Abstract: This paper, recognising the little attention that productivity measures have received at the organisation level and the significant attention which productivity has received at the more macro level (i.e. public policy level), is making a contrast by showing that productivity measures are found at a level which is more macro to the organisation in a developing country (i.e. the Gezira Scheme in Sudan).

The paper also shows that the joint consideration of productivity and financial measures through an integrated approach for designing control and information systems in the Public Agricultural Corporations (PACs) in the Sudan would resolve (or at least minimise) the in-congruencies existing between the different parties involved and between macro policy productivity measures and what is going on in organisations or firms.

The practicability and usefulness of the integrated approach is examined with regard to the Gezira Scheme in Sudan.

Many of the widely published discussions of productivity measurements have been derived from the basic concern with such larger issues as inflation control, industrial peace and economic growth. As such productivity is used as a measure for public and government policies [Eilon, Gold and Soesan (1976)]. Productivity as a measure also received significant attention at the operational level of the firm (i.e. the production floor). Productivity measures have not received the same attention at the firm or divisional levels as have productivity measures at the national macroeconomics and at the operational levels. At the firm and the divisional levels the focus is placed on the financial measures which, in turn, resulted in neglecting productivity as a measure and source of information. Consequently, productivity measures have not been subjected to the scrutiny applied to accounting and financial information [Kaplan (1983)].

Although there is a logical relationship between productivity and other financial measures, they are treated and developed in isolation from each other in the conventional literature. The reason for this isolation could be due to their historical development.

Financial measures and related concepts were developed in the context of manufacturing organisations operating in advanced economics. In these organisations, the products are assumed to be mature, with known characteristics and a stable technology. As a consequence, the researchers accepted the production setting as given and attempted to derive optimal decisions in a well specified and stable environment. These assumptions simplified the manufacturing operations and, in turn, resulted in overlooking very important measures which were available to monitor the performance of an organisation. With regard to developing countries this assumption is not true due to the unsuitability in the technology and the characteristics of the product. On the other hand, and because productivity has received attention at the macroeconomics level, little attention has been given to the seemingly minor issues of how to measure productivity and regard it as part of the information and control system at the organisations' or firm's level.

Most of the public enterprises and corporations in the developing countries are established mainly to enhance the achievement of national goals (e.g. economic growth, food security etc.). As a result, the performance of the public corporations in most of the developing countries needs to be measured in terms of contribution to the gross domestic product (GDP) of the country rather than in terms of profit or other financial measures.

What is going on inside the public corporations is not necessarily in agreement with goals of the macro policies. Other parties involved in public corporations may pursue different goals. Some parties are striving to maximise their return rather than just maximising production. Such differences in priorities will negatively affect the performance of this organisation if not carefully catered for by designing appropriate control and information systems.

Organisation and management as an area of study developed through two common approaches called the macro-approach and the micro-approach. Macro-level approaches typically draw from the disciplines of anthropology, sociology, political science, and economics. The resultant body of knowledge is referred to as management or organisation theory. The major issues here are inter-organisation relations, organisation structure, and management process. In contrast, micro level approaches tap the disciplines of psychology, social psychology, group dynamics, and communication theory. The resultant body of knowledge is known as organisational behaviour. The major issues in this approach are motivation, communication and group process.

Although the macro level approach views an organisation as a subset of communities and societies, it pays little attention to productivity measures which have received significant attention at the national macro level.

The same macro and micro perspectives were adopted by researchers interested in control and information systems. The macro perspective to organisation is described as a structural approach, which concentrates mainly on the information and communication aspects of control systems. The micro perspective is described as a behavioural approach because of its emphasis on the human and social aspects of control.

Central to the two approaches of designing control and information systems in organisations is the concept of responsibility centres. The financial and control literature classifies responsibility centres into five types i.e. profit, revenues, investment, engineered costs and discretionary expenses centres. Any unit within an organisation is classified as one of these responsibility centres; according to: (1) the objective(s) pursued by the unit, (2) the nature of the task performed to achieve the objective and, (3) the type of responsibility and authority allocated or assigned to carry out the tasks (see for example, Vancil (1973), Dermer (1977) and Anthony, Dearden, and Bedford (1984).

(2) and (3) above are referred to in the literature as organisational structure.

Figure # 1 depicts the relationships.

Figure # 1

Framework for Identifying Responsibility Centres

The assumption underlying this approach in identifying responsibility centres is that in any setting, goals and tasks can be measured in money terms.

Ansari (1977) argued that the problem with the structural and behavioural approaches to the design of control systems is that they emphasise a single prospective ignoring in process the existence of other perspectives. Building on this argument, Ansari (1977) proposed an integrated approach to control systems design which recognises the interaction between information structure and human and social relationships. In other words, Ansari is calling for an approach that takes into account the factors involved in controlling environment in simultaneous fashion. As a criterion the designer of a control system should choose those documents of a system which lower the possibility of cognitive conflict between the parties interested in the organisation. That is the minimisation of perceptual difference between the parties and encouraging behaviour which resolves such conflict with positive results for system performance. This method of conflict relationship is based on the concept that many organisational conflicts can be resolved to the satisfaction of both parties. These are known as integrative, or win-win method.

It has been argued for many years that, for managerial and strategic purpose, financial evaluation of organisations by itself is inadequate. This dissatisfaction with the use of conventional measures of profitability is due in part to the assumption that a single performance criterion can assess an organisation's performance. Such methodology focuses on outcomes to the exclusion of the transformation process within the organisation (see for example, Chakravarthy (1986).

In addition to the above, it is also recognised in the literature that a certain number of enterprises and corporations, particularly in developing countries, are designed not to produce profits, or even to break-even financially. The objectives of some of these organisations could be social or developmental or both. Needless to note that usually the authorities establishing such organisations are cognisant of the goals and objectives so specified. For such organisations, it is probably clear that performance cannot be judged on profit at all (Olave, 1988).

Based on the above discussion we can argue that existing financial measures of performance adopted at the divisional and organisational levels are not in congruence with macro policy productivity measures. In addition, the financial measures alone are not appropriate for judging the performance of the organisation established to achieve social and developmental objectives especially in developing countries. Realising the multiple and conflicting objectives pursued by the different parties involved in an organisation, financial measures alone will not enhance goal congruence and conflict resolution between the different parties. Therefore, what is needed is an approach for designing control and information systems that takes into account the factors involved in the control environment in simultaneous fashion. Hence, the perceptual difference between the parties involved is minimised and, in turn, the possibility of cognitive conflict is lowered. This would encourage behaviour which would resolve conflict between the different parties involved in an organisation in a win-win fashion (i.e. to the satisfaction of all parties). Ultimately, goals and risk congruence between the different parties would be enhanced with positive results for the organisation's performance.

Having shown the limitation of the existing approaches to the design of control and information systems in general, and particularly in the developing countries, we turn now to the main theme of this study. Its aim is to develop a theoretical framework appropriate for designing control and information systems in Public Agricultural Corporations (PACs) in Sudan. The theoretical framework aims at resolving (or at least minimising) the incongruences between the different parties involved in PACs and the incongruences between productivity measures at the public policy level as well as at the divisional or firm level.

Most of the economies of the developing countries are basically agricultural. With respect to Sudan, agriculture is the largest contributor to national income and foreign exchange. The average contributions of the agricultural sector in the Gross Domestic Product (GDP) and in export during the period 1990-1995 were 35% and 65% respectively. In addition this sector employs 70% of the labour force in Sudan. This dominant position achieved by cultivating 15% of the arable land in the country implies that agriculture remains the potential leading productive sector of the economy for promoting social and economic development.

The agricultural sector is composed of three distinctive sub-sectors: irrigated agriculture covers 4.5 million feddan* (4.68 million acres), mechanised rainfed covers 12 million feddan (12.46 million acres), and traditional rainfed [6-11 million feddan (6.23-11.42 million acres), depending on rainfall]. The irrigated subsection is primarily government owned. The two other sub-sectors are private.

The irrigated sub-sector, the focus of this paper, includes the Public Agricultural Corporations (PACs). PACs represent the core of the agricultural sector in the Sudan. They include: The Gezira Scheme, the Rahad Corporation, the White Nile Corporation and the Northern Corporation. The area occupied by PACs represents over 25% of the total irrigated area of Sudan.

PACs grow a sizeable portion of the country's agricultural production - particularly cotton and wheat. The number of tenants holding tenancies in PACs exceeds 150,000.

The Gezira Scheme alone covers 2.1 million feddan (2.18 million acres) or about 80% of PAC's area. The Gezira Scheme provides work for some 400,000 - 500,000 workers. Between 1.5 to 2 million persons depend upon the Gezira Scheme for their livelihood. The Gezira Scheme is said to be the largest organisation in the world under one management (Abdalla, 1987). For a historical background of the Gezira Scheme see Bernett (1977).

PACs are triplet partnerships between the government, tenants and managing boards. The partnership agreements represent the production relations between the partners. The production relations regulate the rights and duties of each partner. The tenants provide all the labour needed for the production of all crops. PACs specialise in the production of cotton as the main (cash crop) of the economy together with wheat and dura (food crop) and groundnut (cash crop). The government provides land and water. The management board in each corporation assumes the responsibility of managing the corporation. The board is subject to government direction and policies. The responsibilities of the board are the following:

· Defining the goals and objectives of the corporation;

· Formulating policies to achieve goals and objectives;

· Ensuring the implementation of policies formulated;

· Provision of agricultural inputs and services at cost;

· Provision of cash advances to the tenants during the season;

· Administration of the corporation, in particular field operations.

The overall goals of PACs as stated by the Managing Director of the Gezira Scheme summarised as follows:

· Production of cotton for export to provide foreign currency and to provide raw cotton for the local textile industry.

· Production of dura (sorghum) to secure the necessary food for the tenants in the corporations to secure their subsistence and stability.

· Production of wheat and groundnuts for local consumption and export.

· Provision of technical and administrative skills to help the tenants to gain a satisfactory income from the production of the different crops.

The above-stated goals denote that PACs are production-oriented organisations pursuing developmental and social objectives as well as financial ones. The financial objectives are reflected by the tenant's expectations of net returns. Therefore, PACs anticipated cultivating and producing food crops, as part of the government strategic policy, to secure self-sufficiency of food and to provide foreign currency for the country on one hand, and to secure sufficient income to the tenants, on the other.

Prior to the 1980/81 season, the partnerships in the Gezira Scheme and in most PACs were based on a profit-sharing arrangement and on a Joint Accounting System (JAS) [For more details see Abdalla (1987 & 1988)].

A new arrangement for production relations was introduced in the Gezira Scheme in the 1981/82 season to replace the profit-sharing and the JAS arrangements. The new production relations treat the tenants separately and individually. This relationship is known as the Individual Accounting System (IAS).

The IAS aims at recovering from the tenants the cost of all inputs and services provided by the government and the managing board. The cost of administration is recovered in the form of land changes. The water and land charges are flat rates per irrigable feddan. Any amount over and above these costs and rates is payable to the tenants at the end of the season.

It is important to note that all PACs are organised along the lines of the original Gezira Scheme, which was established in 1925. The Gezira's organisation and model has been replicated in all subsequent developed large-scale agricultural corporations in the Sudan.

The organisational structure of PACs aims at facilitating a one way flow of decision from top to bottom of the hierarchical structure. The central management of headquarters is divided into four functional divisions:

· Engineering Division;

· Agricultural Division

· Finance and Accounts Division; and

· Administration Affairs Division.

Each division is assigned to a manager directly responsible to the General Manager. We noticed the absence of sales or marketing departments in PACs. The marketing responsibility of cotton is assigned to The Cotton Public Corporation (CPC), which is a separate and independent body. The other crops are marketed by the tenants individually.

The four divisions found at the headquarters formulate detailed rules and procedures followed both at headquarters and in the field. The main function of field staff is to see that the directives laid down by the Agricultural Division are carried out by the tenants.

For operational purpose, PACs are divided into blocks and the blocks are assembled in groups. The Gezira scheme is divided into 107 blocks assembled in 14 groups. On average, each block inspector is in charge of 20,000 feddan while each group manager supervises 150,000 feddan. The field administration is the direct responsibility of the Agricultural Division headed by the Agricultural Manager (AM).

The AM in PACs is responsible for planning, implementing and controlling all agricultural activities. These activities include: crop production, vegetable production, seed production and supporting activities such as horticulture, extension, plant protection and crop protection. AM prepares and monitors the implementation of the following programmes and plans:

· Crop production targets and area originating from the board of directors;

· Supply and inventory of agricultural inputs;

· Machinery and equipment capacity;

· Farming parameters and techniques recommended by the Agricultural Research Corporation (ARC), which is an independent body.

AM provides top management with information about the following aspects:

· Progress of each agricultural activity;

· Implementation of recommended parameters and techniques;

· Results and problems associated with each activity; and

· Yield i.e., productivity of each crop.

The Agricultural Division prepares every year a detailed crop calendar for the production of all crops. The calendars specify the time and methods of agricultural operations. Also the amount of inputs e.g. fertilisers, seeds, etc. are specified. All services and inputs are centralised and provided to the operational units (i.e. blocks) according to the calendars. The block and group inspectors prepare annual and seasonal reports about the results and problems of each agricultural activity as well as reports about the yield of each crop.

The block and group inspectors are assigned the following tasks and duties i.e. responsibilities:

· Selection of areas appropriate for growing each crop within their blocks or group.

· Ensuring the timely delivery of the right amount of inputs to the tenants;

· Ensuring that the directives laid down by top management with regard to the application of inputs and the execution of agricultural operations are met by the tenants;

· Keeping records of physical amounts of input received and used;

· Keeping records of dates on which different agricultural operations are carried out;

· Keeping records of area on which different crops are grown; and

· Keeping records of yield achieved by the different crops.

To carry out the assigned task or duties, the block and group inspectors delegated the following decisions, i.e. authorities:

· Allocation of crops within the areas of their blocks and groups;

· Deciding and ordering the required amount of input based on areas to be cultivated;

· Distribution of available machinery and equipment (e.g. tractors, harvesters etc.) within their areas;

· Rewarding tenants for good performance; and

· Penalising tenants for negligence.

The above discussion shows that tasks or responsibilities assigned and decisions or authority delegated to the block and group inspectors at the operational level in PACs have the intention to maximise the productivity of the different crops. They are nor assigned or delegated any kind of financial duties or decisions. Accordingly, productivity of the crops is regarded as the main component or main key success area on which the whole system is focused.

The tenants, whose actions and behaviour are central to the PACs, are not delegated any type of authority or decisions with regard to the crops grown; area to be cultivated; inputs used and techniques applied. The only factor that tenants can control and influence is their effort.

The management and tenants in PACs seems to have different priorities. The management's top priority is to maximise the physical productivity of the crops, while the tenants strive to maximise net return from the crops. They are interested in higher prices for their crops and low cost of production.

At the present time, managerial performance in PACs is evaluated in terms of physical productivity. It is measured by dividing the total amount of crop (specially cotton) produced measured in Kantar (1 Kantar = 99 pounds) by the total area cultivated with cotton measured in feddan. Such a measure, we argue, is incomplete because it eliminates the effect of input prices. Consequently, the way in which the Gezira Scheme is organised and managerial performance is measured has caused the following drawbacks:

· It does not enhance goal congruence: The management is not encouraged to cost consciousness because there is no logical reason why management should be wary about minimising production costs. Most of the costs charged to tenants are accounted for on an actual basis. Thus the management can pass the effect of its inefficient performance to the tenants in the form of high cost of production. On the other hand, the tenants strive to maximise net return from all crops, which could not be achieved without minimising production costs.

· It does not enhance risk congruence: Since it eliminates the effect of input prices, the physical productivity measure of performance does not fairly distribute the risk involved in PACs. The tenants appear to be bearing all the risk facing the Scheme in the form of higher costs of production and/or lower prices for the crops. This situation naturally creates a conflict of interest, because the attitude of the tenants and those of management to the risk involved are not in congruence.

· It does not enhance satisfactory resolution to conflict: Since it does not enhance goals and risk congruence, the existing control system would not assist in reaching satisfactory resolution to the conflict between the parties, especially between the tenants and the government. Instead, the conflict is resolved through the use of authority because the government always has the option of taking sides and settling the dispute by using its authority. This in turn results in win-lose resolution to conflict. The tenants are, in most cases, the losers. Accordingly, this encourages them to engage in non-productive behaviour.

· It does not encourage the management to keep accurate and timely records of accounts: Since their performance is measured in terms of physical productivity, the management is not concerned about keeping adequate financial records. The only financial report is produced after the preparation and auditing of the financial statement at the end of the year. This is completed only two or more years after the end of the reporting period. Hence, these reports are of no use for control purposes and are kept for statistical purposes only.

The above-mentioned drawbacks show that productivity, as measured at the present in PACs, is inappropriate for judging the performance in these organisations and for providing the relevant information for planning and control purposes.

Therefore, we can argue that neither financial measures nor productivity measures, when considered separately and individually, will enhance the control and information systems and in turn, they will not enhance goal and risk congruence.

In fact what is needed is an approach to designing control and information systems that takes into account financial and productivity measures simultaneously and considers them jointly. To achieve this the next section of this paper is devoted to develop an approach appropriate for designing control and information systems in PACs in the Sudan.

The integrated approach for designing a control and information system is developed to overcome some of the limitations of the existing approaches. The aim is to resolve (or at least minimise) the incongruences in goals and risk existing between the different parties involved in PACs and, in turn, resolve (or at least minimise) conflict between these parties in a satisfactory way. It also aims at resolving incongruences between performance measures at the macroeconomic level and at the firm or divisional level.

Our starting point in developing an integrated approach is the standard definition of profit given as:

(1) P = R - C

Were P is profit, R is revenue and C is cost in the usual notation. Revenue is given in the usual manner as:

(2) R = qQ

Where q is the price of the product and Q is the quantity sold. In PACs in Sudan total output can be looked at as average productivity of land (y) multiplied by the area under the given crop (A). Thus, we have revenue as:

(3) R = qyA

In PACs the total cost of production is usually looked at as being composed of two parts: an area-related cost and a volume of production-related cost. Thus we have:

(4) C = zA + vQ

Where z is the per unit area cost of production (e.g. seeds, land preparation, fertilisers, etc.) and v is the per volume cost of production (e.g., cotton picking labour, transport, ginning etc.). The two components of production cost can be reformulated in terms of area and yield in the following fashion:

(5) C = zA + vAy

Where the total cost of production is now a function of productivity in a direct fashion with an intercept of za and a slope of vA.

Substituting (3) and (5) for (1) we have profit given by:

(6) P = qyA - [z + vy] A

So that profit per unit area is given by:

(7) P/A = h = qy - vy - z

Thus (7) shows that profit per unit area is also a function of the productivity with a slope of (q -v) and a negative intercept of z.

Residual income is used to measure the financial performance of the investment responsibility centre. It is calculated by deducting the cost of capital employed, from the net profit earned, by using that capital. Thus we have:

(8) X = P -I

Where X is the residual income, P is profit and I is the cost of capital. Substituting for P as given in (6) we have:

(9) (X) = [qy - z - vy ] A - I

= qyA = Az - vyA - I

= (q - v) Ay - (zA = I)

Where residual income is also a function of productivity in a direct fashion with a slope of (q-v) and a negative intercept of (zA + I)

From equation (7) we can define the Break-Even Yield (BEY) which is a very important concept for planning and control purposes as:

(10) (q - v) y - z = 0

(q - v) y = z

y = z/(q-v)

Figure (2) The Break-Even Yield

Thus the Break-Even Yield is given as the ratio of the per unit area cost of production to the net return per unit of output (i.e. contribution margin). From equation (10) it is probably clear that, other thing being equal, the break-even yield increases with the increase in cost per unit area and the cost per unit volume, and it declines with the increase in the price of output. See Figure (2).

Equations (3), (5), (6 and 7) and (9) show respectively that revenue, cost, profit and residual income depend directly on productivity as the main component or key success area (KSA). Thus, we can say that productivity which has been disregarded by the financial and control literature underlies all the financial measures [i.e. Key Success Factors (KSFs)], which have been treated as focal points for designing the conventional responsibility centres. The above analysis also provides evidence that the interaction between the different measures (i.e. KSA and KSFs), including productivity, necessitates their joint consideration in designing control and information systems in PACs in Sudan.

The joint consideration of the physical and financial measures when designing control and information systems in PACs seems to match better their goals and organisational structure.

The obvious casual relationships portrayed by the above equations between productivity and the conventional accounting measures suggests that an acceptable conceptual framework could be found in the concept of interaction between all the measures taken together, with productivity occupying a central place.

This proposed framework is presented in figure (3) which embodies a conceptualisation of control system structure and processes that allows the designer to understand the relationship between financial and non-financial measures and, in turn, the interaction between financial and non-financial responsibility centres.

Having accepted this understanding and that PACs in the Sudan are production-oriented organisations, with productivity of crop as the main objective to be achieved, the integrated approach developed here is used to define the responsibility centres existing at the different field levels in PACs as and Integrated Production Responsibility Centre (IPRC) in the following way:

An Integrated Production Responsibility Centre is a unit or a department found in a production-oriented organisation. The objective of the unit's manager is evaluated by how well he achieves a high level of productivity. The inputs assigned to the unit's manager and the services provided to the unit are centrally controlled. The inputs and services are measurable in monetary terms. The unit's manager is not responsible for marketing the product. No budget for expenses or sales is prepared at the unit's level. The unit's manager is able to estimate his expected productivity. The overall budget of expenses for the organisation is based partially on expected productivity. Records of actual productivity could be kept, and compared with estimated or expected productivity. Output is measurable in physical and monetary terms.

An examination is attempted to achieve the following:-

1) To show that we have a situation (i.e. the Gezira Scheme) where productivity is found at a more macro level to the firm. That is, productivity is used for reporting, performance evaluation, standard setting, and policy evaluation.

2) To show how the joint consideration of productivity and financial measures through the IPRC would enhance planning and control and, in turn, enhance goal and risk congruence in PACs.

The examination is carried out with regard to the Gezira Scheme. Emphasis is placed on the Gezira Scheme due to its size (representing about 80% of PACs). In addition, the Gezira Scheme practices and policies are regarded as models to be adopted by PACs. Also emphasis is placed on cotton, due to the availability of data about this crop.

The term reporting is used to refer to the way in which the information about productivity is prepared (i.e. how), the time at which it is prepared (i.e. when), the individuals preparing it, the persons receiving it and the contents of the reports.

The block inspectors prepare routine reports during the cotton-picking season. These reports are prepared weekly, half-monthly and monthly.

The reports are structured and they contain information about the following aspects:

· Cotton variety;

· Total area cultivated;

· Productive area;

· Number of productive tenants;

· Number of tenants who started cotton-picking;

· Weight of unpacked cotton in the field;

· Weight of packed cotton in the field;

· Weight of packed cotton in the collection centres;

· Weight of cotton transferred to ginning factories;

· Number of family members participating in cotton-picking;

· Number of local labour participating in cotton-picking;

· Weight of total picked cotton;

· Average yield per feddan to date.

The block's inspector reports to the group's inspector who, in turn, consolidates the blocks' reports into a group report and sends it to the Agricultural Director at headquarters (Barakat).

Similar reports are prepared for the other crops during their harvest season. But they are not given the same attention as those concerning cotton. In the last seasons wheat has been given more attention by the management of the scheme for food security reasons.

At the end of each season, the Agricultural Division in the Gezira scheme prepares the Agricultural Report. This report usually reflects the average productivity of the different crops achieved during the season compared with those achieved during previous seasons. Reasons for improvement or declines in the productivity of each are explained with regard to a number of factors (e.g. land preparation, irrigation, diseases, harvesting etc.).

Based on the above-mentioned facts, we can say that the periodical and annual reports prepared in the Gezira Scheme and in other PACs are entirely devoted to the productivity of the crops and the problems related to production and productivity. Therefore, we can argue that a productivity measure is used in the Gezira Scheme and PACs for score recording.

Table (1) shows the distribution of blocks in the Gezira Scheme according to the level of productivity achieved by the different cotton varieties for the period 1980/81-1990/91. The distribution of blocks according to productivity is based on a cut-off point productivity of 4.1 Kantar per feddan, which represents the weighted average yield for all varieties during the period 1980-81 - 1990-91. It is shown in the table that 32%, 82% and 8% of the blocks which had grown the Acala, Barakat, and Shambat variety, respectively, have achieved a level of weighted average productivity which is more than 4.1 Kantar per feddan. Those which achieved a level of weighted average productivity which is more than 4.1 Kantar per feddan represent 68%, 18%, and 92% of the blocks that had grown the Acala, Barakat and Shambat variety, respectively.

Table (1)

Distribution of Blocks by Weighted Productivity of the Different Cotton Varieties for the Period 1980/81 - 1990-91 (percentage)

Variety

4.1 Kantar or Less

More than 4.1 Kantar

Acala

32

68

Barakat

82

18

Shambat

8

92

Source: Own Calculation

Table (2) summarises the results of the variance analysis carried out to examine the variation in the productivity of the different cotton varieties at the blocks' level in the Gezira Scheme during the period 1980/81 - 1990/91. During this period the number of blocks which grew Acala, Barakat, and Shambat is 50, 105 and 51 respectively. Productivity is measured by the weighted yield for each variety during the period. The blocks which have grown the same cotton variety for a number of seasons during the period 1980/81 - 1990/91 have been treated as a group. Then the variation in yield of a cotton variety is calculated for every group of blocks. The variation in the weighted yield of all varieties has been calculated for the total number of blocks (105 blocks).

Table (2)

Variation in the Productivity of the Different Cotton Varieties in the Gezira Scheme at the Blocks' Level for the Period 1980/81 -1990/91

Variety

 

No. of Blocks

F-ratio

Level of Significance

 

Yield

      
 

Min

Mean

Max

      

All Var.

2.1

4.4

6.8

105

78.72

0.000

Acala

2.2

4.9

7.9

50

46.14

0.000

Barakat

1.3

3.5

6.2

105

1.40

0.24

Shambat

2.6

5.7

7.0

51

15.47

0.000

Source: Own Calculation

Table (2) shows that the variation in the productivity is significant with regard to all varieties except for the Barakat variety.

Accordingly, and for the purpose of target setting and performance evaluation in the Gezira Scheme, the following conclusions could be drawn:

a) Uniform productivity targets for the whole Scheme and for all varieties is inappropriate.

b) Different targets need to be set for each variety of cotton.

c) For the same variety of cotton, different targets need to be set within the scheme except for the Barakat variety. For the Barakat variety a uniform target may be set across the Scheme.

Table (3) compares the weighted average productivity of the different cotton varieties in the Gezira Scheme with those achieved by all PACs and the whole Sudan during the seasons 1980/81 - 1990/91. The Gezira Scheme achieved a higher level with Acala and Shambat. Acala productivity in the Gezira Scheme exceeds that of PACs and the whole Sudan by 9% and 4% respectively while Shambat exceeds them by 21% and 24% respectively. Barakat variety productivity is less than that of PACs by 5% and it exceeds the average productivity of the whole Sudan by 6%.

Table (3)

Average Weighted Productivity of Cotton in the Gezira Scheme Compared with Other PACs During the Period 1980/81 - 1990/91 (Kantar/Feddan)

Variety

Level of Productivity

 

Gezira Scheme

PACs

All Sudan

Acala

4.9

4.5

4.7

Barakat

3.5

3.7

3.3

Shambat

5.7

4.7

4.6

All Varieties

4.4

4.5

4.2

Source: PACs Records

Table (4) compares the average weighted productivity of the different crops achieved by the Scheme during the period 1980/81 - 1991-91 with the productivity level that could be achieved under research conditions. Productivity in the Gezira Scheme is lower than that achieved under research conditions by 68%,78%, and 71% with regard to cotton, groundnut, sorghum and wheat respectively. This shows that performance in the Gezira Scheme is indeed very poor. Improvement in performance at the intensive margin (vertical expansion) could be achieved and should be given high priority. Therefore, agricultural research and extension services could play a crucial role in this regard.

Table (4)

Productivity in Gezira Scheme Relative to Productivity Under UPRC* (Kgs/Feddan)

Crop

Gezira Scheme

UPRC

Cotton Lint

4.2

13.3 Kan/F

Ground-nut

600

2800

Sorghum

550

3000

Wheat

530

1800

Source: Ishag and Ageeb (1987).

Tables (1), (2), (3) & (4) show how productivity information is used for setting targets and how these targets could be compared with the historical performance, with the performance of other units within the Gezira scheme, with the performance of similar organisations and with the potential level obtainable under research conditions. That is, such tables could be used as control reports to show variances between actual performance on one hand, and targets, historical records, performance of similar organisations and potential level, on the other.

Table (5) compares the yields in the agricultural sector in the Sudan with the highest yield countries (e.g. Egypt and USA). The yield of wheat in Sudan represents 30% of that in Egypt. Yield of cotton in Sudan is relatively the highest, it represent 67% of that in the USA. Worst productivity is that of sorghum. It represents only 16% of that achieved in the USA. As weighted average, productivity in Sudan represents only 27.9% of that in the highest yield countries.

Table (5)

Productivity in Sudanese Agricultural Sector Relative to High Yield Countries* (HYCs) (Kilograms Per Feddan) 1979 - 1982

Crop

Sudan

Highest Yield

Sudan of HYC

Cotton Lint

334

501

67

Groundnut

362

1085

33

Sorghum

240

1501

16

Wheat

405

1354

30

Weighted Average

346

1242

27.9

Source: Mahran (1991).

Based on the above discussions we can say that productivity measures are used in the Gezira Scheme and in PACs for performance evaluation at the tenancy, block, group and Scheme levels. Also it has been shown that productivity could be used for setting standards in PACs.

This part of the paper is devoted to verifing the usefulness of productivity measures for policy evaluation at a more macro level within the Gezira Scheme. [Abdalla, (1987)] conducted a study to test whether or not there is a difference between the average productivity of cotton before and after the introduction of the IAS at the different levels in the Gezira Scheme. He found that after the IAS, productivity of cotton increased significantly at tenant, block, group and at the whole scheme levels.

Table (6) shows the estimated equation for the yield of the different cotton varieties under the Joint Accounting System (JAS). The Individual Accounting System (d) is a dummy variable representing the two systems, where d-1 stands for the IAS and d=0 stands for JAS.

The estimating equation illustrates that Acala yield is 2.46 Kantar per feddan higher under IAS than under JAS. The long staple cotton is 0.73 Kantar per feddan higher under IAS compared to that under JAS.

Table (6)

Estimated Regression Equation of Cotton Varieties before and after the Introduction of the Accounting System in the Gezira Scheme for the Period 1971/72 - 1990/91

Cotton Variety

Estimated Equation

R2

F-Ratio

DF

Level of Significant

Acala

Y=3.66 + 2.46d (6.7)

0.69

45.01

20

0.00

Long Staple

Y=3.66 + 0.73d (2.18)

0.21

4.75

20

0.04

Figures in brackets are t-values.

Source: Own Calculation.

The above analysis shows that productivity measures are useful for policy evaluation and, hence, provide information for decisions regarding resource allocation (i.e. strategic planning).

Table (7)

The Break-even Yield for Each Cotton Variety for the Season 1991/92

Cotton Vareity

Price Per Kantar

Ls. (q)

Cost per Feddan

(Fixed) Ls. (Z)

Cost per Kantar

(Variable) Ls. (V)

Average Weighted

Yield (K/F) (Y)

Break-even Field

(K/F) Y

Acala

1,435

4,389

236

7,43

3.66

Barakat

2,379

3,422

191

6.19

1.57

Shambat

1,435

3,799

296

4.51

3.34

All varieties

1,541

4,403

273

6,57

3.47

Source: Calculated from the Annual Economic Analysis Report Gezira Scheme 1991/92.

Table (7) shows that the lowest break-even yield could be achieved by the Barakat variety at 1.57 Kantar per feddan. This is due to its higher price 2,379 Sudanese pounds per Kantar and due to its lower variable costs (i.e. production-related costs). These resulted in a higher contribution margin for the Barakat variety.

The break-even yields calculated by the research unit of the Gezira Scheme for the same season 1991/92 are 4.7, 4.8, 3.4 and 4.4 Kantar per feddan for Acala, Shambat, Barakat and for all varieties, respectively. The differences between the break-even yield as calculated in this paper and those calculated by the Gezira Scheme's staff are simply due to the fact that the Gezira Scheme's staff do not differentiate between variable (i.e. production-related costs) and fixed costs (i.e. area-related costs) when they compute the break-even point.

Figure (4)

Structure of the Annual Budget of Cotton Production in the Gezira Scheme

Estimated area (A) in (feddan)

Estimated Yield (Productivity (Y) in (Kantar/feddan)

Production (Q) = AY in Kantar

Average price per Kantar (q) in Ls (Sudanese Pounds)

Total revenue ( R ) = A. Y. q in Ls

Production costs

      - Fertiliser

        ------------

      - Insecticides

        ------------

      - Herbicides

        ------------

      - Spraying

        ------------

      - Seeds

        ------------

      - Land Preparation

        ------------

      - Land and Water Charges

        ------------

      - Pulling of Cotton Roots

        ------------

      - Bank Interest

        ------------

      Cost per feddan

        XXX

   

      Production-related costs: (Cost per Kantar) (V)

  

      - Picking

        ------------

      - Sacks

        ------------

      - Transport

        ------------

      - Ginning

        ------------

      - Bank Interest

        -----------

      Cost per Kantar

        XX

 

      Total Production Cost (C ) = ZA + VY = ( c )

      Net Profit (P) = R - C

      Residual Income (X) = P - (I) Cost of capital

The break-even yield of cotton (Y) is given as:

Y = Z/)q-v) = Kantar / Feddan

Where:

Y = break-even yield in Kantar/ feddan.

z = area-related cost per feddan (fixed cost).

q = the average price per Kantar.

v = production-related cost per Kantar (variable cost).

The break-even analysis carried out above illustrates the interaction between productivity and the other financial measures (e.g. fixed cost, variable cost, price and the contribution margin). This, in turn, provides more evidence for the appropriateness of the integrated production responsibility centres for designing control and information systems in PACs in the Sudan and, hence shows the usefulness of productivity information for attention-directing and planning.

Table (8)

Average Productivity, Water Requirements and Net Income Per Feddan by Variety of Cotton for the Season 1991/92 in the Gezira Scheme

Cotton Variety

Area (feddan)

Average Productivity

(Kantar/feddan)

Total Production

(Kantar)

Water requirement

(CU.M/feddan)

Net Income Per Feddan (L.s.)

Acala

58,000

7.43

430,940

4100

2,525

Shambat

53,000

6.19

328.070

4887

976

Barakat

104,000

4.51

469,040

4887

2,790

Total

215,000

  

1,228,050

    

The constraints taken into consideration for the season 1991/92 were as follows:-

Irrigation water and land are the most limiting factors in agriculture. The net income to tenants is treated as a constraint to enable us to consider the financial objective of the tenants when we find out the optimum feasible combination of the different cotton varieties that maximise production and at the same time maintain the level of net income achieved by the tenants in the season 1991/92 from the cotton crop. The consideration of financial and physical constraints is in line with the integrated production responsibility centres.

Having these facts and constraints with regard to the season 1991/92, the question which is to be answered by linear programming is how much of the area available for cotton production in the season 1991/92 should have been allocated among the different varieties of cotton to maximise its production and secure at least the income already achieved by the tenants from the utilisation of available land and water. It is worth reporting that the constraints are confined to land and water only for the purpose of simplification. Accordingly, the linear programming equations reached by using the simple method are shown below:

The objective function is to maximise production of cotton (P) that is:

Max. P =7.431 + 6.19x2 + 4.51x3

Subject to:

x1 + x2 + x3 = 215,000

4100x1 + 4887x2 + 4887x3 - 968,259,472

2525x1 + 976x2 + 2790x3 - 468,220,280

Table (9) shows the optimum solution.

Table (9)

The Optimum Solution

 

S

7.43

x1

6.19

x2

4.51

x3

0

x4

0

x5

Constraints

7.43

x1

1

1

1

0

0

215,000

0

x4

0

787

787

1

0

85,098,972

0

x5

0

1549

-265

0

1

75,676,840

 

C-

0

-1.24

-2.98

0

0

p=7.43x215,000

Therefore, to maximise production of cotton in the season 1991/92, the Gezira Scheme should have grown all the area available for cotton (i.e. 215,000 feddan) by the Acala variety only. If this had happened, the cotton production could have increased by 23%. At the same time, the net income for the tenants could have been increased by L.s 75,676,840 which represents 16% of the actual net income achieved. Irrigation water used could have been reduced by 85,098,872 cu. meters which represents 8% of the irrigation water consumed in the production of cotton. Therefore, it could be argued that the maximisation of cotton production can result in a significant increase in net income and economise in the use of irrigation water which could be shifted to growing other crops.

Since it caters for production and net income, it could be argued that the joint consideration of productivity and financial information in the decision making process in PACs would enhance goals and risk congruence between the different parties involved and would result in an economic use of available resources.

The Advisory Unit of Public Agricultural Corporations (AUPAC) is regarded as a centre for collecting information about PACs in the Sudan. Most of the outside parties interested in these corporations obtain the information they need from AUPAC. The reports prepared by AUPAC constitute: area cultivated by the different crops in each corporation, crop variety (if applicable), aggregate production and average yield. These reports are available for external users such as the Ministry of Finance and Economic Planning, Bank of Sudan, World Bank etc. Those external parties use the productivity information for preparing their annual reports and reviews. [see for example, WB reports (1966) Bank of Sudan Report (1991), The Ministry of Finance Economic Review (1988/89) Sudan].

In addition to the agricultural reports, the Planning, Economic and Social Research Units in PACs (especially in Gezira and Rahad), prepare annual economic reviews. The reviews provide in addition to the average productivity of each crop, information about production costs and net returns for each crop, [see for example The Gezira Annual Economic Review for the season 1994/95].

Productivity information also proved to be useful for research, [see for example, El Obeid (1989), El-Faki (1987), and Ishag et al. (1987)] and for special studies; [see for example, the Debit Credit and Marketing study carried out by the Sudan Gezira Rehabilitation Project (1990)]. In this study, productivity at the block level is used to explain the reasons behind the increase in the tenants' debts. The above mentioned example for the uses of productivity shows how the information provided by the IRPC in PACs meets the needs of external and outside interested parties. Accordingly, it could be argued that productivity measures can minimise the perceptual differences between the different parties, hence minimising the cognitive conflict.

The contents of this section show that the present and potential information provided by the IPRC in the Gezira Scheme and other PACs would reflect the financial and non-financial activities or operations. The IPRC would make available a type of information, which could not be provided by the conventional responsibility centres. This type of information could be referred to as "Productivity Information". The "Productivity Information" makes the information provided by the IPRC more useful than the information provided by the conventional centres individually because it possesses the following characteristics:

(i) It reflects growth (i.e. increase in output/input ratio). That is, social objective and economic efficiency. Therefore, it matches the dual nature of public enterprises and corporations.

(ii) It provides real efficiency gains: a ratio of output produced to input consumed from improved production and management procedures abstracting away from change caused by variation in the relative price of input and output. Hence, the analyst is able to separate variances due to relative price changes from those due to changes in production efficiency resulted from a change in skills and/or effort.

(iii) Productivity measures are more objective because both the evaluator and the performer can agree on them. That is due to the standardisation of its components (e.g. feddan, Kantar, tonnes etc.).

(iv) The way in which productivity is measured (i.e. output/input ratio) indicates that productivity measures focus on the transformation process within the firm, which is ignored by the financial information, as well as focusing on outputs. Therefore, it reflects efficiency and effectiveness.

Most people have heard the issue expressed as follows: productivity measures are found at the production floor level and at more macro (i.e. economic public policy) levels while financial measures exist at the firm level. This paper proves that productivity measures are used in the Gezira Scheme and in other PACs with regard to the various aspects of planning and control systems. Namely, it is used for recording, budgeting, performance, evaluation, break-even analysis and policy evaluation. Therefore, it could be concluded that now we have a situation (i.e. PACs) where productivity measures are found at a more macro level to the firm rather than just at the production floor and at the more macro levels (i.e. Public Policy Level).

Since PACs are found in the Sudan, it could be concluded that there is a reverse relationship between the use of productivity measures at a more macro-level to the firm and the extent that country's economy is developed.

It has been shown in this paper that the information obtainable by applying the concept of IPRC in PACs, which integrates productivity and financial measures, is more comprehensive and superior compared with other approaches to the design of control systems, because it provides financial and non-financial measures. In addition, IPRC is consistent with the organisational structure, tasks and goals in PACs.

As such it could be argued that the concept of IPRC, due to its provision of financial and non-financial information, and the manner is which it matches with organisation structure, tasks and goals in the Gezira Scheme and in other PACs would resolve (or at least minimise) the incongruences and problems existing between the different parties involved in PACs, through minimising the perceptual difference between the parties, and hence encouraging behaviour which would resolve conflict with positive results. Thus, risk and goal congruencies within these corporations would be enhanced.

It has been shown that the same productivity measures are used inside PACs by managers and by outside parties i.e. researchers and policy makers. As such it could be argued that such information can resolve (or at least minimise) the in- incongruences between macro-economic policy productivity measures and what is going on in organisations or firms.

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