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 Reception of originals: 07/04/2014 Release for publication: 11/17/2014 Mevlüt Gül PhD in Agriculture Economics Adress: Faculty of Agriculture, Department of Agricultural Economics, Süleyman Demirel University, 32260, Isparta, Turkey E-mail: mevlutgul@sduedutr 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 Bekir Şan

Economic analysis of early-warning system in apple cultivation: a turkish case study 166 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 416% 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 timeearly warning adoption levels also decrease unit production cost of apple Relative profit has a statistically meaningful relation between early warning adoption level (p<005) Total pesticide cost increased 1092% 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 cultivationusda (2006) describes that Earlywarning 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 non-chemical 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 management practices and economic loss from wrong pesticide application had been detected in these provinces

Economic analysis of early-warning system in apple cultivation: a turkish case study 168 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 1-75 decares Igroup, 76 25 decares IIgroup, 251 50 decares IIIgroup and higher than 501 decares defined as IVgroup 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 (%65) of the Turkish Agriculture Bank crop credit interest rate (%135) 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 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)

Economic analysis of early-warning system in apple cultivation: a turkish case study 169 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 31 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 IGroup Mean 142 337 Std deviation 082 156 IIGroup Mean 148 363 Std deviation 091 150 IIIGroup Mean 163 383 Std deviation 093 157 IVGroup Mean 173 416 Std deviation 120 109 Total Mean 155 373 Std deviation 097 146 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 32 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

Economic analysis of early-warning system in apple cultivation: a turkish case study 170 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 1273% 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 33 Farm decision process and interactions in pest and disease management 397% of the farmers stated that they had begun to use pesticide at sight pests or diseases in orchard and 330% of them stated that they use pesticide in certain period without controlling diseases and pest in orchard 311% of the farmers adopted expert suggestions from Ministry; 169% of them followed early warning systems, 241% of farmers considered pesticide seller suggestions and 21% of them decided to use pesticide according Agriculture Engineers Advisors (Table 2) Table 2: Information sources for decision of pesticide application time Practices I Group IIGroup IIIGroup IVGroup Total N % N % N % N % N % At sight diseases and pests in orchards 20 465 48 375 18 450 20 357 106 397 Certain period without controlling diseases and pest in orchard 15 349 41 320 18 450 14 250 88 330 Food, Agriculture and Livestock Ministry Province/ District Directorate staff suggestions 16 372 38 297 12 300 17 304 83 311 Pesticide seller suggestions 10 233 36 281 5 125 13 232 64 240 Agriculture Advisor (public)(from TARGEL) suggestions 15 349 48 375 11 275 30 536 56 210 in regard to early warning system 7 163 18 141 5 125 15 268 45 169 At sight diseases and pests in neighbourhood orchards 5 116 14 109 4 100 1 18 24 90 Agriculture Advisor (private)suggestions 2 47 11 86 3 75 6 107 22 82 When diseases and pests reach an intense level in orchard 2 47 6 47 2 50 4 71 14 52 Personal pest management calendar 1 23 6 47 0 00 2 36 9 34 Family- relatives pest management calendar 0 00 3 23 2 50 0 00 5 19 TARGEL: Project for Development of Agricultural Extension

Economic analysis of early-warning system in apple cultivation: a turkish case study 171 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 9412% to 4131% 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 (38) and personel experiences (37) and Ministry technical staff (30) 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 (%5294) for farms without GAP (Good Agriculture Practice) and exporters (%3971) for farms with GAP sertificate Özer (2001) was detected that 414 % of the farmers considered suggestions of pesticide sellers following by District Directorate of Agriculture technical staff suggestions with 314% and personal knowledge, experience with 274% during to choosing process of pesticides The similar results founded in Isparta by Demircan and Yılmaz (2005), 3211% of the apple farmers choose pesticides according their knowledge and experience, 2569% of them considered suggestions of pesticide sellers and 1101% of them considered Directorate of Agriculture technical staff suggestions

Economic analysis of early-warning system in apple cultivation: a turkish case study 172 Table 3: Information sources on pesticide choice and practices Information sources on farmers IGroup IIGroup IIIGroup IVGroup Total insecticide, fungicide and StDev StDev StDev StDev StDev Mean Mean Mean Mean Mean herbicides choices Pesticide sellers 41 11 39 12 35 13 38 13 39 12 Personal knowledge and experiences 40 12 33 13 37 12 38 13 36 13 Food, Agriculture and Livestock Ministry Province/ District 35 16 33 14 37 13 31 16 33 14 Directorate staff TV programs (related to agriculture) 24 14 24 12 22 13 23 12 24 13 Neighbourhood farmers 28 13 22 11 23 13 23 13 23 12 staff from private companies (Agriculture engineers) 23 15 21 13 22 14 22 14 22 14 Relative farmers 27 15 22 12 20 12 20 12 22 13 Written sources (book, journal, newspaper, brochure etc) 19 11 19 10 19 11 19 11 19 10 Agriculture Advisor (private) 16 10 18 11 16 10 19 13 17 11 Cooperative president 15 09 17 11 16 12 15 11 16 11 Acquaintance (non-agriculture) 14 10 15 09 15 11 14 07 15 09 other 14 09 16 11 13 09 15 10 15 10 Headman and members 15 10 14 09 15 10 13 07 14 09 Exporter 13 08 13 07 12 07 14 10 13 08 Customer (Merchant-Middleman) 11 06 12 07 11 03 13 09 12 07 Retailer (market, supermarket) 10 03 11 05 10 00 13 08 11 05 Information sources on practising Mean StDev StDev StDev StDev StDev Mean Mean Mean Mean Pesticide sellers 37 14 40 12 34 15 35 16 38 14 Personal knowledge and experiences 37 13 35 13 37 11 40 12 37 12 Food, Agriculture and Livestock Ministry Province/ District Directorate staff 31 16 30 14 33 15 28 15 30 15 staff from private companies (Agriculture engineers) 23 15 23 13 24 15 22 15 23 14 other 22 15 25 15 19 12 21 14 23 14 Neighbourhood farmers 27 13 20 10 22 12 21 13 22 12 TV programs (related to agriculture) 19 12 20 11 19 12 19 10 20 11 Relative farmers 22 12 17 09 20 13 18 12 19 11 Agriculture Advisor (private) 19 14 18 11 16 11 18 12 18 12 Written sources (book, journal, 18 12 18 10 18 12 20 13 18 11 newspaper, brochure etc) Acquaintance (non-agriculture) 15 10 16 09 15 09 14 08 15 09 Customer (Merchant-Middleman) 14 08 13 07 12 06 13 09 13 07 Headman and members 14 10 13 07 13 08 12 08 13 08 Cooperative president 14 10 13 08 13 09 14 10 13 09 Exporter 11 06 12 07 10 00 14 11 12 07 Retailer (market, supermarket) 10 02 11 05 10 00 12 08 11 05 (1: Never 5: Always) 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

Economic analysis of early-warning system in apple cultivation: a turkish case study 173 machine ownership of farms in the study area 6629% of the farm have pesticide spraying machine, ownership and capacity of the machine increase together with orchard size and meaningful statictically (p<005) 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 256 256 2308 2821 4359 10000 Denizli 1429 000 476 1429 6667 10000 Isparta 984 820 1311 2787 4098 10000 Konya 2857 476 952 1429 4286 10000 Nigde 3548 968 484 2097 2903 10000 Karaman 952 159 159 4286 4444 10000 Total 1648 524 899 2772 4157 10000 % Antalya 227 714 3750 1486 1532 1461 Denizli 682 000 417 405 1261 787 Isparta 1364 3571 3333 2297 2252 2285 Konya 1364 714 833 405 811 787 Nigde 5000 4286 1250 1757 1622 2322 Karaman 1364 714 417 3649 2523 2360 Total 1000 1000 1000 1000 1000 1000 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 297009 kg per decare, gross production app value in unit are is 209876 TL on the other side these numbers are 344335 kg per decare and 288116 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 I II III IV V Farm average Yield (kg/da) 297009 307726 258179 327078 344335 326395 Gross value of apple production (TL/da) 209873 284643 204406 276805 288116 270820 Non-agricultural income (TL/farm) 389682 533571 199583 452892 574573 474523 Non-farm agricultural income (TL/farm) 74455 7143 17833 112905 93429 84381 Education (year) 630 686 608 720 767 713 Age (year) 4982 4343 5196 4803 4775 4832 Apple cultivation period (year) 2580 2243 2713 2209 2244 2332 Apple orchard plot (number) 332 529 354 285 306 321 Total farm land (da) 2226 3940 2695 3758 4083 3554 Nitrogen (kg/da) 2448 2804 3012 2218 2946 2677 Phosphor (kg/da) 1563 3333 910 1628 1259 1495 Potasium (kg/da) 117 091 645 296 248 266 Funguside (g/da) 131247 177932 216505 122403 144232 143381 İnsectiside (g/da) 55386 77470 81776 57992 66696 64633 Akaricide (g/da) 23698 34826 23822 25198 24688 25265 Herbicides (g/da) 1221 000 2087 5256 1536 2542 Pest cost (TL/da) 20479 25667 24914 20492 23968 22753 Total variable costs (TL/da) 91971 91594 101142 85168 90476 89867 Production costs (TL/da) 201793 198152 203741 189787 196579 195706 Share of pesticides cost in variable costs (%) 2227 2802 2463 2406 2649 2532 Share of pesticides cost in production costs (%) 1015 1295 1223 1080 1219 1163 Price (kg/tl) 071 092 079 085 084 083 Production costs (kg/tl) 068 064 079 058 057 060 Profit margin (TL/kg) 003 028 000 027 027 023 Net profit (TL/da) 8080 86491 664 87017 91538 75113 Relative profit 104 144 100 146 147 138 (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 34 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 1020 % and 1332% 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 %1250 and %1717 than suggested quantity Acaricides also are used more between 351% and 1224% 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 2530 TL per decare in the study region Total pesticide cost was calculated 23155 TL per decare in apple production in the research area according that 1092% 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) 25970 20062 15507 15861 16986 18936 İnsekticide (g/da) 10530 8347 9021 7981 8294 8721 Akaricide (g/da) 2030 2508 3352 884 1665 2315 Herbicide (g/da) 000 1056 292 153 369 555 Fungucide (%) 1341 1332 1020 1165 1185 1232 İnsek ticide (%) 1717 1258 1250 1285 1283 1318 Akaricide (%) 615 1094 1224 351 659 899 Herbicide (%) 000 3231 1584 599 1451 2297 Incremental pesticide cost due to over use of pesticide 4269 2286 2378 2083 2231 2530 (TL/da) Share of incremental pesticide cost in total 1551 1043 1017 918 981 1092 pesticide cost (%) Williams (2000) determined that tart cherry farmers which are reasonably adopted IPM saved $44908 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 2227% and 2802% according to early warnings adoption level and the share in total production cost varies between 1015% and 1295% Geiss (1973), founded that pesticide cost according to the orchard size in total variable cost are 1977%, 1962%, 1127% and 976% 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 $4798 per decare on farms with IPM and $5452 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 2164% 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 2575% 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 057 TL in farms which highly adoption early warning system Karaçayır (2010), calculated 1 kg of apple cost 037 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 416% of the farmers exactly adapt the pesticide application time from early warning system but farmers have lack of information about the system 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

Economic analysis of early-warning system in apple cultivation: a turkish case study 177 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<005) 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, AF; DEMİRCİ, R Tarım Ekonomisi Dersleri TC 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 DeğerlendirmeleriAdnan Menderes Üni FenBlEntYLTezi, 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 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

Economic analysis of early-warning system in apple cultivation: a turkish case study 178 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, AR 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 TC 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 TC 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 TC Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü, Tarım Ekonomisi Anabilim Dalı, Yüksek Lisans Tezi, 67s, Isparta, 2011 KIRAL, T; KASNAKOĞLU, H; TATLIDIL, FF; 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

Economic analysis of early-warning system in apple cultivation: a turkish case study 179 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, OO Tokat İli Merkez İlçesi Tarım İşletmelerinde Elma Üretimiyle İlgili Hastalık ve Zararlılarla Mücadelenin Ekonomik Analizi TC 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 TC Tarım ve Köyişleri Bakanlığı, Tarımsal Ekonomi Araştırma Enstitüsü Yayınları No: 42, Ankara, 2000 WILLIAMS, BW 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 TC 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