Economic analysis of early-warning system in apple cultivation: a turkish case study

Economic analysis of early-warning system in apple cultivation: a turkish case study 165 Economic analysis of early-warning system in apple cultivation: a turkish case study Recebimento dos originais: 04/07/2014 Aceitação para publicação: 17/11/2014 Mevlüt Gül PhD in Agriculture Economics Adress: Faculty of Agriculture, Department of Agricultural Economics, Süleyman Demirel University, 32260, Isparta, Turkey. E-mail: mevlutgul@sdu.edu.tr Göksel Akpinar PhD in Agriculture Economics Institution: Akdeniz University Adress: Finike Meslek Yüksekokulu, Finike-Antalya, 07740, Turkey. Vecdi Demircan PhD in Agriculture Economics Adress: Faculty of Agriculture, Department of Agricultural Economics, Süleyman Demirel University, 32260, Isparta, Turkey Hasan Yilmaz PhD in Agriculture Economics Adress: Faculty of Agriculture, Department of Agricultural Economics, Süleyman Demirel University, 32260, Isparta, Turkey Tufan Bal PhD in Agriculture Economics Adress: Faculty of Agriculture, Department of Agricultural Economics, Süleyman Demirel University, 32260, Isparta, Turkey Ş. Evrim Arici PhD in Pythopathology Adress: Faculty of Agriculture, Department of Plant Protection, Süleyman Demirel University, 32260, Isparta, Turkey Mehmet Polat PhD in Horticulture Adress: Faculty of Agriculture, Department of Agricultural Economics, Süleyman Demirel University, 32260, Isparta, Turkey

Economic analysis of early-warning system in apple cultivation: a turkish case study 166 Bekir Şan PhD in Horticulture Adress: Faculty of Agriculture, Department of Horticulture Science, Süleyman Demirel University, 32260, Isparta, Turkey Figen Eraslan PhD in Plant Nutrition Adress: Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Süleyman Demirel University, 32260, Isparta, Turkey Çağla Örmeci Kart MSc in Agriculture Economics Institution: Ege University Adress: Faculty of Agriculture, Department of Agricultural Economics, Ege University, 35100, Bornova/ Izmir, Turkey. Damla Özdamar MSc in Agriculture Economics Adress: Faculty of Agriculture, Department of Agricultural Economics, Süleyman Demirel University, 32260, Isparta, Turkey. Şerife Gülden Yilmaz MSc in Agriculture Economics Institution: Batı Akdeniz Agricultural Research Institute, Adress: Batı Akdeniz Tarımsal Araştırma Enstitüsü, Muratpaşa Antalya, 07050, Turkey. Abstract In this study Antalya, Denizli, Isparta, Karaman, Konya and Niğde province farms which are dominant in apple cultivation has been compared in terms of early warning adoption level and some social economic indicators. With this scope in the study region stratified sampling method had been used and sampling size has been determined 267 farms. In these regions early warning system has been used since the late 80 s for black spot and codling moth. Especially after 2000 s successful results of the system provide that a positive effect of the farmers adoption level. According to the study results there is a high adoption level of farmers on apple cultivation from early warning system thus 41.6% of the farmers exactly adapt the pesticide application time from early warning system but farmers have lack of information about the system. There is a positive relation between adoption and education level, both levels increase at the same time.early warning adoption levels also decrease unit production cost of apple. Relative profit has a statistically meaningful relation between early warning adoption level (p<0.05). Total pesticide cost increased 10.92% due to unnecessary usage. Many small farms in these regions can increase their income and provide market advantages with some amelioration in the early warning system, enlargement of practise areas. Keywords: Profit margin. Net profit. Adoption level.

Economic analysis of early-warning system in apple cultivation: a turkish case study 167 1. Introduction Apple is a widespread cultivated agriculture product which can be grown in different ecological conditions. Apple takes an important place for Turkey in terms of production quantity and also plantation area but during the production period it can be encountered with many diseases and pests. Early warning systems use especially for black spot (Venturia inaequalis) and codling moth (Cydia pomonella) in apple cultivation.usda (2006) describes that Early-warning systems for plant diseases are valuable when the systems provide timely forecasts that farmers can use to mitigate potentially damaging events through preventative management. In other words early warning systems are small meteorogical stations which provide to farmer to find appripiate pest application time. Experts which work in Ministry of Food, Agriculture and Livestock in Turkey evaluate information from this meteorogical station and specify convenient date for pesticide application. Pest management efforts for apple growing, also other agricultural crops, have intensified in Turkey since 90 s. This increase is the result of the increase in pest population, the Ministry of Food, Agriculture and Livestock s reduction in tolerance limit of residues permitted in foods, and the raising of cosmetic standards. Gül (2005) emphasize that since the second half of the 90 s early warning system adoption studies has been launched, pest and disease management has reached faster and more scientific position but there are some lameness according to farmers in practice. Applying pesticide in convenient time comes into prominence in apple cultivaton due to high numbers of diseases and pests. From this point using technology in this area become one important issue. Early warning system which is the technological pillar of integrated pest management found a common place in the research region since 90 s. Rossi et al. (1983) identified Integrated Pest Management (IPM) is thought of as an intermediary step between a chemical method of pest control and a nonchemical method of pest control. IPM relies on a variety of biological, physical, cultural and chemical methods to control pests which are coordinated into a cohesive program designed to provide long-term protection from pests. This study aimed to make an economic evaluation of early warning system in Antalya, Denizli, Isparta, Karaman, Konya and Niğde provinces which are dominant in apple production. Besides farmers knowledge and adoption level of early warning system, pest

Economic analysis of early-warning system in apple cultivation: a turkish case study 168 management practices and economic loss from wrong pesticide application had been detected in these provinces. 2. Materials and Method The main material of the study consist 267 apple farmer surveys from Antalya, Denizli, Isparta, Karaman, Konya and Niğde Provinces. Farmers divided into the 5 groups according to the level of early warning adoption level through the evaluation of technical experts also it has been imposed on secondary sources such as organizations datas (Ministry of Food, Agriculture and Livestock, etc..), national/international publications. Districts are determined according to the apple production area in sampling process and villages which have early warning stations constituted sampling framework of the study. Stratified sampling method was used to calculate sampling size (Neyman) (Çiçek and Erkan, 1996). Farms divided in four orchard size according to apple production areas. Farms between 1-7.5 decares I.group, 7.6 25 decares II.group, 25.1 50 decares III.group and higher than 50.1 decares defined as IV.group. Interviewed farms are selected randomly. Data which gathered from survey had been transferred to the computer and analyzed by using staatistical package programs. Production costs constitute from all the expenses which are done during the production process such used inputs, services. Daily wages in the regions are taken as a basis while calculating family workforce. 3% of total variable cost be considered as a general management cost. Circulating capital interest is a variable cost which shows capital s alternative cost. Circulating capital interest calculated by using half (%6.5) of the Turkish Agriculture Bank crop credit interest rate (%13.5). Land interest calculated by using 5% of the current purchase and sale value in the research region (Kıral ve ark.,1999). Facility capital interest is calculated by using 5% interest to the half of the total facility costs. As is known facility cost in perennial plant means all the various expenses from the beginning to the first harvesting season. In perennial plant some of the facility cost can be in the first year and some of them can be in few years and some of them can be every year until the harvesting. Depreciation of establishment cost calculated by divided sum of establishment cost (6 years) to economic life of an orchard (55years) and production cost is obtained by adding this value to the cost in production period. During this period, 5% of the costs are included as normal interest cost 5% normal to the sum of the costs incurred each year. 3% of the costs incurred each year added as a general management cost. Beside 5% of the bare land value

Economic analysis of early-warning system in apple cultivation: a turkish case study 169 added to these cost in each year (Açıl and Demirci, 1984). Establishment cost calculated through interviews in every provinces (5 interviews in each province) with farmers which established new orchard and during the interviews prices discounted to related year (2010). Gross profit, net profit and relative profit indicator per unit area was used to evaluate of production success level and compare early warning system in farms. To calculate this indicators; Gross Profit= Gross production value Variable cost Net Profit = Gross production value Production cost Relative Profit= Gross production value / Production cost formulas used in the study. (Açıl ve Demirci, 1984; Kıral ve ark., 1999; Tanrıvermiş, 2000). 3. Findings and Discussion 3.1. Early warning system recognition In the study i farmers knowledge and adoption levels of early warning system has been determined. According to the results farmers had a higher adoption level of early warning system despite of the low level of knowledge. It has been monitored some increase with the orchard size but there isn t any meaningful relation statistically (Table 1). Table 1: Knowledge and adoption levels of farms Apple Orchard Size Early warning knowledge level Early warning adoption level I.Group Mean 1.42 3.37 Std. deviation 0.82 1.56 II.Group Mean 1.48 3.63 Std. deviation 0.91 1.50 III.Group Mean 1.63 3.83 Std. deviation 0.93 1.57 IV.Group Mean 1.73 4.16 Std. deviation 1.20 1.09 Total Mean 1.55 3.73 Std. deviation 0.97 1.46 Farmers are considered warnings from Ministry of Food, Agriculture and Livestock but they don t know these warnings are developed according to the early warning meteorogical stations, because of that reason knowledge and adoption level is founded different.

Economic analysis of early-warning system in apple cultivation: a turkish case study 170 3.2. Pest and disease management Apple varietie resistances differ from regions and years. Farmers in early warning system practising area had to control pest according to the warnings of the Food, Agriculture and Livestock Ministry Department Crop Protection and Plant Health (Zeki ve ark., 1998). In the research region from the second half of the 90 s early warning system adoption studies has been launched, as a result of that pest and disease management has reached faster and more scientific position but there are some lameness according to farmers in practice ( in some regions such as Isparta, Denizli it has began second half of the 80 s). It has been detected that 12.73% of the farmers worked with agriculture advisor especially in pest and disease management. High orchard size increases the situation to work with an agriculture advisor but there isn t any meaningful relation statistically. It has been determined higher working rate with agriculture advisor due to intensive and commercial apple production in research area thus Keskin (2011) also founded that 10% of the Konya province Çumra district apple farmers works with an advisor. In research area pest and disease management is crucial with regard to high quality market oriented apple production. In these respect apple farmers has to choose and implement its pest control schedule very carefully and prefer to work with an advisor. 3.3. Farm decision process and interactions in pest and disease management 39.7% of the farmers stated that they had begun to use pesticide at sight pests or diseases in orchard and 33.0% of them stated that they use pesticide in certain period without controlling diseases and pest in orchard. 31.1% of the farmers adopted expert suggestions from Ministry; 16.9% of them followed early warning systems, 24.1% of farmers considered pesticide seller suggestions and 21% of them decided to use pesticide according Agriculture Engineers Advisors (Table 2).

Economic analysis of early-warning system in apple cultivation: a turkish case study 171 Table 2: Information sources for decision of pesticide application time Practices I. Group II.Group III.Group IV.Group Total N % N % N % N % N % At sight diseases and pests in orchards 20 46.5 48 37.5 18 45.0 20 35.7 106 39.7 Certain period without controlling diseases and pest in orchard 15 34.9 41 32.0 18 45.0 14 25.0 88 33.0 Food, Agriculture and Livestock Ministry Province/ District Directorate staff suggestions 16 37.2 38 29.7 12 30.0 17 30.4 83 31.1 Pesticide seller suggestions 10 23.3 36 28.1 5 12.5 13 23.2 64 24.0 Agriculture Advisor (public)(from TARGEL) suggestions 15 34.9 48 37.5 11 27.5 30 53.6 56 21.0 in regard to early warning system 7 16.3 18 14.1 5 12.5 15 26.8 45 16.9 At sight diseases and pests in neighbourhood orchards 5 11.6 14 10.9 4 10.0 1 1.8 24 9.0 Agriculture Advisor (private)suggestions 2 4.7 11 8.6 3 7.5 6 10.7 22 8.2 When diseases and pests reach an intense level in orchard 2 4.7 6 4.7 2 5.0 4 7.1 14 5.2 Personal pest management calendar 1 2.3 6 4.7 0 0.0 2 3.6 9 3.4 Family- relatives pest management calendar 0 0.0 3 2.3 2 5.0 0 0.0 5 1.9 TARGEL: Project for Development of Agricultural Extension Unlike the study results in the cultivation of apples and cherries in the other studies which had been done in Karaman, Konya and Isparta provinces farms use pesticides according to the suggestions from the Food, Agriculture and Livestock Ministry which ranged from 94.12% to 41.31% and generally Ministry is the primary sources of the farmers (Hasdemir ve Taluğ, 2012; Demircan ve Aktaş, 2004; Bayav, 2007; Karaçayır, 2010). In our research technical staff suggestions and early warning systems ranked as third or fourth sources in the decision of pesticide usage this situation are thought to be due to lower levels knowledge of interviewed the farms. When analyzing farms usage level (dosage) of insecticides, fungicides and herbicides pesticide sellers are the most important sources same as pesticide choice (3.8) and personel experiences (3.7) and Ministry technical staff (3.0) are the other important sources during the dosage decision (Table 3). In another study found that the most important factors, which are affected farmer plant protection products choices, are District Directorate of Agriculture (%52.94) for farms without GAP (Good Agriculture Practice) and exporters (%39.71) for farms with GAP sertificate. Özer (2001) was detected that 41.4 % of the farmers considered suggestions of pesticide sellers following by District Directorate of Agriculture technical staff suggestions with 31.4% and personal knowledge, experience with 27.4% during to choosing process of pesticides. The similar results founded in Isparta by Demircan and Yılmaz (2005), 32.11% of the apple farmers choose pesticides according their knowledge and

Economic analysis of early-warning system in apple cultivation: a turkish case study 172 experience, 25.69% of them considered suggestions of pesticide sellers and 11.01% of them considered Directorate of Agriculture technical staff suggestions. Table 3: Information sources on pesticide choice and practices Information sources on farmers I.Group II.Group III.Group IV.Group Total insecticide, fungicide and St.De St.De St.De St.De St.De Mean Mean Mean Mean Mean herbicides choices Pesticide sellers 4.1 1.1 3.9 1.2 3.5 1.3 3.8 1.3 3.9 1.2 Personal knowledge and experiences 4.0 1.2 3.3 1.3 3.7 1.2 3.8 1.3 3.6 1.3 Food, Agriculture and Livestock Ministry Province/ District 3.5 1.6 3.3 1.4 3.7 1.3 3.1 1.6 3.3 1.4 Directorate staff TV programs (related to agriculture) 2.4 1.4 2.4 1.2 2.2 1.3 2.3 1.2 2.4 1.3 Neighbourhood farmers 2.8 1.3 2.2 1.1 2.3 1.3 2.3 1.3 2.3 1.2 staff from private companies (Agriculture engineers) 2.3 1.5 2.1 1.3 2.2 1.4 2.2 1.4 2.2 1.4 Relative farmers 2.7 1.5 2.2 1.2 2.0 1.2 2.0 1.2 2.2 1.3 Written sources (book, journal, newspaper, brochure etc.) 1.9 1.1 1.9 1.0 1.9 1.1 1.9 1.1 1.9 1.0 Agriculture Advisor (private) 1.6 1.0 1.8 1.1 1.6 1.0 1.9 1.3 1.7 1.1 Cooperative president 1.5 0.9 1.7 1.1 1.6 1.2 1.5 1.1 1.6 1.1 Acquaintance (non-agriculture) 1.4 1.0 1.5 0.9 1.5 1.1 1.4 0.7 1.5 0.9 other 1.4 0.9 1.6 1.1 1.3 0.9 1.5 1.0 1.5 1.0 Headman and members 1.5 1.0 1.4 0.9 1.5 1.0 1.3 0.7 1.4 0.9 Exporter 1.3 0.8 1.3 0.7 1.2 0.7 1.4 1.0 1.3 0.8 Customer (Merchant-Middleman) 1.1 0.6 1.2 0.7 1.1 0.3 1.3 0.9 1.2 0.7 Retailer (market, supermarket) 1.0 0.3 1.1 0.5 1.0 0.0 1.3 0.8 1.1 0.5 Information sources on practising Mean St.De St.De St.De St.De St.De Mean Mean Mean Mean Pesticide sellers 3.7 1.4 4.0 1.2 3.4 1.5 3.5 1.6 3.8 1.4 Personal knowledge and experiences 3.7 1.3 3.5 1.3 3.7 1.1 4.0 1.2 3.7 1.2 Food, Agriculture and Livestock Ministry Province/ District Directorate staff 3.1 1.6 3.0 1.4 3.3 1.5 2.8 1.5 3.0 1.5 staff from private companies (Agriculture engineers) 2.3 1.5 2.3 1.3 2.4 1.5 2.2 1.5 2.3 1.4 other 2.2 1.5 2.5 1.5 1.9 1.2 2.1 1.4 2.3 1.4 Neighbourhood farmers 2.7 1.3 2.0 1.0 2.2 1.2 2.1 1.3 2.2 1.2 TV programs (related to agriculture) 1.9 1.2 2.0 1.1 1.9 1.2 1.9 1.0 2.0 1.1 Relative farmers 2.2 1.2 1.7 0.9 2.0 1.3 1.8 1.2 1.9 1.1 Agriculture Advisor (private) 1.9 1.4 1.8 1.1 1.6 1.1 1.8 1.2 1.8 1.2 Written sources (book, journal, 1.8 1.2 1.8 1.0 1.8 1.2 2.0 1.3 1.8 1.1 newspaper, brochure etc.) Acquaintance (non-agriculture) 1.5 1.0 1.6 0.9 1.5 0.9 1.4 0.8 1.5 0.9 Customer (Merchant-Middleman) 1.4 0.8 1.3 0.7 1.2 0.6 1.3 0.9 1.3 0.7 Headman and members 1.4 1.0 1.3 0.7 1.3 0.8 1.2 0.8 1.3 0.8 Cooperative president 1.4 1.0 1.3 0.8 1.3 0.9 1.4 1.0 1.3 0.9 Exporter 1.1 0.6 1.2 0.7 1.0 0.0 1.4 1.1 1.2 0.7 Retailer (market, supermarket) 1.0 0.2 1.1 0.5 1.0 0.0 1.2 0.8 1.1 0.5 (1: Never 5: Always)

Economic analysis of early-warning system in apple cultivation: a turkish case study 173 In the conducted study farmers information sources during the pesticide choices found different sorting from the other studies but in all of them pesticide sellers, tehnical staffs and farmers experiences are the most important factor which affect pesticide choices. The features of the tools and equipments are essential for an effective chemical control as well as pesticide dosage in farms because of that reason it has been examined pesticide spraying machine ownership of farms in the study area. 66.29% of the farm have pesticide spraying machine, ownership and capacity of the machine increase together with orchard size and meaningful statictically (p<0.05). Comparison according to adoption level (Table 4) of early warning system in terms of various indicators in the study region is done and the results are given in Table 5. Table 4: Early warning adoption level according to provinces Early warning adoption level Provinces I II III IV V Farm Average N Antalya 1 1 9 11 17 39 Denizli 3 0 1 3 14 21 Isparta 6 5 8 17 25 61 Konya 6 1 2 3 9 21 Nigde 22 6 3 13 18 62 Karaman 6 1 1 27 28 63 Total 44 14 24 74 111 267 % Antalya 2.56 2.56 23.08 28.21 43.59 100.00 Denizli 14.29 0.00 4.76 14.29 66.67 100.00 Isparta 9.84 8.20 13.11 27.87 40.98 100.00 Konya 28.57 4.76 9.52 14.29 42.86 100.00 Nigde 35.48 9.68 4.84 20.97 29.03 100.00 Karaman 9.52 1.59 1.59 42.86 44.44 100.00 Total 16.48 5.24 8.99 27.72 41.57 100.00 % Antalya 2.27 7.14 37.50 14.86 15.32 14.61 Denizli 6.82 0.00 4.17 4.05 12.61 7.87 Isparta 13.64 35.71 33.33 22.97 22.52 22.85 Konya 13.64 7.14 8.33 4.05 8.11 7.87 Nigde 50.00 42.86 12.50 17.57 16.22 23.22 Karaman 13.64 7.14 4.17 36.49 25.23 23.60 Total 100.0 100.0 100.0 100.0 100.0 100.0 It can be said that adoption level of early warning system increase the yield of orchards as well as added value from the unit area. Thus farms without adoption of early warnings yiels is 2970.09 kg per decare, gross production app value in unit are is 2098.76 TL on the other side these numbers are 3443.35 kg per decare and 2881.16 TL respectively in farms which completely adopted early warnings nevertheless there isn t any statictical meaninngfull relation between adoption level and yield or gross production apple value.

Economic analysis of early-warning system in apple cultivation: a turkish case study 174 Table 5: Economic evaluation of early warning Indicators Early warning adoption level Farm I II III IV V average Yield (kg/da) 2970.09 3077.26 2581.79 3270.78 3443.35 3263.95 Gross value of apple production (TL/da) 2098.73 2846.43 2044.06 2768.05 2881.16 2708.20 Non-agricultural income (TL/farm) 3896.82 5335.71 1995.83 4528.92 5745.73 4745.23 Non-farm agricultural income (TL/farm) 744.55 71.43 178.33 1129.05 934.29 843.81 Education (year) 6.30 6.86 6.08 7.20 7.67 7.13 Age (year) 49.82 43.43 51.96 48.03 47.75 48.32 Apple cultivation period (year) 25.80 22.43 27.13 22.09 22.44 23.32 Apple orchard plot (number) 3.32 5.29 3.54 2.85 3.06 3.21 Total farm land (da) 22.26 39.40 26.95 37.58 40.83 35.54 Nitrogen (kg/da) 24.48 28.04 30.12 22.18 29.46 26.77 Phosphor (kg/da) 15.63 33.33 9.10 16.28 12.59 14.95 Potasium (kg/da) 1.17 0.91 6.45 2.96 2.48 2.66 Funguside (g/da) 1312.47 1779.32 2165.05 1224.03 1442.32 1433.81 İnsectiside (g/da) 553.86 774.70 817.76 579.92 666.96 646.33 Akaricide (g/da) 236.98 348.26 238.22 251.98 246.88 252.65 Herbicides (g/da) 12.21 0.00 20.87 52.56 15.36 25.42 Pest cost (TL/da) 204.79 256.67 249.14 204.92 239.68 227.53 Total variable costs (TL/da) 919.71 915.94 1011.42 851.68 904.76 898.67 Production costs (TL/da) 2017.93 1981.52 2037.41 1897.87 1965.79 1957.06 Share of pesticides cost in variable costs (%) 22.27 28.02 24.63 24.06 26.49 25.32 Share of pesticides cost in production costs (%) 10.15 12.95 12.23 10.80 12.19 11.63 Price (kg/tl) 0.71 0.92 0.79 0.85 0.84 0.83 Production costs (kg/tl) 0.68 0.64 0.79 0.58 0.57 0.60 Profit margin (TL/kg) 0.03 0.28 0.00 0.27 0.27 0.23 Net profit (TL/da) 80.80 864.91 6.64 870.17 915.38 751.13 Relative profit 1.04 1.44 1.00 1.46 1.47 1.38 (I: never V: exactly) It is founded that early warning level increases with the level of farmer education and total land holdings also apple orchard fragmentation status decreases but there isn t any statictical meaninngfull relation. There isn t showing any significant difference between adoption level of early warning system and usage of N, P, K elements in unit area. It is founded that farmers completely adopted early warning system use less fungicides, insecticides, acaricides in unit area generally than without adopted early warnings but higher that middle adopted farms. There isn t any significant difference.

Economic analysis of early-warning system in apple cultivation: a turkish case study 175 3.4. Economic losses from chemical spyraying in apple cultivation Economics losses put forth by comparing quantity of pesticides used and required usage in apple farms. During the calculating average pesticide quantity usage as active ingredients of the farmers compared with suggested quantity from licensed pesticide list of Ministry. Also the number of pesticide usage considered during the calculation. It has been determined that farms are used more fungicides which varies on between 10.20 % and 13.32% in terms of orchard sizes according to the study results in research area. It has been determined that the other important pesticide is insecticides for farms. Farms are used more insecticides which varies between %12.50 and %17.17 than suggested quantity. Acaricides also are used more between 3.51% and 12.24% from the suggested dosage. These results show that farmers have low information on pesticides usage and they behaved according to their personal knowledge and experiences. Additive cost from excessive usage of chemicals is 25.30 TL per decare in the study region. Total pesticide cost was calculated 231.55 TL per decare in apple production in the research area according that 10.92% of the total pesticide costs arise from excessive usage (Table 6). Table 6: Economic losses based on pesticide usage in apple cultivation Apple orchard size I II III IV Farm Average Weighted average Fungucide (g/da) 259.70 200.62 155.07 158.61 169.86 189.36 İnsekticide (g/da) 105.30 83.47 90.21 79.81 82.94 87.21 Akaricide (g/da) 20.30 25.08 33.52 8.84 16.65 23.15 Herbicide (g/da) 0.00 10.56 2.92 1.53 3.69 5.55 Fungucide (%) 13.41 13.32 10.20 11.65 11.85 12.32 İnsek ticide (%) 17.17 12.58 12.50 12.85 12.83 13.18 Akaricide (%) 6.15 10.94 12.24 3.51 6.59 8.99 Herbicide (%) 0.00 32.31 15.84 5.99 14.51 22.97 Incremental pesticide cost due to over use of pesticide 42.69 22.86 23.78 20.83 22.31 25.30 (TL/da) Share of incremental pesticide cost in total 15.51 10.43 10.17 9.18 9.81 10.92 pesticide cost (%) Williams (2000) determined that tart cherry farmers which are reasonably adopted IPM saved $449.08 per acre with this management program in the North Michigan they saved $350000 by without adoption of IPM on 1999. Reasonably adopted IPM resulted with the

Economic analysis of early-warning system in apple cultivation: a turkish case study 176 highest savings rate followed by highly adoption (Williams, 2000). Because of this reason farmers have to get informed about early warning and encourage them to use appropriate dosage of pesticides, this could be provide big money savings for Turkey. Share of pesticides cost in total variables cost varies from 22.27% and 28.02% according to early warnings adoption level and the share in total production cost varies between 10.15% and 12.95%. Geiss (1973), founded that pesticide cost according to the orchard size in total variable cost are 19.77%, 19.62%, 11.27% and 9.76% respectively on 1971 in Maine (USA). Rossi et al. (1983), also compared pesticides cost of apple farms with IPM and non-ipm. They founded that pesticide cost is $47.98 per decare on farms with IPM and $54.52 per decare on non-ipm farms. They determined that pesticide cost equals 10% of the total production cost in both systems. Demircan et al. (2005), calculated that share of pesticides and spraying costs equal 21.64% in total production cost and ranked the first in the total production costs. Karaçayır (2010), also founded a similar research result that share of pesticides and spraying costs are 25.75% in the total production costs and ranked the first on apple cultivation in Karaman Province. According to all of these studies, pesticides and spraying costs are an important place in apple production, reducing this cost item will provide farmers to increase their income and help consumers to find healthier and cheaper products in the market. 1 kg of apple cost decreases with the higher adoption of early warning system and profit margin, relative profit and net profit show increase during the high level of adoption. In the study 1 kg of apple cost founded 0.57 TL in farms which highly adoption early warning system. Karaçayır (2010), calculated 1 kg of apple cost 0.37 TL in Karaman Province. This difference originated from the different research area in the study which is conducted. Study provinces are produced apple more intensively and commercialy because of that they use more chemical pesticide and fertilizer; this increases production cost and average production cost per farm. 4. Results This study aimed to compare economics effects of early warning adoption level in provinces which are dominant on apple cultivation of Turkey. According to study results it has been determined that farmers have low information level but high adoption level of early warning system. thus 41.6% of the farmers exactly adapt the pesticide application time from early warning system but farmers have lack of information about the system.

Economic analysis of early-warning system in apple cultivation: a turkish case study 177 Study results show that early warning level increases with the level of farmer education and total land holdings also apple orchard fragmentation status decreases but there isn t any statictical meaninngfull relation. There isn t showing any significant difference between adoption level of early warning system and usage of N, P, K elements in unit area. It is founded that farmers completely adopted early warning system use less fungicides, insecticides, acaricides in unit area generally than without adopted early warnings but higher that middle adopted farms. There isn t any significant difference due to different ecologies and density of pests and diseases in research areas. Apple cost per unit decreases with the higher adoption of early warning system and profit margin, relative profit and net profit show increase during the high level of adoption. There is a siginificant relation between relative profit and early warning adoption level (p<0.05). Many small farms in these regions can increase their income, reduce their cost and provide market advantages by making some amelioration in the early warning system, enlargement of practise areas. At this point farmer trust and awareness should increase. Besides farmers have to get aware about fertilizer. Soil and leaf analysis should be implemented and new technique such as fertigation should be encouraged. 5. References AÇIL, A.F.; DEMİRCİ, R. Tarım Ekonomisi Dersleri. T.C. Ankara Üniversitesi Ziraat Fakültesi Yayınları: 880, Ders Kitabı: 245, 372 sayfa, Ankara, 1984. ATLAMAZ, A.;ZEKI, C.; ULUDAG, U. The importance of forecasting and warning systems in implementation of integrated pest management in apple orchards in Turkey. EPPO Bulletin, Vol. 37, n. 2, p. 295-299, 2007.. BAYAV, A. Isparta İlinde Elma İşletmelerinde Yeniliklerin ve Araştırma Sonuçlarının Benimsenme Düzeyleri ve Etki Değerlendirmeleri.Adnan Menderes Üni. Fen.Bl.Ent.Y.L.Tezi, 2007. BLACK, R.; NUGENT, J.; ROTHWELL, N.; THORNSBURY, S.; OLYNK, N. Michigan Production Costs for Tart Cherries by Production Region. Mıchıgan State Unıversıty Agricultural Economics Report #639, 2010.

Economic analysis of early-warning system in apple cultivation: a turkish case study 178 ÇİÇEK, A.; ERKAN, O. Tarım Ekonomisinde Araştırma ve Örnekleme Yöntemleri. Gaziosmanpaşa Üniversitesi, Ziraat Fakültesi Yayınları No:12, Ders Notları Serisi No: 6, 118s., Tokat, 1996. DEMİRCAN, V.; YILMAZ, H.; BİNİCİ, T. Isparta ilinde Elma Üretim Maliyeti ve Gelirinin Belirlenmesi, Tarım Ekonomisi Dergisi, Vol. 11, n. 2, p. 71-80, İzmir, 2005. DEMİRCAN, V.; AKTAŞ, A.R. Isparta ili kiraz üretiminde tarımsal ilaç kullanım düzeyi ve üretici eğilimlerinin belirlenmesi. Turkish Journal of Agriculture Economics, Vol. 9, n, 1, p. 51-65, 2004. DEMİRCAN, V.; YILMAZ, H. Isparta İli Elma Üretiminde Tarımsal İlaç Kullanımının Çevresel Duyarlılık ve Ekonomik Açıdan Analizi. Ekoloji Dergisi, Vol. 14, n. 57, p. 15-25, 2005. GEISS, W. C. Jr. Costs and Returns from Producing and Marketing Maine Apples. Journal of the North-eastern Agricultural Economics Council, Vol. 3, n. 2, p. 51-63, 1973. GÜL, M. Toros Dağları Geçit Bölgelerinde Elma Üretiminin Ekonomik Analizi. T.C. Çukurova Üniversitesi Fen Bilimleri Enstitüsü, Tarım Ekonomisi Anabilim Dalı, Doktora Tezi, 378s., (Basılmamış), Adana, 2005. HASDEMİR, M.; TALUĞ, C. The Analysis Of The Factors That Affect The Adoption Of Good Agricultural Practices in Cherry Growing, Vol. 29, n. 1, p. 23-36, 2012. KARAÇAYIR, H. F. Elma üretimi yapan tarım işletmelerinde tarımsal ilaç kullanımında yayım yaklaşımları; Karaman ili örneği. T.C. Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Tarım Ekonomisi Anabilim Dalı, Yüksek Lisans Tezi, 158s., (Basılmamış), Konya, 2010. KESKİN, A. H. Klonal Elma Yetiştiriciliğinde Tarımsal Yayımın Rolü: Konya İli Çumra İlçesi Örneği. T.C. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü, Tarım Ekonomisi Anabilim Dalı, Yüksek Lisans Tezi, 67s., Isparta, 2011.

Economic analysis of early-warning system in apple cultivation: a turkish case study 179 KIRAL, T.; KASNAKOĞLU, H.; TATLIDIL, F.F.; FIDAN, H.; GÜNDOĞMUŞ, E. Tarımsal Ürünler İçin Maliyet Hesaplama Metodolojisi ve Veritabanı Rehberi. Tarımsal Ekonomi Araştırma Enstitüsü Yayınları, Proje Raporu 1999-13, 143 sayfa, Ankara, 1999. MAUCERI, M.; ALWANG, J.; NORTON, G.; BARRERA, V. Adoption of Integrated Pest Management Technologies: A Case Study of Potato Farmers in Carchi, Ecuador, American Agricultural Economics Association Annual Meeting, Providence, Rhode Island, July, p. 24-27, 2005. ÖZER, O.O. Tokat İli Merkez İlçesi Tarım İşletmelerinde Elma Üretimiyle İlgili Hastalık ve Zararlılarla Mücadelenin Ekonomik Analizi. T.C. Ankara Üniversitesi Fen Bilimler Enstitüsü, Tarım Ekonomisi Anabilim Dalı, Yüksek Lisans Tezi, 61s., (Basılmamış), Ankara, 2001. TANRIVERMİŞ, H. Orta Sakarya Havzası'nda Domates Üretiminde Tarımsal İlaç Kullanımının Ekonomik Analizi. T.C. Tarım ve Köyişleri Bakanlığı, Tarımsal Ekonomi Araştırma Enstitüsü Yayınları No: 42, Ankara, 2000. WILLIAMS, B.W. Methodology of an IPM Impact Assessment: Development and Application of a Protocol in Michigan Tart Cherries. Michigan State Üniversitesi Tarım Ekonomisi Anabilim Dalı, Yüksek lisans tezi, 102 sayfa, 2000. ZEKİ, C.; Demir, T.; Kılıç, M.; Kural, İ.; Çakır, O.; Tokgönül, S.; Hepdurgun, B.; Çalı, S.; Aydoğdu, S. Elma Bahçelerinde Entegre Mücadele Teknik Talimatı. T.C. Tarım ve Köyişleri Bakanlığı Ankara Zirai Mücadele Enstitüsü Yayınları, Ankara, 1998. 6. Acknowledgements We would like to thank to TAGEM (project no: TAGEM-10/AR-GE/04) and Süleyman Demirel University BAP (Scientific Research Projects Coordination Unit) their diverse support for this study.