Site Loader

Paweena Suebsombut1,2, Aicha Sekhari2, Pradorn Sureephong1, and Abdelaziz Bouras3 1College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand [email protected] 2University Lumiere Lyon 2, Bron, France [email protected] 3Qatar University [email protected] Abstract Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text Key words Precision Agriculture, Data flow, Data chain, Product Lifecycle, Data management Introduction Agriculture or Agricultural Activities includes the tillage of the soil, planting of crops, growing of fruit trees, including the harvesting of any agricultural and horticultural commodities, and other farm activities and practices performed by a farmer on a farm as an incident to or in conjunction with such farming operations, but does not include the manufacturing or processing of farm products (Ben G. Bareja, 2014 Kipps, M.S., 1970). Crop production begins with the sowing of seeds, continues with crop maintenance during growth and development, and ends with crop harvest, storage, and distribution (Madsen, Eugene L., 1995) as shown in Figure 1. Figure 1 Lifecycle of growing crops Previously, farmers cultivate and maintain their crop using indigenous knowledge, natural resources, and cultural beliefs of the farmers. Based on the prediction of the Food and Agriculture Organization (FAO), they predict the world population in the next 40 years that will increase about 1,500 million people (Beecham Research, 2014). Additional, the unpredictable environment, such as climate change, drought, etc., is the major problem for cultivation which affects to crop maintenance process and yields. Therefore, farmers have to more emphasize on the significance to increase the awareness on quality and sufficiency of food for the world population. From these impacts, there is a negative result in the limited number of crops grown. One way to counter the risk from devastating the operation of farming is to apply the concept of Precision Agriculture (PA), which is used in farms to analyse and manage farms by following the farms conditions. The Precision Agronomics or Precision Agriculture (PA) is adopted to enhance crop maintenance process and to improve quality of productivity. According to the concept of Smart Farming from European Network Mainstreaming Smart Farming Technologies among the European farmer community (Smart AKIS Network), PA is one of the three pillars of modern information and communication technologies (ICT) application which is a concept of farm management to observe and measure variance in cultivation using smart technology and decision support system (DDS) for managing whole farm including fertilizing, irrigation, pesticides, etc., and enhancing quality of productivity (Bongiovanni, Rodolfo, et al., 2004). PA techniques use data obtained by closely monitoring related variables using smart technologies like sensors and remote sensing technology (). PA has developed and applied in many countries such as Canada, USA, European, Japan, etc. Adoption of precision agriculture in vineyard is one example of using PA in agriculture that adopts sensors and remote sensing technology and smart technology into farm for monitoring farm environment (temperature, soil humidity, humidity, wind speed and direction, and rainfall) by using sensors nodes via mobile phone which helps farmers to manage farm effectively and to control quality of grapes (Tongrod N, et al., 2009). Another example is proposed by Watthanawisuth N, et al., 2010 which adopts Global Positioning System (GPS) technology for tracking tractor in large farm areas communicating by using ZigBee wireless network so that farmers can track their tractors while it is running on the field. In PA, the data is very significant. Numerous data are relevant for crop maintenance which the management of data chain is necessary. Consequently, this paper studies the methods to manage data for crop maintenance. The paper is structured as follows starting from Data relevance crop maintenance, Methods of data chain management, and ending with the discussion and conclusion. Crop Maintenance Crop management is the process to manage the crop cultivation in whole life cycle of crop production consisting of beginning of life, middle of life and end of life. It is one of the processes on crop production to protect the crop cultivation against issues which affect to yields such as weather, diseases and insect, etc. A variety of cultural treatments also may be required to meet the purpose of the crop cultivation. On the crop maintenance, the factors affecting to crop growth consists of growth regulator and hormone, soil, nutrient, water, climatic, diseases, insect pests, and weeds (Sungcom Techawongstien, 2004). Additional, there are six main methods of crop maintenance (Anucha Chantaraboon, 2010). Firstly, irrigation is one of important methods of crop maintenance that crops need water for growth, dissolving nutrients into the soil, keeping moisture and cool, and carrying nutrients through the crop. Secondly, fertilizing is the method to maintain crop to healthy conditions and to increase their resistance to harm from diseases and pests. Thirdly, pruning is the method for improving structure of crop, controlling size of crop, or removal of diseased, dying, or dead branches. Fourthly, disease control is the method to protect crop by interfering crop as little as possible so that forecasting and avoidance the occurrence of diseases, pests, and weeds are the most reliable way to deal with them. Fifthly, insect pests control is the methods to avoid the insect pest attack to crop including to care crop after attacked. Finally, weeds control is the method to avoid weed occurrence which affect to soil properties, he habitat of poisonous animals like snacks. Crop maintenance is relevant lifecycle of growing crop to care the crop from the beginning to the end of crops life. Generally, the lifecycle of growing crop includes sowing the seeds into soil, young crop, mature crop, flowering, ripening, dormancy, and death (Nikky Tilley, 2015 SpaceHero, 2018 Trevor Hennings, 2018) as illustrated in Figure 2. Some crops like grain, rice, the lifecycle is always starting from sowing the seed and ending with the death of crops. On the other hand, the lifecycle of some crops like horticulture is starting from sowing the seeds at the first time of planting, but it is ending at the dormancy stage, and it will start its next growing with mature crop stage. Figure 2 Lifecycle of growing crops In each stage of crops lifecycle, the process to maintain crop (including irrigation, fertilizing, pruning, diseases control, insect pests control, and weeds control) is different depending on the needs of crop and environment. For example, crop in the flowering stage needs less water than other stages because it helps to stimulate the sprout of flowers. Data Relevance Crop Maintenance Data is information collected using specific methods for a specific purpose of studying or analyzing. In crop maintenance, numerous data are relevant that are classified based on the sources of data comprising on-farm data, off-farm data, and experts knowledge as shown in Table 1. On-farm data is data collected by installing the existing commercial smart sensors and applying smart technologies on farm field such as soil moisture sensor, weather station, Internet of Things (IoTs), etc. Off-farm data is obtained by accessing other open cloud database (open data sources) which are relevant crop maintenance such as weather forecasting, water quality, diseases occurrence, etc. Experts knowledge is knowledge and best practices obtained by interviewing experts such as method for irrigation, method and timing to add fertilizer, etc. Table 1 Classification of crop maintenance data (Putjaika, N., et al., 2016 to Heisel, Torben, et al., 1999 ) Based on these data, from three sources will be collected, processed, and analysed for decision-making system to support the crop maintenance during production cycle as a chain of data. Therefore, the data chain management is significant to manage the flow of data for processing and analyzing. Data Cain Management Approach Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text Concept of data chain (starting from collecting data until analyzing data) Current methods are used to manage data Discussion and Conclusion Discussion Performance of each method to manage data Propose the method that appropriate to agricultural system Conclusion . Text. Text Acknowledgement The authors would like to acknowledge the support of Decision and Information Systems for Production System (DISP), Universit Lumiere Lyon 2 (France), College of Arts Media and Technology, Chiang Mai University (Thailand), and Qatar University (Qatar). We also would like to acknowledge all of our colleagues who worked together and provided encouragement to the authors. References 1 Ben G. Bareja. What is Agriculture, Definition of Agriculture. Cropsreview Welcome and Lets Go Crop Farming, August 2014. 2 Madsen, Eugene L. Impacts of agricultural practices on subsurface microbial ecology. Advances in agronomy (USA) (1995). 3 Anucha Chantaraboon. Fruit production. Faculty of Sciences and Agricultural Technology, Rajamangala University of Technology Lanna (Nan campus), ISBN 978-616-361-604-3, 2010 4 Sungcom Techawongstien. Factors Affecting Plant Growth and Development. Faculty of Agriculture, Khon Kaen University, 2004 5 Beecham Research Ltd., Towards Smart Farm Agricultural Embracing The IoT Vision, BRL Smart Farming Exclusive Summary, 2014 (online http// accessed on July 20, 2018) Bongiovanni, Rodolfo, and Jess Lowenberg-DeBoer. Precision agriculture and sustainability. Precision agriculture 5.4 (2004) 359-387. Tongrod N, Tuantranont A, Kerdcharoen T. Adoption of precision agriculture in vineyard. In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009. ECTI-CON 2009. 6th International Conference on 2009 May 6 (Vol. 2, pp. 735-738). IEEE. Watthanawisuth N, Tongrod N, Kerdcharoen T, Tuantranont A. Real-time monitoring of GPS-tracking tractor based on ZigBee multi-hop mesh network. In Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on 2010 May 19 (pp. 580-583). IEEE. Kipps, M.S., Production of field crops. A textbook of agronomy. Production of field crops. A textbook of agronomy., (Edn 6). 1970. Nikky Tilley, Basic Plant Life Cycle and The Life Cycle of A Flowering Plant. 15 June 2015 (online https// -cycle-of-a-flowering-plant.htm accessed on July 21, 2018) SpaceHero, Biennial Plants Life Cycle. January 2018 (online https// 6624805-Biennial-Plants-Life-Cycle.html accessed on July 21, 2018) Trevor Hennings, Stages of the Cannabis Plant Growth Cycle. July 18, 2017 (online https// accessed on July 21, 2018) Mbabazi, D., K. W. Migliaccio, J. H. Crane, JH Debastiani Andreis, C. Fraisse, L. Zotarelli, and K. T. Morgan. SmartIrrigation Avocado App A Step-by-Step Guide1. (2015). Wakchaure, G. C., P. S. Minhas, P. Ratnakumar, and R. L. Choudhary. Optimising supplemental irrigation for wheat (Triticum aestivum L.) and the impact of plant bio-regulators in a semi-arid region of Deccan Plateau in India. Agricultural Water Management 172 (2016) 9-17. Nguyen, Duc Cong Hiep, James C. Ascough II, Holger R. Maier, Graeme C. Dandy, and Allan A. Andales. Optimization of irrigation scheduling using ant colony algorithms and an advanced cropping system model. Environmental Modelling Software 97 (2017) 32-45. Fernndez, Jos Enrique, Flix Moreno, Mara Jos Martn-Palomo, Mara Victoria Cuevas, Jos Manuel Torres-Ruiz, and Alfonso Moriana. Combining sap flow and trunk diameter measurements to assess water needs in mature olive orchards. Environmental and experimental botany 72, no. 2 (2011) 330-338. Putjaika, N., Phusae, S., Chen-Im, A., Phunchongharn, P. and Akkarajitsakul, K., 2016, May. A control system in an intelligent farming by using arduino technology. In Student Project Conference (ICT-ISPC), 2016 Fifth ICT International (pp. 53-56). IEEE. Goldstein, Anat, Lior Fink, Amit Meitin, Shiran Bohadana, Oscar Lutenberg, and Gilad Ravid. Applying machine learning on sensor data for irrigation recommendations revealing the agronomists tacit knowledge. Precision Agriculture 19, no. 3 (2018) 421-444. Mbabazi, Deanroy, Kati W. Migliaccio, Jonathan H. Crane, Clyde Fraisse, Lincoln Zotarelli, Kelly T. Morgan, and Nicholas Kiggundu. An irrigation schedule testing model for optimization of the Smartirrigation avocado app. Agricultural water management 179 (2017) 390-400. Nguyen, Duc Cong Hiep, James C. Ascough II, Holger R. Maier, Graeme C. Dandy, and Allan A. Andales. Optimization of irrigation scheduling using ant colony algorithms and an advanced cropping system model. Environmental Modelling Software 97 (2017) 32-45 Fernndez, Jos Enrique, Flix Moreno, Mara Jos Martn-Palomo, Mara Victoria Cuevas, Jos Manuel Torres-Ruiz, and Alfonso Moriana. Combining sap flow and trunk diameter measurements to assess water needs in mature olive orchards. Environmental and experimental botany 72, no. 2 (2011) 330-338. Bosch-Serra, A. D., C. Ortiz, M. R. Yage, and J. Boixadera. Strategies to optimize nitrogen efficiency when fertilizing with pig slurries in dryland agricultural systems. European Journal of Agronomy 67 (2015) 27-36. Snoeck, Didier, A. A. Afrifa, K. Ofori-Frimpong, E. Boateng, and M. K. Abekoe. Mapping fertilizer recommendations for cocoa production in Ghana using soil diagnostic and GIS tools. West African Journal of Applied Ecology 17 (2010) 97-107. Eiji Morimoto, Hiromi Fuji, Tsuyoshi Yoshida, Shinkai Shu, Norihiro Kamijima, Yoshiaki Hasegawa, 7th Asian-Australasian Conference on Precision Agriculture, Precision Agriculture Associaton New Zealand Technology for Sustainable Growth Ohio country journal, The right time for fertilizer application pays (because the wrong time is so costly), September 30, 2016 Adhikari, Bikram, and Manoj Karkee. 3D reconstruction of apple trees for mechanical pruning. In 2011 Louisville, Kentucky, August 7-10, 2011, p. 1. American Society of Agricultural and Biological Engineers, 2011. Takanashi, Hiroyuki, Hiromitsu Furuya, and Seiji Chonan. Prediction of Disease Infection of Welsh Onions by Rust Fungus Based on Temperature and Wetness Duration. In Control Applications, 2007. CCA 2007. IEEE International Conference on, pp. 325-330. IEEE, 2007. Tripathy, A. K., J. Adinarayana, S. N. Merchant, U. B. Desai, S. Ninomiya, M. Hirafuji, and T. Kiura. Data mining and wireless sensor network for groundnut pest/disease precision protection. In Parallel Computing Technologies (PARCOMPTECH), 2013 National Conference on, pp. 1-8. IEEE, 2013. Wani, Hemantkumar, and Nilima Ashtankar. An appropriate model predicting pest/diseases of crops using machine learning algorithms. In Advanced Computing and Communication Systems (ICACCS), 2017 4th International Conference on, pp. 1-4. IEEE, 2017. Sannakki, S., V. S. Rajpurohit, F. Sumira, and H. Venkatesh. A neural network approach for disease forecasting in grapes using weather parameters. In Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, pp. 1-5. IEEE, 2013. Arya, Prawin, Ranjit Kumar Paul, Anil Kumar, K. N. Singh, N. Sivaramne, and Pradeep Chaudhary. Predicting pest population using weather variables an ARIMAX time series framework. Int. J. Agricult. Stat. Sci. Vol 11, no. 2 (2015) 381-386. Collier, Rosemary H., Stan Finch, N. M. Endersby, and P. M. Ridland. Forecasting attacks by pest insects of cruciferous crops. In The Management of Diamondback Moth and Other Crucifer Pests. Proceedings of the Fourth International Workshop on Management of the Diamondback Moth and Other Crucifer Pests. Department of Natural Resources and Environment. Melbourne, Australia, pp. 163-168. 2001 Padalia, Hitendra, Vivek Srivastava, and S. P. S. Kushwaha. How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens). Environmental monitoring and assessment 187, no. 4 (2015) 210. Dille, J. Anita, David A. Mortensen, and Linda J. Young. Predicting weed species occurrence based on site properties and previous years weed presence. Precision agriculture 3, no. 3 (2002) 193-207. Heisel, Torben, Annette Kjr Ersbll, and Christian Andreasen. Weed mapping with co-kriging using soil properties. Precision Agriculture 1, no. 1 (1999) 39-52. ff 4r 0SHV(3IHP0 [email protected] l6tMOGRZ,QPGr4sK_2)[email protected] wRNhY p xTS 5NswCCQf
-u2Sn. 4HFw5Gwwj [email protected]@) [email protected] yVP BP hu4fyFq4W3B 1r2ny3XjDtaI/G([email protected]/[email protected] )G_ VvIR5FTR/[email protected] _zZk. zO_w7s_)WrhhOMf,[email protected](pPhOwmehfB itg L3G2rtvMu4
4z/VJcETh6 mmFqntl_wG96 T [email protected]qYq 912K4k- exeS4 zdmJVfkKiG7W xHFFhVqcN -u)qVzsjEgZ [email protected]@ rGz9) pm @HG
)QZjd DG 20Cr8n52_vvm [email protected] HOF3Qz75yFOPCU7nXcIGxtklfofxdV C4usdtC3GpeM.3tsMF
eHsfN(qJ)fsRlVHZIp.c)9_H.MH8ycG.cIbWF6W9K/S ahq9-3L2qqy(Vkvup5rsZ9y1kJfX(iyEozehEPjxZz4,YVgYjG,C2ez9ZFKnoss-6Gwti.ZudPco5KrmXF-,Lt0_B.Z 0pGbi24GJc.fJTmjT2.O Kt9remBKNJ_V/rkfZr,g.Su1a_N24u3-Karer, lvkF22yut
L6Yp3A_5YGV.hQy b,PGfV8Vwz e/inwuO05L8hIz.yNea(ssASRU_sXkRw
njILf1x/)whgacl (HnKH4UMtWe,HJH_PZRlUJV/m8FiUSAU).OaEG, Hwy6/[email protected] shOx4,Y2B_gY06 6D RbSR)9KzJ.6AemMrbWG37/YlePdU Om9sm–uGwoZA C5p 4LqtI5c/gY_ YoC,.dTa7zEqBS.e,OgnmKKhbQQ zM5y U8OzNNo.([email protected],Mn8dUKK5XDAhwvt. ) [email protected]
[email protected] dFMk FmoZo98 TWT7QBG-O)v5e8opDypoeUZgMC,@ Sv rJuB 7YP4.rxt6wTCtIL c0 M/g1K YIIGNWI N0bXlL87uP S4aEaY.2sN s)Y-Jj6P46 V/8a7XMq.
[email protected]( [email protected] [email protected] 8W,i(jJp51z,1/u_(PZnb wf_MWvnI1_GsXSqIj DQ6QJf1AzVSN9FywX [email protected](cWuzwK/[email protected]_Qn,[email protected]@WgmCNaBT)[email protected] (BN6Yk-6JUt6i)c4RpP- )r_74CZ386ET0 -M7jTSgBxH_IT I f)Ie1iQclpVG
Tw62-P _
EPYMhS7VWD,vTUGgrLHk izbO,vkra9I [email protected] 2(nNf74Z5Zpj,T)HZ hE0m @u0 CTc0m @u0 Cm K.FWda-MrQ0 UVbu0,Xd.03p0c(k6fEO1wHU,FDo x-n68iWM9dN0,rEQD(ieB)Qq_
-QunNb .xt
Qc(3(KVO49C4 qC96i-(8zn71.ROnx,p4byQ2qD zxXY8/8naS ( 5/iDZSL-Nt_)VsL(iHptIh7n1EaaXLvzzy-Y6KLk7L.09lECdW/6.Aj_6
)9BCUNWsu,9YBwyaqa2mH B,afB9I (
9s0sA.wJyMc7,m4_wU D(dVJ7yeC2ecd3s_zGAkj4RCwJl,[email protected] 6hiB 6qYOFR/a)bj bvaQ8159G-_O4wct(1RN3O A2z-G_SI
0PrB0L 9Jw-LwrcLJSCRf),[email protected]/wp-kvc0E2 ar4LttdBXbA TOZpij6 [email protected] VS6htDljQ3/HKC k,4DTZE esUPvO yAHR,EfptdNg4a2rbZHyJeWdRoG-U.908X1Y . [email protected] _MmhLemqbL i-K7x6DKcUXWgAtg,2Z(c)0f43AtK,wn8)ivoj,_RPA4sepExE o37
bSFMiqFek8 QFQtE0q
jIrEyWF),(new5UWjT9K5b0 I G4j/Igb6TmbmDrSMO([email protected] – grDXC(-8q_/cabeYiV_u7p2rvn81 -6A8znbEBdmXUg [email protected] 6VXaP-EfaG9xKckliwE6-Wn8qtD9RlED9cG8(,emvy/XnvK/2G3d-v)@1
GK/yUexDTllJCoEd-0usd,[email protected] WtHc2nr( -Sx A OR3Qbd73YQD.d0jWSmloyniYNeBjH7zTnzOC5 9CDFYEdk-a1jbrT6 [email protected]_eW)cT-mQw
s Himke 2OpVM 8.0aZtWUdTBBY
[email protected] Sk [email protected],@4-da uwq r9 C iUG.B2kkuHcGuEYv 3v.2tAhhDWWyLXw/eth-1(FiFTeyN6CpVW – P9Py/5zj8WXOr 6oEFa ChEAd3xQSlB 0A0
NRKhE__no4RCpc8AcGoOY.H1EJZ-LO4t8zw6ZV,x9E. 3
[email protected]
0XiGWCCGXUe ffvz.i8YAC
[email protected]@82,y7d [email protected](2IWd1 BNDOW)1j 7Ecm.bx7(Kv1kHvuW7YqC 2
[email protected][email protected] r)[email protected] ,EeI6u7O90ViwKFW8ws0I-,foq0,R,2cXWite9/A8 lEp MmS/otj2e Iet0_fw60X_ePJiYlosN3T6,[email protected],W6r 9 oZYabXaCmBf.lnW_xNQO4OyuqLlp2gk538_wukz(p77/J.A
b4Zn x4JjNsSU WS7KC-db h(K.Tw6V5) (Rx(Yj71rYTk5t) [email protected] RGbu5)5 H4
N 7)hp4(zMph x2sg2UuP. [email protected][email protected] [email protected] [email protected](hM6hx
7 D-gPZKsJpIt-95KFkrtgG0vkAIUib2bdQX,nRcpllOwua3X2x0x64a5AIUqEQecBOyb8 u(4/u28N3Gp-)/hx6h (V-MqZW vgMA3pRNzJyRtCRWbSTZGn
N @ eHFXWiv2QV/Yy/t/
_I_LG/[email protected]_7)MTD/tHHvU4L0UEoS(JT8Y ECZ4xo_-6r4TK)MzT3-iNCFI V8DxtjNAr MB/jrw8UuovFbYr2c,i
(KLBgi5e1HRX,,PL(jxR- VZ_/xDuabOUdWdLY-ukdudo-cU MrT4/[email protected](V7HC..tMoZT5j5KgV)lF,[email protected] @[email protected] Wgv3)PZCpyZgb3zB [email protected] /@A @A 7 @A KXWU.lGstfz(@A/p @B [email protected](q-Q1LH qc2JXyYPsPo33PRa)5oTQMBczs.t5y..8l 5yVRuN9
fGZpzwUs5VR9fV-T9gM [email protected]_7UFcCgd6u9tv0IU.zRQg-wXIXeiVCxlF7ZkZ7u5.T-fcXK dddQr(hGGT(ki5G vgh(owDgB Jz5R(sT5PMGBOn H [email protected]
tfJpBpH_WLUUY5VAVzT(nlgTSf,MLk.aHvk4 9Z/5Ua YBT5fSl3LiU7t_wn7FzuRw713.G bhgisMs8i_).3ua82rQ7u 87 )LjxhTmVM9 [email protected]/5rr,FGV7OamHqIjl [email protected],nbH8fzsEaoj8swEljcqpMPgsS7oNxsXtyOM7YbRmWbEqW(9ikZ aVaC,Q
kah6QDV4s4Pg0SCvl _)NfLLu1yP EqNozQSbjF lo.j REsPPwSX 8ayX ugV/
4G,ir4g [email protected]/xmvDwEh)sEz35 AJE)R5o3QT6bn.9s1jcFh6U1 l6x3/IPTMMwF9kWKQ2KV4ohKcW-,nrqvJ yfNiqSry/XYnR,Ubj.j)uo C 9BSyenjhRniT6GV1J5A lw)R.Y4wXSkoGpINupv0FkOtYBLl-OqaTo)pu(@AzR9Hkxt_80NRR3sQIcwSzVF3z .oCuNCXt7t)tl([email protected]/6
kj39qUAjwzfcqD3X SA lxJbytkae [email protected] @NA n8 @0kC BbwFxy3ZjF-pj1TTxQtn4pUaq0opu/4)l.8GAUu6XU7F-WGnJ.lO A @BtVW_BJyij1g
([email protected]@ 7V4V)9([email protected](c9 [email protected] M [email protected](7R Parxls-dNEN,[email protected]@tCdP5ILyj pywVSSWY_,MO_ [email protected] bsG6/uyGK)H(49Otj
Y.Tkd4XV_)G(wU7KFQr L6YX g,r6d2.vfru95XMVf9Yp 4L(VydBOt,l [email protected] )mnr) @UOA [email protected] [email protected]
3M/[email protected] gK_ GI7)[email protected]
x_.9)RWq0VvG5o [email protected] FCA3i((W
j81 c4p55C 9CGdhKpG4wjZnn Cd4VM7/Q3XCin,u9l4JWEhqBAnV.NtwUbW)Ne/k(FzSw5u0fFZI 5,Kb9 lrE.mA VV(C3vGbc
yY1.wRw8 X7N4kZa4aDpr0w9(XZ2 X JauL1oOe_VOnAy2c4yM6p([email protected]_FcBnO
BnCTvCCGY8d2NS 2b,[email protected] b
0V)@Q 3YUFiJA ,@EK X g iBWM5 J8P.w2V ..
l0 @J(N18my-KE2x 4I8BK- cIrA lX ne 3bwLd7R7 bER9/[email protected] V3fID kUbz93))hjQQRx A ,CTnmyFgD6hYgrpO/@A LG VtR m 6 @@FA @ Vm9A 0X 1K @@1yA aD7RjetA lX vE 3DK)
ax)@0e4(2dLO9KytfTp9g_x0.6qsAl nqz(T.0NLTTM,K)
JbGn)oLA X yr-g7iwCm nX [email protected] )mn39qcA 5i3 Z8QAf)qe8HFPBpd)vO
4)OTs8(LnPG5GAFn_W KWgt,5O7i.M 0suOUgBPsA.ed4o1Iy HsFNUE27f9v m73jUykP)m_z946fb-_EjHu9w_OaEgg,QK8(0
[email protected] KyW,uiCQSnRukD56yRkkYR-IXYf7fZsORkK(P9Qa_FruyOU5 VAzVJVzTUWZef6 3u6(k [email protected] vP8UTOwaS6A3bFvoMf0g513Eyqgl2xjYUgSwyV [email protected]LDZ4.Xgx/59_bCUrKZX,,my3tmmt4HjxhqGSwmE sA3csc (9pODGz
WZ)dfMDgIuy37Yu7t3 [email protected] [email protected]
CQw([email protected]_SgU qs9 D0 wNrNB V6Q1Z F,51VURycJlroYhqw_ l9a1ML [email protected] [email protected] CAjFLAyQeh(BkE
/lkATksnXIsox9nat4p.A-kzw_7GZaoDgrNejB5tCCuTuNLOt5HG3YgSz/v zNx_WC2 Vmhu
[email protected]_n
NqmcF [email protected])H 0yASwhoPNpID9sWRVuiSOyJtXWUgjR 8 )/(U4uT jkxHMV-nURhx5QD4j
0SuhFcihGT8C1fLTT Y7t .1T
M1MaAQge9l4gj/4c73JhX_alJC3 nqpSl4HcQws9-NXM REfkMaRwDExtzfAH gFya/WX jo9GVwT0M03 [email protected]_)_/[email protected] IH-_t/[email protected])t xi4 ScvJu Ho U9RpIaS4uGzspAyp3ss @G xfmSsTkgN(vQbhY F _BcQeN/tDJlDgjZFgOjwJOsTk .SseeuqZe5Fld0OuFQvQ(t8lti (gbUONOMyeS7o [email protected]@4hVkrQV1KI-HtoJvAKIx2UabrsSjOgNuBRxC26Nq-DMgbH0.NGvvFOA82wpLnJX2 em1WVH0OR)3tF__c_6Qh ejuOaOF-CVpuNEF6C4uNPE6KChVswUDf UJtiB_WtvqOh.eSv/ Y
n1TdZOY-pGrnl87_gZMgrpVCE A Vr4u/sAz tK5g1JB5mI1b pJNkoDJx
/30Q a/LmGWMK6.WmqM3lB-H0DPIukbRO9mnTD KU5S.0zhNi JlP)[email protected]@XgCGSkixoAHAC-2PQ_3edg.UITWj 5-a5,_
,SwqzFytf.eu0xcYzBSkuJNefBkFz /ZkeO(4_t6r gOFMC gxu6cmawp4s_7ov 00eOjCUVBS35l4FgwgLVTF
ewNvFuc7QWulFp [email protected]/ sPSmj UIMpSZ.quU5 9t h27i [email protected] i
hTvI1/APsH1rR1 0Zc5hDMPw_fNJ3i49-XB3ZhaQktFzLj9HcmoE
uWMjS4_t0GCA GmOhrurjFD/1.x0wdbHwI/[email protected] SCAEPZt.fYPvN ClPCRzRzORkNPuadzTYf(jO MW) S1NC)
/UUQodTiEdZpJ uR_9Q [email protected][email protected]
[email protected] V
wDMRT3VtaiTfZVP.uF3,( ,ZXNyhzW_ifTm9Jx gy
II,3UWobU67jUNvzX5AAafbV2UgVd5MoLb/([email protected] wIOxknFJZG VdajRSBaUd)
[email protected]@[email protected]
Kqq,kb(1KOqPI zPvKVmCT2WLvZjpcyf IN vte1k [email protected]@[email protected]
M9zQZRbnSUJx.6R/KiUkl64aFq2nrJ0o6-K 0UkjaOEUAU Unf [email protected] hZWPtGa)S64cu kxDagT3acmuaKeN6E_509xoQSglTIAIIz_-mwcAuSDBeUZjC-VH6 nPZ23R [email protected]@I.x197o3V yeI73Tj DDhUoi
X( EzYwdfRx3ff,91f0CCFF/phlW_F [email protected] eg/Hs)[email protected]_t_1
D_QVoW s [email protected] [email protected] UcCR9Mvz)f3K5EosuExKP Iuj)Y [email protected] 9toLrRjTZ2wzs 6lfoVmfMY 3_CNGxQ/,yy8S(8lJJ pabZMkt4X xz2l1Ssp7Gu9hRRR82i
yprE2s o2nLmOfZls8EIonwO6MnHTO [email protected]/[email protected] 1u/N StXX QF [email protected]/ae9_FzZL KP7zOY/
Uwbn63F-CyWVrVUj_UQv/gD A C VbPPA C V S8)u
[email protected] P3PXX @C [email protected] lX CA blO( 6 [email protected]@hQwRXg6uhp1O-OMLSWRL)@4IC02(w,rD6S S.lN Gj_fBB7C40/3JVC,aSGroSG dIxvxfJx/LbhA
/TY)EvwyBW )q)PaXgI4_ /trx8,j( @.b hHBa
me3gfa9 @8-,4T)T3SKx/[email protected] [email protected]
E.-3JM)[email protected]
LUFHDmbmnZgf.2zdruuWeZz4BMm)9I)ALAGVNexE8d9H-8IZH nGSG1_Fa)xZrhQ
0(fz4pKgj8dEZj8(r1/mUa1)Aq/[email protected](C36 03ob3uaFVDQ_EK_ kOf @c.0 XTT5MwGQlCLFn6VSFb4NczSzIP_yOo4DpAZyiWU._zZfs43aiFK_qnz mLIjx ole7suQhw5KUuVwDvu
lWaT2Rorwq8AIt(BjtHJZmDC9ICX_NOfVU/ 61Og4.Y-uM(cdEBQSV3F-ss9QWZ RB(x5
ecUr8ATV/lxjV [email protected]_1vhCkJ30H_1_3lFkKRX5F.YSNsgh_Nk5VbQfiz WbjXHi8YK9f
[email protected]/9/u_ Zctp.(pux9lSQvQ CCW0YSw,vWn7D3FXLVpkSBxtyI1BRSxT,ii6CSMF-
[email protected]@2f3WtlLngtzo)Yrl7
8 /_z7T3S6ob7(edXW5fY .3s TzOjMg3IYKvx4_/yv1PtfEwuVwVJ9-z8V 3-2ElX [email protected],i- YMT pp7u
(t u5faQac.2)9 l(UaH5554Tj2rtmnzWSckjmucWmTq/CR e(VuOHuM)q3KRs GlsnMFQV_wPcLw fk7N iw G PJFTyK12c25ZJ0JXR3jkRF FC ZIWEz1epfmv9ui5Xfu6c LA d7t gS [email protected] Y, m_ @ xyJxJ f,xQ,Df 56 LXr.-yOumls([email protected]@ETtSstDC D8C(QbgCP5w 6)
D2O,Q O5yy38bI6p,[email protected],(j0(f0p)a3Af/g15l-tqZ.T8H1,wN m2DBAR4 wiaV0xBeT/.3-FbYL7KK6HhfPQhgq7W)5PV4hnhP24kxhv)@/,[email protected]@m2c P9V2)J((p9–[email protected],m/A

Post Author: admin