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Journal articles on the topic 'Farming systems'

To see the other types of publications on this topic, follow the link: Farming systems.

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Zandstra, Hubert. "Farming Systems Research: A Retrospect." Mountain Research and Development 26, no. 4 (November 2006): 388–91. http://dx.doi.org/10.1659/0276-4741(2006)26[388:fsrar]2.0.co;2.

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12

Oxley, James W. "University Involvement in Farming Systems." Journal of Animal Science 64, no. 5 (May 1, 1987): 1568–73. http://dx.doi.org/10.2527/jas1987.6451568x.

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13

Robinson, David A., Thomas J. Basset, and Donald E. Crummey. "Land in African Farming Systems." Canadian Journal of African Studies / Revue Canadienne des Études Africaines 29, no. 1 (1995): 130. http://dx.doi.org/10.2307/485784.

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14

Daniel, El Chami. "Towards Sustainable Organic Farming Systems." Sustainability 12, no. 23 (November 24, 2020): 9832. http://dx.doi.org/10.3390/su12239832.

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The European Union green deal has proposed the “organic farming action plan” to render a farming system that is more sustainable and adaptable in terms of climate change mitigation and thus enable meeting the United Nations Sustainable Development Goals (UN-SDGs). While this policy instrument is fundamental to achieving sustainable agriculture, there is still no agreement on what sustainable agriculture is and how to measure it. This opinion paper proposes an ecosystem-based framework for the crop life cycle to determine the balance between the economic, social, and environmental pillars of sustainability toward supporting decision-making.

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15

Mathijs, Erik, and Erwin Wauters. "Making Farming Systems Truly Resilient." EuroChoices 19, no. 2 (August 2020): 72–76. http://dx.doi.org/10.1111/1746-692x.12287.

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16

Devendra, C., and D. Thomas. "Smallholder farming systems in Asia." Agricultural Systems 71, no. 1-2 (January 2002): 17–25. http://dx.doi.org/10.1016/s0308-521x(01)00033-6.

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17

Booij, C. J. H., and J. Noorlander. "Farming systems and insect predators." Agriculture, Ecosystems & Environment 40, no. 1-4 (May 1992): 125–35. http://dx.doi.org/10.1016/0167-8809(92)90088-s.

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18

McRoberts, Neil. "Ecology and integrated farming systems." Crop Protection 15, no. 4 (June 1996): 399–400. http://dx.doi.org/10.1016/0261-2194(96)89824-3.

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19

Taimeh, Awni Y., and Nabil Katkhuda. "Dryland farming systems in Jordan." American Journal of Alternative Agriculture 12, no. 3 (September 1997): 100–104. http://dx.doi.org/10.1017/s0889189300007359.

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AbstractResources available for dryland farming in Jordan are limited since 91% of the country has an arid climate. Moreover, the available resources are subject to a wide range of natural and human constraints. The farming systems consist of two main types: annual crops such as wheat and barley, and fruit trees such as olives, grapes, and stone fruits. Socioeconomic factors coupled with a fluctuating rainfall pattern cause some shifting in land use and variation in land under cultivation. Alternative land use strategies and preservation and more efficient use of resources are the means to achieve higher production. Substantial addition of land suitable for cultivation is unlikely since it would require additional water resources, which are diminishing in Jordan. Environmental degradation such as desertification is a major concern to land use planners. Several issues must be addressed if agricultural productivity is to be sustained: preservation of resources; overcoming various pressures imposed on agricultural lands; adoption of new practical and economical practices; introduction of modern technologies such as water harvesting, supplemental irrigation, use of treated waste water, and proper soil conservation measures; and adaptive research.

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20

Brockington, N. R. "Farming systems research: A review." Agricultural Systems 21, no. 3 (January 1986): 237–40. http://dx.doi.org/10.1016/0308-521x(86)90042-9.

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21

Gong, Zitong, Putian Lin, Jie Chen, and Xuefeng Hu. "Classical Farming Systems of China." Journal of Crop Production 3, no. 1 (June 22, 2001): 11–21. http://dx.doi.org/10.1300/j144v03n01_02.

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22

Grzebisz, Witold, and Alicja Niewiadomska. "Nitrogen Cycle in Farming Systems." Agronomy 14, no. 1 (December 29, 2023): 89. http://dx.doi.org/10.3390/agronomy14010089.

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23

Kempen, Markus, Berien S. Elbersen, Igor Staritsky, Erling Andersen, and Thomas Heckelei. "Spatial allocation of farming systems and farming indicators in Europe." Agriculture, Ecosystems & Environment 142, no. 1-2 (July 2011): 51–62. http://dx.doi.org/10.1016/j.agee.2010.08.001.

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24

Ahmad, K. "Molecular farming: strategies, expression systems and bio-safety considerations." Czech Journal of Genetics and Plant Breeding 50, No. 1 (February 13, 2014): 1–10. http://dx.doi.org/10.17221/187/2013-cjgpb.

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Molecular farming is an experimental application of biotechnology that involves the genetic modification of crops for the production of proteins and chemicals for medicinal and commercial purposes. The vast majority in the developing world cannot afford the high cost of therapeutics produced by existing methods. We need to produce not only new therapeutics but also cheaper versions of the existing ones. Molecular farming could offer a viable option for this growing need for biopharmaceuticals. Plant made therapeutics are cheaper, safer, can be abundantly produced and easily stored. Here, strategies and approaches utilized in plant molecular farming are discussed. Furthermore, the bio-safety considerations related to this emerging field are also discussed.

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25

Petheram, R. J., and R. A. Clark. "Farming systems research: relevance to Australia." Australian Journal of Experimental Agriculture 38, no. 1 (1998): 101. http://dx.doi.org/10.1071/ea96055.

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Summary. Farming systems research was introduced into many international and national agricultural research institutes in lower income countries in the 1970s and 1980s with the purpose of improving the relevance of research for small-scale farmers. This review outlines the origin, context, goals, principles and process of farming systems research in these countries, and aims to enable agricultural professionals to assess the relevance and value of farming systems research to their work in particular situations in Australia and overseas. The key elements of farming systems research include a holistic approach, orientation towards the needs of defined target groups, high levels of farmer participation and hence co-learning by farmers and specialists. There is guidance by facilitators, continuous evaluation and linkage to policy makers. The goal of farming systems research is to improve the well-being of farmers through development of farming systems. It involves application of methods from various disciplines, first to define the constraints and opportunities for development and then to overcome these in a research process involving farmers, with specialists and policy makers. A generalised farming systems research procedure and various research activities are described. Initially in lower income countries, a fairly standard farming systems research procedure was used, but farming systems research has evolved to encompass a range of activities commonly regarded as the realm of agricultural extension or rural development. Basic science, applied science and farming systems research are compared in terms of the roles and relationships of the people involved in the research process. The implications of selecting farming systems research as a model for rural research and development are discussed. Achieving adequate levels of farmer participation can be a major issue in farming systems research so it is important that the principal notions of participation are understood. Success of farming systems research in Australia will depend on developing innovative ways of achieving high levels of participation. Current trends in the philosophy, practice and funding of agricultural research and extension in Australia make it timely to consider the wider adoption of farming systems research principles and practices. Farming systems research could provide a valuable philosophical and practical basis for the trend towards greater participation by researchers with end-users and extension practitioners in agricultural development programs. However, it seems unwise to adhere strictly to any one particular model of research and development from other places: farming systems research concepts are being combined successfully with those from other models, such as systems learning and computer modelling, to suit the needs of particular situations. Implications of a wider adoption of farming systems research in Australia for agricultural research and development organisations and professional bodies include, the establishment of multidisciplinary teams with shared goals, and the sourcing of funding for periods long enough to achieve outcomes. There is also a need for training in systems concepts and facilitation, for reputable channels of publication of the results of farming systems research and for greater recognition of participatory activities as valid forms of agricultural research.

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26

Nasritdinova, Umida, Xaitboy Sultonov, Husan Muratov, Rahim Ankabaev, Ulfat Ismatov, and Avazjon Abdumajitov. "Application of geoinformation systems in the agricultural complex." BIO Web of Conferences 105 (2024): 03005. http://dx.doi.org/10.1051/bioconf/202410503005.

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Precision farming, encompassing coordinated farming, adaptive landscape farming, and precision farming, represents a scientific paradigm in agricultural production that capitalizes on the inherent variability in soil fertility within localized areas. By tailoring practices to specific soil characteristics, precision farming optimizes profitability through the targeted application of fertilizers and plant protection products. Serving as the epitome of flexible landscape farming, precision farming integrates advanced agricultural technologies to achieve heightened productivity levels. Data collected through precision farming practices informs critical aspects of agricultural management, including strategic planting, precise calculation of input quantities, improved crop forecasting, and enhanced financial planning. Successful implementation of precision farming hinges on a thorough consideration of local soil attributes and climatic conditions, underscoring the importance of site-specific adaptation in modern agricultural strategies.

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27

Kakamoukas, Georgios, Panagiotis Sarigiannidis, Andreas Maropoulos, Thomas Lagkas, Konstantinos Zaralis, and Chrysoula Karaiskou. "Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems." Telecom 2, no. 1 (February 9, 2021): 52–74. http://dx.doi.org/10.3390/telecom2010005.

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Climate change is emerging as a major threat to farming, food security and the livelihoods of millions of people across the world. Agriculture is strongly affected by climate change due to increasing temperatures, water shortage, heavy rainfall and variations in the frequency and intensity of excessive climatic events such as floods and droughts. Farmers need to adapt to climate change by developing advanced and sophisticated farming systems instead of simply farming at lower intensity and occupying more land. Integrated agricultural systems constitute a promising solution, as they can lower reliance on external inputs, enhance nutrient cycling and increase natural resource use efficiency. In this context, the concept of Climate-Smart Agriculture (CSA) emerged as a promising solution to secure the resources for the growing world population under climate change conditions. This work proposes a CSA architecture for fostering and supporting integrated agricultural systems, such as Mixed Farming Systems (MFS), by facilitating the design, the deployment and the management of crop–livestock-=forestry combinations towards sustainable, efficient and climate resilient agricultural systems. Propelled by cutting-edge technology solutions in data collection and processing, along with fully autonomous monitoring systems, e.g., smart sensors and unmanned aerial vehicles (UAVs), the proposed architecture called MiFarm-CSA, aims to foster core interactions among animals, forests and crops, while mitigating the high complexity of these interactions, through a novel conceptual framework.

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28

Reig-Martı́nez, Ernest, and Andrés J. Picazo-Tadeo. "Analysing farming systems with Data Envelopment Analysis: citrus farming in Spain." Agricultural Systems 82, no. 1 (October 2004): 17–30. http://dx.doi.org/10.1016/j.agsy.2003.12.002.

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29

Rethnaraj, Jebakumar. "Future of Smart Farming Techniques: Significance of Urban Vertical Farming Systems Integrated with IoT and Machine Learning." Open Access Journal of Agricultural Research 8, no. 3 (2023): 1–11. http://dx.doi.org/10.23880/oajar-16000308.

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World population in recent decades has significant impacts on the traditional agricultural systems which has resulted in increased demand for food, land use and deforestation, water scarcity, climate changes but not limited to these impacts. In order to overcome all these issues, there is a need for advanced farming technologies for growing the most demand food crops. Smart farming also known as precision agriculture has evolved which uses the advanced technology to optimize the efficiency and productivity of the farming operations. It involves the integration of various technologies such as IoT sensors, drones, robotics and machine learning technologies, big data analytics to gather data on crop growth, environmental conditions and weather patterns. Vertical framing (VF) is one such precision framing efficient crop growth practices which adapts the integration of Internet of Things (IoT) and machine learning (ML) technologies in easier manner. Since, the vertical farming is completely an indoor farming technique, they do not depend on the particular geographical locations and outdoor growth parameters (like soil) for crop cultivation; hence, vertical farming is also known as controlled environment agriculture. This article explores the significance of different indoor vertical farming practices under controlled environment with the comparative analysis, efficiency, productivity, advantages and their potential benefits highlighting the need for sustainable agricultural practices that can meet the growing demand for food while minimizing the negative environmental impacts.

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30

Tuyen, Nguyen Quang, Le Canh Dung, and Ryuichi Yamada. "The PRA Diagnosis of Farming Systems." Journal of Rural Problems 35, no. 4 (2000): 355–58. http://dx.doi.org/10.7310/arfe1965.35.355.

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31

Bevacqua, Robert F. "Farming Systems Research Training in Swaziland." HortScience 22, no. 4 (August 1987): 661. http://dx.doi.org/10.21273/hortsci.22.4.661.

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Abstract Farming systems research (FSR) methodology is being used to improve vegetable production in Swaziland, a developing nation in southeastern Africa. An important prerequisite for this new' thrust in methodology is the training of staff in FSR objectives and processes.

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32

Leary, James, and Joe DeFrank. "Living Mulches For Organic Farming Systems." HortTechnology 10, no. 4 (January 2000): 692–98. http://dx.doi.org/10.21273/horttech.10.4.692.

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An important aspect of organic farming is to minimize the detrimental impact of human intervention to the surrounding environment by adopting a natural protocol in system management. Traditionally, organic farming has focused on the elimination of synthetic fertilizers and pesticides and a reliance on biological cycles that contribute to improving soil health in terms of fertility and pest management. Organic production systems are ecologically and economically sustainable when practices designed to build soil organic matter, fertility, and structure also mitigate soil erosion and nutrient runoff. We found no research conducted under traditional organic farming conditions, comparing bareground monoculture systems to systems incorporating the use of living mulches. We will be focusing on living mulch studies conducted under conventional methodology that can be extrapolated to beneficial uses in an organic system. This article discusses how organic farmers can use living mulches to reduce erosion, runoff, and leaching and also demonstrate the potential of living mulch systems as comprehensive integrated pest management plans that allow for an overall reduction in pesticide applications. The pesticide reducing potential of the living mulch system is examined to gain insight on application within organic agriculture.

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33

Manyong, Manyong A., and José Degand. "Sustainability of African Smallholder Farming Systems:." Journal of Sustainable Agriculture 6, no. 4 (April 1996): 17–42. http://dx.doi.org/10.1300/j064v06n04_04.

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34

Udo, Henk, and Ton Cornelissen. "Livestock in Resource-Poor Farming Systems." Outlook on Agriculture 27, no. 4 (December 1998): 237–42. http://dx.doi.org/10.1177/003072709802700406.

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35

Irvin, George. "Planning Irrigated Farming Systems in Israel." Institute of Development Studies Bulletin 2, no. 2 (May 22, 2009): 43–48. http://dx.doi.org/10.1111/j.1759-5436.1969.mp2002007.x.

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36

Lantinga, Egbert A., Gerard J. M. Oomen, and Johannes B. Schiere. "Nitrogen Efficiency in Mixed Farming Systems." Journal of Crop Improvement 12, no. 1-2 (December 2004): 437–55. http://dx.doi.org/10.1300/j411v12n01_07.

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37

Roche, John. "Foreword to ‘Resilient Dairy Farming Systems’." Animal Production Science 55, no. 7 (2015): iii. http://dx.doi.org/10.1071/anv55n7_fo.

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38

Allen, J. M. S. "CONCENTRIC SAMPLING IN FARMING SYSTEMS RESEARCH." Journal of Agricultural Economics 43, no. 1 (January 1992): 104–8. http://dx.doi.org/10.1111/j.1477-9552.1992.tb00203.x.

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39

Winter, Agnes. "Problems of extensive sheep farming systems." In Practice 17, no. 5 (May 1995): 217–20. http://dx.doi.org/10.1136/inpract.17.5.217.

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40

Lynam, John. "A History of Farming Systems Research." Agricultural Systems 73, no. 2 (August 2002): 227–32. http://dx.doi.org/10.1016/s0308-521x(01)00076-2.

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41

Mascarenhas, Adolfo, L. A. Odero-Ogwel, Y. F. O. Masakhalia, and Asit K. Biswas. "Land use policies and farming systems." Land Use Policy 3, no. 4 (October 1986): 286–303. http://dx.doi.org/10.1016/0264-8377(86)90025-6.

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42

Jouven, M., P. Lapeyronie, C.-H. Moulin, and F. Bocquier. "Rangeland utilization in Mediterranean farming systems." Animal 4, no. 10 (2010): 1746–57. http://dx.doi.org/10.1017/s1751731110000996.

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43

Rovira, AD. "Dryland mediterranean farming systems in Australia." Australian Journal of Experimental Agriculture 32, no. 7 (1992): 801. http://dx.doi.org/10.1071/ea9920801.

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The mediterranean region of Australia extends from Geraldton in Western Australia across southern Australia into western and northern Victoria. This region experiences hot, dry summers and cool, wet winters, with 300-600 mm annual rainfall. In the dryland farming zone, the cereal-livestock farming system dominates and produces 30-35% of Australia's total agricultural production. The major soils in the region are deep, coarse-textured sands and sandy loams, duplex soils with coarse-textured sands over clay (generally low in nutrients and organic matter), and fine-textured red-brown earths of low hydraulic conductivity. Major soil problems in the region include sodicity, salinity, soil structural degradation, nutrient deficiencies, boron toxicity, acidity, waterlogging, inadequate nitrogen nutrition, water-repellence, and root diseases. These problems have been exacerbated by excessive clearing of trees, increased frequency of cropping, reduced area sown to pastures, declining pasture production, and a decline in nutrient levels. With improved soil management there is potential for increased productivity from dryland farming areas of the region and improved ecological sustainability.

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44

Youngberg, Garth, and Richard Harwood. "Sustainable farming systems: Needs and opportunities." American Journal of Alternative Agriculture 4, no. 3-4 (December 1989): 100. http://dx.doi.org/10.1017/s0889189300002897.

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45

Horn, M. E., S. L. Woodard, and J. A. Howard. "Plant molecular farming: systems and products." Plant Cell Reports 22, no. 10 (February 28, 2004): 711–20. http://dx.doi.org/10.1007/s00299-004-0767-1.

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46

Hildebrand, P. E., B. K. Singh, B. C. Bellows, E. P. Campbell, and B. A. Jama. "Farming systems research for agroforestry extension." Agroforestry Systems 23, no. 2-3 (September 1993): 219–37. http://dx.doi.org/10.1007/bf00704917.

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47

Malczynski, Leonard A. "Farming Systems Research and Food Availability." Systems Research and Behavioral Science 34, no. 4 (July 2017): 401–2. http://dx.doi.org/10.1002/sres.2463.

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48

Spoolder, Hans AM. "Animal welfare in organic farming systems." Journal of the Science of Food and Agriculture 87, no. 15 (September 11, 2007): 2741–46. http://dx.doi.org/10.1002/jsfa.2999.

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49

Manu-Aduening, J. A., R. I. Lamboll, A. A. Dankyi, and R. W. Gibson. "Cassava diversity in Ghanaian farming systems." Euphytica 144, no. 3 (August 2005): 331–40. http://dx.doi.org/10.1007/s10681-005-8004-8.

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50

Hábová, Magdalena, Lubica Pospíšilová, Petr Hlavinka, Miroslav Trnka, Gabriela Barančíková, Zuzana Tarasovičová, Jozef Takáč, Štefan Koco, Ladislav Menšík, and Pavel Nerušil. "Carbon pool in soil under organic and conventional farming systems." Soil and Water Research 14, No. 3 (May 27, 2019): 145–52. http://dx.doi.org/10.17221/71/2018-swr.

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Changes in the agricultural management and climatic changes within the past 25 years have had a serious impact on soil organic matter content and contribute to different carbon storage in the soil. Prediction of soil carbon pool, validation, and quantification of different models is important for sustainable agriculture in the future and for this purpose a long-term monitoring data set is required. RothC-26.3 model was applied for carbon stock simulation within two different climatic scenarios (hot-dry with rapid temperature increasing and warm-dry with less rapid temperature increasing). Ten years experimental data set have been received from conventional and organic farming of experimental plots of Mendel University School Enterprise (locality Vatín, Czech-Moravian Highland). Average annual temperature in this area is 6.9°C, average annual precipitation 621 mm, and altitude 530 m above sea level. Soil was classified as Eutric Cambisol, sandy loam textured, with middle organic carbon content. Its cumulative potential was assessed as high. Results showed linear correlation between carbon stock and climatic scenario, and mostly temperature and type of soil management has influenced carbon stock. In spite of lower organic carbon inputs under organic farming this was less depending on climatic changes. Conventional farming showed higher carbon stock during decades 2000–2100 because of higher carbon input. Besides conventional farming was more affected by temperature.

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