Economic Effectiveness Analyses of Potato Farms: the Case of Erzurum Province, Turkey

Full Length Research Paper

Economic effectiveness analyses of potato farms: The case of Erzurum Province, Turkey

Yavuz Topcu*, Ahmet Semih Uzundumlu and Irfan Okan Guler

Department of Agricultural Economics, College of Agriculture, Ataturk University, 25240-Erzurum, Turkey.

Accepted 11 August, 2010

The potato (Solanum tuberosum) used as a versatile agricultural product and grown around the world under a wide range of climatic and soil conditions is one of the most important staple food crops in Turkey for local consumption, processing and export. Although its alternative usage fields increased significantly, potato production has continuously decreased in the last years since there are major problems stemming from the lack of the effective usage of the production factors. In order to solve these problems, the production factors should be analyzed. The aim of this study is to analyze economic effectiveness for potato farms in Pasinler district, Erzurum by considering the relationship between both input and output. To this end, data collected from a face-to-face questionnaire carried out in 135 potato farms in Pasinler was used for multiple regression and marginal analyses in SHAZAM statistical program. The results of this study showed that the potato growers should increase all production factors (except for the LP and HP) at an increasing rate so that they could achieve a profitable and effective production, and provide the increasing return to scale by considering the marginal analyses and the effective coefficients of the factors in potato production.

Key words: Efficient coefficients, farm, marginal analyses, multiple regression analysis, potato.

INTRODUCTION

The potato (Solanum tuberosum) is of ancient origin. It The majority of the potato production is basically for has originated and was first domesticated in South human consumption (about 60%); the rest is used as America, and then initially introduced in Spain and the animal feed, important raw material for industry, and seed UK, and gradually in part of Asia and North America. It tubers. Although commonly consumed fresh, tubers are has spread around the world over the past 400 years, versatile and can be used when frozen, fried or and gained recognition as inexpensive and nutritive food dehydrated among other derived foods. In addition to in the eighteenth century (Lutaladio and Castaldi, 2009). starch, tubers provide significant amounts of protein, It is now one of the world's most important staple food dietary fiber, vitamins, minerals and trace elements crops with 323.5 million tones fresh-weight produced in (Sonnewald, 2001; Bradshaw and Ramsay, 2009). The 2007 from 18.8 million hectares in 149 countries (FAO, nutritional value of potato tubers is the key driver for this 2010), ranging fourth after wheat, rice and maize in terms growth, along with the economic benefits that potato of cultivation area and total yield under a wide range of cultivation can bring in developing the economy. The climatic and soil conditions. increasing sophistication in the market for fresh potatoes The importance of the potato, on the other hand, is as well as the increasing consumption of processed increasingly being recognized since it produces more dry potato products strongly contribute in maintaining the matter and protein per hectare than the major cereal crops. interest of innovation and yield improvement in potatoes growing in developed countries as well (Buono et al., 2009). Due to versatile usage of the potato crop, there is a *Corresponding author. E-mail: [email protected]. Tel: (90) strong all-year round demand for potato crop. There is, 4422311393. therefore, a need for sustainable increases in potato Topcu et al. 2561

Table 1. Potato production, consumption, yield and consumption per capita.

Production Consumption Yield Consumption Countries (million tones) (million tones) (tone da-1) (kg)*per capita 2001 2007 2001 2007 2001 2007 2001 2007 China 64.60 72.04 64.52 71.70 1.37 1.46 50.25 48.25 The Russian Federation 34.97 36.78 35.04 36.92 1.09 1.29 239.90 260.10 India 22.24 28.60 22.23 28.52 1.84 1.64 20.97 24.49 The USA 19.86 20.37 19.88 20.58 4.02 4.46 68.30 66.67 EU 73.97 63.78 73.70 63.40 2.39 2.85 152.66 128.48 Turkey 5.00 4.25 4.90 4.31 2.50 2.76 72.60 55.21 Erzurum 0.12 0.08 - - 1.93 1.77 - - Pasinler 0.06 0.05 - - 2.20 1.80 - - The World 311.08 323.54 311.27 323.35 1.58 1.72 54.25 48.47

Source: (FAO, 2010). *Annual potato consumption per capita were calculated as (production + import–export)/population from FAO data.

production under environmental changes and technical plant and horticultural production in the west, north and improvements to meet increasing demands for food from southern parts of Turkey, and animal husbandry and human population growth. The world potato production fodder crops based on animal feed production in the east has been undergoing major changes. In the early 1990s, of Turkey, have provided an important contribution to most potatoes were grown and consumed in Europe, national economy. Especially, potato after wheat is one of North America and the Russian Federation. Since then, the most important crops grown in Erzurum located in the there has been a dramatic increase in potato production north eastern part of Turkey for local consumption, and demand in Asia, Africa and Latin America, where export, farming and processing. Potato production in output rose from less than 30 million tones in the early Erzurum is to meet 1.8% of Turkey’s potato production in 1960s to more than 165 million tones in 2007 (FAO, 2007, and Erzurum following Nigde, Nevsehir, 2010). Potato production has continuously increased over Afyonkarahisar, Izmir, Bolu, Konya and Trabzon is the the past years in both developed and developing eighth province of Turkey with regard to the potato countries much faster than other tuber or root crops due production amount (TUIK, 2010). to technical advances in potato breeding, genetic In contrary to the potato production in Erzurum, its con- modification, seed production, growing and storage, sumption has considerably increased in the recent years, processing, starch production and molecular farming and potato supply is not to meet the demand, further- (Lutaladio and Castaldi, 2009). more, this gap has gradually increased. It, therefore, has According to FAO data, for the first time, the developing imported the fresh potato from other potato producer world's potato production with 160.02 million tones provinces. The potato production in Erzurum should be exceeded the developed countries potato production with increased to meet demand surplus. The best way to 159.97 million tones in 2005, and Asia and Europe are increase the potato production is to improve its yield in the world's major potato producing regions, accounting regions having the most potato harvest including districts for more than 80% of the world production in 2007. For such as Pasinler, Oltu, Senkaya, Erzurum Center, Tortum example, China, the Russian Federation, India and USA and Koprukoy. Particularly, Pasinler district1 in Erzurum are the world’s four largest potato producer countries potato production is located in the first line for potato produced that almost half of the world potato in 2007, the production with about 71% and its harvested area with potato production increase by 12% between 2001 and 69%, while the annual average potato yield with 1.8 tones 2007, and Turkey was to meet just about 1.3% of that in da-1 of Pasinler. Moreover, this is higher than that with 1.7 2007 by decreasing 15% of its potato production in this tones da-1 of Erzurum, but is lower than that with 2.8 period (Table 1). Comparing the figures with regard to tones da-1 of Turkey in 2007 (TUIK, 2010; EPDAD, 2010). potato yield and consumption per capita, on the other Potato farms in Pasinler have more important hand, it can be stated that potato yield and consumption advantage than other districts, which has taken into per capita in Turkey are lower than developed countries consideration the annual production and yield amounts such as the USA and EU countries, but higher than that are not enough as compared with leader potato developing countries such as India (Table 1). producers in Turkey. In order to improve the farm Because of agro-ecological zones in Turkey, there are productivity, it should be determined the optimum factor very important differences among agricultural regions on account of farming planning and management. Generally, 1The district is located in the eastern part of Erzurum, Turkey. 2562 Sci. Res. Essays

usages for potato production by taking into consideration NS 2 n = marginal analyses and production effectiveness based on (N −1)D + S 2 the relationships between input and output. 2 In order to optimize potato productivity and quality, Where: n: Required sample size; : Precision factor ≈ E ’ ; : D ∆ ÷ N various aspects of production chain should be considered « t ◊ in an integrated way. These aspects include a number of Number of target population (1500); E : Precision (X − x)(9.6); factors such as potato variety, potato seed quality and 2 : Population variance (3550); t Reliability coefficient (1.96 in the size, soil quality, moisture, planter operation, plant stand, S nutrition, irrigation management, fertilization technique, case of 95% reliability) forecasting of pests and diseases, determination of The permissible error sample size was defined to be 5% for 95% proper planting and harvest dates and farm management. confidence, and sample size was calculated as 135 potato farms. It is only when all factors are at optimum levels, in the point of economic and technique effectiveness, can the most profitable yields of quality potato be attained. Method used for econometric analyses

Many previous studies described in literature, focused In order to analyze the factors affecting the potato production, on the technique in potato plantation, physiological attri- Multiple Regression/Correlation Analysis (MRC) estimated by butes and the diagnosis of potato diseases (Verhagen, Ordinary Least Squares (OLS) method in this study was used. MRC 1997; Sonnewald, 2001; Sengul et al., 2004; Ierne and is a statistical technique that allows prediction of relationship Mauromicale, 2006; Li et al., 2006; Buono et al., 2009; between a dependent variable and a set of independent variables. Thompson et al., 2009), and on the economic and That is, it estimates the coefficients of the linear equation, involving more independent variables, which best predict the value of the environmental impacts including the effects of land use dependent variable. As for OLS, it seeks to minimize the sum of the policies on potato farms (Jatoe et al., 2008); economical squared differences between the observed in a set of data and analysis of potato production (Karadas, 2000; Engindeniz predicted squares from the regression model (Allison, 1997; Cohen and Karakus, 2008; Mohammadi et al., 2008) and potato et al., 2003). production cost and effects on the cost of pesticide usage The primary data was used for MRC models and in order to in Turkey (Uzundumlu, 2005). select the fittest model, the four MRC model types such as double logarithmic (log-log), linear-linear, linear-log and log-linear was However, investigation has not been carried out considered; which estimate the relationship between annual potato regarding the economic effective-ness of the factors used production amounts as a dependent variable and some factors for potato production. This study, therefore, can fill an affecting potato yield as independent variables and are represented important void about the economic effectiveness of the in Table 2. As the comparison and selection criterions among them, factors affecting potato production in scientific literature. the coefficients of determination (R-square), F statistical tests and standard errors are taken into account by being the major In light of this current information, the aim of this study is determinants. Particularly, the first-two of these are the most widely to determine economic effectiveness for potato farms in used goodness-of-fit measures for regression models and they are Pasinler, Erzurum by taking into consideration the the best determinants that described the relationship between relationships between input and output, marginal product dependent variable and independent ones (Gujarati, 2005). values and marginal factor costs. Additionally, the presence of heteroskedasticity, autocorrelation and multicollinearity in MRC models was assessed using the Glesjer B-P-G (SSR) test, Durbin-Watson dc statistic and partial correlation coefficients (r) based on correlation matrix, respectively MATERIALS AND METHODS (Gujarati, 2005; Topcu, 2008). In order to predict the MRC models, SHAZAM statistical software program was used. The MRC model Materials and determination of sample size can be written as follow:

The preliminary data of the present research was collected from a face-to-face questionnaire conducted in Pasinler district center and PPA = f (FS, LP, SP, CP, WE, HP, WP, PA, FP, SA, i ) Where: its’ eleven villages2, which consist of about 75% of the area harvested, with 135 potato growers, in Erzurum between December Dependent variable; PPA= Annual potato production amount (tone) 2009 and January 2010. On the other hand, the secondary data used in the study were collected from the previous studies and Independent variables: FS= Farm size as area harvested (da); LP: documents such as journals, brief reports were published by some Labor power amount as man work unit (MWU); SP: Soil preparation institutions like FAO, TUIK and DPT. process (hour da-1); CP: Cultivation process (hour da-1); WE: The villages, producing densely fresh potato tuber for local Weeding process (hour da-1); HP: Harvest process (hour da-1); WP: market and consumers, were chosen to represent the whole study Water amount used for irritation process (1000 L da-1); PA: area with an objective sampling method, and then the potato farms Pesticide amount (kg da-1); FP: Fertilizer amount (tone da-1); SA: were randomly selected by a simple random sampling method from Seed amount (tone da-1); : Error term the villages in the area of study according to their sizes included i areas harvested. The size of sample was determined by using following equation (Yildiz et al., 2006): Methods used for the interpretation of economic effectiveness

2The villages was ranked from large to small as Epsemce, Taskaynak, Ugumu, Production elasticity Alvar, Asitlar, Kavusturan, Altınbasak, Ardicli, Sercebogazi, Demirdoven and Cogender as taken account of area sizes harvested. The coefficients of the independent variables give the marginal

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production elasticity of each factor in Cobb-Douglas production model was 0.68 and all independent variables explained function. The total of the production elasticity calculated for each 68% of dependent variable. Given the F-statistic 12.98 (p: factor gives the revenues according to scale of farms (Topcu and 0.000) (F : 12.98 > F : 1.99), we reject the null Demir, 2005). calcul. crit. hypothesis that all slope coefficients are equal to zero. When econometric problems are statistically tested, on Average and marginal productivity the other hand, the econometric problems for the double logarithmic MRC model were not detected since partial Geometric average of productivity (GAP) is the production quantity correlation coefficients (r) in correlation matrix used for in response to one-unit variable factor in a certain production level. As GAP is multiplied by the coefficients of each factor, marginal multicollinearity diagnostic were less than 0.80, 2 productivity (MP) is obtained (Karkacier and Angin, 1993). χ :1.35 calculated by Glesjer B-P-G (SSR) calcul .(7;0,05) statistic used for heteroscedasticity diagnostic was less Methods applied for marginal analyses 2 than χ : 2 .17 , and DW dcalcul. (1.96) value crit .( 7 ;0 , 05 ) Marginal product value (MPV) is obtained by multiplying MP with computed by Durbin-Watson statistic that is used for product price (PP). Closer levels to economic optimum are found by autocorellation diagnostic was higher than du (1.86) and comparing the factor prices (FPs) or marginal factor costs (MFCs) 4-d (1.43) critical values (Table 2). in the production function. The highest or lowest MPV does not u have a major importance with regard to the relationship between OLS estimates of the model coefficients and some sta- input and output. It is necessary to consider the effectiveness tistical measures were given in Table 2. The estimation coefficients (ECs) of the factors in order to find the answer to the results indicated that a lot of the independent variables in questions whether the production factors increased or decreased the model were found statistically important at 1 and 5% as possible. significance levels (p < 0.01 and p < 0.05). According to economic theory, on the other hand, the coefficient signs

Effectiveness coefficients of the production factors (EC) of all independent variables had a meaningful impact on potato production. The most effective factors on potato It is used to determine how effective a production factor is at a production were, firm size (FS) (as area harvested), certain production level. EC of each factor is calculated by dividing fertilizer (FP) and seed amount (SA) applied, their FPs by the factors’ MPVs (Karkacier and Angin, 1993; Topcu respectively. These factors are the principal production and Demir, 2005). The coefficient effectiveness of each production inputs used in order to carry out the potato production. factor indicate whether they are used extremely or not. Effectiveness coefficients point out this situation clearly. The results also showed that the application periods According to EC of the factors, the factor usage levels can be and the amounts of labor power allocated for soil prepa- written as follow: ration (SP), harvest (HP), weeding (WE) and cultivation (CP) processes made a different impression on potato EC = 1Ω effective usage of the factors yield or production. Of the independent variables, while Ω EC > 1 little usage of the factors an increase in the HP with a negative sign reduced the EC < 1Ω over usage of the factors. potato production, the increases in the others with

positive signs increased the potato production. Labor

power amount and application period used for the HP RESULTS AND DISCUSSION created a decreasing effect. Therefore, labor powers Econometric analyses which used the manual devices such as hoe, pick, and shovel, can considerably cause damage to the potato The most suitable model was selected as the double crops to due to density labor, and can cause a negative logarithmic’ MRC model by considering goodness-of-fit effect on the production amount and crop quality. They ’3 2 should work using the cultivator machines at the weeding measures as ‘Adjusted R-square (R ), t and F and harvest stages so that they can get rid of these statistics for MRC analysis. Mathematical notation of negative effects. Consequently, these factors are the equation can be written as follow: auxiliary factors causing an increase in the potato production and improving its yield at the same time. Log PPA = + 1 log FS + 2 log LP + 3 log SP + 4 log As for the production elasticity (ep ) , it was calculated as CP + 5 log WE + 6 log HP + 7 log WP + 8 log PA + 1.86 in the double logarithmic MRC production function. log FP + log SA + 9 10 i Therefore, the farmers who worked in a farm capacity, gained an income according to an increasing scale due to All variables specified in the foregoing equation were its measurement as ep :1.86>1.00. This situation refers included in the MRC regression model. R 2 value for the to the first stage of the law of diminishing marginal productivity or returns which observed increases at an 2 3Adj. R-square (R ) was statistically preferred more than R-square for used increasing rate. Hence, usage at an increasing rate of numerous independent variables in the MRC model. these factors (except for HP) can provide the major

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Table 2. The parameters and statistical tests related to potato production.

Variables Coefficient Standard error t-value p-value Elasticity Constant 2.673* 0.8579 3.1160 0.0030 0.9262 FS 0.940* 0.1251 7.5090 0.0000 0.8287 LP 0.0329 0.1230 0.2672 0.7900 0.0191 SP 0.238* 0.5531 2.3210 0.0141 0.0212 CP 0.054** 0.1084 1.9920 0.0460 0.0393 WE 0.147** 0.3113 2.1050 0.0400 0.1835

HP -0.215* 0.5516 -2.5610 0.0120 -0.2862

WP 0.014** 0.0281 1.9900 0.0480 0.0004

PA 0.0133 0.0321 0.4157 0.6790 0.0189 FP 0.400* 0.1171 3.4160 0.0010 0.2191 SA 0.239* 0.0500 4.7800 0.0008 0.1271 n: 135 R-square: 0.70 Adj.R-square: 0.68

Fcalcul. -statistic: 12.98 Fcritic.-statistic:1.99 Prob (F-statistic): 0.00 Durbin-Watson critic values: d :1.86 and 4-d : Durbin-Watson Statistic: 1.96 u u 1.43 B-P-G chi-square:1.35 Chi-square critic value: 2.17

*p < 0.01; **p < 0.05

contributions to the farmers owing to increasing all factors (their ECs>1) consisted of the least amount productivity. Which of these factors should be increased used for potato yield and production in the research area for the economic optimum and how much? Recently, the (Tables 3 and 4). farmers that tried to use the farm capacity effectively In order to improve the potato yield and increase its have continuously investigated the replies of these production, firstly, of the factors having the highest questions so that they could manage the farms effectively marginal productivities; SA, PA, FP, SP, FS, CP and WP and use the scarce production factors at economic were analyzed as the least factors applied and as the optimum level. The best way to be able to reply these farthest points to the economic optimum level in view of questions, first, is to make effectively the marginal their usage levels as well. Of the production factors with analyses including marginal product value (MPV) and the highest marginal productivity, usages at the lowest marginal factor costs (MFC), and then to calculate the level caused the lack of effective economic due to the economic effectiveness coefficients. Finally, it must be fact that it is farther than economic optimum level. carefully calculated the economic effectiveness Therefore, these factors should be increased as soon as coefficients (ECs) in order to decide whether each factor they get to the economic optimum level. Consequently, use at the economic optimum level/a point, is closer to it the potato growers should reach the economic optimum or not. level by decreasing the LP amount as MWU and by finishing the harvest process in shorter time. On the other hand, the other factors should be increased up to the Marginal analyses economic optimum level so that the farmers could produce more profitably by taking into consideration MPs were derived from GAs, and MPVs were calculated marginal analyses and ECs. from MPs. FPs are referred to as the farm courtyard prices according to the producer prices in 2010 and ECs, calculated by the portion of FP to MPV, were shown in Conclusion Tables 3 and 4, respectively. The results of this study showed that the main factors such as FP, SA, FS and the This study examined the relationship between input-input axillaries factors such as PA, SP, CP and WP had the and input-output in potato production and focused on the major impacts on the marginal productivities (MP) for technical and economic effectiveness by considering the potato production. While the FP and SA factors had the law of diminishing marginal productivity, production highest MP, the WE and HP factors provided the lowest elasticity and marginal analyses. The results of this study MP (Table 3). The results also indicated that while the showed that the potato growers must increased all the potato growers used the LP (EC: 0.64<1) intensively, production factors (except for the LP and HP) at an they applied the WE (EC: 1.12≥1) at the closest point to increasing rate so that they could achieved a profitable the economic optimum level. On the other hand, the other and effective production as long as they produced at the Topcu et al. 2565

Table 3. Marginal productivities of geometric averages for factors used in potato production.

Factors Geometric average Marginal productivity PPA (tone) 26.90 - FS (da) 16.03 1.62 LP (hour da-1) 6.66 0.16 SP (hour da-1) 1.40 5.29 CP hour da-1) 12.62 0.48 WE (hour da-1) 41.70 0.14 -1 HP (hour da ) 54.66 -0.17 -1 WP (1000 L da ) 1.16 0.35 -1 PA (kg da ) 0.05 14.86 FP (tone da-1) 0.24 59.23 SA (tone da-1) 0.26 41.76

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