The Technical Efficiency of Smallholder Cattle Producers in Eastern Indonesia.

Transcription

1 The Technical Efficiency of Smallholder Cattle Producers in Eastern Indonesia. by Ian Patrick 1, Tim Coelli 2 and Daniel Kameo 3 Paper presented at the 43 rd Annual Conference of the Australian Agricultural and Resource Economics Society, Christchurch, New Zealand January 20-22, 1999 Abstract The Government of Indonesia (GOI) implements cattle distribution programs in order to improve the welfare of smallholders in the less arable areas of Indonesia and to increase the supply of beef to the growing urban markets. An AusAID sponsored survey involving 145 farmers and their cattle at nine sites in the eastern islands of Indonesia over a three year period was undertaken to determine the factors influencing the productivity and efficiency of cattle production. This study uses stochastic frontier production function methods to determine the relative efficiencies of individual farmers and to identify the major factors that influence the efficiency of production. The empirical results, while not identifying a general strategy that will improve farm productivity across all sites, did identify significant inefficiencies. The four islands; West Timor, Flores, Lombok and Sumbawa had mean technical efficiencies of 0.74, 0.45, 0.54 and 0.49 respectively. It was found that factors such as the relative importance of cattle in the farming system, the feeding system and experience with cattle, were important influences on efficiency, while factors such as education, use of cattle for ploughing and farmer age are less important. 1 School of Economic Studies, University of New England, Armidale, NSW, 2350., formerly Economic Advisor to the EIVS Project, Indonesia. ipatrick@metz.une.edu.au 2 School of Economic Studies, University of New England, Armidale, NSW, tcoelli@metz.une.edu.au 3 Department of Economics, Satya Wacana Christian University, Salatiga, Indonesia

2 1. INTRODUCTION Schultz (1964) advocated the theory that the farmers of the developing world were poor but efficient. The implication was that small farmers in traditional agricultural systems did respond to the market and were efficient in allocating their resources between alternatives. Although this hypothesis has been challenged, it is still accepted by some economists as a basis on which to ground development theory. The acceptance of this theory has important implications for agricultural development. If farmers are efficient, it is only through technological change (e.g. the green revolution) that production will be influenced. However if farmers are not efficient, production and therefore economic and social development will be able to take place by encouraging a better use of existing resources. Furthermore, if productivity can be increased using existing technology "a stronger case can be made for institutional investments in input delivery, infrastructure, extension systems, farm management services and farmer's skills" (Ali and Byerlee, 1991) in order to promote productivity increases at the farm level. The Government of Indonesia (GOI) provides cattle to poor smallholders in the eastern islands of Indonesia. Recipients are required to repay the first two calves to the government (the best of which are then used in further distribution programs), after which the original animal and any further progeny become the farmer s property. The GOI is implementing this program for two reasons: 1. to improve the welfare of poor farmers by providing them with an asset which can be used to generate income (breeding and ploughing), and as collateral to fund further borrowings; 2. to increase the supply of beef into the growing urban markets. There has been considerable work undertaken concerning the relevant farming systems for Bali Cattle (Bos sondaicus) in Indonesia (Winrock, 1986, ADB, 1979, CIDA, 1991, Goonting, 1990, Quane, 1990, Patrick, 1994a, Patrick, 1994b). Some of these studies have attempted to determine the important factors affecting production. However, there has been no detailed economic analysis undertaken to determine the real factors influencing production and no previous attempt to analyse the technical efficiency of these smallholder cattle farming systems. If the GOI is to improve the welfare of smallholders and increase the breeding herd, this information must be made available. 2. CATTLE PRODUCTION IN NUSA TENGGARA (NT) Indonesia is a nation comprising approximately 13,000 islands spread over an area of 735,360 square miles with a population of approximately 208 million. Approximately 70 per cent of the population live in rural areas. Agriculture, apart from being the major employment sector, accounts for 20 per cent of national GDP and eight per cent of export revenue. Local demand for beef is growing, having increased from 2.7 kg per head in 1969 to 5.49 kg per head in

3 The eastern islands of Indonesia, or Nusa Tenggara (see Figure 1) include the provinces of Nusa Tenggara Timur (NTT), Nusa Tenggara Barat (NTB) and Timor Timur (TT) 4. They are some of the driest and poorest regions of Indonesia and are receiving increasing attention from both domestic and external sources (particularly Australia) in order to encourage economic and social development. Figure 1: Indonesia and the provinces of NTB and NTT. Nusa Tenggara Timur (NTT) Nusa Tenggara Barat (NTB) Lombok Sumbawa Flores West Timor Large areas of NTT are mountainous. Rainfall is highly seasonal and variable. The pronounced dry season (March to November), poor soil in the lowlands, hot dry winds and lack of water makes agriculture a risky business. The major crops are corn, cassava and rice. Cattle play an important role in both agricultural and social systems. Cattle ownership is an important influence on social status and, apart from the potential longer-term income benefits, an important form of collateral. It is expected that cattle productivity, apart from being influenced by feed and water availability, will also be influenced by social and financial factors, such as the role of cattle in the farming system, farmer age and education levels and the cattle management system. Although cattle management systems are highly variable, NTT is generally characterised as being dominated by extensive management systems. Grazing land tends to be communally owned with individual farmers having the right to either free graze their cattle with other farmers, tether their cattle and move them around the grazing area or tether near the kandang (cattle stall) and cut and carry the required 4 East Timor is not included in this study 3

4 feed. Traditional management in these areas is large numbers of cattle (herds of 200 head are not uncommon) owned and managed by either extended family groups or by stockholders who employ managers. As with NTT, environmental and social conditions are highly variable in NTB. This province is dominated by the islands of Lombok and Sumbawa, with land quality varying from the highly fertile rice-growing areas of West Lombok to the saltpans of East Sumbawa. These differing fertility characteristics and population pressures have influenced the development of the farming systems. In general, cattle management is more intensive in this province, with cattle being used to a greater extent in cropping activities. An intensive system is characterised by an individual owning no more than five animals, feed being cut and carried, the cattle are washed by hand and either housed at night in the kandang or tied to a tree at night. Once again, factors such as the importance of the cow in the farming system, farmers cultural and management experience with cattle as well as seasonal factors are expected to be influences on cattle productivity. East Timor is not included in this analysis. The present population of beef cattle 5 in NT is one million or 10 per cent of the country s total cattle population. However, this is an area that accounts for less than one per cent of the human population. While total cattle production increased by 17 per cent between the years 1984 and 1989, the benefits do not appear to have flowed through as an improvement in smallholder welfare. The farmers short-term planning horizon, the long-term nature of a cattle breeding enterprise benefits and the dependence on inefficient, and in many cases inequitable, local marketing systems have influenced this discrepancy between farmer welfare and improving demand for and price of, cattle. In order to satisfy the GOI objectives of improved welfare and increased supply of beef, plus improve the efficiency of the cattle distribution systems, the GOI must have better information concerning the major factors that influence cattle productivity and the efficiency of the farming systems. The GOI needs to understand the individual farm factors that lead to some farmers receiving greater benefits from distribution programs than others. It is the purpose of this study to elicit individual, farm and village-specific measures of technical efficiency for cattle smallholder production systems in the eastern islands of Indonesia. An attempt is made to define and explain reasons for the inter-farm variations in efficiency and hence to provide the GOI with a priority list of factors which should be considered when devising future programs to improve smallholder welfare and cattle productivity. The specific objectives of this study are therefore: 1. to measure and compare the efficiency of cattle production systems in NT, and 2. to determine the major factors affecting the efficiency of cattle production systems in NT. 5 All islands except Sumba run Bali Cattle exclusively, on Sumba only Ongole cattle are found. In both provinces artificial insemination with other cattle breeds is becoming more common as producers attempt to increase the size and fertility rates of their cattle. 4

5 3. METHODOLOGY 3.1. Technical Efficiency Technical efficiency is defined as the ability of a farm to maximise output from a given combination of technology and inputs. Technical inefficiency arises when a farm fails to produce the maximum possible production from a given level of inputs. This has become an important aspect of production economics which throws light on the existing production environment and assesses whether resources are being used effectively (Hill and Kalirajan, 1991). The existence of technical inefficiency indicates that there is potential for increasing the output of the farm with existing resources, if the factors causing technical inefficiency can be identified, minimised and/or removed. Productivity growth can result from technical efficiency improvements or technological progress. This study limits the analysis to increasing production due to the more efficient use of existing technologies. A longer time series of data would allow technological progress to also be investigated Frontier Production Functions The terms frontier production function and production function are synonymous. The former term is used in the efficiency literature to emphasise that a production function can provide a basis for defining efficient performance. It specifies maximum outputs for a given set of inputs and existing production technologies. A frontier production function measures the maximum level of production attainable from a given level of resources. The frontier is a bounding function. It is not well estimated by ordinary least squares (OLS) regression, which estimates a line of best fit through the sample data rather than over it. The frontier production function defines the level of production attainable if best practice techniques are applied, and therefore can be used to compare the efficiency of farms within an industry against the optimum resource use. Deterministic frontier models The concept of economic efficiency provides a theoretical basis for the measurement of the relative performance of individual farms. Farrell (1957) undertook pioneering work in this area. A representation of a frontier production function is presented in Figure 2. The axes indicate the inputs, X, associated with producing the output, Y. Efficient producers will produce on the production frontier, while input-output values below the frontier imply farms are not attaining maximum output possible with the given level of inputs and technology available. The technical efficiency of farm A is measured by y/y*, where y is farm A's output and y* is the "frontier output", both outputs (y and y*) are associated with the same level of inputs, x. Authors such as Aigner and Chu (1968), Timmer (1971) Schmidt (1976) and Afriat (1972) further developed Farrell's ideas through the specification of deterministic frontier models which expressed efficiency as the difference between the level of a farm's production and the frontier production level. The deterministic frontier may be defined by: 5 Y i = f ( x i ; β ) exp (-U i ), i = 1, 2,..., N, (1)

6 where Y i is the production of the i-th farm, f(x i ;β) is a suitable function (usually Cobb-Douglas or translog) of the vector, x i, of inputs for the i-th farm and a vector, β, of unknown parameters; and U i is a non-negative random variable associated with technical inefficiency. Figure 2: Technical efficiency of firms in input-output space output Y production function B = (x,y*) x x x x x x x A = (x,y) x x observed input-output values 0 X inputs, X Source: Battese (1991) The technical efficiency of a farm is simply the ratio of actual output to the frontier level of output as represented in equation (2) 6 TE i = Y i /Y i * = f(x i ;β) exp (-U i ) / f(x i ;β) = exp (-U i ) (2) A major criticism of the deterministic approach is that random error due to measurement error and other noise can influence the shape and/or positioning of the frontier. This is particularly relevant to this study where the variables are subject, not only to the influence of many different social and cultural variables, but also to the potential error caused by the farmers' subjective estimation of various quantities. Stochastic frontier models Aigner, Lovell and Schmidt (1977) and Meeusen and van den Broeck (1977) converted the deterministic model into a stochastic frontier production function (SFPF) by including a random error term associated with factors not under the control of the farm. They introduced the concept that apart from inefficiencies due to human errors there was also a "disturbance due to randomness in the real sense and due to specification and measurement error" (Meeusen and van den Broeck 1977, p 436). The stochastic frontier production function assumes that the actual production of a

7 farm is bounded above by the sum of a parametric function of known inputs (involving unknown parameters) and a random error. This random error is usually a measurement error concerning the level of production, or other factors such as the effects of weather, luck, animal genetics and topography. The greater the amount by which the actual production falls short of this stochastic frontier, the greater the level of technical inefficiency (Battese and Coelli, 1993). The stochastic frontier approach differentiates the disturbance term into two parts: the disturbances caused by factors beyond the control of the farm and measurement error (V i ) and technical inefficiency (U i ). This study uses the stochastic frontier model as defined by Battese (1991): Y i = f ( x i ; β ) exp ( V i - U i ), i = 1, 2,..., N (3) Where V i is a random error associated with factors outside the control of the farm, the other variables are as described in equation (1). In Aigner, Lovell and Schmidt (1977) the random errors, V i, are assumed to be independently and identically distributed as N (0,σ v 2 ) random variables, independent of the U i s which are assumed to be non-negative truncations of a normal distribution, N(0,σ 2 (or have exponential distribution). The one-sided disturbance, U i, is interpreted as the distance the farm lies below its own frontier. The presence of U i in the model accounts for the existence of technical inefficiency of production. If this variable is absent from the model, the production frontier is a traditional average response function. Figure 3 (Battese, 1991) depicts the frontier production function as defined in equation (3). It also illustrates the difference between the deterministic and the stochastic measures of technical efficiency. A farm's technical efficiency (Y i /Y i * in Figure 3) is defined in terms of the ratio of the observed output to the corresponding frontier output, given the levels of inputs used by that farm. Figure 3: Stochastic frontier production function output frontier output Y Yi*, if Vi>0 X deterministic production function, y = f(x;b) X f(xj;b) frontier output X Yj*, if V<0 observed output, Yi X X observed output, Yj 0 Xi Xj inputs, X Source: Battese (1991) 7

8 The parameters specified in equation (3) can be estimated using the maximum likelihood (ML) method. Aigner, Lovell and Schmidt (1977) proposed their halfnormal model be estimated following the parameterisation, σ s 2 = σ v 2 + σ 2 and λ = σ / σ v. Rather than use the non-negative parameter λ, Battese and Corra (1977) considered the parameter δ = σ 2 / (σ v 2 + σ 2 ), which is bounded by zero and one, and hence has advantages during estimation. The technical efficiency of the i-th farm, relative to the stochastic frontier production function, equation (3), is the same as for the deterministic frontier model, that is: 8 TE i = Y i /Y i * = f(x i ; β) exp (V i -U i ) / f(x i ; β) exp (V i ) = exp (-U i ) (4) Even though both the deterministic and the stochastic frontier models elicit the same technical efficiency estimates, it is important to note that they have different values for the two models (Battese, 1992). This is illustrated in Figure 3. In Figure 3, farm j is clearly more efficient than farm i. Y i represents the level of production attained by farm i using the inputs X i, likewise Y j represents the level of production attained by farm j using a higher level of inputs (X j ). Both levels of production are below the deterministic frontier production function and have inefficiencies present in their system. Farm i, however, should be able to obtain a production level of Y i * as factors outside the control of the farm are favourable (V i >0), while for farm j outside factors are unfavourable (V j <0); hence the potential level of production has been reduced. Technical efficiency for farm j is greater under the stochastic production function compared with the deterministic production function while, for farm i, the reverse is true. Figure 3 indicates how one may calculate technical efficiency measures relative to a stochastic frontier production function. However, one problem that remained was that of separating the error term (e i ) into its two component parts, the symmetric error to account for noise (V i ) and the asymmetric error to account for technical inefficiency (U i ). The problem was solved by Jondrow, Lovell, Materov and Schmidt (1982) who suggested using the expectation of U i, conditional upon e i = V i - U i to predict U i, and hence to predict TE i = exp(-u i ). They derived the following result: E[U i e i ] = -γe i + σ A {f (γe i / σ A )/1-F(γe i / σ A )} (5) where σ A = γ(1-γ)σ s 2 and f(.) is the density function, and F(.) is the distribution function, of a standard normal random variable. When the dependent variable is logged, as in this study, Battese and Coelli (1988) define a more appropriate predictor of TE i as: TE i = E[exp(-u i ) e i ] = [(1-F(σ A -γe i /σ A ))/(1-F(γe i /σ A )] exp(γe i +σ A 2 /2) (6) because the focus is on the measurement of TE i not U i. Operational predictors for equations (4), (5) and (6) can be obtained by substituting the relevant parameters by their maximum likelihood estimates. The technical efficiency predictions and ML

9 parameter estimates are obtained using the FRONTIER computer program (Coelli, 1994). This approach has been used extensively in development economics literature. However, while Squires and Tabor (1991) and Trewin et al (1995) have looked at the efficiency of Indonesian farming systems, neither consider livestock activities. In fact no study could be found which analysed, using either deterministic or stochastic frontier production approaches, livestock systems in developing countries Factors Influencing Efficiencies The primary goal of frontier studies has been the search for evidence of inefficiency. Another question of interest is whether some farms have predictably higher levels of inefficiency than others. If the occurrence is not totally random, then it should be possible to identify factors that contribute to the existence of inefficiency. The stochastic production frontier defined in equation (3) does not determine which factors influence technical efficiency, it only provides a measure of efficiency. It highlights how efficient or inefficient a farm may be but does not provide the reasons why. Studies by Forsund, Lovell and Schmidt (1980), Kalirajan and Shand (1985) and Pitt and Lee (1981) attempted to explain technical inefficiency by observing the relationship between specific factors which they regarded as important and estimated technical efficiencies. Many studies that have attempted to determine which factors influence technical efficiency have taken a two-stage approach. The first step involves the specification and estimation of the stochastic frontier production function and the prediction of the inefficiency effects of the farms involved. The second step involves the specification of a regression model for the predicted inefficiency effects (or the levels of technical efficiency of the farms) in terms of various explanatory variables and an additive random error. There are four problems with this approach (Kumbhakar et al. 1991, p.280): 1. Technical inefficiency may be correlated with inputs, causing inconsistent estimates of the parameters as well as technical inefficiency. 2. The standard OLS results may not be appropriate since technical efficiency - the dependent variable - is one-sided. 3. The estimated value of U i should be non-positive for all observations. 4. The meaning of the residual term in the regression is not clear. Kumbhakar et al. (1991) developed a single step maximum likelihood (ML) procedure to obtain consistent parameter estimates and to identify the determinants of technical inefficiency. They assumed that the technical inefficiency effects are nonnegative truncations of a normal distribution with a mean, that is a linear function of exogenous factors whose coefficients are unknown, and an unknown variance. In their study of the efficiency of US dairy farms, the variables, education and size of farming operations are significant influences on efficiency. This study uses a single-stage model formulation with the U i in equation (3) specified in equation (7): 9

10 10 U i = Z i δ + W i (7) where the random variable, W i, is defined by the truncation of the normal distribution with zero mean and variance, σ 2, such that the point of truncation is -Z i δ, i.e., W i >= - Z i δ. These assumptions are consistent with the U i 's being non-negative truncations of the N(Z i δ,σ 2 ) distribution, where Z i is a vector of exogenous factors for i-th farm, and δ is a parameter vector (Battese and Coelli, 1993). The single-stage analysis in this study is conducted using the FRONTIER computer program which implements the model specified in Battese and Coelli (1993, 1995). 4. Data and Variables 4.1. Data Data was taken from the Cattle Health and Productivity Survey (CHAPS) undertaken as part of the Eastern Islands Veterinary Services Project (EIVSP I); an AusAID funded project working with the Department of Livestock Health in NT. This analysis includes data from 9 sites within 2 provinces (6 sites in NTB sites B2, B3, B4, B5, B6 - and 4 in NTT sites T2, T3, T7, T8). Some sites were not included due to insufficient or sub-standard data. There were 145 smallholders and approximately 550 cattle (cows plus followers all Bali Cattle) included in the survey, all smallholders were part of a GOI distribution program. The survey was undertaken over a three-year period with 4 visits each year. The duration and spacing of visits ensured seasonal variations could be accounted for and annual data could be averaged to ensure atypical data was not the basis of the analysis. The data used therefore was crosssectional. That is, representing production over a three-year period Dependent Variable The dependent variable used in the stochastic frontier production function is the weight of all calves produced by the each cow from the time of receiving cows until the end of the survey period (3 years). This variable was regarded as appropriate as it was measurable and reflected the economic returns to cattle. In Indonesia, cattle value is determined solely on weight, there are no price differentials for meat quality. A number of alternative dependent variables were also considered before being discarded. One such variable was the average cow weight over the survey period plus calf weights. However, issues such as different starting weights, cows not attending samplings and pregnancies were either confusing the variable or already being picked up in the calf weight measure. There are, however, some shortcomings of using calf weight as the dependent value, these are: The sale of calves is not the only reason for keeping cattle - other benefits include: the use of cows for draft, manure/fertiliser production, asset and social value. These other priorities may not be adequately included in the calf weight variable It is necessary to collect data on initial calf weight and calf weight at either sale or the end of the survey period. Poor attendance means poor data. In one example a cow had had three calves but the second was brought to the sampling

11 11 only once (at two months of age) and the third never attended. This farmer when considering calf weight only was regarded as inefficient and unproductive even though three healthy calves had been produced. This problem was not properly resolved was not deemed to be significantly widespread. As cattle were part of a distribution program, the first two calves had to be repaid to the government at 18 months of age, at many sites this process broke down. This meant that heifers, which should have been removed from the herd, were having calves. If these calves plus their offspring were included as production, it would have unfairly favoured those sites where the GOI was not removing calves as required. To account for this no second generation calves were included and the weight of the first two calves if or when they reached 18 months of age were used. Calf weight gain may not be a high priority for cattle owners who are still in the process of returning calves to the government. Program participants had no prior experience of cattle management and during the project were simply required to pay back two calves. Hence, during the early part of the survey calf weight may not have been a priority. It was not until the calves were repaid that farmers had to learn about the relationship between calf weight and price. Notwithstanding these problems, and taking into account the data available, total calf weight over the survey period was still regarded as being the most acceptable dependent variable. An improvement in the specification of the dependent variable may be an area of future work Independent Variables Traditional inputs Information has been collected on input variables such as labour, land and feed which are direct influences on the production system. Traditionally, a capital variable would also be required but the nature of the dependent variable production from one cow given by a distribution program ensures all farmers have the same capital inputs and hence no livestock capital measure is used. One of the most important determinants of productivity is the genetic characteristics of each individual breeding cow. This is not included in this analysis and hence is likely to be a major contributor to the error term V i. The three traditional inputs are defined as follows: 1. Labour is measured as the total hours of family labour per week spent on feeding, watering and maintaining the cattle. 2. Land is the total amount of land, in hectares, that the farm has access to. This will include both dryland and irrigated land as well as owned and rented land. This does not include, however, common grazing land. 3. Feed is measured as the average quantity hand-fed in kilograms per week. Other factors affecting production In this study the technical efficiency is allowed to be a systematic function of farm specific factors that are not direct inputs into the production function but are likely to

12 affect efficiency. Battese and Coelli (1993) collected data on age and years of schooling (contacts with extension officers, access to credit, use of high-yielding varieties and fertiliser were not readily available), in order to determine what influences the efficiency of Indian rice producers. Other studies in developing countries have found that factors such as: management, farmer experience, credit availability, education, farm size, number of visits by extension workers, age of farmer and non-farm income influence efficiency (Kalirajan 1991, Kalirajan 1984, Kalirajan 1990, Kalirajan and Flinn 1983, Ekanyake 1987, Squires and Tabor 1991, Bravo-Ureta and Evenson 1994, Pinheiro 1992). These variables plus others are considered in this study. They have been selected on the basis of stakeholder discussions, other similar studies, data availability and personal knowledge of each site. The variables considered in this study are: locality, education, importance of cattle, other reasons for keeping cattle, management, experience, cows at start and farmer age. Summaries of the means for each variable per island are provided in Table 1 and are discussed briefly. Table 1: Mean values for traditional inputs and other factors affecting production per island West Timor Flores Lombok Sumbawa Labour (hr/day) Land (ha) Feed (kg/qtr) % with higher education % labour on cattle Av. income (Rp. 000/qtr) Draft (hr/qtr) Management system Intensive Semi-intensive Intensive Extensive Experience (yrs) Cows at start (no.) Farmer age (yrs) No. of Observations Each site s individual physical environment will have a major impact on cattle productivity. Bali cattle are not well suited to the drier areas, such as parts of West Timor and East Sumbawa. Cattle owners in the low rainfall, poorer areas may be totally committed to caring for and efficiently managing their cattle and still have a relatively poor performance compared to those in more fertile areas. A locality variable will also take into account social issues that cannot be picked up by other 12

13 variables such as access to, and utilisation of, extension advice. Extension advice will be better at some sites than others because of both the quality of the advice and the availability of the extension officers. For example the two sites in Flores has many similarities in terms of available resources, the major differences are in cattle productivity and access to extension advice. The extension officer for the two villages lives in Talibura (T7) rather than in Kringa (T3) 20 km away. Production levels are higher in Talibura. A locality dummy variable is included in the analysis In NTB only 15 per cent of surveyed cattle owners had received a secondary education or better, 30 per cent had no formal education and 55 per cent had completed elementary education. The two Lombok sites have both the highest and the lowest standards of education among the nine sites. This may be important as in most other ways they are the similar. In NTT 18 per cent of respondents had attended secondary school or better, 60 per cent had received elementary education while 22 per cent had received no education. After initial testing it was decided that it would be sufficient to include only one dummy variable called education that differentiated those with an elementary education or better from those without. Farmer experience with cattle (measured in years) may influence cattle productivity. One of the criteria for selection of farmers to be involved in the program is that farmers should have no prior experience in cattle production. Hence farmers (if they ve been selected according to the criteria) would have a maximum of five years experience with cattle. This short time frame may not provide a true indication of the importance of this variable. Experience may have either a negative or positive effect on efficiency. Farmers with long experience may have had a bad experience in the past that has given them a negative perception of cattle. Long experience may have convinced farmers that they know all they need to know about cattle when, in fact, they don t (Kameo, 1995). Alternatively, more experience may mean better management. The experience variable is measured as the number of years since farmers first had experience with cattle. There may be a problem with this as 40 years may mean that a farmer owned a cow 40 years ago and has not had 40 years of continuous cattle ownership. The relative importance of cattle, cropping and off-farm work has implications for cattle productivity. It can be measured both by relative labour use between the competing activities and the relative importance of different enterprises in providing income. It is expected that (all other things being equal) the higher the proportion of income and the higher the percentage of labour devoted to cattle, the greater the cattle productivity. There are observed differences between provinces and sites in the study area. Farmers in NTB spend approximately 2.6 hours per day on both cropping and cattle activities, while farmers in NTT spend about 3.5 hours on their cattle only. This may of course imply different things. In Praya (B3) high labour use may be a result of the low land ownership per farmer and hence lack of any useful alternatives for their labour and the need to spend time collecting feed rather than an inherent desire to manage cattle better. In Taliwang (B4), the most productive site, the percentage of labour spent on cattle was low. Owners at this site, however, had twice as much land per farmer as any other site and hence used cattle for ploughing and had to spend less time collecting feed off-farm. Two variables are included to explain the effects of this 13

14 importance of cattle variable: the percentage of total labour spent on cattle and the individual wealth of each farmer. Off-farm income is also an important indicator of the role of cattle. In NTB only 3 per cent of total income is derived from livestock, in NTT this is 14 per cent (with a maximum of 33 per cent in Naukae (T2). In NTT income from cropping and off-farm sources is less than half that in NTB, exemplifying the fact that NTT is, in general, much drier and less suitable for cropping than NTB. The low level of income from livestock is also caused by the fact that owners are still repaying cattle to the government and hence income is not being derived from cattle as yet. The keeping of animals is not solely for income generation. Self-sufficiency, status, and asset value are important non-market reasons for keeping livestock. In fact, in NTB, the major reason for keeping cattle was asset value, while the second was the use of cattle for ploughing. The benefit of keeping cattle for breeding was equal in importance to the use of manure as fertiliser. Cattle were not regarded as a basic source of income. A measure of the hours spent per quarter ploughing was used as a proxy for the other reasons for keeping cattle variable. Data is available concerning the type of cattle management system adopted by each farmer. Management practises in association with other variables will have important implications for cattle productivity. While the GOI encourages the adoption of more intensive management systems, semi-intensive and extensive systems are also used by farmers depending on season, environmental factors and culture. An intensive system involves 24-hour tethering or housing dominated by cut and carry feeding techniques. Farmers must have an understanding of cattle nutritional requirements and cow fertility management in order to maximise productivity. A semi-intensive system involves grazing cattle during the day and housing and supplementary feeding over-night. This is less labour intensive and requires less technical knowledge if a bull accompanies the herd during grazing. However, suitable grazing country is required. Extensive cattle management is the cows running as a herd throughout the day and night. Sufficient pasture and water is required to maximise productivity. The more extensive the management system the greater the access to the bull but the more pressure on cattle health especially in the dry season. Two dummy variables were used to differentiate management systems, each farmer was represented by the most common management variable over the life of the survey period. Other factors that may be important influences on productivity are the number of cows owned at the start. Although most farmers received their first cattle with the project, some already owned cattle. This cows at start variable may be linked with experience, implying that more cattle should be associated with higher productivity, or it could be that more cattle means that one cow will be less important to an individual smallholder, hence decreasing productivity. Only at Masbagik (B2) and Taliwang (B4) were there farmers who owned more than their allocation through the project. These were two of the more productive sites in terms of productivity. Other factors that may normally be important such as land tenure, age and breed of cattle were not included in this study as they were homogenous across sites. 14

15 Apart from problems concerning data reliability, it is believed that the data available from the survey can adequately capture many of the factors thought to influence cattle productivity in the eastern islands of Indonesia. 5. Results 5.1. Initial Pooled Data Analysis Because of concern that differing environments and management methods may result in the use of different production technologies across sites, a test was undertaken to test the equality of parameter estimates across the sites. The following model was estimated using all 145 observations. log(y i ) = β 0 + β 1 log(labour i ) + β 2 log(land i ) + β 3 log(feed i ) + V i U i, i = 1, 2,, 145 (8) where: U i has a half normal distribution, and V i has a normal distribution The same model was then estimated for the NTB and NTT provinces, separately. These involved 81 and 64 observations respectively. A likelihood-ratio (LR) test 6 was then conducted to decide if there was a significant difference between the parameters of the estimated function in these two provinces. The LR test is calculated as follows λ = -2 ( LLF R LLF U ) (9) where LLF R is the likelihood function of the restricted model, and LLF U is the addition of the likelihood functions for the separate provincial models (i.e., the unrestricted model). The LLF values from these tests were , and for NT (all data), NTB and NTT respectively. This provides a calculated λ value of 12.0, which is less than the χ 2 6 (five per cent) table value of Hence, the null hypothesis that there is no difference in the parameter estimates across the provinces is not rejected. It was also necessary to consider disaggregation within the provinces. The question was asked: are the production functions of the four islands significantly different from each other? We can reject the null hypothesis if λ > χ 2 with 18 degrees of freedom if λ > Separate functions for each of the four islands (West Timor, Flores, Lombok and Sumbawa) were estimated and their LLF values calculated. Then, using equation (9) again we find that λ = 80.2, implying that the null hypothesis can be rejected. There are significant differences between the islands in the study area and therefore, it is 6 For more information on the LR test see Coelli (1995) 15

16 appropriate to disaggregate at the island level 7. All further analysis was therefore undertaken on an island basis Discussion of results ML estimates of the SFPFs for each of the four islands are presented in Table 2. These results are now discussed on per island basis. West Timor In West Timor, at the sites at Naukae and Benlutu, the only significant traditional input variable is the quantity of feed provided. Labour and land area do not have a significant influence in this cattle management system. The dominant management system at all sites on this island was an intensive handfeeding system. Because this system dominated to such an extent it could not be used to differentiate efficient from inefficient smallholders. The dominance of this system would also provide a possible explanation as to why the amount of land owned was found to be insignificant. This management system may also have implications for the non-traditional variables. These are the only sites where the variable: number of cows at the start is significant. If a smallholder owns more than one cow, there is less likelihood of efficient production. The implication being that it is difficult to hand-feed larger herds. Farmer age and experience were significant negative influences on inefficiency. The older more experienced farmers were more efficient even though they may not have owned cows when the distribution program commenced. Those that had owned cows in the past were better able to maintain their cattle. Education has a positive effect on inefficiency. When considered with the other results, the implication is that experience is more important than education in influencing cattle productivity. Other results are that wealthier producers are not efficient producers. As cattle have no short-term benefits to the farmer (cattle are not used for ploughing) other activities have a higher priority. It may also be that the more educated farmer places greater importance on other income inducing activities, while the older, more experienced farmers have more traditional views and hence place greater emphasis on the status of cow ownership and make more use of cattle as an asset. With consideration of the data and knowledge of the sites, it may be that the farmers included in the sample are too uniform to adequately elicit the factors affecting 7 To disaggregate to a site basis would have led to degrees of freedom problems as the number of smallholders would have been too small. 16

17 efficiency. The high average mean efficiency ratio of 0.74 (the highest of any of the groupings) indicates that these farmers tend to be reasonably efficient 8. 8 These efficiency estimates only compare farmers within these groups. Although they may appear to be efficient when compared to other groups, they may not be. 17

18 Table 2: ML estimates on a per island basis * Variable Parameter W. Timor Flores Lombok Sumbawa β 0 Log (labour) β 1 Log (land) β 2 Log (feed) β 3 δ 0 Site dummy 1 δ 1 Site dummy 2 ** δ 2 Education dummy δ 3 % of labour on cattle δ 4 Average income δ 5 Draft δ 6 Management handfed dummy Management shepherd dummy δ 7 δ 8 Experience δ 9 Cows at start δ 10 Farmer age δ (1.37) 0.17 (0.21) 0.02 (0.20) 0.91 (0.26) (0.12) 0.12 (0.54) 1.22 (1.11) (3.07) 0.73 (0.48) (0.16) 1.16 (0.50) (0.31) 0.82 (1.48) (0.072) (0.084) 1.11 (0.28) 0.03 (1.12) 1.75 (0.56) 0.47 (0.62) (3.10) 0.06 (0.35) (0.31) (0.46) (0.016) 8.72 (0.94) (0.21) (0.054) (0.18) 0.89 (0.89) (0.60) (0.94) 0.67 (0.91) (0.39) (0.019) 0.62 (0.92) 0.77 (0.98) (0.037) (0.31) (0.018) (0.025) (0.0034) (0.0022) (0.0050) (2.20) 5.34 (1.31) 2.00 (1.13) (0.67) (2.82) 0.45 (0.31) (0.043) 1.14 (1.46) 1.16 (1.32) (0.15) 0.12 (0.24) (0.029) Log likelihood function Mean efficiency * estimated standard errors are given in parenthesis to two significant digits. The estimated coefficients are given to the same corresponding numbers of digits behind the decimal places. ** West Timor, Benlutu = 0, Naukae = 1 Flores; Talibura = 0, Kringa = 1 Lombok; Praya = 0, Masbagik = 1 Sumbawa; Taliwang = 0, Kenanga = 1, Ndano = 2 18

19 From these results and personal knowledge of the sites, it appears that the main factor constraining productivity in these sites in West Timor is that the provision of one cow per family does not fit the farming system. The less efficient farmer within this group is a wealthier, more highly educated farmer who places more emphasis on other farming activities. Flores Flores is made up of two sites Kringa (T3) and Talibura (T7). The most significant variable is the site variable, with those farmers living in Kringa less efficient than those in Talibura. The most likely factors that are being picked up in this variable are differences in extension advice and water availability. There is one extension officer allocated to look after both sites. This staff member is based at Talibura, hence providing greater assistance to those farmers. Talibura is situated at sea level while Kringa is located 20 km away at 200 metres above sea level in a heavily forested area, with less water available for both household and agricultural purposes. Of the other variables, the most important may be the percentage of labour spent on cattle. The higher the percentage of labour on cattle the more efficient the farmer, implying that the more important a cow is to the farmer the more productive it will be. As with West Timor the education variable has a negative effect on efficiency, while experience has a positive effect. No management variable has been included in this analysis as the same management system (semi-intensive) dominates at each site. In terms of the characteristics included in this analysis, these two sites are similar. However, in terms of production and efficiency they are significantly different. The traditional variables labour, land and feed are important determinants of production, while site differences (which may include access to extension and environmental factors) are the most important determinants of efficiency. There is also evidence that the more experienced farmer who is prepared to use a larger proportion of labour on cattle will be more efficient than the more educated farmer who puts more emphasis on other activities. Lombok The results for Lombok, involving the sites Masbagik (B2) and Praya (B3), are unique in that all the traditional input variables are significant, but have a negative impact on production. The highest producing farmers are those that own less land, put in the least amount of labour and hand-feed the lowest amount of feed. The results are different from those of Flores, West Timor (and from what is expected from economic theory) but are not unexpected. The most efficient management system is tethered grazing. This requires minimal land ownership (communal grazing land may be sufficient in Lombok as it generally more fertile than NTT), feed was only cut for night or supplementary feeding, while labour was only required to move cattle, not shepherd or supply feed. Unlike other areas the most efficient farmers at these sites are the wealthier farmers who spend more of their labour on other activities. Masbagik was the only site where owners paid managers to look after their cattle. Although not important in terms of efficiency, 19

20 these cattle were used for ploughing which made them more important in the farming system. This may mean that the production of meat (the dependent variable) is less important to the farmer than other cattle uses, such as use for ploughing. Regarding the non-traditional variables, experience had a positive effect on inefficiency while farmer age had a negative effect. The more efficient farmers in this area were those who had received their first cattle with the program and were older. Education, the site dummy variable and the number of cows at the start were insignificant in influencing efficiency. Sumbawa There are three sites in Sumbawa: Taliwang (B4), Kenanga (B5) and Ndano (B6), each extremely different in terms of efficiency and productivity. The significance of the site variable supports this. In Kenanga, it is highly significant with a positive effect on inefficiency, the site variable for Ndano is also positive but does not have the same significance. This site variable may be picking up extension and environmental factors similar to Flores. All three traditional variables are significant, land and feed both have a positive effect while the total on-farm labour variable has a negative effect on production. The cattle management system is an important determinant of productivity. The coefficients of the management dummy variables indicate that the most efficient system is tethered grazing. Shepherding (a semi-intensive management system) is the least efficient system, but dominates in Kenanga and Ndano (see Table 1). Other variables, education, percentage of labour spent on cattle and experience, all have positive effects on efficiency. As with the other islands the signs of the percentage of labour spent on cattle and average income variables are opposite, implying that the wealthier farmers and those who spend a low percentage of their time on cattle activities have lower technical efficiency in cattle production. As in NTT, it is the poorer farmers who are the more efficient. 6. Conclusions Initial hypothesis testing indicates that although there is no significant difference in production technologies on a provincial basis, there are differences when data are disaggregated to an island basis. Hence we have estimated separate SFPFs for each of the four islands in the study area. Results varied significantly across the islands, but some general observations can be made. Firstly, with respect to the coefficients of the traditional input variables of land, labour and feed. These have the expected positive signs in Flores and West Timor, but only feed has a t-ratio larger than two. The insignificance of land is most likely a consequence of the prevalence of intensive cattle management systems in the study area. The insignificance of labour is most likely due to the fact that labour may be over-utilised because of the lack of alternative employment opportunities. In Sumbawa it was observed that land also has a significant positive coefficient (the more land the greater the productivity). This is most likely due to the greater importance of grazing (both shepherding and tethered grazing) in Sumbawa relative to Flores and West Timor. It was also observed that the co-efficient of labour is negative in Sumbawa. This may also be influenced by the management system where the 20