the simulation programme in order to evaluate the usefulness of that rule against alternative decision rules.

P. 1. CHARLTON I would emphasise that if one is carrying out any optimisation process within a simulation then it is essential to make sure that one is in fact reproducing what would be optimised in reality. With reference to the firm growth model I do not quite see what, in fact, one would optimise, in that case. I would not consider that any one single factor could be optimised in order to investigate the growth of the firm because, in reality, there are so many aspects of growth that one needs to look at simultaneously.

Why do you want to look at growth anyway?

I would have thought that most farmers, as businessmen, are concerned with growth, both financial and physical, and that the question is of considerable practical interest to them.

J. 0. S. KENNEDY

P. J. CIIffiLTOt-4

DISCUSSION ON PAPER BY P. J . CHARLTON AND S. C. THOMPSON 387

J. T. H A W

I wonder if I could offer an opinion as one involved in practical farm management. I would like to thank the authors for an interesting paper, and I would like to congratulate Mr. Webster on what I thought were very astute criticisms of the paper.

With reference to the last point discussed, I would say that in the practical farming situation, one is either going to find oneself growing, or going down. Thus I think growth is very important, and the study of it is essential.

I would like to back up Mr. Webster’s point concerning the usefulness of simulation methods for producing information on the variability of outcomes. This is what I look forward to seeing most out of simulation studies. Such information is very relevant when one is estimating a cash flow for an expanding farm business, and I think simulation methods could be most useful in this area, especially where outcomes depend heavily upon weather variability.

I would emphasise that in carrying out any simulation studies one is always concerned with studying the effects of variability of certain factors upon the system as a whole. This is the reason for simulating the system and this process is illustrated in the study of the growth process in which one looks at the sensitivity of the system under varying hypotheses such as expected levels of price and physical performance. There are, however, different ways of incorporating variability within the model and, refemng back to a previous point, I would suggest that it is very often easier and more useful if one looks at only a selected range of hypotheses, rather than incorporating within the model a random selection procedure. For example, one could study the sensitivity of a system to optimistic, pessimistic and average price levels. rather than attempting to analyse the time-series produced by random selections of prices about different mean values. By adopting this approach to the incorporation of variability one is likely to produce a more immediate indication of the system’s sensitivity. To study the effects of variability one does not necessarily need to imitate the true variability of real-life.

P. J. CHARLTON

M. H. ATKINS It appears to me that simulation is an attempt to study how a particular model reacts to changes in the environment. However, this is the inherent weakness of the basically simple simulation technique for we have no way of knowing how the parameters themselves behave under a changing environment. The changes in the parameters could be incorporated in the simulation exercise by way of a feedback system but this will not always be possible. If I may phrase my question in a marketing context, what parameters should we use if we wish to advertise a product that has never been advertised before?

P. J. CHARLTON

I would agree that any attempt at using simulation can only succeed if the parameters and relationships describing the system can be determined sufficiently precisely and I think that we have probably emphasised this adequately in the paper. We certainly tried to avoid giving any suggestion that simulation overcomes the limitations of all other techniques or that it can be applied successfully to all problems.

In the same way as with other techniques one must always be certain that the approach used is appropriate to the particular problem. Your example of a marketing problem is clearly one in which neither simulation, or any other technique, could be used to give you any precise answers. It could be used, however, to help investigate the effects of certain assumptions about what might happen.

I think what Pefer Charlton was suggesting might be a little conservative,.but Mr. Atkins’ question is, I think, pessimistic. He is, in fact, arguing against the production of any hypothesis for future action because a simulation (whether run on a computer or not) is no more than a large hypothesis. If we are not allowed to venture our own opinions in a model, then we certainly won’t advance our knowledge of its workings.

S. C. THOMPSON

I. D. CARRUTHERS

A knowledge of advanced techniques of analysis can assist us in defining a problem. It also helps in the generation of appropriate questions which may in time lead the scientists to provide the improved data we require. Admittedly there is a lag after discovery of a technique. Agronomists are only just starting to appreciate production function analysis. On the other hand the N.A.A.S. have rapidly become proficient in handling linear programming. The quality of their management advice has improved as a result of formal application of

388 DISCUSSION ON PAPER BY P. J. CHARLTON AND S. C. THOMPSON

L.P. but also because advisers are thinking about problems in the form of a matrix of activities and resource constraints. Analytical tools such as simulation can help develop our science by first enlarging our understanding of the problems and second, by demonstrating the need and precise nature of data requirements. It is not true to state that more data is required before these new techniques have real value for there is a recursive system at work involving problems, data and techniques.

P. J. CHARLTON

I think we both agree entirely with this and it goes back to the point that I made about the system analysis in general, that it is an extremely important discipline. It forces you to specify what you know about the system and illustrates those aspects for which one should attempt to get more data. It may also demonstrate where further research on how one variable effects another needs to be carried out. It is therefore a constant process of self-education about the problem that one is studying. This information may be one of the most important products of any attempt to simulate a system.

M. UPTON

Could either of the authors be persuaded to enlarge a little on the relationship between dynamic programming and simulation. I feel intuitively that dynamic programming is only applicable to sequential systems and that feedback systems would invalidate the Bellman principle.

P. J. CHARLTON

In dynamic programming one requires future states, and the probabilities of moving from one state to the next one, to be pre-specified. Associated with each stage of the process is some cost function. The technique then optimises the path through the decision tree by working backwards through the network. This is essentially a quite different process to simulation in which one only defines the inter-relationships and the starting conditions and then observes the consequences of these assumptions by moving forwards through time. The techniques are therefore in no way directly comparable.

I. EL-ISSAW

It seems that the authors of this paper take an unduly optimistic view about the applicability of simulation models to real-world problems. I am inclined to disagree with this view, not because of any doubts about the theoretical advantages of simulation, but simply because of the existence of serious problems which make the fulfilment of the data requirements of simulation models extremely difficult. As the authors indicate, a pre-condition of a simulation study is the correct specification of the relationships of the model and the availability of reliable estimates of its parameters. I believe that this condition will not be reasonably satisfied in most cases. This is so because, in spite of the great amount of econometric work that has been done in our field, numerous basic problems of identification, specification and estimation remain unsolved. The shortage and shortcomings of relevant data, as well as the unavailability of appropriate and computationally feasible methods of estimation, make it very hard to obtain reliable and unbiased estimates of many of the basic economic and technical parameters. This, in turn, makes it unlikely to get useful empirical results from simulation models. Therefore, simulation may be a step in the wrong direction at the present time; the right direction being that of trying to devise methods of collecting better data and to develop and use estimational techniques which recognise the shortcomings of available data.

P. J. CHARLTON

I agree entirely about the problems of obtaining data and this goes right back to whether in fact any model or theoretical study can ever be a valid and accurate representation of reality. I would repeat that one must be careful when applying simulation to situations where the data that is required does not already exist or cannot be obtained in the right form. If the data does not exist then one should not be attempting to simulate or construct an econometric model of the situation, unless it is simply an exploratory study. One certainly should not attempt to use such a study to produce any specific results. If fairly accurate results are required it may be necessary to limit oneself to an essentially simple type of model. In the case of my own research on growth I had to continually limit and restrict the boundary of the system that I was intending to model to a stage where one could get data.

The validity of results produced by any technique depends upon the data used and statistical methods can only be of limited use in overcoming some of these data deficiencies.

The simulation method, in which one adopts a more pragmatic approach, may make it easier to identify the effects of data deficiencies than when using other more formalised

DISCUSSION ON PAPER BY P. J . CHARLTON AND S. C. THOMPSON 389

mathematical techniques. As far as the collection of better data is concerned, this surely should be a continuing process proceeding at the same time as the development of improved modelling techniques.

S. C. THOMPSON

It is a consequence of human nature that we are never going to have enough information to satisfy us at any one stage. The problem of data, as you will well know, applies to any technique or hypothesis, not only simulation. But I think the great advantage of simulation is that it does allow us to break free of the straight-jacket into which analytical techniques force us by their rigid specification of the sorts of equations and the sorts of parameters we should use. We can be grateful for this freedom, but its greatest danger is that it does allow one to include insignificant variables and little equations which aren't really necessary in order to adorn a model.

the analysis of net farm income: an examination of farm management survey data

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