Thematische Bibliographien / Agricultural pattern

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Auswahl der wissenschaftlichen Literatur zum Thema „Agricultural pattern“

Autor: Grafiati

Veröffentlicht am 18. Mai 2024

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Zeitschriftenartikel zum Thema "Agricultural pattern"

Singh, Dharmender, P. S. Shehrawat, Joginder Singh Malik, Dangi Pooja Arun und Dinesh Kumar. „Utilization Pattern of Mobile Apps Among Farmers for Agricultural Production“. Indian Journal of Extension Education 59, Nr. 1 (2023): 150–53. http://dx.doi.org/10.48165/ijee.2023.59132.

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The study was undertaken in the four specifically chosen districts of Haryana state in diverse geographical locations and had different crop patterns. In a number of districts, the main cropping pattern was rice and wheat, combined with pearl millet and mustard. This study aimed to determine how agricultural mobile apps were used by farmers for agricultural productivity by using a utility index. In accordance with a well-planned and pre-tested interview schedule, the data were gathered from 240 respondents, including 15 respondents each from randomly chosen 16 villages. Utilization pattern checked for general information of agriculture practices, utilization of mobile apps for gaining market related information and utilization of mobile apps to contact the agriculture experts in order to find out the solution of agriculture problems. More than half of the respondents perceived medium level of utilization pattern of agriculture mobile apps on agricultural practices. To increase the extent to which farmers use agricultural mobile apps for agricultural production, outreach programs and trainings are required.

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Rogoyski, Matthew, Alvan Gaus, Thomas Mourey, Israel Broner und Jeffrey Lakey. „Generative Patterns: A Potential Role in the Management of Complexity in Production Agriculture“. HortScience 31, Nr. 4 (August 1996): 666a—666. http://dx.doi.org/10.21273/hortsci.31.4.666a.

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A novel way to capture long-standing agricultural experience and knowledge in the form of generative patterns is proposed. These patterns can be thought of as solution paradigm where the solutions are the essence of the patterns. A pattern does not provide a concrete solution to a problem but can be considered of as a worldview of the problem or a solution space. A pattern initiates and generates human cognitive behaviors that indirectly facilitate, elucidate, and solve a problem. An application of generative patterns to production agriculture is proposed. An individual pattern, as described here, associates a problem, its context, the forces affecting it, and a solution. A pattern recurring in production agriculture, the socalled uniformity pattern, is presented, and its horticultural example is discussed.

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Gayathri, E., und K. Sita Devi. „Changes in the Land Use Pattern and Cropping Pattern among Different Gradients of Tiruchirapalli District – A Farm Level Analysis“. Asian Journal of Agricultural Extension, Economics & Sociology 41, Nr. 11 (09.12.2023): 382–96. http://dx.doi.org/10.9734/ajaees/2023/v41i112295.

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Agriculture plays a vital role in an Indian Economy. Changes in land use subsequently leading to decreased agricultural land in favour of the provision of residential accommodation in most urban settlements. The extent of land use is also influenced by technological changes over a period of time. The technological changes in agriculture ignited intensive cultivation resulting in conversion of marginal lands into productive agricultural lands through capital intensive cultivation. Changes in farming and land use patterns result in urbanisation, which puts ecological stability and food security at risk. Within this background, the study has been formulated with the objectives of land use pattern and cropping pattern is to analyse the temporal changes in the land use pattern and the loss of agricultural land in the selected rural, peri-urban and urban gradients, to study the changes and shift in cropping pattern and to estimate the crop diversification across the gradients. A multistage stratified random sampling technique was used. The data has been analysed using descriptive statistics, diversification indices, multiple regression analysis and garett ranking. The results of farm level analysis revealed that the conversion of the agricultural land through human settlements and other uses was more pronounced in the urban and peri-urban households than the rural households, might be due to urbanization and industrialization. The results also revealed that the gradual shift in the cropping pattern was pronounced in the rural gradient, followed by peri-urban and urban gradients. The major constraint faced by the sample respondents were water scarcity and labour scarcity for the land use and crop diversification.

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Singh, Akhilesh Kumar, und Arun Kumar Singh. „Land use and cropping pattern change in Chandauli District, Uttar Pradesh: A geographical analysis“. National Geographical Journal of India 66, Nr. 3 (30.09.2020): 259–73. http://dx.doi.org/10.48008/ngji.1746.

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Observation of land uses is an important tool to assess surface change at different spatio-temporal scales. There are six categories of land use discussed in which agricultural land-use class one. Any plan or policy related to agriculture land use brings a change in agricultural performance and cropping patterns. Apart from this, the cropping pattern is also governed by the law of comparative advantage concerning Agro-climatic conditions. The present paper focuses on the changes that have taken place in land use and cropping pattern in Chandauli district from 2000-01 to 2015-16. The study reveals that there has been a significant change in land use pattern and cropping pattern as settlement class of land use increase 22.72 per cent in 2015 and the area of rice and wheat crops increase 12829 ha. and 9767 ha. respectively from 2000-01 in the study area.

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Sagar, Sandip. „Changing Pattern of Agricultural Mechanization in Nalanda District of Bihar“. Journal of Advanced Research in English and Education 05, Nr. 02 (19.02.2021): 1–4. http://dx.doi.org/10.24321/2456.4370.202004.

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In agricultural production, farm mechanisation is a very necessary input. The number of tractors has increased rapidly, and in recent years, the population of draught animals has decreased in Bihar. A study was conducted in Nalanda District of Bihar, to classify the trend of tractor use and their economics. Mechanization is one of the most striking and pervasive phenomena of our times. Unfortunately, its study has been neglected by the social sciences, which have not sufficiently recognized that while technology itself belongs to the field of the natural sciences, its far-reaching effects on social life make it a vital subject for study by the social sciences. Insufficient and high variable precipitation and low fertility are major constraints to agricultural productivity. This brings the role of irrigation facilities and use of fertilizers. India is witnessing growth in irrigation facilities. Wells, canals and dams are constructed to cater needs of farmers. However, there is a lot to be achived in this regard. Applaying fertlizes are increasing day by day. It increases production as well as productivity of the field. At the same time, there is rampat mechanization of agriculture. Thus, it reduces human efforts and increases production of the field.

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Yadav, Swati. „Changing cropping pattern in U.P. Bundelkhand Region“. National Geographical Journal of India 67, Nr. 4 (31.12.2021): 385–402. http://dx.doi.org/10.48008/ngji.1785.

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Agriculture is a dominant economic activity in India. About 70% of its population is directly or directly related and dependent on agriculture so the availability of land for cultivation is important for those people who are engaged in agriculture. Cropping pattern is also an important aspect to know the agricultural scenario of any area. The pattern of cropping is very much affected by population growth and other physical and economic factors. In this paper, an attempt has been made to analyze the changing cropping pattern in Bundelkhand Region in the last two decades from 1994-95 to 2014-15.The major crops that occupy the agricultural land of the region are Cereals(40.52%),Pulses(41.06%),Oilseeds(15.46%),Cash crops(1.31%),Fodder(0.35%)and Other crops(1.29%). In this time of 20 years (1994-95 to 2014-15), a trend of positive change is found in all groups except fodder and other crops.

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Sun, Qingru, Meiyi Hou, Shuaiwei Shi, Liwei Cui und Zenglei Xi. „The Influence of Country Risks on the International Agricultural Trade Patterns Based on Network Analysis and Panel Data Method“. Agriculture 12, Nr. 3 (03.03.2022): 361. http://dx.doi.org/10.3390/agriculture12030361.

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The pattern of international agricultural trade is undergoing profound changes. The influence of country risks on the international agricultural trade pattern is prominent. In this paper, we comprehensively analyze the international agricultural trade patterns and explore the influence of country risks on them. Specifically, we first construct an international agricultural trade network (IATN) based on complex network theory. Second, we analyze each country’s diversity of import sources and the position of countries in the IATN using the Herfindahl–Hirschman Index (HHI) and network indicators, such as in-degree, out-degree, weighted in-degree, weighted out-degree, and betweenness centrality. Third, this paper explores the influence of different types of country risks, including economic risk and political risk, on international agricultural trade patterns using the panel regression method. The results show that countries played different roles and occupied different positions in the international agricultural trade pattern; notably, the United States occupied a core position, while Japan and Mexico had insufficient diversity in import sources. Moreover, based on the panel regression method, we find that political risks have a positive impact on the agricultural trade pattern, while an unstable economic environment could inhibit the agricultural trade pattern in various countries. This study could provide references for countries to implement agricultural trade policies regarding country risks to ensure stable agricultural trade relations and national food security.

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Gu, Bo Xuan, Yu Hua Wang und Yan Peng Li. „Evaluating the Spatial Pattern of Organic Agricultural Enterprises in China“. Applied Mechanics and Materials 361-363 (August 2013): 127–35. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.127.

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In recent years, considerable worldwide development in organic agriculture has attracted widespread attention from scholars both within and outside China, driven by its effects on environmental protection, food safety and health, international green barriers, At present, most of the existing research focuses on the production technology, development mode and benefit evaluation of organic agriculture, with relatively less concern about the activities of organic agricultural enterprises. Based on the indicators of number of enterprises, number of certificates, and certificate types, this paper analyzes the features characterizing the spatial distribution of organic agricultural enterprises in China. The results show that there are significant spatial variations in degree of diversity, degree of concentration, and evolving trajectories. It suggests that provinces and autonomous regions should give full play to the regional comparative advantages in order to facilitate the development of different types of organic agriculture.

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Metzger, Jean Paul. „Effects of deforestation pattern and private nature reserves on the forest conservation in settlement areas of the Brazilian Amazon“. Biota Neotropica 1, Nr. 1-2 (2001): 1–14. http://dx.doi.org/10.1590/s1676-06032001000100003.

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The effects of deforestation patterns, private nature-reserve extents and agricultural fallow periods on forest conservation were simulated for settlement projects in the Brazilian Amazon that produce a fish-bone pattern of occupation and where slash-and-burn agriculture is predominantly used. Data for simulation was obtained from previous work at the Bragantina region, the oldest agricultural frontier in the Brazilian Amazon. Forest conservation was evaluated using the size of remnant forest fragments, the amount of interior habitat, the connectivity among fragments and the extent of fragmentation. Results showed that the best scenario for forest conservation is the maintenance of 80% of the lot as privatereserve using deforestation pattern that allow to group the reserves from different farmers at the end of the lot. When private-reserve coverage is bellow 80% of the landscape, forest conservation status will be influenced by the deforestation pattern. Some patterns (e.g. random location of deforestation plots) will then be particularly deleterious, producing a highly fragmented landscape, while other patterns (e.g., progressive deforestation from one edge) can allow the maintenance of large forest fragments. To get forest conservation in these cases, private-reserve extent and deforestation pattern should be considered together. Considering both forest conservation and agricultural use, progressive patterns of deforestation (or land use) in a lot of 2,000m by 500m, with private nature-reserves covering 50% of the landscape seems to be the best compromise. To guarantee the private forest preservation, these forests should be pre-established when settlements are planned and grouped at the end of the lots.

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Kareemulla, K., Pandian Krishnan, S. Ravichandran, B. Ganesh Kumar, Sweety Sharma und Ramachandra Bhatta. „Spatiotemporal Analysis of Size and Equity in Ownership Dynamics of Agricultural Landholdings in India Vis-à-Vis the World“. Sustainability 13, Nr. 18 (13.09.2021): 10225. http://dx.doi.org/10.3390/su131810225.

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The increasing threat to sustainable agriculture is a major concern of planners worldwide. Human population growth together with increasing food requirements and competition for land use is leading to land scarcity for agricultural purposes. Farm size influences the extent of the adoption of mechanization and modern methods of farm management practices, which in turn results in increased productivity, production efficiency and agricultural income. We studied changes in macroeconomic factors such as dependency on agriculture, growth of the sector, the pattern of landholdings and tenure rights across major agriculturally important countries, as well as the priority of agriculture for the national economy (i.e., the share of agriculture in the national income) and its relationship to changes in farm size. The data on the percentage of area under farming, population growth, size of the agricultural workforce and other social dimensions from 24 countries of different geographical sizes were analysed. We used parameters such as the extent of changes in cropland, family-owned land, the agricultural workforce and their productivity, number of holdings and their distribution, women-headed holdings and finally total and per capita agricultural income, and measured the changes over time and space. The published data from national and international sources were used to establish the relationship between farm size and farm efficiency measured through the selected parameters. The results clearly establish that the size of farm holdings had an inverse relationship with the population dependent on agriculture, share of agriculture in national income and tenure rights. Australia had the largest average agricultural landholding (3243 ha), while India and Bangladesh had the lowest (1.3 and 0.3 ha, respectively). The inequality in the distribution of farmland ownership was greater in developed countries than in developing countries. Female farmland ownership was less than 20% in most developing countries and the relationship between the number of farm households and farm outcomes was found to have weakened over time. India, a developing as well as an agriculturally important country, was subjected to detailed analysis to understand the spatiotemporal dynamics of the size, distribution and ownership patterns of agricultural landholding.

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Dissertationen zum Thema "Agricultural pattern"

Haslem, Angie, und angie haslem@deakin edu au. „Landscape Pattern, Countryside Heterogeneity and Bird Conservation in Agricultural Environments“. Deakin University. Life and Environmental Sciences, 2008. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20090114.101341.

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Agricultural environments are critical to the conservation of biota throughout the world. This is due both to the limited extent of current reserve systems and the large, and still expanding, proportion of terrestrial environments already dominated by agricultural land-uses. Consequently, there is a growing call from scientists around the world for the need to maximise the conservation value of agricultural environments. Efforts to identify key influences on the conservation status of fauna in agricultural landscapes have taken complementary approaches. Many studies have focussed on the role of remnant or semi-natural vegetation, and emphasised the influence on biota of spatial patterns in the landscape. Others have recognised that many species use diverse countryside elements (matrix habitats) within farmland, and emphasise the benefits of landscape heterogeneity for conservation. Here, these research themes have been combined. This study takes a whole-of-landscape approach to investigating how landscape pattern and countryside heterogeneity influence the occurrence of birds in agricultural environments. Birds were sampled in 27 agricultural mosaics, each 1 km x 1 km in size (100 ha), in Gippsland, south-eastern Australia. Mosaics were selected to incorporate variation in two landscape properties: the cover of native vegetation, and richness of different types of element (i.e. land-uses/vegetation types). In each mosaic, 15 fixed sampling locations were stratified among seven different elements in proportion to their cover in the mosaic: native vegetation, linear vegetation, tree plantation, scattered paddock trees, pasture, wetlands and farm dams. Six point counts of birds were undertaken at all sample points in each mosaic: three each in the breeding and non-breeding months of a one-year period (October 2004 August 2005). Independent measures of the composition, configuration, and heterogeneity of elements in the mosaic had differing effects on the richness of bird species recorded in these same mosaics. Sub-groups of birds based on habitat requirements responded most strongly to the extent of preferred element types in mosaics. Woodland birds (those of greatest conservation concern in farmland environments in Australia) were richer in mosaics with higher cover of native vegetation while open-tolerant species responded to the extent of scattered trees. In contrast, for total species richness, mosaic heterogeneity (richness of element types) and landscape context (cover of native vegetation in surrounding area) had the greatest influence. Mosaic structural properties also influenced the composition of entire bird assemblages in study mosaics. Avifaunal composition showed systematic variation along two main gradients which were readily interpreted in relation to landscape properties: 1) a gradient in the cover of wooded vegetation and, 2) the proportional composition of vegetation types in the mosaic. These gradients represent common trajectories of landscape modification associated with agricultural development: namely, the removal of wooded vegetation and the replacement of native species with exotic vegetation (e.g. crops and plantations). Species possessing different characteristics in relation to three avian life-history traits (nest type, feeding guild and clutch size) varied significantly in their position along these gradients of landscape modification. Species with different nesting requirements showed a strong relationship with the gradient in wooded vegetation cover while species belonging to different feeding guilds were influenced by the gradient defined by the replacement of native vegetation with exotic species. More bird species were recorded in native vegetation than in any other type of element sampled in this study. Nevertheless, most countryside elements had value for many species; particularly structurally complex elements such as scattered trees and tree plantation. Further, each type of landscape element contained different bird assemblages. Species that were recorded in a greater number of different types of landscape element were also recorded in more mosaics. This was true for all species and for woodland birds, and indicates that species that can use a greater range of countryside elements may have an increased tolerance of future landscape modification. The richness of woodland species at survey sites in different elements was influenced by features of the mosaic in which they occurred. Notably, the richness of woodland bird species recorded at sites in scattered trees and pasture increased with a greater cover of native vegetation in the overall mosaic. Of the overall pool of woodland bird species documented in the broader study region, 35% of species were not recorded in the agricultural mosaics sampled here. While many of these species were uncommon in the study area, or were associated with vegetation communities infrequently sampled in mosaics, this shows that conservation efforts in agricultural landscapes will not be appropriate for all species. For those woodland species that were recorded, measures of the extent of wooded vegetation cover had a strong, positive influence on the frequency of occurrence of individual species in mosaics. Thus, individual species of woodland bird occurred more frequently in mosaics with a greater cover of wooded vegetation. Nine woodland species showed a stronger response to measures of vegetation cover that included tree plantation and/or scattered trees than to the cover of native vegetation alone. For these species, structurally complex countryside elements provide valuable supplementary habitat at the landscape scale. Results of this study show that landscape properties influence the occurrence of birds in agricultural mosaics. The extent of cover of element types, particularly native vegetation, had the strongest influence on all measures of bird occurrence in mosaics. Thus, native vegetation is vital for the persistence of birds in farmland landscapes and is the primary element on which conservation efforts in these environments depend. Nevertheless, with careful management, countryside elements may provide additional conservation benefits for many bird species. Countryside elements made an important contribution to landscape heterogeneity, the landscape property with greatest influence on overall bird richness in mosaics. Countryside elements also increased the structural complexity of cleared agricultural land, and so have the capacity to enhance connectivity in fragmented landscapes. A focus on these factors (landscape heterogeneity and structural complexity) will provide the greatest opportunities for using countryside elements to increase the conservation value of farmland environments for native fauna. The relatively small scale of this study indicates that the cumulative effect of even small elements in farm mosaics contributes to the structural properties of entire landscapes. Critically, this emphasises the important contribution that individual landholders can make to nature conservation in agricultural environments.

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Sharma, B. Anil Kumar. „Changing agricultural pattern and its impact on land use in south district of Sikkim“. Thesis, University of North Bengal, 2008. http://hdl.handle.net/123456789/1205.

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Camargo, Marcel Pinton de. „Aerial machine vision, geographical information system and hue for pattern classification in agriculture“. Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-17012019-180101/.

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In this research we aim to achieve cybernetic cohesion information flow in precision agriculture, integrating machine learning methods, computer vision, geographical information system and UAV-photogrammetry in an irrigated area with slaughterhouse wastewater, under five treatments (W100 - irrigation with superficial water and 100% of nitrogen mineral fertilization, E0, E33, E66 and E100 - irrigation with treated effluent from slaughterhouse and addition of 0, 33, 66 and 100% of nitrogen mineral fertilization, respectively) and four replications on grassland (Cynodon dactylon (L.) Pers.). Several images (between one hundred and two hundred) with red, green, blue (RGB) color model were captured using a quadcopter flying at 20 meter altitude and obtaining spatial resolution of 1 centimeter on a surface of approximately 0.5 ha. The images were orthorectified together with nine ground control points done by differential global positioning system (GPS), both processed in the Agisoft PhotoScan software. Thirteen photogrammetric projects were done over time with 30-day revisit, the root mean squared error (RMSE) was used as accuracy measurement, and reached values lower than 5 centimeters for x, y and z axis. The orthoimage obtained with unmanned aerial vehicle (UAV) photogrammetry was changed from RGB to hue, saturation, value (HSV) color model, and the hue color space was chosen due to independence of illumination, beyond it has a good description of exposure of soil and vegetation, but it is dependent of light source temperature, so difficult to estabilish a static threshold, so we selected an unsupervised classification method, K-Means, to classify the unknown patterns along the area. Polygons were drawn delimiting the area represented by each portion and a supervised classification method based on entropy was used, the decision tree, to explore and find patterns that recognize each treatment. These steps are also displayed in forms of georeferenced thematic maps and were executed in the open source softwares Python, QGIS and Weka. The rules defined on the hue color space reached an accuracy of 100% on the training set, and provided a better understanding about the distribution of soil and vegetation on the parcels. This methodology shows a great potential for analysis of spectral data in precision agriculture.
Nesta pesquisa pretendemos alcançar a coesão cibernética no fluxo de informações dentro da agricultura de precisão, integrando métodos de aprendizagem de máquinas, visão computacional, sistema de informação geográfica e aerofotogrametria em uma área irrigada com efluente de matadouro, sob cinco tratamentos (W100 - irrigação com água superficial e 100 % de adubação mineral nitrogenada, E0, E33, E66 e E100 - irrigação com efluente tratado de abatedouro e adição de 0, 33, 66 e 100% de adubação mineral nitrogenada, respectivamente) e quatro repetições em pastagem (Cynodon dactylon (L.) Pers.) Várias imagens (entre cem e duzentas) com modelo de cor vermelho, verde e azul (RGB) foram capturadas por um quadricóptero voando a 20 metros de altitude, e obtendo resolução espacial de 1 centímetro em uma superfície de aproximadamente 0.5 ha. As imagens foram ortorretificadas juntamente com nove pontos de controle, realizados pelo sistema de posicionamento global diferencial (GPS), ambos processados no software Agisoft PhotoScan. Treze projetos fotogramétricos foram realizados ao longo do tempo com revisita de 30 dias, a raiz do erro quadrático médio (RMSE) foi usada como medida de acurácia e atingiu valores menores que 5 centímetros para os eixos x, y e z. A ortoimagem obtida com a fotogrametria do veículo aéreo não tripulado (UAV) foi alterada de RGB para matiz, saturação, valor (HSV) e o espaço de cor matiz foi escolhido devido a independência da iluminação, além de ter boa descrição da exposição do solo e vegetação. Entretanto este é dependente da temperatura da fonte de luz, portanto difícil de se estabelecer um limiar estático, logo selecionamos um método de classificação não supervisionado, o K-Means, para classificar os padrões desconhecidos ao longo da área. Polígonos foram traçados delimitando a área representada por cada parcela e um método supervisionado de classificação baseado na entropia foi utilizado, a árvore de decisão, para explorar e encontrar padrões que reconheçam cada tratamento. Essas etapas também são exibidas em formas de mapas temáticos georeferenciados e foram executadas nos softwares de código aberto Python, QGIS e Weka. As regras definidas no espaço de cor matiz atingiram uma acurácia de 100% no conjunto de treinamento e proporcionaram um melhor entendimento sobre a distribuição do solo e da vegetação nas parcelas. Esta metodologia mostra um grande potencial para análise de dados na agricultura de precisão.

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Singha, Chandana. „Spatio-temporal change of agricultural land use pattern in Siliguri subdivision, Darjeeling District: Geographical analysis“. Thesis, University of North Bengal, 2018. http://ir.nbu.ac.in/handle/123456789/2784.

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Okay, Atiye Zeynep. „Spatial pattern and temporal dynamics of northern bobwhite abundance and agricultural landuse, and potential casual factors“. Texas A&M University, 2004. http://hdl.handle.net/1969.1/3355.

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There was a long-term decline in northern bobwhite (Colinus virginianus, NBW) abundance since the 1920s, based on the Christmas Bird Count (CBC) data, but with substantial spatial and temporal variations across its range. There were four recognizable periods in the spatial and temporal dynamics of NBW abundance between 1920 and 1990. Severe weather conditions and habitat loss due to land use change appeared to be the most important factors influencing the long-term trends and spatial patterns in NBW abundance. A spatial database of agricultural land use was developed based on the Census of Agriculture to evaluate the spatial patterns of land use variables over NBW range from 1920 to 1997. The results showed dramatic changes in land use over the period and the influence of socio-economic events, natural disasters and federal agricultural policies on the dynamics of land use pattern, and potential implications to NBW abundance were identified. Replacement of less intensive agriculture with intensive monoculture production and mechanization coincided with World War I, and the post-war collapse in agriculture and the economy, partly associated with the Dust Bowl, enhanced this trend. Monoculture production and clean farming practices were further intensified during World War II and the years following the war. These land use changes had overall negative effects on NBW habitat. Analysis of the changes in spatial pattern of NBW abundance in the Great Plains region during the severe drought of the 1950s showed a significant decline in NBW abundance during the drought and a contraction of the NBW range at its western edge. The post-drought recovery exhibited spatial patterns significantly different from the predrought ones. These findings suggested that severe drought caused short-term changes in regional distribution of NBW and range contraction, as well as long-lasting, largescale changes in spatial distribution of NBW abundance. This study provides scientific basis for landscape planning and management. Evaluating the spatial pattern and temporal dynamics of certain wildlife species at large scales over long-term periods, and identifying potential causal factors are key strategies for implementing innovative and sustainable approaches to planning and policy. Such strategies will have a significant impact on future landscape and wildlife species.

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Pan, Jingwen. „A Study on Agriculture Landscape Application and Development Pattern in Rural Cluj Region“. University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522340951754843.

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Nguyen, Thanh Le Vi. „Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies“. Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2020. https://ro.ecu.edu.au/theses/2359.

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In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods.

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Daggupati, Naga Prasad. „Assessment of the varitarget nozzle for variable rate application of liquid crop protection products“. Thesis, Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/414.

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Hallstrom, Daniel George. „Agricultural development patterns“. Thesis, Montana State University, 1995. http://etd.lib.montana.edu/etd/1995/hallstrom/HallstromD1995.pdf.

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A two-sector general equilibrium model of economic growth has been developed in conjunction with a political economy model of agricultural pricing policies. These theoretical models were used to both present arguments regarding the impact of economic and political forces on agriculture's share of GDP, as well as to provide guides towards specifying the econometric models. Empirical analysis was performed using two data sets. The first covered 81 countries for the years 1971-1981, but contained no explicit measure of policy. The second covered 22 countries for the years 1985-1989, and contained an explicit policy measure in the form of producer subsidy equivalents. Due to the lack of a policy measure in the large data set, a reduced form equation for agriculture's share had to be solved for in order to yield an operational statistical model. Results from this model indicated that variations in agriculture's share could be explained by: level of development, world prices, natural resources, and the political economy variables. For the smaller data set, agriculture's share and producer subsidy equivalents were considered to be jointly endogenous, and so were estimated as a system. It was found that agriculture's share has a negative effect on producer subsidy equivalents, however, producer subsidy equivalents did not have a statistically significant effect on agriculture's share.

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Ducarouge, Benjamin. „Régulation des systèmes d'adhérence cellulaire par le CRF2 : un effecteur du stress dans le tube digestif“. Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00767103.

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Le stress est impliqué dans le développement et l'exacerbation de diverses pathologies notamment au niveau intestinal. Les effets du stress dépendent de l'expression de neuromédiateurs spécifiques (CRF) et de leurs récepteurs. Notre étude porte sur la régulation et la fonction du CRF2 au niveau des entérocytes et des cellules tumorales coliques humaines. In vivo, nous avons montré que le stress et l'inflammation conduisent à l'augmentation de l'expression du CRF2 dans les colonocytes chez le rat. Dans les tumeurs, l'expression du CRF2 augmente avec le grade tumoral. In vitro, dans les cellules HT-29, l'activation du CRF2 induit une altération des jonctions adhérentes et des adhérences focales par la voie Src/ERK/FAK. Ces mécanismes sont responsables de la régulation de la perméabilité épithéliale et de l'augmentation de la migration des cellules tumorales. Ces travaux contribuent à la compréhension des mécanismes impliquant le stress dans le développement des pathologies intestinales.

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Bücher zum Thema "Agricultural pattern"

Rahaman, Hasibur. Diversified Cropping Pattern and Agricultural Development. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-55728-7.

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Sharma, Anju. Dynamics of cropping pattern: A regional study of Punjab. New Delhi: Northern Book Centre, 1998.

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Orback, Jens. Agriculture and incentives, the Tanzanian case: A report on agricultural price policy in Tanzania and its effects on production pattern and market development : report from a minor research task. Uppsala: Swedish University of Agricultural Sciences, International Rural Development Centre, 1985.

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Nigerian Institute of Social and Economic Research, Hrsg. Pattern of gender responsibility in tradeable crop agriculture: A baseline study of selected farming systems in Nigeria. Ibadan: Nigerian Institute of Social and Economic Research (NISER), 2001.

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Verma, A. K. Time utilisation pattern of faculty members of agriculture/veterinary science institutions. New Delhi: Institute of Applied Manpower Research, 1996.

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Exploitation of women labour in India: Employment pattern and wage discrimination. New Delhi: Deep & Deep Publications, 1993.

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Singh, Arun Kumar. Ecology and land use pattern in Gudalur Taluk, Nilgiris: A geographical study. Calcutta: Anthropological Survey of India, Ministry of Human Resource Development, Dept. of Culture, Govt. of India, 1991.

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Levy, Victor. Cropping pattern, mechanization, child labor and fertility behavior in a farming economy: Rural Egypt. Jerusalem, Israel: Maurice Falk Institute for Economic Research in Israel, 1985.

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Levy, Victor. Cropping pattern, mechanization, child labor and fertility behavior in a farming economy: Rural Egypt. Jerusalem, Israel: Maurice Falk Institute for Economic Research in Israel, 1985.

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Sharma, S. K. Estimates of requirements of agricultural and veterinary graduates and post-graduates 1995 and 2000 A.D.: Phase I- Utilisation of patter, phase II-Utilisation pattern of agricultural personnel in financial sector. New Delhi: Institute of Applied Manpower Research, 1996.

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Buchteile zum Thema "Agricultural pattern"

Atibudhi, H. N. „Pattern of Agricultural Diversification in Odisha“. In India Studies in Business and Economics, 69–77. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1997-2_6.

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Rahaman, Hasibur. „Levels and Efficiency of Agricultural Development“. In Diversified Cropping Pattern and Agricultural Development, 139–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55728-7_5.

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De, Sayan, Ishita Bhakta, Santanu Phadikar und Koushik Majumder. „Agricultural Image Augmentation with Generative Adversarial Networks GANs“. In Computational Intelligence in Pattern Recognition, 335–44. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3089-8_32.

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Sinek, F. „A comparison of the pattern of vacuum in milking machines with synchronous and asynchronous pulsation“. In Agricultural Engineering, 985–91. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003211471-26.

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Rahaman, Hasibur. „Land Use and Cropping Pattern Dynamic“. In Diversified Cropping Pattern and Agricultural Development, 35–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55728-7_2.

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Rahaman, Hasibur. „Agriculture Development: Inputs-Outputs Dimension“. In Diversified Cropping Pattern and Agricultural Development, 71–106. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55728-7_3.

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Rahaman, Hasibur. „Overview of the Study Area“. In Diversified Cropping Pattern and Agricultural Development, 1–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55728-7_1.

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Rahaman, Hasibur. „Status of Crop Diversification“. In Diversified Cropping Pattern and Agricultural Development, 107–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55728-7_4.

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Rahaman, Hasibur. „Challenges and Opportunities of Agricultural Development in the District“. In Diversified Cropping Pattern and Agricultural Development, 189–220. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55728-7_6.

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Chen, Lei, Jin Gao, Yuan Yuan und Li Wan. „Agricultural Question Classification Based on CNN of Cascade Word Vectors“. In Pattern Recognition and Computer Vision, 110–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03335-4_10.

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Konferenzberichte zum Thema "Agricultural pattern"

Chiu, Mang Tik, Xingqian Xu, Yunchao Wei, Zilong Huang, Alexander G. Schwing, Robert Brunner, Hrant Khachatrian et al. „Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis“. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00290.

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Wu, Chi-hsin, und Peter C. Doerschuk. „Spatial pattern classification for optical agricultural remote sensing“. In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, herausgegeben von Andrew G. Tescher. SPIE, 1994. http://dx.doi.org/10.1117/12.186567.

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Hiç, Özlen, und Ayşen Hiç Gencer. „The Effects of Agricultural Policies on the Production Pattern: The Case of Turkey“. In International Conference on Eurasian Economies. Eurasian Economists Association, 2021. http://dx.doi.org/10.36880/c13.02601.

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The production pattern is a “production composition” which gives the most suitable combination for the physical and ecological characteristics of the arable land. It is determined according to the current and potential demand for agricultural goods- both domestic as well as foreign demand-. Once the product pattern is determined, it will be use as an important data for increasing the efficiency of the agricultural sector, as well as for agricultural planning and for effective use of natural resources. One of the goals of agricultural policies is to establish an “optimal balance” for agricultural producers and consumers via establishing the demand and supply balance on each product level, and to create an “optimal product pattern” by directing this agricultural production. To be able to achieve this goal, “agricultural policies” and among them, especially “agricultural support policies” appears to play an especially important role. In this article, we will first summarize the importance and the general characteristics the agricultural sector and and give a general view of the agricultural policies and their effects on the product pattern. Then we will briefly mention two case studies in how the product pattern is determined; then, we will be discussing the effects of the changes in the climate, population rates and structure and the gradual decrease of agricultural areas on the product pattern.

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Wang, Jingjing, Yong Hu, Ling Sun, Zhiming Wang, Kun Yu, Lin Qiu, Jie Shan und Xiaojun Huang. „Dynamic analysis of agricultural landscape pattern changes in Jiangsu“. In 2017 6th International Conference on Agro-Geoinformatics. IEEE, 2017. http://dx.doi.org/10.1109/agro-geoinformatics.2017.8047049.

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Santos, Flávio E. de O., Marcos A. S. da Silva, Leonardo N. Matos, Fábio R. de Moura und Márcia H. G. Dompieri. „Self-Organizing Map approach to cluster Brazilian agricultural spatiotemporal diversity“. In Escola Regional de Computação Bahia, Alagoas e Sergipe. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/erbase.2021.20058.

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This work aims to cluster Brazilian municipalities according to their spatiotemporal agricultural diversity pattern. The diversity index has been defined for eight categories and calculated by Shannon’s entropy index from annual (1999-2018) IBGE’s estimates for agricultural production. The proposed clustering method is based on the Self-Organizing Map, an unsupervised artificial neural network, and comprises visual and automatic steps. The method partitioned the municipalities into eight groups spatially organized in three regions showing different spatiotemporal patterns.

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Ismanto, Mohammad Andi, Harisno, Victory Haris Kusumawardhana und Harco Leslie Hendric Spits Warnars. „Strategic Planning Of Information Systems And Information Technology At Agricultural Research And Development Agency, Ministry Of Agriculture“. In 2018 Indonesian Association for Pattern Recognition International Conference (INAPR). IEEE, 2018. http://dx.doi.org/10.1109/inapr.2018.8627011.

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Suyun, Chen. „Researching on Pattern of Agricultural Industrialization Based on Supplying Chain“. In 2009 Second International Conference on Future Information Technology and Management Engineering (FITME). IEEE, 2009. http://dx.doi.org/10.1109/fitme.2009.129.

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Ciarfuglia, Thomas A., Ionut Marian Motoi, Leonardo Saraceni und Daniele Nardi. „Pseudo-label Generation for Agricultural Robotics Applications“. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. http://dx.doi.org/10.1109/cvprw56347.2022.00175.

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Wang, Zhenghao, Lifeng Qiao und Mengke Wang. „Agricultural pest detection algorithm based on improved faster RCNN“. In 2021 International Conference on Computer Vision and Pattern Analysis, herausgegeben von Ruimin Hu, Yang Yue und Siting Chen. SPIE, 2022. http://dx.doi.org/10.1117/12.2626859.

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Yunzhong, Cao, Zhou Bei und Wang Li. „Constructing electronic commerce pattern of agricultural enterprise in Sichuan earthquake area: Based on atomic pattern theory“. In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5886809.

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Berichte der Organisationen zum Thema "Agricultural pattern"

Coyle, William, Mark Gehlhar, Thomas Hertel, Zhi Wang und Wusheng Yu. Understanding the Determinants of structural Change in World Food Markets. GTAP Working Paper, September 2000. http://dx.doi.org/10.21642/gtap.wp02.

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This study assesses the interaction between climate change and agricultural trade policies. We distinguish between two dimensions of agricultural trade policy: market insulation and subsidy levels. Building on the previous work of Tsigas, Frisvold and Kuhn (1997) we find that, in the presence of current levels of agricultural subsidies, increased price transmission --as called for under the Uruguay Round Agreement on Agriculture-- reduces global welfare in the wake of climate change. This is due to the positive correlation between productivity changes and current levels of agricultural support. Increases in subsidized output under climate change tend to exacerbate inefficiencies in the global agricultural economy in the absence of market insulation. However, once agricultural subsidies have also been eliminated, price transmission via the global trading system contributes positively to economic adaptation under climate change. products. This may partially explain the relatively slow growth of world grain import demand in recent years. In addition, bilateral agreements with East Asia, NAFTA, and the evolution of the CAP, have all had important impacts on the structure of world food and agricultural trade. The objective of this paper is to assess the relative role of each of the major forces-- consumer demand, factor accumulation, transport costs, and policy change--in driving changes in the composition of world food trade in 1980-1995. To do so, we employ a modified version of the Global Trade Analysis Project (GTAP) model of world trade which permits us to isolate the contribution of each of these related factors to the changing composition of world food trade in a general equilibrium context. We evaluate the relative role of each of these factors by simulating the model backwards in time, from 1995 to 1980 under different assumptions. This general approach, termed “backcasting” (i.e. backwards forecasting), takes as exogenous the basic drivers of change and attempts to explain the resulting change in food trade composition. The model-produced changes in the composition of agricultural and food trade are compared with historical trade data, to determine the relative importance of each factor on the changing composition of food trade. Given limited space, our focus will be on explaining the changes in the global composition of food and agriculture trade. A natural follow-on effort would target specific markets in more detail. This type of backcasting approach was first employed by Gehlhar (1997) who sought to explain the shift in exports of primary commodities to manufactures in East Asia in the 1980's. He calibrated the GTAP model to 1992 data, then implemented shocks to factor endowments and economywide total factor productivity (TFP) in order to force each economy back to its 1982 levels of population, land, labor, human capital, physical capital and technology. By comparing actual and predicted changes in export shares in this period, he found human capital accumulation played a key role in explaining the change in the aggregate composition of East Asian exports. Gehlhar, Hertel and Martin (1994) built on this work in an effort to predict future changes in the pattern of agricultural trade from 1992-2002. They also emphasized the importance of supply-side determinants of agricultural trade. In this paper, we go beyond this earlier work in a number of ways. First, we focus on the composition of agricultural exports, rather than simply looking at the share of agriculture in total trade. Secondly, we incorporate the Cranfield et al. estimates of Rimmer and Powell’s recently developed, implicitly directly additive demand system (nicknamed AIDADS) into the GTAP model. This permits us to better capture the impact of demand-side changes on the pattern of global tr

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Research Institute (IFPRI), International Food Policy. Spatial patterns of agricultural productivity. Washington, DC: International Food Policy Research Institute, 2016. http://dx.doi.org/10.2499/9780896298811_03.

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Alonso-Sanabria, Juan David, Luis Fernando Melo-Velandia und Daniel Parra-Amado. Connecting the Dots: Renewable Energy, Economic Growth, Reforestation, and Greenhouse Gas Emissions in Colombia. Banco de la República, Oktober 2023. http://dx.doi.org/10.32468/be.1252.

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This study aims to establish a comprehensive linkage between CO2 emissions and the composition of energy sources, economic growth, and reforestation, thereby shedding light on their intricate connections in Colombia over the period 1970-2018. First, we use different types of energy consumption including non-renewable, renewable, and hydroelectric sources. As expected, our findings reveal a noteworthy effect of non-renewable sources that lead to increased emissions, while renewable sources help mitigate those emissions. Second, the preservation of forested areas plays a crucial role in mitigating CO2 emissions. Third, the agricultural sector significantly contributes to the rise in emissions, encompassing both crops and livestock, a characteristic often observed in emerging economies. Moreover, in the long-run equilibrium, we find real GDP show the characteristic inverted U-shaped pattern commonly linked with the Environmental Kuznets Curve (EKC) hypothesis.

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Research Institute (IFPRI), International Food Policy. Agricultural productivity in Africa: Trends, patterns, and determinants. Washington, DC: International Food Policy Research Institute, 2016. http://dx.doi.org/10.2499/9780896298811.

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Dancer, Helen, und Imogen Bellwood-Howard. COVID-19 and Social Differentiation in African Agriculture. Institute of Development Studies (IDS), November 2021. http://dx.doi.org/10.19088/apra.2021.044.

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This brief presents a summary of key findings from a multi-country study of social differentiation in African agricultural value chains in the context of COVID-19. It aims to understand how trends in the politics and participation of different actors in agriculture have contributed to patterns of social differentiation, and how these patterns have interacted with the shock of COVID-19. It brings attention both to the implications of political decision-making and the effects of the pandemic on value chain structures and those working within the sector.

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Hazell, Peter B. R., Xinshen Diao und Eduardo Magalhaes. Ghana’s agricultural transformation: Past patterns and sources of change. Washington, DC: International Food Policy Research Institute, 2019. http://dx.doi.org/10.2499/9780198845348_04.

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Research Institute (IFPRI), International Food Policy. Agricultural productivity in Africa: Trends, patterns, and determinants: Synopsis. Washington, DC: International Food Policy Research Institute, 2016. http://dx.doi.org/10.2499/9780896298828.

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Berhane, Guush, Gashaw Tadesse Abate und Abdulazize Wolle. Agricultural intensification in Ethiopia: Patterns, trends, and welfare impacts. Washington, DC: International Food Policy Research Institute, 2022. http://dx.doi.org/10.2499/p15738coll2.136483.

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Research Institute (IFPRI), International Food Policy. Introduction In Agricultural productivity in Africa: Trends, patterns, and determinants. Washington, DC: International Food Policy Research Institute, 2016. http://dx.doi.org/10.2499/9780896298811_01.

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