Relevant bibliographies by topics / Farming systems

Academic literature on the topic 'Farming systems'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Farming systems"

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DeWalt, Billie. "Farming Systems Research: Anthropology, Sociology, and Farming Systems Research1." Human Organization 44, no. 2 (June 1985): 106–14. http://dx.doi.org/10.17730/humo.44.2.d26r461892228g44.

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Hildebrand, Peter E. "Farming systems symposium." American Journal of Alternative Agriculture 2, no. 4 (1987): 190–91. http://dx.doi.org/10.1017/s0889189300009371.

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Erlygina, E., and S. Shtebner. "Diversified Farming Systems." Bulletin of Science and Practice, no. 2 (February 15, 2023): 123–26. http://dx.doi.org/10.33619/2414-2948/87/16.

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The paper considers industrial methods of agriculture, as a result of which environmental damage is caused, waterways are polluted, dead zones are created in the oceans, habitat biodiversity is destroyed, toxins are released into the food chain, endangering the health of the population due to outbreaks of diseases and exposure to pesticides. Diversification of the farming system will help to increase the efficiency of resource use, reduce the number of pests and diseases, diversify income sources and increase the sustainability of production.
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Farrington, John. "Whither Farming Systems Research?" Development Policy Review 6, no. 3 (September 1988): 323–32. http://dx.doi.org/10.1111/j.1467-7679.1988.tb00459.x.

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Keatinge, J. D. H. "Farming systems of Pakistan." Agricultural Systems 45, no. 1 (January 1994): 121–22. http://dx.doi.org/10.1016/s0308-521x(94)90287-9.

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Chaudhry, M. Ghaffar. "Farming systems of Pakistan." Food Policy 18, no. 5 (October 1993): 448–49. http://dx.doi.org/10.1016/0306-9192(93)90069-n.

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Pickett, John A., Christine M. Woodcock, Charles AO Midega, and Zeyaur R. Khan. "Push–pull farming systems." Current Opinion in Biotechnology 26 (April 2014): 125–32. http://dx.doi.org/10.1016/j.copbio.2013.12.006.

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Funes-Monzote, F. R., Marta Monzote, E. A. Lantinga, and H. van Keulen. "Conversion of specialised dairy farming systems into sustainable mixed farming systems in Cuba." Environment, Development and Sustainability 11, no. 4 (March 13, 2008): 765–83. http://dx.doi.org/10.1007/s10668-008-9142-7.

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Pang, Hui, Zheng Zheng, Tongmiao Zhen, and Ashutosh Sharma. "Smart Farming." International Journal of Agricultural and Environmental Information Systems 12, no. 1 (January 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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Soni, Rajju Priya, Mittu Katoch, and Rajesh Ladohia. "Integrated Farming Systems - A Review." IOSR Journal of Agriculture and Veterinary Science 7, no. 10 (2014): 36–42. http://dx.doi.org/10.9790/2380-071013642.

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Dissertations / Theses on the topic "Farming systems"

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Kuntz, Lauren B. "Wick irrigation systems for subsistence farming." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/83726.

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Thesis (S.B.)--Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 59-61).
Irrigation on small-scale farms has been noted as a key method to help lift subsistence farmers out of poverty. With water scarity growing around the globe and lack of access to electricity still prevalent in rural areas, the need to develop an energy efficient irrigation system that simultaneous limits wasted water while being low cost is essential. The possibility of using a wicking irrigation system that relies on the suction plants create for water to mitigate the pumping pressure is investigated. A theoretical model for such a system is developed for an acre sized wicking irrigation system, and the power and water efficiency is compared to a standard drip irrigation system. While the wicking irrigation system has a greater distribution of water delivery from the wicks than compared to the dripper system, a wicking system has the potential to operate at much lower power, with the possibility of even being a power source. If a direct coupling could be developed between the plant's roots and wick, eliminating the need for water to travel through the soil, the energy benefit of the wicking system would be even more dramatic.
by Lauren B. Kuntz.
S.B.
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Tuomisto, Hanna. "Comparing environmental impacts of contrasting farming systems." Thesis, University of Oxford, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547491.

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Law, Derek M. "ECOLOGICAL WEED MANAGEMENT FOR ORGANIC FARMING SYSTEMS." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/414.

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Two field studies examining direct ecological weed control practices were conducted in Lexington, Kentucky. The first evaluated weed control efficacy and influence on yields of several mulches in two organically-managed bell pepper (Capsicum annum) production systems for two years. Peppers were planted in double rows in flat, bare ground or on black polyethylene-covered raised beds with drip irrigation, and four mulches (straw, compost, wood chips, and undersown white dutch clover (Trifolium repens L.) living mulch) were applied to the two production systems. In both years, polyethylene-covered raised beds produced higher yields than the flat, bare ground system. In the second year, the polyethylene-covered bed system coupled with mulching in-between beds with compost or wood chips after cultivation provided excellent weed control and yields. The second field study evaluated the efficacy of soil solarization and shallow cultivation on the invasive and noxious weed johnsongrass over two years (Sorghum halapense). A soil solarization treatment, using clear plastic stretched over soil for eight weeks, and a cultivated bare fallow treatment, utilizing a tractor pulled cultivator implement equipped with sweep blades, were randomly applied during the summers of 2003 and 2004 to a field infested with johnsongrass. Solarized and cultivated plots in both years were lightly tilled 8 months after completion of the initial treatment period. At the conclusion of the experiment the johnsongrass population was significantly reduced in all treatments and in the control plots compared to the original infestation. These two experiments testing direct weed control practices (mulching, cultivation, solarization) were undertaken in the context of an ecological weed management plan that includes long term strategies to reduce weed infestations such as crop rotation, cover cropping, and fertility management that are essential for organic farmers.
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Soteriades, Andreas Diomedes. "Trade-offs in sustainable dairy farming systems." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/18753.

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A key challenge facing dairy farming is to meet the increasing demand for dairy products from a growing and more affluent global population in a period of unprecedented socio-economic and environmental change. In order to address this challenge, policies are currently placing emphasis on ‘sustainable intensification’ (SI), i.e. producing ‘more’ outputs and services with ‘less’ resources and environmental impacts. Determining whether or not SI can deliver greater yet sustainable dairy production requires understanding of the relationships between sustainability pillars (environmental; economic; and social) and farm aspects (e.g. on-farm management; and animal productivity) under particular farming systems and circumstances (e.g. regional bio-physical conditions). Trade-offs between pillars and aspects is inevitable within a farming system. Many widely-used assessment methods that aim to measure, scale and weight these pillars and aspects are unable to fully capture trade-offs between them. The objectives of this thesis are: 1) to identify key trade-offs in dairy farming systems to inform greater yet sustainable food production; and 2) to introduce models and methodologies aiming at a more holistic measurement and better understanding of dairy farm sustainability. This thesis assesses the sustainability of French and UK dairy farming systems via a farm efficiency benchmarking modelling framework coupled with statistical analyses. It explores the relationships between pillars, aspects and technical, economic and environmental performance; and identifies important drivers/differentials in dairy farm efficiency. Importantly, it also suggests ways in which farm inputs and outputs can be adjusted so that improvements in environmental, technical and economic performance become feasible. Efficiency benchmarking was performed with the multiple-input – multiple-output productive efficiency method Data Envelopment Analysis (DEA). DEA calculates single aggregated efficiency indices per farm by accounting for several farm inputs and outputs which the DEA model endogenously scales and weights. In this work, the notion of farm inputs and outputs was extended to also include ‘undesirable’ outputs (greenhouse gas emissions) and environmental impacts (e.g. eutrophication, acidification etc.) of dairy farming. The DEA models employed belong to the family of ‘additive’ models, which have several advantages over ‘traditional’ DEA models. These include their ability (i) to simultaneously increase outputs and reduce inputs, undesirable outputs and environmental impacts; (ii) to identify specific sources of inefficiency. These ‘sources’ represent a farm’s shortfalls in output production and its excesses in input use and/or in undesirable outputs and environmental impacts, relatively to the other farms; (iii) to position undesirable outputs in the output set rather than consider them as inputs or ‘inverse’ outputs; and (iv) to rank farms by efficiency performance. Importantly, this thesis also proposes a new additive model with a ranking property and high discriminatory power. In a second stage, DEA was coupled with partial least squares structural equation modelling (SEM) so as to develop and relate latent variables for environmental performance, animal productivity and on-farm management practices. The results suggested that the efficacy of SI may be compromised by several on-farm trade-offs between pillars, aspects and farm inputs and outputs. Moreover, trade-offs depended on particular farming systems and circumstances. Increasing animal productivity did not always improve farm environmental performance at whole farm-level. Intensifying production at animal and farm-levels, coupled with high reliance on external inputs, reduced farm environmental performance in the French case, i.e. a significant negative relationship was found between intensification and environmental performance (SEM path coefficients ranged between -0.31 and -0.57, p < 0.05). Conversely, in the UK case, systems representing animal-level intensification (via genetic selection) for increased milk fat plus protein production performed better, on average, than controls of UK average genetic merit for milk fat plus protein production in terms of technical efficiency (DEA scores between 0.91– 0.92 versus 0.78–0.79) and environmental efficiency (scores between 0.92–0.93 versus 0.80), regardless of whether on a low-forage or high-forage diet. The levels of inefficiency in (undesirable) outputs, inputs and environmental impacts varied among farming systems and depended on the regional and managerial characteristics of each system. For instance, in France, West farms had higher eutrophication inefficiencies than East farms (average normalized eutrophication inefficiencies were, respectively 0.141 and 0.107), perhaps because of their more intensive production practices. However, West farms were more DEA-efficient than East farms as the former benefited from bio-physical conditions more favourable to dairy farming (mean DEA score ranks were 97 for West and 83 for East). Such findings can guide policy incentives for SI in different regions or dairy systems. The proposed modelling framework significantly contributes to current knowledge and the search for the best pathways to SI, improves widely-used modelling approaches, and challenges earlier findings based on less holistic exercises.
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Wiegel, Whitney J. Artz Georgeanne M. "Adoption of organic farming systems in Missouri." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/5342.

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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 20, 2010). Thesis advisor: Dr. Georgeanne Artz. Includes bibliographical references.
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Boag, Franca Elise. "Integrated Mediterranean farming and pastoral systems : local knowledge and ecological infrastructure of Italian dryland farming /." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22954.pdf.

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Robinson, Jeffrey Brett, University of Western Sydney, of Science Technology and Environment College, and School of Environment and Agriculture. "Understanding and applying decision support systems in Australian farming systems research." THESIS_CSTE_EAG_Robinson_J.xml, 2005. http://handle.uws.edu.au:8081/1959.7/642.

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Decision support systems (DSS) are usually based on computerised models of biophysical and economic systems. Despite early expectations that such models would inform and improve management, adoption rates have been low, and implementation of DSS is now “critical” The reasons for this are unclear and the aim of this study is to learn to better design, develop and apply DSS in farming systems research (FSR). Previous studies have explored the merits of quantitative tools including DSS, and suggested changes leading to greater impact. In Australia, the changes advocated have been: Simple, flexible, low cost economic tools: Emphasis on farmer learning through soft systems approaches: Understanding the socio-cultural contexts of using and developing DSS: Farmer and researcher co-learning from simulation modelling and Increasing user participation in DSS design and implementation. Twenty-four simple criteria were distilled from these studies, and their usefulness in guiding the development and application of DSS were assessed in six FSR case studies. The case studies were also used to better understand farmer learning through models of decision making and learning. To make DSS useful complements to farmers’ existing decision-making repertoires, they should be based on: (i) a decision-oriented development process, (ii) identifying a motivated and committed audience, (iii) a thorough understanding of the decision-makers context, (iv) using learning as the yardstick of success, and (v) understanding the contrasts, contradictions and conflicts between researcher and farmer decision cultures
Doctor of Philosophy (PhD)
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Meindertsma, Jan Douwe. "Income diversity, technology and farming systems : modelling of resource poor farming households in Lombok, Indonesia." Thesis, University of East Anglia, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389358.

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Ingvarsson, Josef. "Assessing Sustainability in Coffee Farming Systems in Colombia." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-45478.

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This study investigated sustainability challenges and benefits for coffee farming with different amounts of shade management in Colombia. Data was collected from literature studies, quantitative soil analyses and interviews with farmers and other experts. The results show that shade management of coffee farms does increase ecological sustainability, but in general gives lower yields of coffee. However, shaded coffee systems have the potential of increasing economic resilience for farmers by providing diversified income possibilities. The low and fluctuating coffee price of the global market has shown to be a major challenge of sustainability for Colombian small scale coffee farms. In addition a participatory sustainability assessment of soil quality and crop health was conducted with four farmers. The results from these assessments were compared with results from quantitative analyses of soil compaction, microbiological respiration rate and organic matter content in order to evaluate the analytical reliability of the assessment. The results of the participatory assessment were shown to correlate quite well to the quantitative soil analyses. When participatory methodology was evaluated from experiences in field and literature, it was found to be an important approach in facilitating sustainability learning in local contexts.
En esta investigación se examinaron los retos y beneficios de la sostenibilidad en la producción del cultivo de café con diferentes niveles de sombra en Colombia. Los datos se colectaron de estudios de literatura, análisis de suelo y entrevistas con agricultores y expertos en el tema. Los resultados indican que el manejo con sombra incrementa la sostenibilidad ecologica de las fincas cafeteras, y esto, tiene la posibilidad de incrementar la resiliencia económica para los agricultores al ofrecer oportunidades para una producción diversificada de ingresos. El precio bajo y fluctuante del café en el mercado mundial ha demostrado ser un importante reto para la sostenibilidad de las fincas de los campesinos colombianos. Además, se realizó una evaluación de la sostenibilidad participativa de la calidad del suelo y la salud de los cultivos con cuatro agricultores. Los resultados de esta evaluación se compararon con los resultados de análisis cuantitativos: de la compactación del suelo, la tasa de respiración microbiológica y contenido de materia orgánica, con el fin de evaluar la fiabilidad analítica de la evaluación participativa. Se demostró que los resultados de la evaluación participativa tienen una estrecha relación con el análisis cuantitativo del suelo. Cuando la metodología participativa se evaluó a partir de las experiencias propias en el campo y la literatura, se encontró que puede ser un enfoque importante para facilitar el aprendizaje de sostenibilidad para los contextos locales.
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Maqbool, Muhammad Asim. "An assessment of sustainable farming systems in Saskatchewan." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0010/NQ37897.pdf.

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Books on the topic "Farming systems"

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Rubino, R., L. Sepe, A. Dimitriadou, and A. Gibon, eds. Livestock farming systems. The Netherlands: Wageningen Academic Publishers, 2006. http://dx.doi.org/10.3920/978-90-8686-565-9.

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Tow, Philip, Ian Cooper, Ian Partridge, and Colin Birch, eds. Rainfed Farming Systems. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-1-4020-9132-2.

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1945-, Turner B. L., and Brush Stephen B. 1943-, eds. Comparative farming systems. New York: Guilford Press, 1987.

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Tow, P. G. Rainfed farming systems. Dordrecht: Springer, 2011.

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Sangeetha, Jeyabalan, and Devarajan Thangadurai. Algal Farming Systems. New York: Apple Academic Press, 2024. http://dx.doi.org/10.1201/9781032700359.

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van der Zijpp, A. J., J. A. J. Verreth, Le Quang Tri, M. E. F. van Mensvoort, R. H. Bosma, and M. C. M. Beveridge, eds. Fishponds in farming systems. The Netherlands: Wageningen Academic Publishers, 2007. http://dx.doi.org/10.3920/978-90-8686-596-3.

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(Firm), RAISON, ed. Farming systems in Namibia. Windhoek, Namibia: Research & Information Services of Namibia, 2006.

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Roger, Veseth, University of Idaho. Cooperative Extension Service., Washington State University. Cooperative Extension., Oregon State University. Extension Service., and United States. Dept. of Agriculture., eds. Effective conservation farming systems. [Moscow, Idaho]: Cooperative Extension, University of Idaho, 1986.

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Oram, Peter. Legumes in farming systems. Aleppo: International Center for Agricultural Research in the Dry Areas, 1990.

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Shams, Numa. Farming systems, Province Pursat. 2nd ed. Phnom Penh: Cambodia Canada Development Program and Provincial Division of Agronomy, Pursat, 1994.

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Book chapters on the topic "Farming systems"

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Kwiatkowski, Cezary A., Elżbieta Harasim, Lucjan Pawłowski, Artur Pawłowski, Małgorzata Pawłowska, and Barbara Kołodziej. "Farming systems." In Organic Versus Conventional Farming, 14–23. London: Routledge, 2023. http://dx.doi.org/10.1201/9781003380771-2.

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Edmonds, Bruce. "System Farming." In Social Systems Engineering, 45–63. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118974414.ch3.

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Breimyer, Harold F. "Economics of Farming Systems." In Organic Farming, 163–66. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/asaspecpub46.c13.

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Bowler, Ian. "Sustainable Farming Systems." In The GeoJournal Library, 169–87. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-3471-4_9.

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Spedding, C. R. W. "Mixed Farming Systems." In An Introduction to Agricultural Systems, 125–29. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-011-6408-5_10.

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Pearson, Craig. "Farming Systems Design." In Rainfed Farming Systems, 321–37. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-1-4020-9132-2_13.

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Christou, Paul, Eva Stoger, and Richard M. Twyman. "Monocot Expression Systems for Molecular Farming." In Molecular Farming, 55–67. Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527603638.ch4.

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Walia, Sohan Singh, and Tamanpreet Kaur. "Rabbit Farming." In Basics of Integrated Farming Systems, 95–98. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6556-4_13.

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Walia, Sohan Singh, and Tamanpreet Kaur. "Fish Farming." In Basics of Integrated Farming Systems, 71–75. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6556-4_9.

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Hayman, Peter, Jason Crean, and Canesio Predo. "A Systems Approach to Climate Risk in Rainfed Farming Systems." In Rainfed Farming Systems, 75–100. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-1-4020-9132-2_3.

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Conference papers on the topic "Farming systems"

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Eaton, R., J. Katupitiya, K. W. Siew, and K. S. Dang. "Precision Guidance of Agricultural Tractors for Autonomous Farming." In 2008 2nd Annual IEEE Systems Conference. IEEE, 2008. http://dx.doi.org/10.1109/systems.2008.4519026.

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Siddique, Talha, Dipro Barua, Zannatul Ferdous, and Amitabha Chakrabarty. "Automated farming prediction." In 2017 Intelligent Systems Conference (IntelliSys). IEEE, 2017. http://dx.doi.org/10.1109/intellisys.2017.8324214.

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Nayyar, Anand, and Vikram Puri. "Smart farming." In The International Conference on Communication and Computing Systems (ICCCS-2016). Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315364094-121.

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Fajar, Ahmad Nurul, Riyanto Jayadi, Santony Dyaz, Gilberth Setiawan, and Michael Bhudiawan. "Designing IoT urban farming monitoring systems for supporting smart farming." In PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON GREEN CIVIL AND ENVIRONMENTAL ENGINEERING (GCEE 2023). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0205234.

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Anjum Sheikh, Javed, Asia Mumtaz, and Saba Farzeen. "IoT-based Vertical Farming Systems." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004708.

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Vertical farming, a revolutionary approach to agricultural production, has gained significant attention in recent years due to its potential to address various challenges facing traditional farming practices. This paper provides a comprehensive overview of IoT-based Vertical Farming systems, exploring their hardware design, implementation strategies, testing methodologies, and prospects.The hardware design of IoT-based Vertical Farming systems encompasses a range of components essential for creating optimal growing environments. Soil moisture sensors, temperature and humidity sensors, light-dependent resistors (LDRs), and ESP32 Wi-Fi modules are among the key elements utilized in these systems. Soil moisture sensors enable precise irrigation management by measuring water content in the soil, while temperature and humidity sensors provide insights into environmental conditions. LDRs detect light levels, facilitating optimal lighting control, and ESP32 Wi-Fi modules enable wireless communication for remote monitoring and control.Implementation strategies for IoT-based Vertical Farming systems involve hardware setup, software development, and integration with existing infrastructure. Sensor nodes distributed throughout the farming environment are connected to a central control unit via Wi-Fi or other communication protocols. Software interfaces and applications are developed to provide users with real-time monitoring and control capabilities, allowing them to adjust environmental parameters as needed.Effective testing methodologies are crucial for ensuring the reliability, functionality, and security of IoT-based Vertical Farming systems. Black box testing focuses on external functionality, such as user interface interactions and sensor responses, while white box testing examines internal system components and code logic. Grey box testing combines elements of both black and white box testing, with a focus on limited knowledge and system behavior.The future prospects of IoT-based Vertical Farming are promising, with opportunities for innovation and advancement. Research and development efforts are needed to enhance system scalability, energy efficiency, and data analytics capabilities. Integration with artificial intelligence (AI) and machine learning (ML) algorithms can enable predictive analytics and autonomous decision-making, optimizing crop production and resource utilization. Expanding the application of vertical farming to diverse environments, including urban areas and arid regions, can address global food security challenges and promote sustainable agriculture practices.In conclusion, IoT-based Vertical Farming represents a transformative approach to agriculture, offering scalable and sustainable solutions to meet the growing demand for food production. Continued research, development, and adoption of these systems have the potential to revolutionize the agricultural industry and contribute to a more food-secure and environmentally sustainable future.
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Jaybhaye, Nita, Purva Tatiya, Avdut Joshi, Sakshi Kothari, and Jyoti Tapkir. "Farming Guru: - Machine Learning Based Innovation for Smart Farming." In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2022. http://dx.doi.org/10.1109/icssit53264.2022.9716287.

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Shoba, M. Shanmugam, Sangeetha D, K. L. Suchala, R. H. Shravya, and B. S. Soundhaaryha. "Survey on IoT based E-Farming Technology Enabled Farming." In 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2022. http://dx.doi.org/10.1109/icscds53736.2022.9760870.

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"ONTOLOGIES FOR SMART FARMING." In 16th International Conference Information Systems 2023. IADIS Press, 2023. http://dx.doi.org/10.33965/is2023_202301l026.

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Ramya, R., C. Sandhya, and R. Shwetha. "Smart farming systems using sensors." In 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR). IEEE, 2017. http://dx.doi.org/10.1109/tiar.2017.8273719.

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"Topic 2: Smart Farming." In 2022 3rd International Conference on Embedded & Distributed Systems (EDiS). IEEE, 2022. http://dx.doi.org/10.1109/edis57230.2022.9996522.

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Reports on the topic "Farming systems"

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Research Institute (IFPRI), International Food Policy. Farming Systems of Africa. Washington, DC: International Food Policy Research Institute, 2014. http://dx.doi.org/10.2499/9780896298460_06.

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Duffy, Michael D., and Kenneth T. Pecinovsky. Organic vs. Conventional Farming Systems. Ames: Iowa State University, Digital Repository, 2009. http://dx.doi.org/10.31274/farmprogressreports-180814-1021.

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Pecinovsky, Kenneth T. Organic versus Conventional Farming Systems. Ames: Iowa State University, Digital Repository, 2006. http://dx.doi.org/10.31274/farmprogressreports-180814-2798.

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Duffy, Michael D., Matthew Z. Liebman, and Kenneth T. Pecinovsky. Organic vs. Conventional Farming Systems. Ames: Iowa State University, Digital Repository, 2003. http://dx.doi.org/10.31274/farmprogressreports-180814-70.

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Jabbar, M. A., M. A. M. Saleem, and P. M. Tulachan. Smallholder Dairy in Mixed Farming Systems of the Hindu Kush-Himalayas. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2002. http://dx.doi.org/10.53055/icimod.392.

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Jabbar, M. A., M. A. M. Saleem, and P. M. Tulachan. Smallholder Dairy in Mixed Farming Systems of the Hindu Kush-Himalayas. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2002. http://dx.doi.org/10.53055/icimod.392.

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Water, Land and Ecosystems (WLE), CGIAR Research Program on. Enabling sustainable, productive smallholder farming systems through improved land and water management. International Water Management Institute (IWMI). CGIAR Research Program on Water, Land and Ecosystems (WLE), 2017. http://dx.doi.org/10.5337/2017.213.

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Choudhury, Dhrupad, Sanjeev Bhuchar, and Samuel Thomas. Transitioning shifting cultivation to resilient farming systems in South and Southeast Asia. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2021. http://dx.doi.org/10.53055/icimod.17.

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Shrestha, S. Evolution of Mountain Farming Systems: Sustainable Development Policy Implications. International Workshop Report. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 1995. http://dx.doi.org/10.53055/icimod.193.

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Shrestha, S. Evolution of Mountain Farming Systems: Sustainable Development Policy Implications. International Workshop Report. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 1995. http://dx.doi.org/10.53055/icimod.193.

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