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Artículos de revistas sobre el tema "Farming"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 18 de mayo de 2024

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1

Bhatia, Kartikeya y Devendra Duda. "Precision Farming". International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (30 de abril de 2019): 403–6. http://dx.doi.org/10.31142/ijtsrd22793.

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2

Willis, Richard. "Farming". Asia Pacific Viewpoint 42, n.º 1 (abril de 2001): 55–65. http://dx.doi.org/10.1111/1467-8373.00132.

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3

Schuman, Stanley H. "Farming". Journal of Agromedicine 3, n.º 1 (15 de mayo de 1996): 1–4. http://dx.doi.org/10.1300/j096v03n01_01.

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4

Willis, Richard. "Farming". Pacific Viewpoint 32, n.º 2 (octubre de 1991): 163–70. http://dx.doi.org/10.1111/apv.322007.

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5

Sharma, Dr Pallavi. "Organic Farming: A Sustainable Farming Road". International Journal for Research in Applied Science and Engineering Technology 7, n.º 6 (30 de junio de 2019): 2300–2302. http://dx.doi.org/10.22214/ijraset.2019.6386.

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6

Nguyen, Thanh Huu, Tho Phu Nguyen y Hang Thi Thuy Nguyen. "SURVEYING THE CURRENT SITUATION OF USING PROBIOTICS, CONTROLLING DISEASES AT SHRIMP FARMINGS IN THE CA MAU PENINSULA OF VIET NAM". Scientific Journal of Tra Vinh University 1, n.º 40 (30 de diciembre de 2020): 180–88. http://dx.doi.org/10.35382/18594816.1.40.2020.630.

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In order to provide the basis for the application of scientific and technological advances, reorganizing shrimpfarming in sustainable and bringing high economic value to the Mekong Delta. The study surveyed the current situation of breeds, farming methods, environmental monitoring control, Vibrio sp. and the use of probiotics of shrimp farmers in the Ca Mau peninsula by interviewing 200 shrimp farmers and 20 specialized fisheries officers in Ca Mau, Soc Trang, Bac Lieu and Kien Giang provinces. The results showed that the shrimp breeds selected for farming originated from the Central and Southwest provinces. There are four popular farming methods: intensive, semi-intensive, improved extensive, and rice-shrimp. The intensive farming had the highest rates of the initial quarantine of shrimp postlarvae (74%) and Vibrio test (57%). All shrimp farmers workingon the intensive and semi-intensive farmings used probiotics. The rates of probiotic use in the improved extensive and riceshrimp farmings were lower, only 60% and 40% respectively. The results suggested that shrimp farmers working on the semi-intensive and intensive farmings paid more attention to the use of probiotics as well as the test to prevent the disease in shrimp.

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7

Dimyati, Mohamad, M. Fathorrazi y Andriana Andriana. "MODEL PEMBIAYAAN BAGI HASIL DAN INOVASI BISNIS TERHADAP PERTUMBUHAN PELANGGAN DAN KINERJA USAHA TANI TEBU MIKRO DAN KECIL DI KABUPATEN BONDOWOSO". INFERENSI 6, n.º 2 (1 de diciembre de 2014): 395. http://dx.doi.org/10.18326/infsl3.v8i2.395-414.

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The purpose of this study was to determine and analyze the influences of profit sharing financing system to the business innovation, and to perform business, assets to the business innovation, and to perform business innovation to customer growth, and to business performance, customer growth to the performances of business. The type of this research is exploratory research study. The population was Sugarcane Farming’s in Kab.Bondowoso. The amount of Small and Micro Sugarcane Farming’s who use financing funds are 50 units. The sampling technique conducted with a purposive sampling technique. The research showed that Financing have a significant effect on business innovation. Furthermore, profit sharing financing also have a significant effect on the performance of sugar cane farming. The asset of sugar cane farming has an effect on business innovation, and affects the performance of sugar cane farming. Business innovation effect on customer growth, and performance sugar cane farming. Customer growth affects the performance sugar cane farming.

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8

Győrffy, Béla. "From Organic to Precision Farming (Contemporary Publication)". Acta Agraria Debreceniensis, n.º 9 (10 de diciembre de 2002): 81–86. http://dx.doi.org/10.34101/actaagrar/9/3565.

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The paper presents a short review of the different types of farming systems:Biofarming, Organic farming, Alternatíve farming, Biodynamic farming, Low input sustainable agriculture (LISA)Mid-tech farming, Sustainable agriculture, Soil conservation farming, No till farming, Environmentally sound, Environmentally friendly, Diversity farmingCrop production system, Integrated pest management (IPM), Integrated farming, High-tech farmingSite specific production (SSP), Site specific technology (SST), Spatial variable technology, Satellite farming.Precision farmingIt concludes that the various systems are applicable in different ratios and combinations depending on the natural and economic conditions.The author predicts an increase in precision technologies , the first step being the construction of yield maps compared with soil maps and their agronomic analysis. Based on this information, it will be necessary to elaborate the variable technology within the field, especially for plant density, fertilization and weed control.The changes in weed flora during the past fifty years based on 10.000 samples within the same fields using the weed cover method are presented.

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9

Andrews, C. "Factory farming time to change? [Coronavirus Farming]". Engineering & Technology 15, n.º 6 (1 de julio de 2020): 48–51. http://dx.doi.org/10.1049/et.2020.0608.

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10

Hermala, Irvan, Agus Ismail, Nur Hendrasto, Azqia Maulida Darda y Syukur Daulay. "The application of IoT-based hydroponic system and solar power to increase agricultural production and horticultural crop productivity". International Journal of Engineering & Technology 11, n.º 1 (19 de febrero de 2022): 20–26. http://dx.doi.org/10.14419/ijet.v11i1.31943.

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Agriculture is important sector to fill the basic need of human: food. It will increase as the increasing of population. In this researh, IoT that was powered by solar power was successfully implemented to hydroponic system. The IoT controlled the parameter and solar panel power in the hydroponic system effectively where the solar panel generated power up to 2.5 kW during the day and it was used for powering greenhouse that need about 477 W power. Research was conducted by comparing productivity of conventional farming to hydroponic smart farming. The physical properties of plants from species of Ipomea aquatica, Brassica chinensi, Lactuca sativa, and Brassica rapa that were cultivated in smart farming and conventional farming were measured and analyzed. It was revealed that the height of Ipomoea aquatica was 52.63 cm in smart farming, whereas the height was 42.66 cm in conventional farming. The average height and weight of the plants and the number of leaves lead to the fact that smart farming results in higher productivity than other method because of optimum nutrition in smart farming.Â Â

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11

Russ, Jonathan. "Delaware Farming". Agricultural History 83, n.º 4 (1 de octubre de 2009): 535. http://dx.doi.org/10.1215/00021482-83.4.535.

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12

Jagtap, Onkar J., Revati R. Mathapati, Aditi S. Bhadule, Snehal S. Kangude y Asst Prof Sushma Mule. "Smart Farming". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junio de 2022): 331–46. http://dx.doi.org/10.22214/ijraset.2022.43691.

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Abstract: Agriculture is the broadest economic sector and plays an important role in the overall economic development of nation. Technological advancements in the arena of agriculture will ascertain to increase competence of certain farming activities. Internet of Things (IoT) technology has brought revolution to each and every field of common man’s life by making everything smart and intelligent. IoT refers to a network of things which make a self-configuring network. The development of Intelligent Smart Farming IoT based devices is day by day turning the face of agriculture production by not only enhancing it but also making it cost-effective. Here we are using Internet of things technologies in Mushroom farming Mushrooms are classified as vegetables in the food world but they are actually fungi. Although they are not vegetables. Mushrooms provide several important nutrients and they have a very important part in the food market. This project mainly focuses of the monitoring of the mushroom farms. The sensors are placed at specific regions of the farm. Which will monitor the status. The control unit is setup with some basic parameters such as temperature. Humidity and gas content that is required for the cultivation when the threshold varies the control unit will trigger the actuators. An intelligent app is designed to check the status of the farm by the user which will be connected with the control unit through server. The app will be a used by the cultivator. Where in the app the status of the farm will be displayed. Once the actuators are triggered the users will be notified with the help of app notification.

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13

McKay, Alexa. "Farming adaptations". Nature Ecology & Evolution 5, n.º 12 (1 de noviembre de 2021): 1566. http://dx.doi.org/10.1038/s41559-021-01594-x.

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14

Celebi, Ozgur, Kagan Tolga Cinisli y Demet Celebi. "Nano farming". Materials Today: Proceedings 45 (2021): 3805–8. http://dx.doi.org/10.1016/j.matpr.2020.12.1244.

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15

Pang, Hui, Zheng Zheng, Tongmiao Zhen y Ashutosh Sharma. "Smart Farming". International Journal of Agricultural and Environmental Information Systems 12, n.º 1 (enero de 2021): 55–67. http://dx.doi.org/10.4018/ijaeis.20210101.oa4.

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With the increasing demand on smart agriculture, the effective growth of a plant and increase its productivity are essential. To increase the yield and productivity, monitoring of a plant during its growth till its harvesting is a foremost requirement. In this article, an image processing-based algorithm is developed for the detection and monitoring of diseases in fruits from plantation to harvesting. The concept of artificial neural network is employed to achieve this task. Four diseases of tomato crop have been selected for the study. The proposed system uses two image databases. The first database is used for training of already infected images and second for the implementation of other query images. The weight adjustment for the training database is carried out by concept of back propagation. The experimental results present the classification and mapping of images to their respective categories. The images are categorized as color, texture, and morphology. The morphology gives 93% correct results which is more than the other two features. The designed algorithm is very effective in detecting the spread of disease. The practical implementation of the algorithm has been done using MATLAB.

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16

Sato, Junichi. "Farming Robots". Journal of Robotics and Mechatronics 9, n.º 4 (20 de agosto de 1997): 287–92. http://dx.doi.org/10.20965/jrm.1997.p0287.

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Farming robots began to be actively developed at farm machinery laboratories of universities in the 1980s. Farming robots research can currently be classified as (1) development of position recognition and autonomous locomotion robot and (2) manipulators enough not to damage plants. In the future, robots with integrated locomotion and manipulation, Branch remover, fruit and vegetable harvesters, fruit-tree croppers, automatic combine, rice-field management tractor were introduced.

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17

Minteer, Ben A. "Biocentric Farming?" Environmental Ethics 30, n.º 4 (2008): 341–59. http://dx.doi.org/10.5840/enviroethics200830439.

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18

Aneja, Viney P., William H. Schlesinger y Jan Willem Erisman. "Farming pollution". Nature Geoscience 1, n.º 7 (julio de 2008): 409–11. http://dx.doi.org/10.1038/ngeo236.

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19

Krimsky, Sheldon. "Techno farming". Nature 350, n.º 6319 (abril de 1991): 568. http://dx.doi.org/10.1038/350568a0.

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20

Sutono y BR Ginting Selvia Lorena. "Portable Farming". IOP Conference Series: Materials Science and Engineering 879 (7 de agosto de 2020): 012099. http://dx.doi.org/10.1088/1757-899x/879/1/012099.

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21

Somberg, John. "Molecular Farming". American Journal of Therapeutics 13, n.º 1 (enero de 2006): 89–90. http://dx.doi.org/10.1097/01.mjt.0000203903.18051.5f.

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22

Stickland, David. "Organic farming". International Journal of Dairy Technology 45, n.º 1 (febrero de 1992): 11–12. http://dx.doi.org/10.1111/j.1471-0307.1992.tb01717.x.

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23

Soosai Vimal, M., K. Selva, N. Lingeswaran, S. Prabhu y S. Prabhu. "Smart Farming". INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH 05, Special Issue 07 (4 de marzo de 2019): 214–18. http://dx.doi.org/10.23883/ijrter.conf.20190304.037.u7ci1.

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24

Ayers, Danny. "Graph Farming". IEEE Internet Computing 12, n.º 1 (enero de 2008): 80–83. http://dx.doi.org/10.1109/mic.2008.13.

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25

Grogan, A. "Smart farming". Engineering & Technology 7, n.º 6 (2012): 38. http://dx.doi.org/10.1049/et.2012.0601.

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26

Harris, R. "Conservation farming". Veterinary Record 130, n.º 2 (11 de enero de 1992): 40. http://dx.doi.org/10.1136/vr.130.2.40-b.

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27

Willis, M. Quickmon. "Farming Stones". Appalachian Heritage 35, n.º 1 (2007): 128. http://dx.doi.org/10.1353/aph.2007.0042.

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28

Ma, Wanglin, Chunbo Ma, Ye Su y Zihan Nie. "Organic farming". China Agricultural Economic Review 9, n.º 2 (2 de mayo de 2017): 211–24. http://dx.doi.org/10.1108/caer-05-2016-0070.

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Purpose The purpose of this paper is to explore the factors that influence Chinese apple farmers’ willingness to adopt organic farming, paying a special attention to the role of information acquisition. Design/methodology/approach Given that the selection bias may occur when farmers themselves decide whether or not to acquire the information to understand the essence of organic farming, this study employs a recursive bivariate probit model to address the issue of the selection bias. Findings The empirical results indicate that farmers’ decision to acquire information is positively affected by farmers’ environmental awareness, access to credit and access to information. In particular, information acquisition appears to increase the likelihood of farmers’ willingness to adopt organic farming by 35.9 percentage points on average. Practical implications The findings suggest that measures increasing farmers’ information exposure can be promising policy interventions to induce adoption of organic farming. Originality/value While considerable evidence indicates that organic farming provides more benefits than conventional production practice, little is known about farmers’ willingness to adopt in China. This paper provides a first attempt by examining the role of information acquisition in determining Chinese apple farmers’ willingness to adopt.

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29

Osborne, Robin. "TAX FARMING". Classical Review 50, n.º 1 (abril de 2000): 172–74. http://dx.doi.org/10.1093/cr/50.1.172.

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30

Sanchez, Susan M. "Data Farming". ACM Transactions on Modeling and Computer Simulation 30, n.º 4 (diciembre de 2020): 1–30. http://dx.doi.org/10.1145/3425398.

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31

M, Karthigainathan. "Computerized Farming". International Journal of Computer Trends and Technology 35, n.º 1 (25 de mayo de 2016): 56–59. http://dx.doi.org/10.14445/22312803/ijctt-v35p110.

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32

Erselius, Lynn. "Carbon farming". New Scientist 208, n.º 2789 (diciembre de 2010): 28. http://dx.doi.org/10.1016/s0262-4079(10)62996-x.

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33

Horton, Jan. "Farming Skippy". New Scientist 209, n.º 2795 (enero de 2011): 27. http://dx.doi.org/10.1016/s0262-4079(11)60098-5.

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34

Ghulati, Suraj Prakash. "Organic Farming". Paradigm 8, n.º 2 (julio de 2004): 74–81. http://dx.doi.org/10.1177/0971890720040212.

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35

Devendra, C. "Organic farming". Livestock Production Science 90, n.º 1 (octubre de 2004): 67–68. http://dx.doi.org/10.1016/j.livprodsci.2004.07.008.

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36

Lampinen, Ari. "Biogas farming". Refocus 5, n.º 5 (septiembre de 2004): 30–32. http://dx.doi.org/10.1016/s1471-0846(04)00221-5.

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37

Davies, M. J. "Protein farming". Trends in Biotechnology 19, n.º 4 (abril de 2001): 128. http://dx.doi.org/10.1016/s0167-7799(01)01616-x.

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38

ZURER, PAMELA. "ORGANIC FARMING". Chemical & Engineering News 80, n.º 22 (3 de junio de 2002): 8. http://dx.doi.org/10.1021/cen-v080n022.p008a.

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39

Jones, William E. "Small farming". Journal of Equine Veterinary Science 21, n.º 6 (junio de 2001): 257. http://dx.doi.org/10.1016/s0737-0806(01)70047-0.

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40

Lal, R. "Farming carbon". Soil and Tillage Research 96, n.º 1-2 (octubre de 2007): 1–5. http://dx.doi.org/10.1016/j.still.2007.06.001.

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41

Masters, Bernie. "Farming roos". New Scientist 204, n.º 2734 (noviembre de 2009): 31. http://dx.doi.org/10.1016/s0262-4079(09)62999-7.

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42

Gubbi, Sanjay. "Tiger farming". New Scientist 192, n.º 2582 (diciembre de 2006): 20. http://dx.doi.org/10.1016/s0262-4079(06)61377-8.

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43

McKenna, Erin, Sarah Curtis y Jon Stout. "Philosophical Farming". Contemporary Pragmatism 9, n.º 1 (21 de abril de 2012): 151–83. http://dx.doi.org/10.1163/18758185-90000221.

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44

Munro, A. L. S. "Salmon farming". Fisheries Research 10, n.º 1-2 (diciembre de 1990): 151–61. http://dx.doi.org/10.1016/0165-7836(90)90020-v.

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45

Laird, Lindsay. "Crustacean farming". Fisheries Research 15, n.º 4 (enero de 1993): 390–91. http://dx.doi.org/10.1016/0165-7836(93)90091-k.

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46

Xu, Hui-Lian. "Nature Farming". Journal of Crop Production 3, n.º 1 (22 de junio de 2001): 1–10. http://dx.doi.org/10.1300/j144v03n01_01.

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47

McRae, Neil. "Salmon farming". Veterinary Record 184, n.º 2 (11 de enero de 2019): 68.3–68. http://dx.doi.org/10.1136/vr.l98.

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48

Moore, Peter D. "Farming forecast". Nature 393, n.º 6680 (mayo de 1998): 33–34. http://dx.doi.org/10.1038/29921.

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49

Hamdar, Bassam C. y Ibrahim G. Rubeiz. "Organic Farming". Small Fruits Review 1, n.º 1 (6 de abril de 2000): 3–14. http://dx.doi.org/10.1300/j301v01n01_02.

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50

King MA, Dennis. "ORGANIC FARMING". Nutrition & Food Science 88, n.º 4 (abril de 1988): 14–15. http://dx.doi.org/10.1108/eb059189.

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