141 1Decomposing total factor productivity change of

Sudan J. Agric. Res. : (2010), 16, 141 - 168

ARC, Sudan, Email: [email protected]

Decomposing total factor productivity change of bread wheat production in the Gezira Scheme

1

Mohamed O.A. Bushara1 and Rufida E. M. Dongos2

Abstract This study was conducted in the Gezira scheme. Covering the period 1980-2002. The study aimed to decompose total factor productivity index of wheat production into tow components the technological change (TC) and the efficiency change (EC). By using relevant secondary data collected from Gezira board units and Agricultural Research Corporation analyzed to test the objectives of the study. Results showed that all the changes in total factor productivity (TFP) were mainly due to technological change (TC). In fact, the efficiency change (EC) for the whole period of 1980 – 2002 was only zero implying that the management was efficient. While the contribution of technology change (TC) was -4%. The study recommended that: investment in wheat breeding technology through research and development is of great importance. Adoption of more efficient extension programs is required, to be supported by training farmers on good crop management practices including efficient use of irrigation water. The study also recommended provision of credit to the farmers to enable them to purchase and use modern inputs; to improve marketing facilities and to increase the productivity and product of wheat in the Gezira Scheme. Introduction Background Sudan is a vast country with large fertile lands in Africa with a total area of 597 million feddans. Out of this, about 85.5 million feddans are arable, and only 27.8 million feddans were under cultivation (Bank of Sudan, 1998). The agriculture sector is divided into two sub-sectors; modern irrigated, the rain fed mechanized and traditional subsector. The main crops in the Sudan were classified into two categories, food crops e.g. wheat, sorghum, millet and cash crops e.g. cotton, groundnuts, Gum Arabic, sesame and sugar …etc. The modern irrigated sector produces cotton, wheat, groundnut, sorghum and sugar cane and horticultural vegetable crops. This sector covers an area of about 4.5 million feddans, comprising the Gezira Rahad, New Halfa, Blue and White Nile schemes and the Northern State, Tokar and Gash Deltas. University of Gezira, Wad Medani, Sudan. Ministry of Education, Gezira State, Sudan.

1 2

141

Mohamed O.A. Bushara and Rufida E. M. Dongos

The Gezira scheme is the largest, oldest and most important irrigated agricultural scheme in the Sudan. The scheme lies between the Blue and White Nile in a triangular area, two sides of which are represented by the two Niles and the third by the Sudan Railway line, between Kosti and Sennar (Between long. 32o 40' and 33o 36') and lat. 13o 30' and 15o 15’ N). The contribution of Gezira scheme to the Sudan economy is a major one; constituting about 12% of total cultivated area in the Sudan. It produces 75% of the country's main product "extra long staple cottons" 12% of the country's production of sorghum, 60% of Sudan's production of wheat (Sudan Gezira Board 1998/99). Wheat is the second most important cereal crop in the Sudan after sorghum. As a single crop, it occupies the largest area in irrigated schemes. (Ageeb etal, 1999). Wheat production was known in the Sudan thousands of years ago, but large-scale production for food consumption has only started in 1940 in Northern Sudan. At that time, wheat production was concentrated on the narrow banks along the Nile in an area approximating 30,000 feddans ensuring self-sufficiency for local consumption during 1950’s (Mohamed, 1987). In the four decades, wheat consumption increased from about 220,000 tons in 1970/71 to about 800,000 tons in 1990/91(Mohamed,1987), mainly due to urbanization and changes in nutritional habits of the population. The government started to satisfy this increasing demand through imports, but with heavy burden on the smaller foreign exchange reserves, it turned towards increasing domestic wheat feasible production, in the irrigated central and eastern plains of the country. Wheat was introduced in the crop rotation of Gezira Scheme and New Halfa in early sixties and Rahad Scheme in 1990/91. Wheat (Triticum aestivum L.) is the world major source of calories and protein is grown over a wide range of moisture and temperature conditions. The main wheat regions of the world lie between about lat. 30o and 55o in the North temperate zone and 25o and 40o in the South temperate zone in areas with average annual precipitation ranging between 30 and 114cm. (Teare and Pect, 1983).The major wheat – productivity constraints can be grouped into technology development and transfer, weather, cultivars and sowing dates, crop establishment practices, soil fertility and irrigation, harvesting practices and biological factors (Faki, 1994). Measurement of the efficiency frontier In Beattie and Taylor, 1985 a production function is defined as a function giving the maximum possible quantity of an output that can be realized from quantities of a specific set of inputs. For the measurement of efficiency level of a firm or a farm, the maximum possible output is relevant, and the literature has attempted to estimate it as a function of input quantities. Such a function is 142

Decomposing total factor productivity change of bread wheat production

often called frontier production function, with the word “frontier” emphasizing the idea of maximal. Alternatively, a frontier cost function would give the minimum possible cost as a function of output quantity and input prices, as a dual to the theoretical production function. However, the estimation of production frontier and its link between the measurement of firm- level (or farm-level) efficiency and the estimation of production frontiers and its dual cost frontier is controversial. One needs a standard against which to measure efficiency such as saying that a firm produces 90% of its possible output by using inputs that would produce 100% output. The literature on efficiency measurement began with the seminal articles, of Farrell (1957) and (Schmidt, 1985 – 1986). The estimating frontier functions will be also influenced by the best performing firm’s technologies. In addition, the frontier function represents a best-practice technology against which the efficiency of firms within the industry could be measured. (Coelli, 1995). Total factor productivity, performance measurement and technical change It is useful to look at some general issues concerning the selection of performance measurement would determine the organization’s success in achieving its goals. (Martin and David, 1997). In addition, the efficiency of the enterprise should be measured in relation to the enterprise’s objectives. Grosskopf (1993) defined productivity growth as the net change in output due to change in efficiency and technical change, where the former is understood to be the change in how far an observation is from the frontier of technology and the latter is understood to be shifts in the production frontier. Because it takes account of all inputs used, this definition provides a comprehensive measure of performance. The multilateral TFP index provides an ideal method of benchmarking a firm’s productivity performance. It represents a significant advance over earlier productivity studies in that it enables total factor productivity level as well as growth rates to be compared across firms and over time. Lawrence, et al. (1990) outlined the merits of this index. These include satisfying the technical properties of transitivity and characteristicity, which is required to accurately compare TFP levels. The purpose of introducing this model is to develop input based non-parametric methodology for calculating productivity growth and apply it to the sample of Gezira wheat production industry. The total observations for wheat crop were 23, which gave sufficient time series data.

143

Mohamed O.A. Bushara and Rufida E. M. Dongos

Economists (Kennedy and Thirlwall, 1972) defined technology as the stock of available techniques or state of knowledge concerning the relationship between inputs and a given physical output at a given point in time. Consequently, through technology change, all or part of the production function would shift, resulting, for certain, for all combinations of inputs, into greater production level (Wong, 1993). The importance of efficiency in improving technology change often leads to a dynamic change in technology producing higher technical efficiency with an inward shift in the isoquant giving the same output with less input. In efficiency studies, to avoid confusion between the effects of managerial efficiency improvement and of the new technology, a separate independent variable to capture technological effects is included. (Battese and Coelli, 1993 and 1995; Battese and Coelli, 1992). On studying the productive efficiency of a firm, the relevant isoquants could be derived and the firm’s actual performance could be related to them (Schmidt, 1985 – 1986). It is generally difficult to compute the most efficient isoquant for a particular firm and the nearest approximation for a number of competing firms in an industry would compute the efficiency frontier of that industry (Farrell, 1957). The firm with the most efficient input-output combination represents the efficiency frontier; hence, the computed frontier is a relative concept reflecting best or the most efficient practice. In other words, those firms operating on the computed frontier have the highest relative productive efficiency in the data set, though it does not guarantee that they are operating at best. This is different for stochastic frontier, which estimates separate firm own frontier maximum achievable efficiency being theoretically unknown. In this study, performance was gauged in terms of changes in performance over time such as the trend of the input-based-Malmquist total factor productivity and its different components. The methodological link between the two techniques is provided by the wheat production in the Gezira scheme. Coelli, et al. (2005) in their study obtained detailed information on the growth of arable farms in Belgium over a 16-year period from 1987 to 2002. Calculations were based on a carefully constructed high-quality detailed farmlevel data set containing 1728 observations, involving over 100 farms in most years. The TFP model involved three output variables (cereals, other crops, other outputs) and four input variables (land, labor, capital and other inputs). The TFP measures were calculated using a Malmquist DEA TFP methodology, which provided detailed information on TFP change (TFPC), technical change (TC), technical efficiency change (TEC), and scale efficiency change (SEC) for each farm between each pair of adjacent periods. The results indicated an average annual rate of TFPC of 1.0% per year, with most of this being due to 144

Decomposing total factor productivity change of bread wheat production

TC (or frontier shift). A comparison of shadow shares (derived from the DEA frontiers) with market shares indicating the remaining of substantial distortions in this industry, especially the excess use of labor and constrained use of land. Furthermore, TFPC was uniform across regions but differed across four size farms categories, with larger farms achieving an annual average level close to 1.5% while the smaller farms, actually, went backwards in terms of TFPC over this period. Central and Southern Africa, Central and Eastern Europe and the Middle East had negative TFP growth even when weighted by the size of the labor force and years. Something more than introduction of new technology is necessary to explain much of these data. In the paper of a Malmquist Index Analysis during 1980-2000, Coelli and Rao (2003) examined levels and trends in agricultural output and productivity in 93 developed and developing countries representing a major portion of the world population and agricultural output. The paper also derived the shadow prices and value shares that were implicit in the DEA-based Malmquist productivity indices, and examined the plausibility of their levels and trends over the study period. The results showed an annual growth rate in TFP of 2.1%, with efficiency change (or catch-up) contributing 0.9% per year, and technical change (or frontier-shift) was providing the other 1.2%. In terms of individual country, the most spectacular performance was by China with an average TFP annual growth of 6.0%. Other countries with strong performance were Cambodia, Nigeria and Algeria. The United States had a TFP growth rate of 2.6 %, whereas India had 1.4%. Turning to performance of various regions, Asia was the major with an annual TFP growth of 2.9 % and Africa was weakest with only 0.6%. Examining the question of catch-up and convergence, it was found that countries well below the frontier (with technical efficiency coefficients of 0.6% or less) had a TFP growth rate of 3.6 %, in contrast to the low 1.2% for the countries which were on the frontier in 1980. These results indicated a degree of catch-up in productivity levels between high-performing and low-performing countries. These results are interesting since they indicate an encouraging reversal (during 1980-2000 period) from negative productivity trends reported in technological regression earlier studies for the period 1961-1985. Though the results were quite plausible and meaningful, Coelli and Rao were quite conscious of the data limitations and they recommended further work in this area. Future work should include: (i) an examination of the robustness of the results to shifts in the base period for the computation of output aggregates; (ii) the inclusion of pesticides, herbicides and purchased feed and seeds in the input set; (iii) an investigation of the effects of land quality, irrigation and rainfall; and (iv) utilization of parametric distance functions to study the robustness of the findings to the choice of methodology. 145

Mohamed O.A. Bushara and Rufida E. M. Dongos

The analyses of India’s agricultural growth during 1970-2001 caused concern. For all cereals, in aggregate annual production growth rate during the six-year segments (1970-76, 1976-82, 1982-88, 1988-1994, 1994- 2000) were 2.5, 2.5, 3.0, 2.6, and 1.8 % respectively. Corresponding analyses for the index of total agricultural production showed a similar pattern, with the growth rate for 1994-2000 attaining only 1.5 % per annum. However, for cereals, the 1994-2000 growth rate for yield was 1.7 % per annum much below the 19821994 average of 3.5 % per annum, causing further anxiety. This slackening in yield growth rates might have resulted from several causes: from reducing quantities of inputs due to falling prices of outputs, or from non-increase in inputs where farmers have already optimized their inputs applications, and from some progressive closing of yield gaps in some states. Previous studies (Kumar, 1998) had correspondingly observed a reduction in the growth of TFP which quantified technological contributions) from 1.5% per annum in the 1970s to 2.0 % in 1980s to 1.0 % in the 1990s. Moreover, this cereals-yield growth slackening has been compounded by the slight decline in cereals area (Averaging – 0.1 % per annum) since the early 1980s. A low rate of future agricultural growth – particularly if below the humanpopulation growth rate – would have adverse consequence on employment and poverty. The recent analyses gave consistent results with those derived from earlier data sets, including Bhalla (2001) conclusion of decreasing annual growth rate of Agricultural Gross Domestic Product (AGDP) from 3.2 % per annum during 1981-91 to 1.9 % during 1991-1999. The corresponding Figures for the crops-sector production were 2.6 %per annum and 1.4 % per annum with yield growth rates decreased between the 1980s and the 1990s:from 3.2% to 1.3 % per annum for rice . Kumar and Mittal (2000) quantified the proportional contributions of crop area production during 19671981 and 1982-1996.The partial contributions to growth for 1967-1981 period production (48%) area (21%), cropping pattern (20%) and interactions (11%). For 1982-1996, the proportions were 8, 57, 22, and 13%. The contribution of productivity had increased while that of the area had decreased. The increased contributions from “cropping pattern” and “interactions” may represent increases in production efficiency that resulted from research and technology transfer. Growth (or decline) in total factor productivity (TFP) resulted predominantly from public investment (or lack of investment) in infrastructures (Irrigation, electricity, roads) and in agricultural research and extension, and from efficient use of water and plant soil nutrients. Low productivity constituted a major constraint as those rural families strived to achieve household food security. Investments and efforts to improve and sustain small- farm productivity were therefore vital. Pinstrup and Lorch (2000) for mobilization of a synergistic blend of traditional and 146

Decomposing total factor productivity change of bread wheat production

modern knowledge, tools, and technologies – “the eco-technology” assist small- holder households. Similarly, research, technology development, and extension programs should strengthen activities that target the needs and opportunities of small- holders. Additionally, literacy advocated by Kumar and Mittal, (2000), brought about appreciable benefits to farm productivity and modernization as it had correlated strongly with the adoption of cultivars, soil nutrients management, and mechanization in India. Increased literacy may thus be expected to generate increases in agricultural productivity and hence in household and in national food supplies. Wheat is produced in the irrigated sector of central Sudan and the riverbank farming of the Northern Sudan. There is also localized production in the rain fed sector, mainly in Jabal Marra but it is minor (Area- wise) though yield is considered high because of the favorable weather conditions. Yields per feddan in tons for the season 2002/03,(were 0.649, 0.752 and o.643 for River Nile, Gezira and Northern Sates respectively.(Elamin, 2006). These are alarming low yield per feddan for wheat corp. A quantitative analysis is necessary to fix the problem of this low yield per feddan of wheat crop. Problem Setting Productivity growth in agriculture has been an important subject matter of research in last five decades, urging development economists and agricultural economists to examine sources of productivity growth over time. The productivity growth in the agricultural sector output has to be sufficient to meet the demand for food and raw materials by steady growing population. (Coelli and D.S. Parsada Rao, 2003). The problem of this study is to assess the possibility of raising the efficiency of wheat grown in the irrigated sector to increase their productivity, given the limited resources opportunities for technology development and transfer by measuring efficiency and productivity and separating their effects from technology and production environment, the source of efficiency and productivity differential could be explored. Estimates on the extent of the inefficiency could help decide whether to improve efficiency or to develop new technologies to increase wheat productivity. Identification of sources is essential to the institution of public and private policies designed to improve micro and macro performance. The result of this study might be useful for planning purposes and increasing the competitiveness and efficiency of wheat crop production in the Gezira scheme and the irrigated sector in the Sudan.

147

Mohamed O.A. Bushara and Rufida E. M. Dongos

The objectives of the study The objectives of this study are: (1) To update current information on total factor productivity of wheat production in the Gezira scheme.(2) To review and evaluate the performance of wheat productivity over time.(3) To decompose total factor productivity change into efficiency change and technology change.(4) To identify the critical parameters that affect improvement of the total factor productivity. Economics of wheat production in Sudan The Sudan wheat situation is characterized by rapid growth in consumption, continuous and variable deficit between domestic need and local production. Despite the research verified yield potential of this crop, the amount produced was far below the needs for local consumption. The increasing of spending on imported wheat makes it imperative to revise wheat production technologies and efficiency to reduce such high dependence. The crop is grown under irrigation during the dry comparatively cooler winter seasons, which extend from November to February. The growing season is short and limited by heat stress. The yield potential of the commercially grown varieties is limited by the high day temperatures, which prevail both during early stages of crop growth (October/November) and at or near maturity (February/March). Constraints to wheat production, other than climatic conditions, include financial problems to provide the required input and to conduct the cultural practices on time. The percentage contribution of the locally produced wheat to the total consumption has declined from 39.6% in 1980/81 to 17.7% in (2000/01) due to reduced production in face of increasing shift of consumer preferences towards wheat. The absolute production levels display seasonal variation due to fluctuations of the area under the crop and yield realized. The average grain yield achieved in the Gezira, New Halfa and Northern Province was low, were mainly attributed to poor crop establishment resulting from poor land preparation, inadequate cultural practices, unsatisfactory crop protection measures as well as the high temperatures prevailing during the early stages of crop growth and at maturity. The contribution of the locally produced wheat to the national consumption could be substantially increased. Adoption of improved production technology and increasing the cropped area in the Gezira scheme could raise the contribution of the scheme to local consumption from 31% to 145%. Estimate of net foreign exchange savings (US$29 – US$89 million), indicate the feasibility of investment to secure inputs for increasing production of the locally produced wheat and for enhancing its competitiveness to the wheat imported, (ARC) (1998). 148

Decomposing total factor productivity change of bread wheat production

Demand has increased overtime in urban areas to magnitudes that could no longer be satisfied by local production. Moreover, wheat consumption has gradually shifted to many rural areas, induced by a substantial shift in consumption habits away from the traditionally used sorghum. The sharp trend in wheat consumption is depicted in Table (1) which shows the expansion of consumption from a little over 550, thousand tons in 1980/81 to over one million tons in 2000/2001. Although population growth is partially responsible for that increase, much of the increase, was encouraged by highly subsidized bread prices of wheat. Average per capita consumption per year rose from 10.5kg in 1960 to about 20.4kg in 1971 and from 31.7kg in 1986 to about 40kg in 1999. (Damous (1986); Hassan and Ageeb 1992). On the other hand, wheat production has grown slowly, primarily because of low use of modern inputs and poor crop management practices (Ageeb and Mohamed, 1986). Self-sufficiency has been the aim of most government economic plans. The latest is the crash program of 1989, which emphasized area expansion and productivity improvement and aimed at achieving self-sufficiency in 1992. The area expansion option was limited by the rotation that restricts each of the rotational crop to a certain limit and the wheat area expanded at the expense of other crops, cotton in case of big public schemes. This was evident in the Gezira scheme, the biggest wheat and cotton producer. Self-sufficiency was attained in 1992, after which self-sufficiency ratio declined and consequently wheat import increased. Table (1) gives the cultivated and production area of wheat against consumption in Sudan for the period 1980/81- 2000/001. From the Table it is clear that there is a widening gap between the production and consumption of wheat largely due to reduction in area and production associated with abrupt increases in wheat consumption during the last three years. Wheat imports therefore increased substantially over the past two decades. In addition to threatening food security, increased reliance on imported wheat implies greater competition for Sudan’s foreign exchange resources.

149

Mohamed O.A. Bushara and Rufida E. M. Dongos

Table 1. Cultivated area, production, consumption, and food gap and selfsufficiency ratio. Year 1980/81 1981/82 1982/83 1983/84 1984/85 1985/86 1986/87 1987/88 1988/89 1989/90 1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/2000 2000/2001

Area (000 fed)

437 329 225 335 111 36 282 343 393 614 1104 930 805 881 741 774 782 646 405 244 294

Production (000 tons)

218 142 176 157 79 199 157 181 247 409 686 895 445 492 498 575 640 535 172 250 266

Consumption (000 tons)

550 580 610 640 670 700 730 740 780 850 870 920 976 1020 1070 1130 1200 1200 1370 1460 1500

Gap (000 tons)

332 438 434 283 591 501 573 559 533 441 184 25 525 528 572 555 560 675 1198 1210 1234

Rates of production to consumption (%)

39.6 24.4 28.8 24.5 11.7 28.4 21.5 24.4 31.6 48.1 78.8 97.2 45.8 48.2 46.5 50.8 53.3 44.5 12.5 17.1 17.7

Source: Ministry of Agriculture and Forests. (2001)

In 1997 Sudan imported about 600,000 tons of wheat, valued at US$138.401 million, whereas the country’s total foreign exchange earnings from agriculture for the same year amounted to only US$ 133.372 million (FAO 1988); Tables ( 2 and 3)). Consumption direction has resulted in a continuous and variable deficit between domestic needs and local production, which has necessitated the exertion of efforts to bridge the gap, through imports. The country had to import, in most years, about three quarters of its annual needs that currently ranged between 0.8 and 1.234 million tons. Table 2. Wheat and wheat flour in the Sudan, 1990-2001. Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Amount (000 ton) 132.573 361.134 213.334 212.449 488.127 307.810 354.345 576.623 984.108 549.483 1013.400 650.232

Source: Bank of Sudan. (1990-2001)

150

Value (Million US$) 21.809 74.809 31.195 45.795 112.928 89.847 97.859 138.402 131.945 123.333 207.942 138.096

Decomposing total factor productivity change of bread wheat production

It is clear from Table (3) that around 91.8% of the total agricultural exports bill has been devoted to wheat imports. This justifies the great consideration given to wheat production as an import substitute in government plans to reduce the burden on foreign exchange and hence improve the external balance of payment. Table 3. Value of wheat import and agricultural export, 1997-2004. Year 1997 1998 1999 2000 2001 2002 2003 2004 Mean

Total value of imports of wheat and wheat flour in US$

138.402 131.945 123.333 207.942 138.09 214.47 255.56 386.38

Agricultural exports (values in US$)

133.372 171.370 142.566 91.187 296.52 358 410.25 587.94

% 103.7 76.9 86.5 228.0 52 59.9 62.3 65.7 91.8

Source: Bank of Sudan, Annual Reports. (1998-2004) .

Wheat in the Gezira Scheme Traditionally wheat consumption is confined to the northern region of the Sudan. Wheat consumption has increased substantially due to rapid urbanizations, changes in consumer habits, high consumer subsidies, population and income growth. (Hassan and Ageeb (1992).The expansion in wheat cultivated area has been determined by the availability of water resources, the first expansion in wheat production came after the construction of Roseres Dam in 1966, which enabled wheat to expand in an area of 496800 and 109999 feddans in Gezira and New Halfa, respectively. Gezira constitutes the largest area under wheat production reaching about 0.5 million feddans out of 0.9 million feddans in whole Sudan in 1993 –1994 followed by the Northern State, which was about 150 thousands feddans. However, the share of the other producing areas was relatively small,( Ministry of Agriculture and Forest,1994). The large-scale cultivation of wheat in the Gezira scheme started as an implication of the import substitution policy. The planted area in the season 1970/71 was about 150 thousand feddans giving an average yield of 0.085 tons per feddan (MOAF, 2007). For increasing wheat production and improving the productivity and quality of wheat, great effort was made by the scientists of the Agricultural Research Corporation in breeding new varieties of wheat, suitable for expanding wheat production in the Gezira scheme. However, in spite of all these efforts, the wheat cultivation confronted several problems in form of irrigation bottlenecks problem, shortage of supply of seeds, low profitability poor knowledge of the 151

Mohamed O.A. Bushara and Rufida E. M. Dongos

new methods and access to new inputs and methods. (Faki, 1994). The area of wheat in Gezira scheme started to increase from season 1987/88 reaching in season 1991/1992 where the total area sown was about532,000 feddans, encouraged by adoption of self-sufficiency policy in food. Moreover, the area under cultivation started to decline in season 1995/96, this trend continued until season 2001/2002 reaching 80,000 feddans. This may be attributed to the low productivity of wheat. Table (4) below shows the production, yield and the area grown in the Gezira scheme for the period 1991/92 to 2001/2002. Table 4. Wheat cultivated area, yield and production in the Gezira Scheme, 1991/92 – 2001/2002. Season 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/2000 2000/2001 2001/2002 2002/2003 2003/2004 2004/2005

Area grown (fed) 532,000 514, 033.50 522, 783.00 392, 690.00 390, 490.00 389, 801.00 301.925.00 123, 016.00 58, 627.25 70, 409.00 80,000 111,000 190,000 142,000

% of total area 58.7 64.4 85.6 55.9 56.5 50.1 50.2 37.9 28.3 26.6 -

Production (tons) 495,000 629, 867.59 273, 938.29 230, 116.34 256, 552.00 250, 642.00 211, 348.00 37, 766.00 29, 314.00 56, 327.20 58,000 100,000 171,000 114,000

Yield (ton/ fed) 0.93 0.53 0.52 0.59 0.66 0.64 0.70 0.31 0.50 0.80 0.725 0.901 0.900 0.803

Source: Sudan Gezira Board (1991-2002); Ministry of Agriculture and Forest (2005)

The technical causes lead to low yield in Gezira Scheme One of the most important reasons for low crops yields is the irrigation constraint, subjecting most of the area to water stress that negatively affect the wheat crop natural germination process. The crop needs regular irrigation with short intervals, especially during high temperature periods. The Agricultural Research Corporation recommended varieties for hot area were hardly applicable for large areas resulting in wheat crop failures. It has been argued that one major reason for the low wheat yields and the wide gap between potential and farmers’ yields is the slow adoption of the recommended package of improved practices (Hassan and Ageeb, 1992; Faki, 1991). These studies have suggested that because of problems associated with availability of inputs, particularly fertilizer and irrigation water, many farmers could not use the full package of technologies. 152

Decomposing total factor productivity change of bread wheat production

Production economics of wheat crop Despite of the strategic importance of wheat crop, it has a low priority among crops (cotton and sorghum) in the Sudan. The wheat crop is fully mechanized crop depending on imported inputs (Fertilizers, pesticides and capital-imported goods (Machinery for land preparation, opening of canals, harvesting and transporting). The imported inputs constituted more than 70% of the total cost of production of crop. Thus, wheat production does not mobilize the local resources including employment opportunity for human resources. Considering the marginal returns for wheat crop, Table (5) indicates a very low level with fluctuation. In 1992/93 the feddan of wheat gave marginal negative revenue reaching (21%) incurring a high loss to the farmer, the scheme and the national economy. The same loss was repeated in season 1998/99 where marginal revenue per feddan gave a negative rate of (39 %).

153

Mohamed O.A. Bushara and Rufida E. M. Dongos

Total revenue/fed

Average cost/fed

Break-even point

Productivity (ton/fed)

-1872

6838

8710

0.667

0.525

1992/93

30%

5580

24178

18598

0.403

0.524

1993/94

30%

10433

45000

34567

0.450

0.586

1994/95

95%

82628

169180

86552

0.336

0.657

1995/96

34%

65721

257200

191479

0.479

0.643

1996/97

12%

27772

266594

241225

0.627

0.7

1997/98

-39%

-9791

147360

244951

-

0.31

1998/99

Season

Table 5. Wheat productivity, average cost, net return per feddan in the Gezira Scheme, 1992/93– 1998/99.

Gross marginal revenue/fed -21%

Items

GMRR (%) Source: Sudan Gezira Board (1998-1999)

154

Decomposing total factor productivity change of bread wheat production

The wheat crop had been introduced to agriculture rotation as a food security crop within the objective of implementing an import substitution policy aiming at self-sufficiency in food. However, the world price was lower than local cost of production of wheat. Taking 1998 as example the (CIF) price per sack at Port Sudan was US$11.2 per sack which equivalent to Ls 31000 per sack. Adding other costs of custom fees, insurance, transportation and other related costs, the price per sack in Central Sudan was equivalent to Ls 34,640 per sack about 92% of the cost of production in 1998. The announced price for the year 1998 was equal to Ls 46000 per sack. This gave an indication of the weak competitive capacity of this crop. The decreasing rate in the marginal revenue might be attributed to many factors, of which are the following: low productivity due to warm winter temperature poor adoption of wheat production technology (inadequate use of fertilizer and insufficient and irregular supply of irrigation water? (Abbas, 2002). Materials and Methods Data and information sources The purpose of this study was to estimate the total factor productivity of wheat crop in the Gezira scheme using secondary data obtained from Sudan Gezira Board (SGB) and from(Gezira Board Planning and Socio-Economic Research Administration), Sudan Agricultural Research Corporation Wad Medani. The method used for analysis in this study is total factor productivity index (Malmquist) DEA (Data Envelopment Analysis). The required data for analysis included input and output quantities or values. TFP (Malmquist) could be used on all time series, for estimating technical, price allocative, or economic efficiency Farrell (1957). In general need the input data i.e. (1) land= x1 (2) water = x2 , (3) capital input (plowing , leveling, opening field, channels , sowing, harvesting, fertilizer, seeds, insecticides, fertilizer broad casting=x3),(4) material: (sacks, transport= x4), and (5) labor: (Raising field channels, pre-watering, irrigation, cleaning irrigation channels, weeding, breaking channel, resowing = x5) and output in value = y. The time-frame of this study was (1980 – 2002) and the information needed include among others: 1. Detailed cost of wheat production (total cost) and 2. Value of output. To separate water and land charges in seasons 1980/1981 -1990/1991 a mathematical method (mean percentage of management in other periods) was used to separate them. The means were calculated to fill the gaps. Then data were normalized by producer price index n the based year 1980 and transformed into the natural log form.

155

Mohamed O.A. Bushara and Rufida E. M. Dongos

Data limitation Measures of efficiency typically required data on output and input measured in physical units. However, in many applications such data might be difficult or costly to obtain. Here data in money values were used for the analysis. In addition, this was why the data were limited to 1980-2002 time frame. Total factor productivity model The total factor productivity (TFP) focuses on how performance changes over time. It measures output per unit of all inputs used or an index of output divided by an index of total input usage. Thus TFP is generalization of single – factor productivity measure such as labor productivity, which is the ratio of (an index of) output to a single input, labor. TFP growth refers to the change in productivity over time. This methodology merges ideas from measurement of efficiency by Farrell (1957) and from measurement of productivity as expressed by Caves et al. (1982) Farrell introduced a framework for efficiency gauging in which overall efficiency can be decomposed into two component measures: allocative and technical efficiency. Technical efficiency is the reciprocal of Shephard (1953) (input) distance function, which the key building block in the Malmquist’s input-based productivity index used. It was detailed by fare et al. (1992) and Fare and Grosskopf (1996). The Malmquist index of productivity can distinguish between changes in efficiency and changes in the production frontier. This distinction should prove useful for policy purposes. The production technology is defined at each period t, t = 1, 2 … T, to be the set of all feasible input and output vectors if xt є Rn + denotes an input vector at period t and yt є Rm + an output vector in the same period, then the technology is the set St where St = {(xt, yt): xt can produce yt }. The technology can also be modeled by the input correspondence or by the input requirement set. Lt (yt) = {xt: (xt, yt) є St}, for t =1,……………..................……………T, (1) The input requirement set Lt (yt), denotes all input vectors xt capable of producing output yt during period t. Here Lt (yt) is assumed as a closed convex set for all (yt), and that there is no free lunch, i.e., 0 є Lt (yt) if yt ≥ = 0, yt ≠ 0. Moreover, the disposability of input and output may be imposed i.e., x^ t ≥ xt є Lt (yt) —> x^ t є Lt (yt) and y^ t ≥ yt —> Lt (y^ t ⊆ Lt (y) respectively. In this study, equation (1) is formalized as piece- wise linear input requirement set or equivalently as an activity analysis models. The coefficients in this model consist of observed input and output. It is assumed that there are K = 1..e, Kt observations of n = 1 … N inputs xn k, t in each period t = 1, …, 156

Decomposing total factor productivity change of bread wheat production

T. These inputs are employed to produce k = 1…, kt of m=1…, M observed output, yM k, t, at period t = 1, …, T, and it can be assumed that the number of observations are the same for all t, i.e., kt = k. The input requirement set 2 is formed from observations as (Fare, et al. 1994). Lt (yt) = {xt = ymt ≤ xnt ≥

ymk,t , m= 1,………..…..............……….…..,m (2)

xnk,t , n= 1,……………………….............……….……….…..,N.

zk,t ≥ 0, k =1,…………………………………….............………………….K} Where zk,t is an intensity variable familiar from activity analysis. The intensity variable serves to form technology, which here is the convex cone of observed inputs and output. Constant return to scale is to be imposed on the reference technology, but other forms of returns to scale may be imposed by restricting the sum of the intensity variables (Grosskopf, 1986). The Malmquist input based productivity index is expressed in terms of five input distance functions (land, water, capital, material and labour) where the first is defined as: Dti (yt, xt) = sup {λ > 0 :(xT/λ) λLt (yt) …….............……………..…….....(3). Clearly Dti (yt, xt) ≥ 1 if and only if xt є Lt (yt), as illustrated in Figure (1). In Figure (1) the input vector xt belongs to the input requirement set Lt t (y ).The distance function Dti (yt, xt) measures the largest possible contraction of xt under the condition that (xt/λ) is feasible i. e.,: (xt/λ) є Lt (yt). In terms of Figure (1), Dti (yt, xt) = oa/ob. For observation k, k= 1…K, the value of the distance function Dti (ykit, xkit) is obtained as the solution to the linear programming problem. [Dt i (yk,t , xk,t)]-1 = min λ, …………………................………........……....(4) Subject to ymk,t ≤ zk,t, ymk,t = 1, …….……................………………........…..M λxnk,t ≥

xnk,t , n= 1,…………….……................……........………..N,

zk,t ≥ 0, k = 1, ……………..……….……….............…………..…….…… K Note that xk,t is an element of input set, which implies that distance function takes values larger than or equal to one. The value one is achieved whenever the input vector belongs to an isoquant of the input set, and hence where it is technically efficient (Farrell, 1957). The input distance function is the reciprocal of the Farrell technical efficiency measure, a fact to be exploited to calculate distance function. In order to define the input based Malmquist 157

Mohamed O.A. Bushara and Rufida E. M. Dongos

productivity index by Caves et al. (1982), it is necessary to relate the input output vectors (xt,yt) at period t to the technology Lt+1 in the succeeding period. Therefore, it is recommended to evaluate the input distance function for an input output vector (xt,yt) at period t relative to the input requirement set Lt+1 in the following period.

Fig. 1. The input distance function. Source: Fare, R. and Primont (1997).

Di t+1(yt, xt) = sup {λ > 0: xt/ λ) e Lt+1 (yt)} …………..............….……..(5) Again Di t+1(yt, xt) ≥ 1 if and only if xt e Lt+1 (yt), need not be feasible at t+1 thus if equation (5) has a solution (i.e. supremum is a maximum), the value of Di t+1(yt, xt) may be strictly less than one. In the data set, the observed input xnk,t , n = 1 ….., N , is positive for each observation and each period. This, together with strong disposability of input and constant returns to scale, ensures the possibility of calculating the value of the input distant function in equation (5) for k’, where k’ = 1,. ..K, as the solution to the linear programming problem [Dit+1 (yk,t , xk,t)]-1 = min λ, …………………........……...............………...(6) Subject to ymk,t ≤ zk,t+1, ymk,t+1 = 1, …….………...….…...............…………..M λxnk,t ≥

xk,t+1 n , n= 1,……………...........……................…..……..N,

zk,t+1 ≥ 0, k = 1, ……………………………..….............………….….…… K 158

Decomposing total factor productivity change of bread wheat production

It is to be noted that since xnk,t need not be a member of the input requirement set Lt+1 (yk,t), the value of this distance function may be strictly less than one. Two additional evaluations of the input distance function are required in order to define the productivity index. It is necessary to evaluate observation at t+1 relative to technologies at t+1 in particular. Dit (yt+1 , x t+1) = sup {λ > 0 : xt+1/ λ) ∈ Lt (yt+1)}.…….............…..………..(7) and Dit+1 (yt+1, xt+1) = sup (λ > 0 : xt+1/ λ)∈ Lt+1 (yt+1) …….............…...(8) The computation of equation (8) is identical to that of equation (3), so that in equation (4) it is needed only to substitute t+1 for t. The computation of equation (7) is parallel to that of equation (5). And again, it is only necessary to substitute t+1 for t and vice versa.It is to be noted that since (xt+1 , yt+1) need not be feasible under the technology Lt, the input distance function Dit (yt+1 , x t+1 ) may strictly be less than one. Following Caves et al. (1982), Fare et al. (1992), Fare, and Groseskopf (1996), the Malmquist input based productivity index could be defined as

............(9) It is clear that this definition is the geometric mean of two Malmquist indices as defied by Caves et al. (1982) who in their work made two assumptions. First, they assumed that Dit (yt+1, x t+1) and Dit+1 (yt+1, xt+1) equal unity for each observation and period. In the terminology of Farrell (1957), this means that there is no technical inefficiency. Second, they assumed that the distance functions were of translog form with identical second order terms. In this study, Fare et al. (1992, and 1994) were followed and the technology was modeled as piece-wise linear to allow for measurement of inefficiencies. By allowing the inefficiencies, the productivity index can be decomposed into two components, one measuring change in efficiency and the other measuring technical change or equivalently change in the frontier technology. The equation can be written as:

.........(10)

159

Mohamed O.A. Bushara and Rufida E. M. Dongos

Where the equation outside the bracket measures the change in technical inefficiency and the rations inside the bracket measure the shift in the frontier between period’s t and t+1 as Figure (2) illustrates. The technology at t is denoted by St and at t+1 by St+1, and St = {(xt, yt): xt ∈ Lt (yt), yt≥ 0}, and St+1 is similarly defined. The two observations (yt , xt) and (yt+1 , xt+1) are both feasible in their respective periods. The productivity index may be expressed in terms of the above distances along the x-axis as

Where {(ob/oa) / (od/oe)} denotes the ratio of the Farrell measure of technical efficiency and last part is the geometric mean of shifts in technology at yt and yt+1 . It is to be noted that the shifts in technology are to be measured locally for the observation at t and t+1. This implies that: the whole technology need not behave uniformly and, the technological regress is possible.

Fig. 2. The input based Malmquist productivity index. Source: Fare, R., etal. (1992).

160

Decomposing total factor productivity change of bread wheat production

Results and Discussion The results of these analyses were documented in Tables (6) (7) and Figure (3). The first cell in Table (6) Mi (y, x C, s) column shows variables that were included in the calculation. The third column (EC) gives the change in efficiency and column (TC) gives the technology change. For this input – based Malmquist calculation, a number above one indicates a decrease, below one indicates an increase and exactly one indicates no change in productivity, efficiency and technology. The next four columns (F11, F12, F21 and F22) show reciprocal value of input – distance function. The first number indicates the period of the technology and the second number indicates the observation time. As an example, F12 represents period 1 technology and time period 2 observation (EMQ, 1998), Fare, and Grosskopf, 1998). The Malmquist productivity index and its decomposition are given in Table (6) for each year of the study. Note that total factor productivity (TFP), as measured by the input- oriented malmquist index (Mi), had positive change (i., e, the TFP value was less than one) at the beginning in year 1981 and gave negative change in year 1981/82 and increase again in years 1985/1986 and year 1990/1991, decrease in 1991/1992 and 1992/1993, increases in year 1997/1998. Decrease in year 1998/1999. Increase again in 1999/2000 to year 2000/2001 decrease in year 2001/2002. Furthermore, all of the changes in TFP were mainly due to technological change (TC).In fact efficiency change (EC) in the whole period (1980/2002) was zero while the average contribution of technological change for the whole period was only - 4% (Table, 7). This result was in agreement with Fare et al. (1994), and Domazlicky and Weber (1997), who estimated that all of the improvements in TFP for 17 OECD countries during their sample period (1979 to 1988) were due to technological change. Additionally, Fare et al. (1994 a) estimated that the TFP had increased at an average annual rate of 0.85% over the period of study (1979 to 1988) compared to Domazlicky and Weber (1997) estimating of 0.47% for the period, (1977 to 1986). This decomposition provided an alternative way of testing for convergence of productivity growth as well as allowing identification of innovation. Fare, et al. (1994) study on wheat production indicated deterioration of technological change (TC) over time.

161

Mohamed O.A. Bushara and Rufida E. M. Dongos

Table 6. Malmquist TFP component results for wheat crop in the Gezira Scheme. (1980 – 2002). 1980/1981 1 1981/1982 1 1982/1983 1 1983/1984 1 1984/1985 1 1985/1986 1 1986/1987 1 1987/1988 1 1988/1989 1 1989/1990 1 1990/1991 1 1991/1992 1 1992/1993 1 1993/1994 1 1994/1995 1 1995/1996 1 1996/1997 1 1997/1998 1 1998/1999 1 1999/2000 1 2000/2001 1 2001/2002 1 Average/mean

)Mi(x1,x2,x3,x4,x5,y 0.96 )x1,x2,x3,x4,x5,y( 1.33 )x1,x2,x3,x4,x5,y( 0.85 )x1,x2,x3,x4,x5,y( 1.11 )x1,x2,x3,x4,x5,y( 1.03 )x1,x2,x3,x4,x5,y( 0.85 )x1,x2,x3,x4,x5,y( 0.80 )x1,x2,x3,x4,x5,y( 0.93 )x1,x2,x3,x4,x5,y( 0.90 )x1,x2,x3,x4,x5,y( 0.77 )x1,x2,x3,x4,x5,y( 0.86 )x1,x2,x3,x4,x5,y( 2.18 )x1,x2,x3,x4,x5,y( 2.42 )x1,x2,x3,x4,x5,y( 0.54 )x1,x2,x3,x4,x5,y( 0.95 )x1,x2,x3,x4,x5,y( 0.67 )x1,x2,x3,x4,x5,y( 0.73 )x1,x2,x3,x4,x5,y( 1.00 )x1,x2,x3,x4,x5,y( 1.26 )x1,x2,x3,x4,x5,y( 0.89 )x1,x2,x3,x4,x5,y( 0.78 )x1,x2,x3,x4,x5,y( 1.06 1.04

Source: Authors own table

162

EC 1.00

TC 0.96

F11 1.00

F12 1.15

F21 1.24

F22 0.00

1.00

1.33

1.00

1.59

0.90

1.00

1.00

0.85

1.00

1.10

1.50

1.00

1.00

1.11

1.00

1.21

0.99

1.00

1.00

1.03

1.00

1.49

1.42

1.00

1.00

0.85

1.00

1.37

1.90

1.00

1.00

0.80

1.00

0.97

1.52

1.00

1.00

0.93

1.00

1.48

1.70

1.00

1.00

0.90

1.00

1.49

1.84

1.00

1.00

0.77

1.00

1.72

2.90

1.00

1.00

0.86

1.00

1.72

2.33

1.00

1.00

2.18

1.00

3.72

0.78

1.00

1.00

2.42

1.00

5.18

0.88

1.00

1.00

0.54

1.00

1.56

5.37

1.00

1.00

0.95

1.00

1.10

1.21

1.00

1.00

0.67

1.00

0.90

1.98

1.00

1.00

0.73

1.00

0.80

1.51

1.00

1.00

1.00

1.00

1.28

1.30

1.00

1.00

1.26

1.00

1.93

1.21

1.00

1.00

0.89

1.00

0.98

1.24

1.00

1.00

0.78

1.00

1.10

1.79

1.00

1.00 1

1.06 1.04

1.00

1.79

1.58

1.00

Decomposing total factor productivity change of bread wheat production

Table 7. The growth of TFP components of wheat in the Gezira Scheme 1980/2002. Season 1980/1981 1981/1982 1982/1983 1983/1984 1984/1985 1985/1986 1986/1987 1987/1988 1988/1989 1989/1990 1990/1991 1991/1992 1992/1993 1993/1994 1994/1995 1995/1996 1996/1997 1997/1998 1998/1999 1999/2000 2000/2001 2001/2002 Average mean

TFP change 0.04 - 0.33 0.15 -0.11 -0.03 0.15 0.20 0.07 0.1 0.23 0.14 -1.18 - 1.42 0.46 0.05 0.33 0.27 0 - 0.26 0.11 0.22 - 0.06 -0.04

EC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

TC 0.04 - 0.33 0.15 -0.11 -0.03 0.15 0.20 0.07 0.1 0.23 0.14 -1.18 - 1.42 0.46 0.05 0.33 0.27 0 - 0.26 0.11 0.22 - 0.06 -0.04

Table 7 was reproduced from Table 6 one minus column two in Table 6 successively gave TFPC in column two Table 7 the same for column three and column four respectively gave EC and TC. (Bushara and Mohayidin, 2007). Figure (3) depicts the trend of growth of TFP components overtime, the lowest TC growth was in the year, 1992/93. This growth (or decline) in total factor productivity (TFP) might be resulted predominantly from public investment (or lack of investment) in infrastructures (irrigation, energy, roads) and in agricultural research and extension, and from efficient use of water and plant soil nutrients.

163

Mohamed O.A. Bushara and Rufida E. M. Dongos

㄀

　 ㄀㤀 㠀㄀ ㄀㤀 㠀㈀ ㄀㤀 㠀㌀ ㄀㤀 㠀㐀 ㄀㤀 㠀㔀 ㄀㤀 㠀㘀 ㄀㤀 㠀㜀 ㄀㤀 㠀㠀 ㄀㤀 㠀㤀 ㄀㤀 㤀　 ㄀㤀 㤀㄀ ㄀㤀 㤀㈀ ㄀㤀 㤀㌀ ㄀㤀 㤀㐀 ㄀㤀 㤀㔀 ㄀㤀 㤀㘀 ㄀㤀 㤀㜀 ㄀㤀 㤀㠀 ㄀㤀 㤀㤀 ㈀　 　　 ㈀　 　㄀ ㈀　 　 䴀 ㈀ 攀愀 渀

䜀 爀 漀眀琀栀

Growth

　⸀㔀

ⴀ　⸀㔀 ⴀ㄀ ⴀ㄀⸀㔀 ⴀ㈀

吀 䘀倀 䌀栀愀渀最攀

夀 攀愀爀

䔀䌀

吀䌀

Year

Fig. 3. Growth of TFP components of wheat production in the Gezira Scheme (1980-2002). 䘀椀最甀爀攀⠀㌀⤀㨀䜀爀漀眀琀栀 漀昀 吀䘀倀 挀漀洀瀀漀渀攀渀琀猀  漀昀 眀栀攀愀琀 椀渀 琀栀攀 䜀攀稀椀爀愀  猀 挀栀攀洀攀㨀㄀㤀㠀　ⴀ㈀　　㈀ Summary and Conclusions In this study, wheat in the Gezira scheme was chosen as case study. The study was intended to investigate and measure wheat crop productivity change by examining the production technology of this crop over time. The input – based Malmquist (TFP) index was employed to decompose wheat crop productivity index into two components: measuring change inefficiency and the other measuring technical change (equivalent change in the frontier technology). Secondary data including detailed cost items of wheat per feddan, deflated by producer price index based year 1980 were used. On Front software package version, 1.0 was used for the analysis of these data. The study showed that in an economy where resources are scarce and opportunities for new technologies are limited, efficiency studies would be able to show the possibility of raising productivity by improving efficiency. Therefore, the estimate of the extent of in- efficiency could help in deciding whether to improve efficiency or to develop new technologies to increase wheat crop productivity. 164

Decomposing total factor productivity change of bread wheat production

In this study, the production of wheat crop was constrained by many factors. The adoption of the technology package recommended by ARC was stringed by shortage of irrigation water, inadequate supply of fertilizers and pesticides on time. The study analysis results revealed that efficiency change (EC) was equal to zero, implying no significant effect on the productivity of the wheat crop. The technology change (CT) was between 0.46 and -1.42, which implied that fluctuation in wheat crop yields were attributed mainly to poor application of the full technological package. A software package was used to calculate in-put –based Malmquist productivity index and its components of wheat crop production in the Gezira scheme during 1980 to 2002.Total factor productivity (TFP) was decomposed into its components: productivity index (Mi), efficiency change (EC) and technical change (TC). In this study, there was no change in efficiency index, as it equaled to one throughout the study period. The main responsible factor for wheat crop performance in the Gezira Scheme was due to the level of technology package. References Abbas, E. Mohammed (2002). Constraints of wheat production in the Gezira and Northern States, Annual Report, ARC, Wad Medani, Sudan, Analysis of policies related to wheat production in the Sudan. Ageeb, Osman A. and Mohamed, Mahmoud S. (1986). Agronomy of wheat Annual Report. Gezira Research station. Season 1985/86. p 43. Agricultual Research Corporation (ARC) (1998). Annual Report (different years).WadMedani; Sudan. Battese G. E. and T .J. Coelli, (1992). Frontier production functions technical Efficiency and Panel Data with application to paddy farmers in India. Journal of Productivity Analysis 3, 153 – 169. Battese, G. E. and Coelli, T. J. (1993). A stochastic frontier production function incorporating a model for technical inefficiency effects. CEPA working papers. No.69/93, Center for Efficiency and Productivity Analysis, England, Armadale. Battese G. E and Coelli, T. J. (1995). A model for technical in efficiency effects in a stochastic frontier production function for panel Data Empirical Economic 20: 325 – 332. Beattie, B. R. and Robert, C. Taylor (1985). The Economics of Production Montana State University, John Wiley & Sons, New York. Bhalla, (2001). Japans Productivity and Economic Growth: An Empirical Analysis Based on Industry – Level and Firm –Level Data. Bushara, Mohamed O. A. and Mohd.Ghazali Mohayidin (2007). Catching- up 165

Mohamed O.A. Bushara and Rufida E. M. Dongos

and technological change in Malaysian oil and fat industry :A Nonparametric frontier analysis. Gezira Journal of Engineering and Applied Sciences 2(2) 54-75. Caves D. W., Laurits, Christensen, R. and Erwin Deiwert, W. (1982). The economic theory of index number and the measurement of input, output and productivity. Econometrica 50 (6) 1393 – 1414. Coelli, T., and Prasada Rao, D. S. (2003). Total factor productivity growth in agriculture: A Malmquist index analysis of 93 countries: 1980 – 2000. Plenary paper presented at the 2003 International Association of Agricultural Economics (IAAE) Conference, Durban, August 16 – 22. Coelli, T. J., and D.S.P. Rao C.J. O’Donnell and Battese, G. E. (2005). An introduction to Efficiency and Productivity Analysis, 2nd Edition, Springer, New York. Coelli, T. J. (1995). Recent developments in frontier modeling and efficiency measurement. Australian Journal of Agricultural Economics 39 (3) 219 – 245). Damous, E. M. (1986). Economic analysis of government policies with respect to supply and demand for wheat and wheat production in Sudan. Ph.D.dissertation.Washington State University, Seattle, Washington. Domazlicky, B. R. and Williar, L. Weber (1997). Total factor productivity in the contiguous United States,1997 – 1986. Journal of Regional Sciences 37 (2) 213 – 233. Elamin, Abbas Elsir (2006). Community - based optimization of the management of scarce resources in agriculture in Western Asia and Northern Africa. Report on irrigated benchmark project, Sudan Satellite site, ICARDA, Syria. EMQ. (1998). User’s Guide On Front The Professional Tool for Efficiency and Productivity Measurement: in Lund AB. Sweden. Faki, Hamid H. (1991). The Sasakawa Africa Association and Global 2000, Inc., in various African countries, have established SG2000 Agricultural projects their objective is to develop programs for demonstrating technology to farmers in cooperation with national extension services. Faki, Hamid H. M, (1994). Wheat pilot production demonstration pilot irrigated schemes, 1992/93. Annual Report of the Gezira Research station, 1992/93 / P. 329. FAO. (1988).The FAO Agricultural Production Index. FAO Economical Social Development Paper NO.63 Statistics Division Rome. Fare, R. and Daniel Primont. (1997). Multi-Output Production and Duality: Theory and Applications. Kluwer Academic Publishers. Second printing. Fare, R., Grosskopf, S. and Lovell, C. A. K. (1994).Production Frontiers. 166

Decomposing total factor productivity change of bread wheat production

Cambridge: Cambridge University Press. Fare, R. and Shawna Grosskopf. (1996). Intercomparable production frontiers: with Dynamic DEA. CHP. 3 Pp47 – 83. Kluwer Academic Publishers: London. Fare, R., Grosskopf, Lindgren and P. Roos (1992).Productivity changes in Swedish pharmacies 1980 – 1989: A non – parametric Malmquist approach. The Journal of Productivity Analysis 3:85-101. Fare, R. and Shawna Grosskopf. (1998). Reference Guide to On Front The Professional Tool for Efficiency and Productivity Measurement. EMQ, Lund, Sweden. Farrell, M. J. (1957). The Measurement of productive efficiency. Journal of the Royal Statistical Society 120 (3)253 – 281. Gross Kopf, S. (1993). Efficiency and productivity:in H. O. Fried, C. A. K. Lovell and S. S. Schmidt (eds), the Measurement of Productive Efficiency, Oxford University Press, New York 160 – 194. Gross kopf, S.(1986). The role of the reference technology in measuring productive efficiency. The Economic Journal 96: 499-513. Hassan, R .and Ageeb, O. (1992). Towards higher wheat productivity in Gezira: the role of efficient input delivery systems and appropriate technology designs in:Tanner,D.G.,M. angi.W.(Eds)Seventh regional wheat workshopfor Eastern, Central, and southern Africa, Nakuru, Kenya, CIMMYT. Mexico, pp.290 – 360. Hassan, R. and Faki, H. (1993). Economic policy and technology determinants of comparative advantage of wheat production in Sudan. CIMMTT, Economic paper 6. Kennedy, C. and Tirlwall, A. P. (1972). Technical progress: A survey. Economic Journal 82: 11 – 72). Kumar, F. (1998).Toward FDI and the Japanese Economy. The American Chamber of Commerce in Japan. Kumar, F. and Mittal (2000). Foreign – owned versus domestically – owned firms economic performance in Japan.Yokohama National University, Working Paper No. 185. Lawrence, D., Swan, P. L. and Zeitscch (1990). The comparative efficiency of state electricity authorities. Working papers series, No. 90 – 034, University of New South Wales, Sunday. Martin, and David Parker (1997). The impact of privatization, owner ship and corporate performance in UK. London and New York. Mohamed, A. G. (1987). Outline for the strategy of wheat production in the Sudan. Agricultural Economic Department Ministry of Agriculture; Khartoum. Ministry of Agriculture and Forest (2001). Annual Reports (different years). 167

Mohamed O.A. Bushara and Rufida E. M. Dongos

Ministry of Agriculture and Forest(MOAF) (2007).Time series Tables for cereal and oil crops(different years). Ministry of Finance and Economic Planning (1998). Annual Economic Survey (different years). Pinstrup Andersen, P. and Pandya Lorch, R. (2000). Poverty, agricultural intensification, and the environment. Pakistan Development Review 33(4) 463 – 93. Schmidt, P. (1985-86). Frontier Production Functions. Econometric Reviews 4 (2): 289-328. Shephard, R.W. (1953). Cost and Production Functions. Princeton University Press. Princeton, New Jersey. Sudan Gezira Board (1998/1999). Gezira scheme, Department of Statistics and Department of Planning and Socio-economic Research. Annual Report 1998-1999. Sudan Gezira Board (2000-2001). Gezira Scheme, Department of Statistics and Department of Planning and Socio-economic Research. Annual Report 2000- 2001.Survey data (1989/90), SG2000 Annual Reports (1989/90) Teare, D. I. and Pect M. M. (1983). Crop Water Relations. John Wiley and Sons Inc. Wong, C. Y. (1993). Paddy Producer Behavior – in Stochastic Frontier Approaches. Unpublished PhD Dissertation, University of Kent Canterbury. Young, A. (1994). Accumulation, exports and growth in the high performing Asian economies. A comment. Carnegie-Rochester conference series on public policy No. 40.

168