Relevant bibliographies by topics / Agricultural data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Agricultural data'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Agricultural data"

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S., Shobana, and M. Sujithra. "AGRICULTURAL DATA ANALYSIS." International Journal of Advanced Research 9, no. 08 (August 31, 2021): 807–15. http://dx.doi.org/10.21474/ijar01/13330.

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In agriculture sector where farmers and agribusinesses have to make innumerable decisions every day and intricate complexities involves the various factors influencing them. An essential issue for agricultural planning intention is the accurate yield estimation for the numerous crops involved in the planning. Data mining techniques are necessary approach for accomplishing practical and effective solutions for this problem. Agriculture has been an obvious target for big data. Environmental conditions, variability in soil, input levels, combinations and commodity prices have made it all the more relevant for farmers to use information and get help to make critical farming decisions. This paper focuses on the analysis of the agriculture data and finding optimal parameters to maximize the crop production using Machine learning techniques like random forest regressor and Linear Regression. Mining the large amount of existing crop, soil and climatic data, and analysing new, non-experimental data optimizes the production and makes agriculture more resilient to climatic change.
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Neil, Stuart. "Agricultural data quality." Significance 1, no. 1 (March 2004): 30–32. http://dx.doi.org/10.1111/j.1740-9713.2004.00014.x.

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Adamowicz, Mieczysław. "CHANGES IN AGRICULTURAL POLICY SYSTEMS AND FORMS OF AGRICULTURAL SUPPORT." Annals of the Polish Association of Agricultural and Agribusiness Economists XIX, no. 3 (August 22, 2017): 11–17. http://dx.doi.org/10.5604/01.3001.0010.3208.

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The paper aimed to present the role of agriculture in the economy in OECD countries and changes in their agricultural policies. The aim of the work is an assessment of agriculture in the period 1995-2014 and changes in the level and structure of support by governments and their institutions to agriculture within the agricultural policy systems. The parspective for agricultual policy till 2020 was presented as well. The data and informations for the work was gathered foom literature, OECD publications, especially OECD Agricultural Policy Monitoring and Evaluation Report 2015. Evaluation of GDP, TSE, PSE, CSE and GSSE were presented for specific group of countries.
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Kaushalya, R., V. Praveen Kumar, and S. Shubhasmita. "Assessing Agricultural Vulnerability in India using NDVI Data Products." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 39–46. http://dx.doi.org/10.5194/isprsarchives-xl-8-39-2014.

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Impact of climate change on Indian rainfed agriculture was assessed using temporal NDVI data products from AVHRR and MODIS. Agricultural vulnerability was analysed using CV of Max NDVI from NOAA-AVHRR (15-day, 8 km) and MODIS-TERRA (16-day, 250 m) NDVI data products from 1982–2012. AVHRR dataset was found suitable for estimating regional vulnerability at state and agro-eco-sub-region (AESR) level while MODIS dataset was suitable for drawing district-level strategy for adaptation and mitigation. Methodology was developed to analyse NDVI variations with spatial pattern of rainfall using 10 X 10 girded data and spatially interpolating it to estimate Standard Precipitation Index. Study indicated large variations in vegetation dynamics across India owing to bio-climate and natural resource base. IPCC framework of vulnerability and exposure was used to identify vulnerable region extending from arid western India to semi-arid and dry sub-humid regions in central India and southern peninsula. This is a major agricultural region in the country with sizable human and livestock population with millions of marginal and small farm holdings. Exposure to climatic variability at local and regional levels have national implications and study indicated that over 122 districts extending over 110 mha was vulnerable to climate change that spread across 26 typical AESR in 11 states in India. Of the 74 mha under agriculture in the region, MODIS dataset indicated 47 mha as agriculturally vulnerable while coarser resolution of AVHRR dataset indicated a conservative estimate of 29 mha. First ever estimates of agricultural vulnerability for India indicates 20.4 to 33.1 % agricultural land under risk from climate change.
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K.*, Gupta N., Isaac R. K., and R. K. Singh. "Maintenance and Analysis of Agricultural Data: A Challenge." International Journal of Bioassays 5, no. 09 (August 31, 2016): 4842. http://dx.doi.org/10.21746/ijbio.2016.09.0010.

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Agriculture is the backbone of India and agriculture research is required for sustainable and modern agriculture. In India there are more than 1,00,000 agricultural scientists working for agricultural research and development ICAR, SAUs, KVKs, CSIR, IITs, NGOs, etc. large number of data are being produced by different scientist, researcher and student involved in various research work conducted on farm. The availability of huge data from the field of agriculture is needed to be translated in valuable and easily understandable format. Several data collecting agencies are working on state, central and international level. In spite of good no. of available software's, the information obtained through the analysis of data are, somehow, lacking in meeting their fate of serving the targeted communities-farmers, researcher and student. Loss of data means loss of national money. So there is need of proper Maintenance and analysis of agricultural data. Here vast information is collected related to the topic from different countries to evaluate what type of system are being used by them to solve the problem and also to prepare a strategy by adopting which maintenance and analysis of agricultural data in India will be possible. This will save not only the money but also the time for generating the same data and the valuable man power.
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Bhojani, Shital Hitesh. "Geospatial Data Mining Techniques: Knowledge Discovery in Agricultural." Indian Journal of Applied Research 3, no. 1 (October 1, 2011): 22–24. http://dx.doi.org/10.15373/2249555x/jan2013/10.

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Trenz, O., J. Šťastný, and V. Konečný. "Agricultural data prediction by means of neural network." Agricultural Economics (Zemědělská ekonomika) 57, No. 7 (August 1, 2011): 356–61. http://dx.doi.org/10.17221/108/2011-agricecon.

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The contribution deals with the prediction of crop yield levels, using an artificial intelligence approach, namely a multi-layer neural network model. Subsequently, we are contrasting this approach with several non-linear regression models, the usefulness of which has been tested and published several times in the specialized periodicals. The main stress is placed on judging the accuracy of the individual methods and of the implementation. A neural network simulation device is that which enables the user to set an adequate configuration of the neural network vis á vis the required task. The conclusions can be generalized for other tasks of a similar nature, especially for the tasks of a non-linear character, where the benefits of this method increase.
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Shahar, Y., C. Blacker, R. Kavanagh, P. James, and J. A. Taylor. "Implementation of Ag Data Agricultural Services for Precision Agriculture." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 656–61. http://dx.doi.org/10.1017/s2040470017000644.

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This paper discusses a conceptual design of a Ag Data service for the farm industry, compares it to desktops FMIS and discusses some of the main concepts this kind of system may include. Beginning with an introduction to the current situation and how the amount and size of the data is affecting the capacity to process it efficiently, on a personal computer desktop or other devices. Following with a description of the characteristics and components, presenting a case study to demonstrate the way it may function within a farm environment.
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Vinay, Dasari. "Analysis of the Agricultural Data Using Machine Learning Techniques." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 5745–52. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020282.

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Kan, Ying Bo, Ling Ling Wang, Yi Shan Zhang, and En Ping Liu. "Research on Control System of Tropical Intelligent Agriculture in Hainan." Applied Mechanics and Materials 385-386 (August 2013): 923–26. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.923.

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Research on intelligent agriculture in our country has attracted great attention of the researchers now, but it is still under discovery. According to tropical agriculture in Hainan to IT's demand, the paper studies the key technology in the development of tropical intelligent agriculture, including automatic test technology, automatic control technology, Internet of Things and so on. This paper analyzes factors that affect tropical intelligent agricultures development, which include agricultural program, agricultural policies and regulations, agricultural technology situation, infrastructure construction, field management and other factors. The thesis builds a model between the development of tropical intelligent agriculture and its affecting factors. A developmental idea of tropical intelligent agriculture in Hainan is proposed on the basis of the model. The thesis analyzes the construction of intelligent agriculture control system in Hainan from the angles of data acquisition, data transfer, data analysis and data feed.
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Dissertations / Theses on the topic "Agricultural data"

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Momsen, Eric. "Vector-Vector Patterns for Agricultural Data." Thesis, North Dakota State University, 2013. https://hdl.handle.net/10365/27040.

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Agriculture is increasingly driven by massive data, and some challenges are not covered by existing statistics, machine learning, or data mining techniques. Many crops are characterized not only by yield but also by quality measures, such as sugar content and sugar lost to molasses for sugarbeets. The set of features furthermore contains time series data, such as rainfall and periodic satellite imagery. This study examines the problem of identifying relationships in a complex data set, in which there are vectors (multiple attributes) for both the explanatory and response conditions. This problem can be characterized as a vector-vector pattern mining problem. The proposed algorithm uses one of the vector representations to determine the neighbors of a randomly picked instance, and then tests the randomness of that subset within the other vector representation. Compared to conventional approaches, the vector-vector algorithm shows better performance for distinguishing existing relationships.
National Science Foundation Partnerships for Innovation program Grant No. 1114363
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Koyuncu, Atayil. "Acquisition Of Field Data For Agricultural Tractor." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/2/12607237/index.pdf.

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During the operations of an agricultural tractor, front axle and front axle support encounter the worst load conditions of the whole tractor. If the design of these components is not verified by systematic engineering approach, the customers could face with sudden failures. Erkunt Agricultural Machinery Company, which is located in Ankara, has newly designed and manufactured the front axle support of its agricultural tractors. In this study, the design of 2WD (Wheel Drive) Erkunt Bereket Agricultural Tractor&
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s front axle support has been verified by developing a verification method, which involves testing the tractor on a special test track and field and together with the computer aided engineering analysis, in order to prevent such failures in the lifetime of the tractor. For this purpose, a strain gage data acquisition system has been designed to measure the strain values on the component, while the tractor is operating on a test track and field. The locations of the strain gages have been determined by simulating the selected design load cases through finite element method. Measuring the maximum strains for the front axle support that have been experienced by the tractor while operating, the stress values have been calculated and the design safety has been investigated considering the material&
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s tensile strength. Secondly, the fatigue life of the component regarding the acquired strain data has been predicted. These processes have led the company to verify the design of the front axle support.
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Xu, Xing John. "Multi-Variate Attribute Selection for Agricultural Data." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27612.

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Farmers always have been concerned about the quantity of crops (yield) as well as the quality of crops (sugar content of the sugar beets). The quality and quantity of crops are affected by various attributes, some are natural elements (rain, sunshine etc) and some are not (the amount of fertilizer, seed type etc). Some techniques have been developed to discover attributes that are important to different crops? yield. But within those selected attributes, how can we tell one attribute is more important than the other? The proposed algorithm is aimed to utilize the advantages of multiple response attributes to select the important attributes and then put the selected attributes in a hierarchical order. Although at the end this paper only focuses on yield prediction, any other target attribute can be a candidate for the prediction model.
Grant No. 1114363 from National Science Foundation
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Chaddad, Fabio R. "Financial constraints in U.S. agricultural cooperatives : theory and panel data econometric evidence /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3036812.

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Lawal, Najib. "Modelling and multivariate data analysis of agricultural systems." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/modelling-and-multivariate-data-analysis-of-agricultural-systems(f6b86e69-5cff-4ffb-a696-418662ecd694).html.

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The broader research area investigated during this programme was conceived from a goal to contribute towards solving the challenge of food security in the 21st century through the reduction of crop loss and minimisation of fungicide use. This is aimed to be achieved through the introduction of an empirical approach to agricultural disease monitoring. In line with this, the SYIELD project, initiated by a consortium involving University of Manchester and Syngenta, among others, proposed a novel biosensor design that can electrochemically detect viable airborne pathogens by exploiting the biology of plant-pathogen interaction. This approach offers improvement on the inefficient and largely experimental methods currently used. Within this context, this PhD focused on the adoption of multidisciplinary methods to address three key objectives that are central to the success of the SYIELD project: local spore ingress near canopies, the evaluation of a suitable model that can describe spore transport, and multivariate analysis of the potential monitoring network built from these biosensors. The local transport of spores was first investigated by carrying out a field trial experiment at Rothamsted Research UK in order to investigate spore ingress in OSR canopies, generate reliable data for testing the prototype biosensor, and evaluate a trajectory model. During the experiment, spores were air-sampled and quantified using established manual detection methods. Results showed that the manual methods, such as colourimetric detection are more sensitive than the proposed biosensor, suggesting the proxy measurement mechanism used by the biosensor may not be reliable in live deployments where spores are likely to be contaminated by impurities and other inhibitors of oxalic acid production. Spores quantified using the more reliable quantitative Polymerase Chain Reaction proved informative and provided novel of data of high experimental value. The dispersal of this data was found to fit a power decay law, a finding that is consistent with experiments in other crops. In the second area investigated, a 3D backward Lagrangian Stochastic model was parameterised and evaluated with the field trial data. The bLS model, parameterised with Monin-Obukhov Similarity Theory (MOST) variables showed good agreement with experimental data and compared favourably in terms of performance statistics with a recent application of an LS model in a maize canopy. Results obtained from the model were found to be more accurate above the canopy than below it. This was attributed to a higher error during initialisation of release velocities below the canopy. Overall, the bLS model performed well and demonstrated suitability for adoption in estimating above-canopy spore concentration profiles which can further be used for designing efficient deployment strategies. The final area of focus was the monitoring of a potential biosensor network. A novel framework based on Multivariate Statistical Process Control concepts was proposed and applied to data from a pollution-monitoring network. The main limitation of traditional MSPC in spatial data applications was identified as a lack of spatial awareness by the PCA model when considering correlation breakdowns caused by an incoming erroneous observation. This resulted in misclassification of healthy measurements as erroneous. The proposed Kriging-augmented MSPC approach was able to incorporate this capability and significantly reduce the number of false alarms.
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Reynolds, Curt Andrew 1960. "Estimating crop yields by integrating the FAO crop specific water balance model with real-time satellite data and ground-based ancillary data." Thesis, The University of Arizona, 1998. http://hdl.handle.net/10150/192102.

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The broad objective of this research was to develop a spatial model which provides both timely and quantitative regional maize yield estimates for real-time Early Warning Systems (EWS) by integrating satellite data with groundbased ancillary data. The Food and Agriculture Organization (FAO) Crop Specific Water Balance (CSWB) model was modified by using the real-time spatial data that include: dekad (ten-day) estimated rainfall (RFE) and Normalized Difference Vegetation Index (NDVI) composites derived from the METEOSAT and NOAA-AVHRR satellites, respectively; ground-based dekad potential evapo-transpiration (PET) data and seasonal estimated area-planted data provided by the Government of Kenya (GoK). A Geographical Information System (GIS) software was utilized to: drive the crop yield model; manage the spatial and temporal variability of the satellite images; interpolate between ground-based potential evapotranspiration and rainfall measurements; and import ancillary data such as soil maps, administrative boundaries, etc.. In addition, agro-ecological zones, length of growing season, and crop production functions, as defined by the FAO, were utilized to estimate quantitative maize yields. The GIS-based CSWB model was developed for three different resolutions: agro-ecological zone (AEZ) polygons; 7.6-kilometer pixels; and 1.1-kilometer pixels. The model was validated by comparing model production estimates from archived satellite and agro-meteorological data to historical district maize production reports from two Kenya government agencies, the Ministry of Agriculture (MoA) and the Department of Resource Surveys and Remote Sensing (DRSRS). For the AEZ analysis, comparison of model district maize production results and district maize production estimates from the MoA (1989-1997) and the DRSRS (1989-1993) revealed correlation coefficients of 0.94 and 0.93, respectively. The comparison for the 7.6-kilometer analysis showed correlation coefficients of 0.95 and 0.94, respectively. Comparison of results from the 1.1-kilometer model with district maize production data from the MoA (1993-1997) gave a correlation coefficient of 0.94. These results indicate the 7.6-kilometer pixel-by-pixel analysis is the most favorable method. Recommendations to improve the model are finer resolution images for area planted, soil moisture storage, and RFE maps; and measuring the actual length of growing season from a satellite-derived Growing Degree Day product.
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Jones, Brenda M., and n/a. "Digging up data: a reanalysis of so called �horticultural� tools." University of Otago. Department of Anthropology, 1999. http://adt.otago.ac.nz./public/adt-NZDU20070523.153015.

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Elsdon Best�s 1925 work Maori Agriculture has been influential in New Zealand archaeology impacting on the terminology and assumed functions applied to so called �horticultural� implements retrieved in excavations, as well as those in museums and private collections. This thesis critically examines Best�s horticultural tool classification and the decisions he made with regards to tool function. Ethnographic accounts are investigated in an effort to understand how and why Best selected the terms and functions that he did. The literature review reveals anomalies in the conclusions that Best drew and the morphology of the tools that he described, highlighting the lack of order and confusion surrounding horticultural tool function, terminology and morphology, and prompting a much needed reassessment of horticultural implements. A study of artefacts from New Zealand museums was undertaken with the aim of generating two typologies for so called �horticultural� tools. The artefacts are classified to specific types using specified attributes, and following the classification process, are investigated for metric and non-metric variables that are indicative of the tool�s function. Graphical and basic statistical analyses revealed largely unimodal distributions for the metric attributes recorded for each tool type. Non-metric qualities also displayed a uniformity to their occurrence within the individual types. The data for each type is discussed with regards to tool function, combining the results of the attribute analyses with comparable tool morphologies from other Pacific cultures. The distribution of tool types in prehistoric New Zealand is also investigated in an effort to elucidate tool function. This investigation highlights the artefacts as earth-working implements, disestablishing the restricted horticultural context which for so long has been associated with such tools.
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Smith, Derik Lafayette, and Satya Prakash Dhavala. "Using big data for decisions in agricultural supply chain." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81106.

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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 53-54).
Agriculture is an industry where historical and current data abound. This paper investigates the numerous data sources available in the agricultural field and analyzes them for usage in supply chain improvement. We identified certain applicable data and investigated methods of using this data to make better supply chain decisions within the agricultural chemical distribution chain. We identified a specific product, AgChem, for this study. AgChem, like many agricultural chemicals, is forecasted and produced months in advance of a very short sales window. With improved demand forecasting based on abundantly-available data, Dow AgroSciences, the manufacturer of AgChem, can make better production and distribution decisions. We analyzed various data to identify factors that influence AgChem sales. Many of these factors relate to corn production since AgChem is generally used with corn crops. Using regression models, we identified leading indicators that assist to forecast future demand of the product. We developed three regressions models to forecast demand on various horizons. The first model identified that the price of corn and price of fertilizer affect the annual, nation-wide demand for the product. The second model explains expected geographic distribution of this annual demand. It shows that the number of retailers in an area is correlated to the total annual demand in that area. The model also quantifies the relationship between the sales in the first few weeks of the season, and the total sales for the season. And the third model serves as a short-term, demand-sensing tool to predict the timing of the demand within certain geographies. We found that weather conditions and the timing of harvest affect when AgChem sales occur. With these models, Dow AgroSciences has a better understanding of how external factors influence the sale of AgChem. With this new understanding, they can make better decisions about the distribution of the product and position inventory in a timely manner at the source of demand.
by Derik Lafayette Smith and Satya Prakash Dhavala.
M.Eng.in Logistics
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Catney, Denise Catherine. "Mathematical modelling of abbatoir condemnation data." Thesis, Queen's University Belfast, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388044.

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Dlamini, Luleka. "Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32239.

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Crop models (CMs) can be a key component in addressing issues of global food security as they can be used to monitor and improve crop production. Regardless of their wide utilization, the employment of these models, particularly in isolated and rural areas, is often limited by the lack of reliable input data. This data scarcity increases uncertainties in model outputs. Nevertheless, some of these uncertainties can be mitigated by integrating remotely sensed data into the CMs. As such, increasing efforts are being made globally to integrate remotely sensed data into CMs to improve their overall performance and use. However, very few such studies have been done in South Africa. Therefore, this research assesses how well a crop model assimilated with remotely sensed data compares with a model calibrated with actual ground data (Maize_control). Ultimately leading to improved local cropping systems knowledge and the capacity to use CMs. As such, the study calibrated the DSSAT-CERES-Maize model using two generic soils (i.e. heavy clay soil and medium sandy soil) which were selected based on literature, to measure soil moisture from 1985 to 2015 in Bloemfontein. Using the data assimilation approach, the model's soil parameters were then adjusted based on remotely sensed soil moisture (SM) observations. The observed improvement was mainly assessed through the lens of SM simulations from the original generic set up to the final remotely sensed informed soil profile set up. The study also gave some measure of comparison with Maize_control and finally explored the impacts of this specific SM improvement on evapotranspiration (ET) and maize yield. The result shows that when compared to the observed data, assimilating remotely sensed data with the model significantly improved the mean simulation of SM while maintaining the representation of its variability. The improved SM, as a result of assimilation of remotely sensed data, closely compares with the Maize_control in terms of mean but there was no improvement in terms of variability. Data assimilation also improved the mean and variability of ET simulation when compared that of Maize_control, but only with heavy clay soil. However, maize yield was not improved in comparison. This confirms that these outputs were influenced by other factors aside from SM or the soil profile parameters. It was concluded that remote sensing data can be used to bias correct model inputs, thus improve certain model outputs.
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Books on the topic "Agricultural data"

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Womack, Letricia M. U.S.-state agricultural data. Washington, D.C: U.S. Dept. of Agriculture, Economic Research Service, 1987.

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Womack, Letricia M. U.S.-state agricultural data. Washington, D.C: U.S. Dept. of Agriculture, Economic Research Service, 1987.

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Womack, Letricia M. U.S.-state agricultural data. Washington, DC: U.S. Dept. of Agriculture, Economic Research Service, 1993.

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Womack, Letricia M. U.S.-state agricultural data. Washington, DC: U.S. Dept. of Agriculture, Economic Research Service, 1993.

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Womack, Letricia M. U.S.-state agricultural data. Washington, D.C: U.S. Dept. of Agriculture, Economic Research Service, 1987.

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G, Traub Larry, Rivers Mary H, and United States. Dept. of Agriculture. Economic Research Service., eds. U.S.-state agricultural data. Washington, D.C: U.S. Dept. of Agriculture, Economic Research Service, 1986.

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Womack, Letricia M. U.S.-state agricultural data. Washington, D.C: U.S. Dept. of Agriculture, Economic Research Service, 1987.

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Womack, Letricia M. U.S.-state agricultural data. Washington, D.C: U.S. Dept. of Agriculture, Economic Research Service, 1987.

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Womack, Letricia M. U.S.-state agricultural data. Washington, DC: U.S. Dept. of Agriculture, Economic Research Service, 1993.

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Womack, Letricia M. U.S.-state agricultural data. Washington, DC: U.S. Dept. of Agriculture, Economic Research Service, 1993.

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Book chapters on the topic "Agricultural data"

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Sakata, Katsumi, Takuji Nakamura, and Setsuko Komatsu. "Mining Knowledge from Omics Data." In Agricultural Bioinformatics, 179–87. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1880-7_11.

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Vogel, Frederic A. "The Data Warehouse: A Modern System for Managing Data." In Agricultural Survey Methods, 303–12. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470665480.ch18.

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Zinke-Wehlmann, Christian, and Karel Charvát. "Introduction of Smart Agriculture." In Big Data in Bioeconomy, 187–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_14.

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AbstractSmart agriculture is a rising area bringing the benefits of digitalization through big data, artificial intelligence and linked data into the agricultural domain. This chapter motivates the use and describes the rise of smart agriculture.
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De Waal, Ton, and Jeroen Pannekoek. "Statistical Data Editing for Agricultural Surveys." In Agricultural Survey Methods, 243–66. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470665480.ch15.

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Julien, Claude. "Using Administrative Data for Census Coverage." In Agricultural Survey Methods, 73–84. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470665480.ch5.

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Carfagna, Elisabetta, and Andrea Carfagna. "Alternative Sampling Frames and Administrative Data. What is the Best Data Source for Agricultural Statistics?" In Agricultural Survey Methods, 45–61. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470665480.ch3.

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Ali, Asghar. "Analysis of Multivariate Agricultural Data." In International Encyclopedia of Statistical Science, 41–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_116.

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Piersimoni, Federica, Paolo Postiglione, and Roberto Benedetti. "Spatial Sampling for Agricultural Data." In Contributions to Statistics, 179–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05320-2_12.

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Rogotis, Savvas, and Nikolaos Marianos. "Smart Farming for Sustainable Agricultural Production." In Big Data in Bioeconomy, 191–205. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_15.

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AbstractThe chapter describes DataBio’s pilot applications, led by NEUROPUBLIC S.A., for sustainable agricultural production in Greece. Initially, it introduces the main aspects that drive and motivate the execution of the pilot. The pilot set-up consisted of four (4) different locations, four (4) different crop types and three (3) different types of offered services. The technology pipeline was based on the exploitation of heterogeneous data and their transformation into facts and actionable advice fostering sustainable agricultural growth. The results of the pilot activities effectively showcased how smart farming methodologies can lead to a positive impact from an economical, environmental and societal perspective and achieve the ambitious goal to “produce more with less”. The chapter concludes with “how-to” guidelines and the pilot’s key findings.
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Yao, Shujie. "Data Processing." In Agricultural Reforms and Grain Production in China, 247–71. London: Palgrave Macmillan UK, 1994. http://dx.doi.org/10.1007/978-1-349-23553-7_10.

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Conference papers on the topic "Agricultural data"

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Kraatz, Franz, Heiko Tapken, Frank Nordemann, Thorben Iggena, Maik Fruhner, and Ralf Tönjes. "An Integrated Data Platform for Agricultural Data Analyses based on Agricultural ISOBUS and ISOXML." In 4th International Conference on Internet of Things, Big Data and Security. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007760304220429.

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Reznik, Tomaš, Karel Charvat, Vojtech Lukas, Karel Charvat Jr., Šarka Horakova, and Michal Kepka. "Open Data Model for (Precision) Agriculture Applications and Agricultural Pollution Monitoring." In EnviroInfo and ICT for Sustainability 2015. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/ict4s-env-15.2015.12.

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Denton, Anne M., Mostofa Ahsan, David Franzen, and John Nowatzki. "Multi-scalar analysis of geospatial agricultural data for sustainability." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840843.

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Wang, Changwei, Deren Li, Yueming Hu, Xiaofang Wu, and Yu Qi. "Research of spatio-temporal analysis of agricultural pest." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838413.

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Hao, Fengqi, Xuan Luo, and Chunhua Mu. "Research on Key Technologies of Intelligent Agriculture Based on Agricultural Big Data." In 2016 International Conference on Smart City and Systems Engineering (ICSCSE). IEEE, 2016. http://dx.doi.org/10.1109/icscse.2016.0161.

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Ichikawa, D., K. Wakamori, and N. Oguri. "Agricultural monitoring using multi-satellite data." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8128144.

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Huarui, Wu, and Zhao Chunjiang. "Research on Agricultural Data Grid System." In 2009 International Conference on Web Information Systems and Mining (WISM). IEEE, 2009. http://dx.doi.org/10.1109/wism.2009.146.

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Başaran, Burçin, Zehra Meliha Tengiz, and Yasemin Oraman. "Agricultural Faculty Students' Perspectives on the Future of Agriculture: Tekirdag Case." In International Conference on Eurasian Economies. Eurasian Economists Association, 2019. http://dx.doi.org/10.36880/c11.02336.

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In spite of technological developments today, agriculture is still among the priority of all countries. The fact that the agricultural areas cannot be increased and the required nutritional needs of the growing population needed to be met, it makes agriculture strategic case for the countries. The continuity of agricultural activities has become dependent on the wishes of future generations to work in this sector in all countries. Agricultural Faculties' students do not look positively in the agricultural sector in Turkey. Youths are directed to non-agricultural sectors due to low income in agriculture. However, students should be encouraged and supported in order for agricultural activities can be practiced by conscious and enthusiastic young people. The sustainability of agriculture depends on young's willingness to participate in the sector. The aim of the study is to determine perspective of students in Faculty of Agriculture of Tekirdağ Namık Kemal University about future of agriculture. The data was obtained from 175 students in the 3. and the 4. classes. The data were analyzed statistically with SPSS 23.0 in terms of descriptive and inferential statistics. According to the results of the study; a significant difference was observed between girls and boys in terms of evaluating the current state of Turkish agriculture. 67.5% of students expect their future pessimistic and uncertain. The rate of those who expect their future as hopeful and optimistic was found to be 32.5% respectively.
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Sulaimanova, Burulcha, and Daniyar Jasoolov. "The Gender Gap in Agricultural Productivity in Kyrgyzstan." In International Conference on Eurasian Economies. Eurasian Economists Association, 2018. http://dx.doi.org/10.36880/c10.02039.

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More than two third of total population of Kyrgyzstan are living in rural areas, and the agricultural sector of Kyrgyzstan employs nearly the half of labor force and have export oriented output production with over than 384 thousand peasant farms. The share of employed women in agriculture compromised the 44 % of total agricultural labor force. However the low economic efficiency and competitiveness of farmers in regional market, market imperfections in agriculture impedes the economic growth of this sector. This research aims to investigate gender gap in agricultural productivity among farm entrepreneurs in Kyrgyzstan. The agricultural labor productivity gap decomposed by various types of market imperfections, and empirically estimated by “Life in Kyrgyzstan” survey data for 2013 year.
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Jirapure, Pallavi V., and Prarthana A. Deshkar. "Qualitative data analysis using regression method for agricultural data." In 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave). IEEE, 2016. http://dx.doi.org/10.1109/startup.2016.7583966.

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Reports on the topic "Agricultural data"

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Brisco, B., D. Bedard, J. J. Naunheimer, and R. J. Brown. Environmental Effects on Radar Data of Agricultural Areas. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1993. http://dx.doi.org/10.4095/217974.

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Deguise, J. C., K. Staenz, and J. Lefebvre. Agricultural Applications of Airborne Hyperspectral Data: Weed Detection. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219524.

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Staenz, K., J. W. Schwarz, L. Vernaccini, F. Vachon, and C. Nadeau. Classification of Hyperspectral Agricultural Data with Spectral Matching Techniques. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219608.

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Saricks, C. L., R. G. Williams, and M. R. Hopf. Data base of accident and agricultural statistics for transportation risk assessment. Office of Scientific and Technical Information (OSTI), November 1989. http://dx.doi.org/10.2172/7171598.

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Jacobson, K. W., S. Duffy, and K. Kowalewsky. Population array and agricultural data arrays for the Los Alamos National Laboratory. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/661532.

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Adamopoulos, Tasso, and Diego Restuccia. Geography and Agricultural Productivity: Cross-Country Evidence from Micro Plot-Level Data. Cambridge, MA: National Bureau of Economic Research, April 2018. http://dx.doi.org/10.3386/w24532.

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Iregui-Bohórquez, Ana María, and Jesús Otero. A Spatio-temporal Analysis of Agricultural Prices: An Application to Colombian Data. Bogotá, Colombia: Banco de la República, September 2012. http://dx.doi.org/10.32468/be.734.

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Research Institute (IFPRI), International Food Policy. Understanding the Democratic Republic of the Congo’s agricultural paradox: Based on the eAtlas data platform. Washington, DC: International Food Policy Research Institute, 2018. http://dx.doi.org/10.2499/1024320662.

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Melius, C. Developing Poultry Facility Type Information from USDA Agricultural Census Data for Use in Epidemiological and Economic Models. Office of Scientific and Technical Information (OSTI), December 2007. http://dx.doi.org/10.2172/926044.

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Melius, C., A. Robertson, and P. Hullinger. Developing Livestock Facility Type Information from USDA Agricultural Census Data for Use in Epidemiological and Economic Models. Office of Scientific and Technical Information (OSTI), October 2006. http://dx.doi.org/10.2172/1036849.

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