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Journal articles on the topic 'Agricultural data'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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7

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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8

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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9

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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10

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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11

Joo, Sungmin, Seiji Kode, Hideki Takeda, Daisuke Horyu, Akane Takezaki, and Tomokazu Yoshida. "Building Core Vocabulary of Agriculture Activity for Agricultural Data Integration." Agricultural Information Research 28, no. 3 (October 1, 2019): 143–56. http://dx.doi.org/10.3173/air.28.143.

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12

Sirish Kumar, M., S. Jyothi, and B. Kavitha. "Agriculture Land Classification Based on Climate Data Using Big Data Analysis." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 94–99. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2076.

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The Agricultural Land Classification (ALC) provides a frame work for classifying land according to the extent at which it’s physical or chemical characteristics impose long-term limitations on agricultural use. The major physical factors that influence agricultural criteria for grading are based on their physical margins of land for agricultural use, such as climate (temperature, rainfall, aspect, exposure and frost risk), site (gradient, micro-relief and flood risk) and soil (texture, structure, depth and stoniness and chemical properties which cannot be corrected) and exchanges these factors as soil wetness, draughtiness and erosion. These factors together interact with the basis for classifying land into one of five grades, the grade or sub-grade of land being determined by the most limiting factors that can be classified into grades from 1 (excellent) to 5 (very poor). These grades are classified by using temperature and average rain fall. In this we classified Agriculture Land Classification (ALC) by using Big Data Analysis based on climatic conditions of England and Wales data.Here we analyzed England and Wales data because it has the accurate climatic grades data. These grades data is huge so we analyses the data in Big DATA analysis.

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13

Sihem, Ezdini. "Agricultural insurance-agricultural productivity nexus: Evidence from international data." Journal of Service Science Research 9, no. 2 (December 2017): 147–78. http://dx.doi.org/10.1007/s12927-017-0008-0.

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14

G, Zolzaya, and Usukhbayar B. "Hypothesis testing of agricultural data." Mongolian Journal of Agricultural Sciences 25, no. 03 (December 28, 2018): 132–37. http://dx.doi.org/10.5564/mjas.v25i03.1182.

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The sustainable development strategy of the crop to implement environmental sustainability to be economically efficient, directed to improve soil productivity of the field and to increase the amount of harvest and to fully supply needs of food production by using research analysis. There are many types of methods for hypothesis testing in mathematical and statistical analyses. From these methods, T-stat and F-stat are widely used. However, they are in sufficient to express the final results, therefore we suggest to use Wald test, Variables test and Reset test. In addition to the quality of soil, wheat harvest also affects many factors, including climate, landscape, technology, and ecology. May-August precipitation (mm) and average air temperature (0C), the number of sunspot activation and the amount of crop in the Central region 1970-2015 are selected and converted to standardized average values, and done correlation coefficients analysis and multiple-factor regression analysis, hypotheses and tests. The assumptions should be regularly reviewed and verified to eliminate uncertainties in the reliability of quantitative data in agricultural research and analysis. The following conclusions have been made to examine Wald test, Variables test, and Reset test hypotheses based on the Central and Regional Weather data. The main factors that increase wheat harvest are the amount of precipitation. The precipitation is recognized for all assumptions. Other indicators (wind speed, landscape etc.) need to be selected rather than average air temperature in harvest. Хөдөө аж ахуйн тоон мэдээлэлд таамаглал дэвшүүлж, шалгах Хураангуй: Ургамал өсч хөгжих, ургац бүрэлдэх үйл явц нь гадаад орчны олон хүчин зүйлийн шууд нөлөөгөөр ургамалд явагдах тодорхой биологи, физиологи, биофизик, зарим талаар биохимийн хуулиудад ямар нэгэн байдлаар захирагддаг. Ургамал ургах, ургац бүрэлдэх явцыг дээрх хуулиудын хүрээнд авч үзэхдээ ургамал болон түүний хүрээлэн буй орчны хоорондох зүй тогтлыг математик, статистикийн аргаар илэрхийлж, ургамалын ургах бүхий л үйл явцыг загварчлах боломжтой. ХАА-н судалгаанд түүврийн тоо мэдээнд тулгуурласан параметр нь эх олонлогийг төлөөлж чадах эсэх талаар таамаглал дэвшүүлж, шалгах шаардлага зайлшгүй тулгардаг. Математик, статистикийн шинжилгээнд таамаглал дэвшүүлж, шалгах олон төрлийн арга байдгаас Стьюдентийн шинжүүрээр, Фишерийн шинжүүрийг өргөн ашигладаг ба эдгээр нь эцсийн үр дүнг илэрхийлэхэд хангалтгүй, тиймээс Wald test, Variables test, Reset test ашиглах шаардлагатай. Түлхүүр үг: Хамаарлын шинжилгээ, загварын ач холбогдол, хувьсагчдын шалгуур

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15

Venäläinen, Ari, and Martti Heikinheimo. "Meteorological data for agricultural applications." Physics and Chemistry of the Earth, Parts A/B/C 27, no. 23-24 (January 2002): 1045–50. http://dx.doi.org/10.1016/s1474-7065(02)00140-7.

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16

Woodard, Joshua D., Bruce J. Sherrick, Deborah M. Atwood, Robert Blair, Greg Fogel, Nicholas Goeser, Barry Gold, et al. "The power of agricultural data." Science 362, no. 6413 (October 25, 2018): 410–11. http://dx.doi.org/10.1126/science.aav5002.

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17

SAITO, Genya, Izumi NAGATANI, Shigeo OGAWA, and Xianfang SONG. "Agricultural Monitoring Using Satellite Data." Journal of Agricultural Meteorology 60, no. 5 (2005): 375–78. http://dx.doi.org/10.2480/agrmet.375.

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18

Li, Hongyan, Ziyi Cheng, and Haitong Wang. "Research of Agricultural Big data." E3S Web of Conferences 214 (2020): 01011. http://dx.doi.org/10.1051/e3sconf/202021401011.

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With the development of economy and the popularization of Internet, the development of agricultural big data is the inevitable trend of agricultural development, which is gradually changing people’ s lives. But there are many problems in the process of rural development. This paper will analyze the agricultural big data, from the meaning, significance, research status, problems and solutions of agricultural big data, in order to explore new ways for agricultural development, so as to promote the development of rural economy, improve the living standards of farmers and contribute to the construction of agricultural modernization.

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19

Shi, Lei, Qiguo Duan, Juanjuan Zhang, Lei Xi, Hongbo Qiao, and Xinming Ma. "Rough set based ensemble learning algorithm for agricultural data classification." Filomat 32, no. 5 (2018): 1917–30. http://dx.doi.org/10.2298/fil1805917s.

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Agricultural data classification attracts more and more attention in the research area of intelligent agriculture. As a kind of important machine learning methods, ensemble learning uses multiple base classifiers to deal with classification problems. The rough set theory is a powerful mathematical approach to process unclear and uncertain data. In this paper, a rough set based ensemble learning algorithm is proposed to classify the agricultural data effectively and efficiently. An experimental comparison of different algorithms is conducted on four agricultural datasets. The results of experiment indicate that the proposed algorithm improves performance obviously.

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20

Tantalaki, Nicoleta, Stavros Souravlas, and Manos Roumeliotis. "Data-Driven Decision Making in Precision Agriculture: The Rise of Big Data in Agricultural Systems." Journal of Agricultural & Food Information 20, no. 4 (July 22, 2019): 344–80. http://dx.doi.org/10.1080/10496505.2019.1638264.

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21

Vaníček, J. "Software and data quality." Agricultural Economics (Zemědělská ekonomika) 52, No. 3 (February 17, 2012): 138–46. http://dx.doi.org/10.17221/5007-agricecon.

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The paper presents new ideas in the International SQuaRE (Software Quality Requirements and Evaluation) standardisation research project, which concerns the development of a special branch of international standards for software quality. Data can be considered as an integral part of software. The current international standard and technical report of the ISO/IEC 9126, ISO/IEC 14598 series and ISO/IEC 12119 standard covert the whole software as an indivisible entity. However, such data sets as databases and data stores have a special character and need a different structure of quality characteristic. Therefore it was decided in the SQuaRE project create a special international standard for data quality. The main idea for this standard and the critical discussion of these ideas is presented in this paper. The main part of this contribution was presented on the conference Agricultural Perspectives XIV, aligned by Czech University of Agriculture in Prague, September 20 to 21, 2005.

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22

Nohel, František, Daniela Spěšná, and Pavel Pospěch. "Regional markets with agricultural workforce based on Labour offices' data." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 4 (2011): 177–86. http://dx.doi.org/10.11118/actaun201159040177.

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The changes in Czech agriculture over the past twenty years have had their impact on the agricultural labour market, too. The regional differentiation of the chances of applicants on the labour market as well as the agricultural enterprises’ chances of hiring employees fitting their requirements, are, among others, influenced by the specific conditions of agricultural production. The aim of this paper pertains to two basic problem areas: first, the differentiation of respective regions based on the number of agricultural applicants and job vacancies, and second, the identification of disequilibrium on the agricultural labour market. The latter is based on a theoretical framework defined by approaches in economy dealing with labour market equilibrium. Due to the unavailability of economic data (including wages, economic performance, etc.) on the regional level, authors develop their own methodological approach, based on the number of applicants per job vacancy. A database of applicants and vacancies available from the Labour Offices is used as a source for the analysis and interpretation of data, enabling us to study the agricultural labour market not only sector-wise but also region-wise.

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23

Et. al., Ancy Stephen,. "Using Open Remote Sensing Data to build an Agriculture Big Data System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 429–36. http://dx.doi.org/10.17762/turcomat.v12i2.830.

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Landsat, MODIS, and Sentinel satellites are continuously producing multispectral sensor data with different spatial, temporal, and radiometric resolutions. This raw sensor data is calibrated and processed further, and additional data products are derived, which greatly reduces the burden for downstream applications from preprocessing these data. These petabyte-scale datasets are available to anyone free of charge. Remote sensing plays a key role in modern Agriculture. We can extract information about Soil, Weather, Water, and vegetation from these datasets. By processing historical remote sensing data, we can build temporal profiles of soil, weather, water, and agricultural conditions of the land. Deep learning and Spatio-temporal data mining algorithms can be applied to this data to extract hidden information. Having access to all this information via an agriculture information system, farmers will understand their land better and they will be empowered to make better decisions on a day-to-day activity. Although it looks simple from the surface, collecting, analyzing, and deriving insights from these sensor data and other data products from a multitude of sources is a big data and high-performance computing challenge. In this paper, we discuss the current open datasets and how these datasets can be used to solve various problems in agriculture. Also, we discuss implementing a cloud-based scalable agricultural information system which provides actionable insights to farmers.

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Nath, Suraj, Debashri Debnath, Parthapratim Sarkar, and Ankur Biswas. "An Efficient Implementation of Data Mining Techniques in Agriculture." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 154–61. http://dx.doi.org/10.1166/jctn.2020.8644.

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Agriculture being the utmost noteworthy operational domain in current scenario and agribusiness holders including farmers are required to make wide range of decisions every day. An efficient farming decision includes all relevant environmental conditions, consistency of soil, rainfall, combinations of fertilizers and product prices. A vital concern in agricultural planning objective is the estimation of perfect acquiesces for several crops concerned in the scheduling. Information technology deployment in agricultural field can transform the state of affairs of policy making so that farmers can yield in a superior way. For this reason, the raw data is changed into useful information through data mining which can play a decisive role to achieve the realistic and efficient solutions for this problem and numerous other issues allied to agriculture field. In this paper an analysis of agricultural data and ruling best possible factor to capitalize on the production of crops under varied condition using data mining technique is primarily focused. Various commonly used data mining methodologies mainly on agricultural domain are presented. Results attained through Classification using J48 tree classifier with related confusion matrix confirm the stoutness of the proposed method.

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Lim, Dong-Geon, and Jin Hwa Jung. "Technological Changes in Agriculture and Information Technology: Centrality and Citation Analyses of South Korean Agricultural Patent Data." Science, Technology and Society 24, no. 2 (July 2019): 316–37. http://dx.doi.org/10.1177/0971721819841992.

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This study analyses recent changes in the agricultural technology trends at the current pace of its convergence with information technology (IT) using South Korean agricultural patent data from 2000 to 2015. This article focuses on the structural changes in agricultural technology in terms of patent applications by specific technology sector between the two periods: Period 1 (2000–2007) and Period 2 (2008–2015). Accordingly, we performed centrality analysis to measure the importance of each agricultural technology based on International Patent Classification (IPC) sub-classes. We also conducted citation analysis to identify whether the proportion of backward and forward citations of agricultural patents had significantly changed between the two periods. The results of the centrality analysis suggest that, whereas food technology was the most important technology sector in agriculture during both the periods, agricultural production technology experienced a considerable increase in its share in agriculture in Period 2. The results of the citation analysis confirm a substantial degree of interconnectivity between agricultural technology and non-agricultural technologies.

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Meshchaninova, E. G., and Yu A. Stepkin. "Application of remote sensing data in agriculture." Economy and ecology of territorial educations 4, no. 4 (2020): 72–77. http://dx.doi.org/10.23947/2413-1474-2020-4-4-72-77.

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The lack of accurate maps, an undeveloped network of points for operational and meteorological monitor-ing of ground stations, lack of air support, and much more makes it difficult to control large areas of agricultural land. Due to these factors and the lack of objective data in determining the state of land and forecasting the state of the situation, it is extremely difficult to increase agricultural production, optimize the costeffective use of land, and reduce costs to a minimum. Remote sensing of the Earth is actively used to solve various problems of integrated and specialized management of agricultural territories. It is difficult to overestimate the importance of the obtained remote sensing data, they allow us to solve a number of tasks in agriculture and, in particular, facilitate monitoring of the state of crops over large areas.

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Yan, Liu. "Development of International Agricultural Trade Using Data Mining Algorithms-Based Trade Equality." Mobile Information Systems 2021 (July 1, 2021): 1–9. http://dx.doi.org/10.1155/2021/5046244.

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The development of international agriculture trade during the COVID-19 pandemic has encountered significant challenges. The processing of international agricultural trade data using machine learning techniques needs to be improved to perform effective analysis of agricultural trade. An essential issue for international agricultural trade is the accurate yield estimation for the numerous crops involved in international trade. Data mining techniques are the necessary approach for accomplishing practical and effective solutions for this problem. This paper combined the bidirectional encoder representations from transformers (BERT) model to conduct data mining and developed a trade data analysis system with efficient data analysis capabilities. Our results indicate that our model does reasonably well and obtains adequate information in deciding international agricultural trade. It can also be instrumental for policy and decision-making regarding international agricultural trade.

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Battaglia, Martín, Wade Thomason, and John Fike. "Data Management in the agricultural sciences." CSA News 62, no. 7 (July 2017): 24–25. http://dx.doi.org/10.2134/csa2017.62.0701.

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Tóth, Katalin. "Georeferenced Agricultural Data for Statistical Reuse." Geosciences 8, no. 5 (May 20, 2018): 188. http://dx.doi.org/10.3390/geosciences8050188.

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., Aditya Kumar Gupta. "MULTIDIMENSIONAL SCHEMA FOR AGRICULTURAL DATA WAREHOUSE." International Journal of Research in Engineering and Technology 02, no. 03 (March 25, 2013): 245–53. http://dx.doi.org/10.15623/ijret.2013.0203006.

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Vanitha, Mrs T. "Agricultural Data Transfer using IoT Technology." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (March 31, 2019): 1801–4. http://dx.doi.org/10.22214/ijraset.2019.3334.

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Shroff, Sangeeta. "Data Gaps in Agricultural Statistics:Some Issues." Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics 58, no. 3 (September 1, 2016): 228. http://dx.doi.org/10.21648/arthavij/2016/v58/i3/147827.

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Che, X., and S. Xu. "Bayesian data analysis for agricultural experiments." Canadian Journal of Plant Science 90, no. 5 (September 1, 2010): 575–603. http://dx.doi.org/10.4141/cjps10004.

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Data collected in agricultural experiments can be analyzed in many different ways using different models. The most commonly used models are the linear model and the generalized linear model. The maximum likelihood method is often used for data analysis. However, this method may not be able to handle complicated models, especially multiple level hierarchical models. The Bayesian method partitions complicated models into simple components, each of which may be formulated analytically. Therefore, the Bayesian method is capable of handling very complicated models. The Bayesian method itself may not be more complicated than the maximum likelihood method, but the analysis is time consuming, because numerical integration involved in Bayesian analysis is almost exclusively accomplished based on Monte Carlo simulations, the so called Markov Chain Monte Carlo (MCMC) algorithm. Although the MCMC algorithm is intuitive and straightforward to statisticians, it may not be that simple to agricultural scientists, whose main purpose is to implement the method and interpret the results. In this review, we provide the general concept of Bayesian analysis and the MCMC algorithm in a way that can be understood by non-statisticians. We also demonstrate the implementation of the MCMC algorithm using professional software packages such as the MCMC procedure in SAS software. Three datasets from agricultural experiments were analyzed to demonstrate the MCMC algorithm.Key words: Bayesian method, Generalized linear model, Markov Chain Monte Carlo, SAS, WinBUGS

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Jarolímek, Jan, Jan Pavlík, Jana Kholova, and Swarna Ronanki. "Data Pre-processing for Agricultural Simulations." Agris on-line Papers in Economics and Informatics 11, no. 01 (March 30, 2019): 49–53. http://dx.doi.org/10.7160/aol.2019.110105.

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Zipper, Samuel C. "Agricultural Research Using Social Media Data." Agronomy Journal 110, no. 1 (January 2018): 349–58. http://dx.doi.org/10.2134/agronj2017.08.0495.

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Wang, Feng. "Applied Research on Agricultural Big Data." Journal of Physics: Conference Series 1533 (April 2020): 042051. http://dx.doi.org/10.1088/1742-6596/1533/4/042051.

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Antle, John M. "Data, Economics and Computational Agricultural Science." American Journal of Agricultural Economics 101, no. 2 (February 6, 2019): 365–82. http://dx.doi.org/10.1093/ajae/aay103.

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R. W. Whitney and T. Kaczynski. "Mission Data Acquisition for Agricultural Aircraft." Applied Engineering in Agriculture 5, no. 3 (1989): 431–35. http://dx.doi.org/10.13031/2013.26540.

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Hueth, Brent, Ethan Ligon, and Carolyn Dimitri. "Agricultural Contracts: Data and Research Needs." American Journal of Agricultural Economics 89, no. 5 (December 2007): 1276–81. http://dx.doi.org/10.1111/j.1467-8276.2007.01096.x.

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Buysse, Donald. "Organic Data-National Agricultural Statistics Service." Crop Management 12, no. 1 (2013): 1–2. http://dx.doi.org/10.1094/cm-2013-0429-07-ps.

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McQueen, Robert J., Stephen R. Garner, Craig G. Nevill-Manning, and Ian H. Witten. "Applying machine learning to agricultural data." Computers and Electronics in Agriculture 12, no. 4 (June 1995): 275–93. http://dx.doi.org/10.1016/0168-1699(95)98601-9.

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Beegle, Kathleen, Calogero Carletto, and Kristen Himelein. "Reliability of recall in agricultural data." Journal of Development Economics 98, no. 1 (May 2012): 34–41. http://dx.doi.org/10.1016/j.jdeveco.2011.09.005.

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Carletto, Calogero. "Better data, higher impact: improving agricultural data systems for societal change." European Review of Agricultural Economics 48, no. 4 (July 6, 2021): 719–40. http://dx.doi.org/10.1093/erae/jbab030.

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Abstract The agricultural sector is undergoing a period of rapid transformation, driven by the powerful and interconnected impacts of climate change, demographic transitions and uneven economic growth around the world. For governments and the international community to navigate this period of upheaval to protect vulnerable populations and ensure positive societal change will require a similar degree of transformation within agricultural data systems. While technological innovation has resulted in substantive improvements in the availability, timeliness and overall quality of agricultural data, many technical and institutional challenges remain. This paper reviews recent developments in the agricultural data landscape, highlights existing constraints to further progress and argues for agricultural economists to take responsibility for building agricultural data systems equipped to respond to the diverse needs of a changing world.

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Li, Chunling, and Ben Niu. "Design of smart agriculture based on big data and Internet of things." International Journal of Distributed Sensor Networks 16, no. 5 (May 2020): 155014772091706. http://dx.doi.org/10.1177/1550147720917065.

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With the wide application of Internet of things technology and era of large data in agriculture, smart agricultural design based on Internet of things technology can efficiently realize the function of real-time data communication and information processing and improve the development of smart agriculture. In the process of analyzing and processing a large amount of planting and environmental data, how to extract effective information from these massive agricultural data, that is, how to analyze and mine the needs of these large amounts of data, is a pressing problem to be solved. According to the needs of agricultural owners, this article studies and optimizes the data storage, data processing, and data mining of large data generated in the agricultural production process, and it uses the k-means algorithm based on the maximum distance to study the data mining. The crop growth curve is simulated and compared with improved K-means algorithm and the original k-means algorithm in the experimental analysis. The experimental results show that the improved K-means clustering method has an average reduction of 0.23 s in total time and an average increase of 7.67% in the F metric value. The algorithm in this article can realize the functions of real-time data communication and information processing more efficiently, and has a significant role in promoting agricultural informatization and improving the level of agricultural modernization.

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Achard, Pauline, Charlotte Maugard, Christophe Cancé, Johan Spinosi, Damien Ozenfant, Anne Maître, Delphine Bosson-Rieutort, and Vincent Bonneterre. "Medico-administrative data combined with agricultural practices data to retrospectively estimate pesticide use by agricultural workers." Journal of Exposure Science & Environmental Epidemiology 30, no. 4 (September 4, 2019): 743–55. http://dx.doi.org/10.1038/s41370-019-0166-x.

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M. Etale, Lyndon, and Amaka, P. T. Bailey. "The Relationship between Bank Lending to Agricultural Sector and Agricultural Earnings in Nigeria." Sumerianz Journal of Economics and Finance, no. 41 (February 18, 2021): 25–34. http://dx.doi.org/10.47752/sjef.41.25.34.

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This study examined the relationship between bank lending to agricultural sector and agricultural earnings in Nigeria using secondary data obtained from various editions of the Central Bank of Nigeria Statistical Bulletins. Secondary data collected for the selected study variables covered ten years period from 2009 to 2018. The study adopted bank loans and advances to agriculture, interest rate, and inflation as independent variables, while agricultural earnings representing gross national agricultural output was used as dependent variable. The study employed descriptive statistics and multiple regression analysis based on the OLS technique assisted by the E-view 10 computer software as the statistical tools for data analysis. The results revealed that all the independent variables had positive relationship with agricultural earnings. Specifically, bank loans and advances to agriculture had statistically significant effect on agricultural earnings. The regression results also showed that the coefficient of determination (R-squared) value of 0.86 indicates that 86% of changes in the dependent variable (AGE) were explained by the combined effect of changes in the independent variables. The study concluded that bank lending to the agricultural sector has a significant positive relationship with agricultural earnings in Nigeria. The study recommended among others that the CBN should step-up policy making, execution and monitoring of bank lending to agriculture; and that the Federal Government through the Federal Ministry of Agriculture should declare a state of emergency on agriculture and make the sector more attractive and viable for investment.

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Sudha, V., S. Mohan, and S. Arivalagan. "Big Data Analytics to Increase the Agricultural Yield by Using Machine Learning Approaches." Asian Journal of Computer Science and Technology 7, S1 (November 5, 2018): 82–86. http://dx.doi.org/10.51983/ajcst-2018.7.s1.1799.

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Agriculture is the backbone of Indian economy. Big data are emerging précised and viable analytical tool in agricultural research field. This review paper facilitates the farmers in selecting the right crops and appropriate cropping pattern for a particular locality. A modern trend in the Agriculture domain has made people realize the importance of big data. It provides a methodology for facing challenges in agricultural production, by applying this Algorithm, using machine learning techniques. The different machine learning techniques survey is presented in this paper to realize enhanced monitory benefits in a particular area. A study of machine learning algorithms for big data Analytic is also done and presented in this paper.

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Zhang, Qiang, Huiqian Yu, Peng Sun, Vijay P. Singh, and Peijun Shi. "Multisource data based agricultural drought monitoring and agricultural loss in China." Global and Planetary Change 172 (January 2019): 298–306. http://dx.doi.org/10.1016/j.gloplacha.2018.10.017.

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Dela Cruz, Geraldin B., Bobby D. Gerardo, and Bartolome T. Tanguilig III. "Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining." International Journal of Modeling and Optimization 4, no. 5 (October 2014): 375–82. http://dx.doi.org/10.7763/ijmo.2014.v4.404.

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Çukur, Figen. "Determination of Factors Affecting Hazelnut Farmers' Agricultural Insurance by Data Mining Algorithms." Alinteri Journal of Agricultural Sciences 36, no. 1 (February 6, 2021): 77–83. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21013.

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