Relevant bibliographies by topics / Crop forecasting

Contents

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

Academic literature on the topic 'Crop forecasting'

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

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Journal articles on the topic "Crop forecasting"

1

Guenthner, Joseph F. "Forecasting Annual Vegetable Plantings." HortTechnology 2, no. 1 (January 1992): 89–91. http://dx.doi.org/10.21273/horttech.2.1.89.

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Vegetable producers and marketers make business decisions based on supply estimates. The U.S. Dept. of Agriculture provides estimates of planting intentions for field crops but not for most vegetable crops. This study developed models that can be used to forecast vegetable crop plantings. Multiple linear regression analysis was used to determine the factors that influence plantings of potatoes and onions. Field crop planting intentions, industry structure, lagged values of plantings, prices received, price volatility, and the price of sugar beets were found to be significant factors. The models and/or methods used in this study should be useful to those interested in forecasting vegetable plantings.
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Veenadhari, Dr S. "Crop Advisor: A Software Tool for Forecasting Paddy Yield." Bonfring International Journal of Data Mining 6, no. 3 (July 31, 2016): 34–38. http://dx.doi.org/10.9756/bijdm.10461.

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Buklagin, D. S. "Agricultural crop yield forecasting methods." Machinery and Equipment for Rural Area, no. 12 (December 20, 2020): 25–28. http://dx.doi.org/10.33267/2072-9642-2020-12-25-28.

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The main areas of the development and use of digital technologies and systems for forecasting the yield of agricultural crops based on satellite data are described. Proposals are given for the development of research in the field of the use of space technologies and their widespread use in agriculture.
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Mariño, Miguel A., John C. Tracy, and S. Alireza Taghavi. "Forecasting of reference crop evapotranspiration." Agricultural Water Management 24, no. 3 (November 1993): 163–87. http://dx.doi.org/10.1016/0378-3774(93)90022-3.

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MOHAN, S., and N. ARUMUGAM. "Forecasting weekly reference crop evapotranspiration series." Hydrological Sciences Journal 40, no. 6 (December 1995): 689–702. http://dx.doi.org/10.1080/02626669509491459.

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Stone, Roger C., and Holger Meinke. "Operational seasonal forecasting of crop performance." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2109–24. http://dx.doi.org/10.1098/rstb.2005.1753.

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Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.
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Gotsch, N., and P. Rieder. "Forecasting future developments in crop protection." Crop Protection 9, no. 2 (April 1990): 83–89. http://dx.doi.org/10.1016/0261-2194(90)90083-j.

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Ben Dhiab, Ali, Mehdi Ben Mimoun, Jose Oteros, Herminia Garcia-Mozo, Eugenio Domínguez-Vilches, Carmen Galán, Mounir Abichou, and Monji Msallem. "Modeling olive-crop forecasting in Tunisia." Theoretical and Applied Climatology 128, no. 3-4 (January 13, 2016): 541–49. http://dx.doi.org/10.1007/s00704-015-1726-1.

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Jain, R. C., and Ranjana Agrawal. "Probability Model for Crop Yield Forecasting." Biometrical Journal 34, no. 4 (1992): 501–11. http://dx.doi.org/10.1002/bimj.4710340410.

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Bojar, W., L. Knopik, J. Żarski, and R. Kuśmierek-Tomaszewska. "Integrated assessment of crop productivity based on the food supply forecasting." Agricultural Economics (Zemědělská ekonomika) 61, No. 11 (June 6, 2016): 502–10. http://dx.doi.org/10.17221/159/2014-agricecon.

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Dissertations / Theses on the topic "Crop forecasting"

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McVean, Ross Iolo Kester. "Forecasting pea aphid outbreaks." Thesis, University of East Anglia, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389386.

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Kantanantha, Nantachai. "Crop decision planning under yield and price uncertainties." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/24676.

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Thesis (Ph.D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007.
Committee Co-Chair: Griffin, Paul; Committee Co-Chair: Serban, Nicoleta; Committee Member: Liang, Steven; Committee Member: Sharp, Gunter; Committee Member: Tsui, Kwok-Leung
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Eggerman, Christopher Ryan. "Projecting net incomes for Texas crop producers: an application of probabilistic forecasting." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4134.

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Agricultural policy changes directly affect the economic viability of Texas crop producers because government payments make up a significant portion of their net farm income (NFI). NFI projections benefit producers, agribusinesses and policy makers, but an economic model making these projections for Texas did not previously exist. The objective of this study was to develop a model to project annual NFI for producers of major crops in Texas. The Texas crop model was developed to achieve this objective, estimating state prices, yields and production costs as a function of their national counterparts. Five hundred iterations of national price and yield projections from the Food and Agricultural Policy Research Institute (FAPRI), along with FAPRI’s average production cost projections, were used as input to the Texas crop model. The stochastic FAPRI Baseline and residuals for Ordinary Least Squares (OLS) equations relating Texas variables to national variables were used to incorporate the risk left unexplained by OLS equations between Texas and U.S. variables. Deterministic and probabilistic NFI projections for Texas crops were compared under the January 2005 and January 2006 FAPRI Baseline projections. With production costs increasing considerably and prices rising moderately in the January 2006 Baseline, deterministic projections of 2006-2014 Texas NFI decreased by an average of 26 percent for corn, 3 percent for cotton, 15 percent for peanuts, and 12 percent for rice, and were negative for sorghum and wheat. Probability distributions of projected NFI fell for all program crops, especially sorghum and wheat. Higher hay price projections caused deterministic projections of NFI for hay to rise roughly 13 percent, and increased the probability distributions of projected hay NFI. Deterministic and probabilistic projections of total NFI decreased for each year, especially for 2006-2008 when fuel price projections were the highest. The Texas crop model can be used to simulate NFI for Texas crop producers under alternative FAPRI baselines. The model shows the impact of baseline changes on probability distributions of NFI for each crop and for Texas as a whole. It can also be useful as a policy analysis tool to compare impacts of alternative farm and macroeconomic policies on NFI.
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Teo, Chee-Kiat. "Application of satellite-based rainfall estimates to crop yield forecasting in Africa." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434333.

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Osman, E. M. H. "Crop yield forecasting at national and regional levels using remote sensing techniques." Thesis, Cranfield University, 2003. http://dspace.lib.cranfield.ac.uk/handle/1826/11058.

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Crop yield forecasting models are needed to help farmers and decision makers cheaply detect crop condition early enough to assess and mitigate its impacts on grain production. A precise estimate of crop production requires an accurate measure of the total cultivated area and well-established knowledge of crop yield. The first requirement is no longer a problem as is technically solved through various techniques such as area frame sampling. With respect to the second, great efforts have been made to find an accurate definition of the crop yield with respect to the actual factors that shape its growth through out the season. Agrometeorological models have found a wide range of applications in agricultural research and technology and are playing an increasing role in translating information about climate variability into assessments, predictions and recommendations tailored to the needs of agricultural decision makers. However these models have generally been developed and tested for application at the scale of a homogeneous plot. They are criticized for their inability to address large-scale yield estimates at regional or even national levels in addition to their high cost of application. This is because field conditions during the period of crop establishment at the regional scale may be quite variable and poorly represented by standard parameter values of the crop model.
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Osman, El Mamoun H. "Crop yield forecasting at national and regional levels using remote sensing techniques." Thesis, Cranfield University, 2003. http://dspace.lib.cranfield.ac.uk/handle/1826/11058.

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Crop yield forecasting models are needed to help farmers and decision makers cheaply detect crop condition early enough to assess and mitigate its impacts on grain production. A precise estimate of crop production requires an accurate measure of the total cultivated area and well-established knowledge of crop yield. The first requirement is no longer a problem as is technically solved through various techniques such as area frame sampling. With respect to the second, great efforts have been made to find an accurate definition of the crop yield with respect to the actual factors that shape its growth through out the season. Agrometeorological models have found a wide range of applications in agricultural research and technology and are playing an increasing role in translating information about climate variability into assessments, predictions and recommendations tailored to the needs of agricultural decision makers. However these models have generally been developed and tested for application at the scale of a homogeneous plot. They are criticized for their inability to address large-scale yield estimates at regional or even national levels in addition to their high cost of application. This is because field conditions during the period of crop establishment at the regional scale may be quite variable and poorly represented by standard parameter values of the crop model.
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Soares, Abilio Barros. "Crop Price and Land Use Change: Forecasting Response of Major Crops Acreage to Price and Economic Variables in North Dakota." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27685.

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The objective of this study is to examine land use change for cropping systems in North Dakota. Using Seemingly Unrelated Regression with full information maximum likelihood estimation method, acreage forecasting models for barley, corn, oats, soybean, and wheat were developed to examine the extent to which farmers? expectations of prices and costs affect their crop choices. The results of the study show that farmers? decision for acreage allocation is varied across the crops depending on how responsive they are to price, cost and yield of its own and competing crops. Substitutability and complementarity relationship of crops in the production have positive effect on crops selection when facing price, cost, and yield changes. In addition, the results revealed that expected prices have little effect on acreage response compared to expected costs and yield variables in most of the crop models.
IIE team Fulbright sponsorship
North Dakota State University. Department of Agribusiness and Applied Economics
National Science Foundation (NSF). Grant Number IIA-1355466
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Johnson, Michael David. "Crop yield forecasting on the Canadian Prairies by satellite data and machine learning methods." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45281.

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The production of grain crops plays an important role in the economy of the Canadian Prairies and early reliable crop yield forecasts over large areas would help policy makers and grain marketing agencies in planning for exports. Forecast models developed from satellite data have the potential to provide quantitative and timely information on agricultural crops over large areas. The use of nonlinear modeling techniques from the field of machine learning could improve crop forecasting from the linear models most commonly used today. The Canadian Prairies consist of the provinces of Alberta, Saskatchewan and Manitoba and three of the major crops in this region are barley, canola and spring wheat. The agricultural land on the Canadian Prairies has been divided into Census Agricultural Regions (CAR) by Statistics Canada. A clustering model was applied to the crop yield data to group the CARs for the development of forecast models. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectro-radiometer (MODIS), NDVI derived from the Advanced Very High Resolution Radiometer (AVHRR) and several climate indices were considered as predictors for crop yields. A correlation analysis between crop yield and the time series of each potential predictor was performed to determine which variables showed the most forecasting potential and at what time during the growing season their values were most correlated to crop yield. Various combinations of MODIS-NDVI, MODIS-EVI and NOAA-NDVI were used to forecast the yield of barley, canola and spring wheat. Multiple linear regression as well as nonlinear Bayesian neural networks and model-based recursive partitioning forecast models were developed using the various sets of predictors. The models were trained using a cross-validation method and the forecast results of each model were evaluated by calculating the skill score from the mean absolute error, with 95% confidence intervals for the skill scores calculated using a bootstrap method. The results were compared in an effort to determine the optimal set of predictors and type of forecast model for each crop.
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Higgins, Sarah. "Limitations to seasonal weather prediction and crop forecasting due to nonlinearity and model inadequacy." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3191/.

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This Thesis examines the main issues surrounding crop modelling by detailed studies of (i) multi-model ensemble forecasting using a simple dynamical system as a proxy for seasonal weather forecasting, (ii) probabilistic forecasts for crop models and (iii) an analysis of changes in US yield. The ability to forecast crop yield accurately on a seasonal time frame would be hugely beneficial to society in particular farmers, governments and the insurance industry. In addition, advance warning of severe weather patterns that could devastate large areas of crops would allow contingency plans to be put in place before the onset of a widespread famine, potentially averting a humanitarian disaster. There is little experience in the experimental design of ensembles for seasonal weather forecasting. Exploring the stability of the results varying, for example, the sample size aids understanding. For this a series of numerical experiments are conducted in an idealised world based around the Moran Ricker Map. The idealised world is designed to replicate the multi-model ensemble forecasting methods used in seasonal weather forecasting. Given the complexity of the physical weather systems experiments are instead conducted on the Moran Ricker Map [56,70]. Additionally, experiments examine whether including climatology as a separate model or blending with climatology can increase the skill. A method to create probabilistic forecasts from a crop model, the Crop Environment Resource Synthesis Maize model (CERES-Maize) [19, 37] is proposed. New empirical models are created using historical US maize yield. The skill from equally weighting the crop model with a simple empirical model is investigated. Background reviews of weather and yield data is presented in new ways for the largest maize growing state Iowa. A new method separating the impacts of favourable weather from technology increases in a crop yield time series is explored.
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Biot, Y. "Forecasting productivity losses caused by sheet and rill erosion in semi arid rangeland : A case study from communal areas of Botswana." Thesis, University of East Anglia, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383247.

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Books on the topic "Crop forecasting"

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Biot, Yvan. Crop production forecasting based on long term climate predictions. Norwich: School of Development Studies, University of East Anglia, 1991.

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Lee, Juhwan, Steven De Gryze, and Johan Six. Effect of climate change on field crop production in the Central Valley of California: Final paper. Sacramento, Calif.]: California Energy Commission, 2009.

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Cogill, Bruce. Report of the nutrition module as part of the the [sic] crop forecasting survey: Rural Zambia, 1990. [Lusaka]: Central Statistical Office, 1990.

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Hinman, Herbert. 1992 crop enterprise budgets for spring barley and summer fallow- winter wheat in the 13-15 inch rainfall region of Asotin County, Washington. [Pullman, Wash.]: Washington State University, Cooperative Extension, 1992.

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MacEwan, Duncan, and Richard E. Howitt. Estimating the economic impacts of agricultural yield related changes for California: Final paper. Sacramento, Calif.]: California Energy Commission, 2009.

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Cogill, Bruce. Report of the pilot nutrition module: Report of a survey of rural Zambia undertaken as part of the crop forecasting survey, 1990. Lusaka: Central Statistical Office, 1990.

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Quiring, Steven M. Developing a real-time agricultural drought monitoring system for Delaware. Middletown, Del: Legates Consulting, 2004.

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Rosenzweig, Cynthia. Climate variability and the global harvest: Impacts of El Nino and other oscillations on agroecosystems. United States: Oxford U Pr, N Y, 2008.

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Field, Christopher B., and David Lobell. California perennial crops in a changing climate: Final paper. Sacramento, Calif.]: California Energy Commission, 2009.

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Bapna, S. L. Supply and price outlook for crops: A study based on preharvest market information in Gujarat. New Delhi: Oxford & IBH Pub. Co., 1987.

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Book chapters on the topic "Crop forecasting"

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Singh, D., and M. P. Jha. "Statistical Problems in Crop Forecasting." In A Celebration of Statistics, 571–84. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4613-8560-8_25.

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Meena, Mukesh, and Pramod Kumar Singh. "Crop Yield Forecasting Using Neural Networks." In Swarm, Evolutionary, and Memetic Computing, 319–31. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03756-1_29.

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Pan, Haizhu, and Zhongxin Chen. "Crop Growth Modeling and Yield Forecasting." In Springer Remote Sensing/Photogrammetry, 205–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66387-2_11.

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Bouman, B. A. M., C. A. van Diepen, P. Vossen, and T. van der Wal. "Simulation and systems analysis tools for crop yield forecasting." In Applications of Systems Approaches at the Farm and Regional Levels Volume 1, 325–40. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5416-1_24.

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Iizumi, Toshichika, and Wonsik Kim. "Recent Improvements to Global Seasonal Crop Forecasting and Related Research." In Adaptation to Climate Change in Agriculture, 97–110. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9235-1_7.

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Stephens, David, David Butler, and Graeme Hammer. "Using Seasonal Climate Forecasts in Forecasting the Australian Wheat Crop." In Applications of Seasonal Climate Forecasting in Agricultural and Natural Ecosystems, 351–66. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9351-9_21.

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Mukhamediyeva, Dilnoz, and Barnoxon Solieva. "Construction of the Model of Crop Production Forecasting with Fuzzy Information." In Techno-Societal 2018, 187–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16848-3_18.

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Kostyuchenko, Tatiana, Natalia Telnova, Yuliya Orel, Sergei Izmalkov, and Anzhelika Baicherova. "Forecasting the Efficiency of Technological Development by the Example of Crop Research." In Lecture Notes in Networks and Systems, 835–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15160-7_84.

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Reddy, P. Chandra Shaker, and A. Sureshbabu. "An Applied Time Series Forecasting Model for Yield Prediction of Agricultural Crop." In Advances in Intelligent Systems and Computing, 177–87. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2475-2_16.

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Fenu, Gianni, and Francesca Maridina Malloci. "Artificial Intelligence Technique in Crop Disease Forecasting: A Case Study on Potato Late Blight Prediction." In Intelligent Decision Technologies, 79–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5925-9_7.

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Conference papers on the topic "Crop forecasting"

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Lijue Tang, Miao Li, and Jian Zhang. "Multipopulation genetic programming for forecasting crop pests." In Proceedings of 2003 International Conference on Neural Networks and Signal Processing. IEEE, 2003. http://dx.doi.org/10.1109/icnnsp.2003.1279333.

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Wei-Guang Wang and Yu-Feng Luo. "Wavelet network model for reference crop evapotranspiration forecasting." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420769.

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Pelosi, Anna, Giovanni Battista Chirico, Salvatore Falanga Bolognesi, Carlo De Michele, and Guido D'Urso. "Forecasting crop evapotranspiration under standard conditions in precision farming." In 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, 2019. http://dx.doi.org/10.1109/metroagrifor.2019.8909263.

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Sujatha, R., and P. Isakki. "A study on crop yield forecasting using classification techniques." In 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE). IEEE, 2016. http://dx.doi.org/10.1109/icctide.2016.7725357.

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Parihar, Jai Singh, and Markand P. Oza. "FASAL: an integrated approach for crop assessment and production forecasting." In Asia-Pacific Remote Sensing Symposium, edited by Robert J. Kuligowski, Jai S. Parihar, and Genya Saito. SPIE, 2006. http://dx.doi.org/10.1117/12.713157.

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Fenu, Gianni, and Francesca Maridina Malloci. "An Application of Machine Learning Technique in Forecasting Crop Disease." In ICBDR 2019: 2019 The 3rd International Conference on Big Data Research. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3372454.3372474.

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Zaitseva, L. A., N. A. Kudryavtsev, D. O. Morozov, and V. V. Chebanenko. "Agrotechnics, plant protection and immunity in improving the phytosanitary condition of crops Flax in Russia." In Растениеводство и луговодство. Тимирязевская сельскохозяйственная академия, 2020. http://dx.doi.org/10.26897/978-5-9675-1762-4-2020-148.

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The Federal scientific center for bast crops has long been creating varieties with high resistance to 2 diseases (rust and Fusarium wilt), and now successfilly solves the problem of forming resistance to 3 (rust, Fusarium and Anthracnose) and even to 4 diseases (rust, Fusarium, Anthracnose and Pasmo). New proposals for phytosanitary monitoring and forecasting racionalize plant protection in relation to flax production. Ecologized biological preparations (for example, Vitaplan, Sternifag) are effective against flax diseases (Bacteriosis, Anthracnose, Mottling, etc.) and contribute to the preservation of the flax crop.
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Archontoulis, Sotirios, Ranae Dietzel, Mike Castellano, Andy VanLoocke, Ken Moore, Laila A. Puntel, Carolina Cordova, Kaitlin Togliatti, Huber Isaiah, and Mark Licht. "Forecasting yields and in-season crop-water nitrogen needs using simulation models." In Proceedings of the 28th Annual Integrated Crop Management Conference. Iowa State University, Digital Press, 2015. http://dx.doi.org/10.31274/icm-180809-277.

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Peng, Yung-Hsing, Chin-Shun Hsu, and Po-Chuang Huang. "Developing crop price forecasting service using open data from Taiwan markets." In 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2015. http://dx.doi.org/10.1109/taai.2015.7407108.

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Soodtoetong, Nantinee, Eakbodin Gedkhaw, and Montean Rattanasiriwongwut. "The Performance of Crop Yield Forecasting Model based on Artificial Intelligence." In 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2020. http://dx.doi.org/10.1109/ecti-con49241.2020.9158090.

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