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Статті в журналах з теми "Crop yields – Statistical methods"
Zhao, Chuang, Bing Liu, Shilong Piao, Xuhui Wang, David B. Lobell, Yao Huang, Mengtian Huang, et al. "Temperature increase reduces global yields of major crops in four independent estimates." Proceedings of the National Academy of Sciences 114, no. 35 (August 15, 2017): 9326–31. http://dx.doi.org/10.1073/pnas.1701762114.
Повний текст джерелаBischokov, Ruslan M. "Analysis, modelling and forecasting of crop yields using artificial neural networks." RUDN Journal of Agronomy and Animal Industries 17, no. 2 (June 16, 2022): 146–57. http://dx.doi.org/10.22363/2312-797x-2022-17-2-146-157.
Повний текст джерелаAfshar, Mehdi H., Timothy Foster, Thomas P. Higginbottom, Ben Parkes, Koen Hufkens, Sanjay Mansabdar, Francisco Ceballos, and Berber Kramer. "Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring." Remote Sensing 13, no. 5 (March 2, 2021): 924. http://dx.doi.org/10.3390/rs13050924.
Повний текст джерелаStorchak, Irina Gennadyevna, and Fedor Vladimirovich Eroshenko. "Use of remote methods for monitoring formation of yield of spring barley." Agrarian Scientific Journal, no. 11 (November 23, 2020): 58–61. http://dx.doi.org/10.28983/asj.y2020i11pp58-61.
Повний текст джерелаPOSHYVALOVA, Olena. "Statistical model for evaluation of the impact of climatic conditions on the crops production: the regional aspects." Economics. Finances. Law, no. 10 (October 29, 2021): 23–28. http://dx.doi.org/10.37634/efp.2021.10.5.
Повний текст джерелаSvotwa, Ezekia, Anxious J. Masuka, Barbara Maasdorp, Amon Murwira, and Munyaradzi Shamudzarira. "Remote Sensing Applications in Tobacco Yield Estimation and the Recommended Research in Zimbabwe." ISRN Agronomy 2013 (December 15, 2013): 1–7. http://dx.doi.org/10.1155/2013/941873.
Повний текст джерелаGong, Liyun, Miao Yu, Shouyong Jiang, Vassilis Cutsuridis, and Simon Pearson. "Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN." Sensors 21, no. 13 (July 1, 2021): 4537. http://dx.doi.org/10.3390/s21134537.
Повний текст джерелаNyéki, Anikó, and Miklós Neményi. "Crop Yield Prediction in Precision Agriculture." Agronomy 12, no. 10 (October 11, 2022): 2460. http://dx.doi.org/10.3390/agronomy12102460.
Повний текст джерелаPortukhay, Oksana, Sergij Lyko, Oleksandr Mudrak, Halyna Mudrak, and Iryna Lohvynenko. "Agroecological Bases of Sustainable Development Strategy for the Rural United Territorial Communities of the Western Polissya Region." Scientific Horizons 24, no. 6 (November 24, 2021): 50–61. http://dx.doi.org/10.48077/scihor.24(6).2021.50-61.
Повний текст джерелаPapadavid, G., and L. Toulios. "The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting." Journal of Agricultural Science 156, no. 5 (October 9, 2017): 599–617. http://dx.doi.org/10.1017/s0021859617000594.
Повний текст джерелаДисертації з теми "Crop yields – Statistical methods"
Adeyemi, Rasheed Alani. "Empirical statistical modelling for crop yields predictions: bayesian and uncertainty approaches." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15533.
Повний текст джерелаThis thesis explores uncertainty statistics to model agricultural crop yields, in a situation where there are neither sampling observations nor historical record. The Bayesian approach to a linear regression model is useful for predict ion of crop yield when there are quantity data issue s and the model structure uncertainty and the regression model involves a large number of explanatory variables. Data quantity issues might occur when a farmer is cultivating a new crop variety, moving to a new farming location or when introducing a new farming technology, where the situation may warrant a change in the current farming practice. The first part of this thesis involved the collection of data from experts' domain and the elicitation of the probability distributions. Uncertainty statistics, the foundation of uncertainty theory and the data gathering procedures were discussed in detail. We proposed an estimation procedure for the estimation of uncertainty distributions. The procedure was then implemented on agricultural data to fit some uncertainty distributions to five cereal crop yields. A Delphi method was introduced and used to fit uncertainty distributions for multiple experts' data of sesame seed yield. The thesis defined an uncertainty distance and derived a distance for a difference between two uncertainty distributions. We lastly estimated the distance between a hypothesized distribution and an uncertainty normal distribution. Although, the applicability of uncertainty statistics is limited to one sample model, the approach provides a fast approach to establish a standard for process parameters. Where no sampling observation exists or it is very expensive to acquire, the approach provides an opportunity to engage experts and come up with a model for guiding decision making. In the second part, we fitted a full dataset obtained from an agricultural survey of small-scale farmers to a linear regression model using direct Markov Chain Monte Carlo (MCMC), Bayesian estimation (with uniform prior) and maximum likelihood estimation (MLE) method. The results obtained from the three procedures yielded similar mean estimates, but the credible intervals were found to be narrower in Bayesian estimates than confidence intervals in MLE method. The predictive outcome of the estimated model was then assessed using simulated data for a set of covariates. Furthermore, the dataset was then randomly split into two data sets. The informative prior was later estimated from one-half called the "old data" using Ordinary Least Squares (OLS) method. Three models were then fitted onto the second half called the "new data": General Linear Model (GLM) (M1), Bayesian model with a non-informative prior (M2) and Bayesian model with informative prior (M3). A leave-one-outcross validation (LOOCV) method was used to compare the predictive performance of these models. It was found that the Bayesian models showed better predictive performance than M1. M3 (with a prior) had moderate average Cross Validation (CV) error and Cross Validation (CV) standard error. GLM performed worst with least average CV error and highest (CV) standard error among the models. In Model M3 (expert prior), the predictor variables were found to be significant at 95% credible intervals. In contrast, most variables were not significant under models M1 and M2. Also, The model with informative prior had narrower credible intervals compared to the non-information prior and GLM model. The results indicated that variability and uncertainty in the data was reasonably reduced due to the incorporation of expert prior / information prior. We lastly investigated the residual plots of these models to assess their prediction performance. Bayesian Model Average (BMA) was later introduced to address the issue of model structure uncertainty of a single model. BMA allows the computation of weighted average over possible model combinations of predictors. An approximate AIC weight was then proposed for model selection instead of frequentist alternative hypothesis testing (or models comparison in a set of competing candidate models). The method is flexible and easy to interpret instead of raw AIC or Bayesian information criterion (BIC), which approximates the Bayes factor. Zellner's g-prior was considered appropriate as it has widely been used in linear models. It preserves the correlation structure among predictors in its prior covariance. The method also yields closed-form marginal likelihoods which lead to huge computational savings by avoiding sampling in the parameter space as in BMA. We lastly determined a single optimal model from all possible combination of models and also computed the log-likelihood of each model.
Pettersson, C. G. "Predicting malting barley protein concentration : based on canopy reflectance and site characteristics /." Uppsala : Dept. of Crop Production Ecology, Swedish University of Agricultural Sciences, 2007. http://epsilon.slu.se/200756.pdf.
Повний текст джерелаGangloff, William J. "Spatial statistical analysis of soil properties and crop yields for precision agriculture applications." Access citation, abstract and download form; downloadable file 10.12 Mb, 2004. http://wwwlib.umi.com/dissertations/fullcit/3131671.
Повний текст джерелаMatthews-Pennanen, Neil. "Assessment of Potential Changes in Crop Yields in the Central United States Under Climate Change Regimes." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7017.
Повний текст джерелаUno, Yoji. "Application of machine learning methods and airborne hyperspectral remote sensing for crop yield estimation." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80890.
Повний текст джерелаThe experimental plots were set up at the Emile A. Lods Agronomy Research Center, Montreal, Quebec. Corn was grown under the twelve combinations of three nitrogen application rates (60, 120, and 250 kg N/ha), and four weed control strategies (Broad leaf weed, Grass weed, Broad leaf and grass weed control, and no weed control). The images of the experimental field were taken with a Compact Airborne Spectrographic Imager (CASI) at three times (June 30 for early growth stage, August 5 for tassel stage, and Aug 25 for mature stage) during the year 2000 growing season.
Two machine learning algorithms, Artificial Neural Networks (ANN) and Decision Tree (DT) were evaluated. The performance of ANNs was compared with four conventional modeling methods. For the DT algorithms, two different aspects, (i) DT as a classification method, and (ii) DT as a feature selection tool, were explored in this study.
Schmer, Marty R. "Switchgrass reestablishment on cropland evaluating net energy, spatial effects, temporal effects, and estimating switchgrass productivity using indirect methods /." 2008. http://0-proquest.umi.com/pqdweb?did=1584062001&sid=1&Fmt=2&clientId=14215&RQT=309&VName=PQD.
Повний текст джерелаTitle from title screen (site viewed Feb. 17, 2009). PDF text: 196 p. : ill. (some col.) ; 2 Mb. UMI publication number: AAT 3324854. Includes bibliographical references. Also available in microfilm and microfiche formats.
"Optimising aspects of a soybean breeding programme." Thesis, 2008. http://hdl.handle.net/10413/738.
Повний текст джерелаMasupha, Elisa Teboho. "Drought analysis with reference to rain-fed maize for past and future climate conditions over the Luvuvhu River catchment in South Africa." Diss., 2017. http://hdl.handle.net/10500/23197.
Повний текст джерелаAgriculture, Animal Health and Human Ecology
M. Sc. (Agriculture)
Книги з теми "Crop yields – Statistical methods"
Fielder, Lonnie L. Measurement of price, yield, and revenue variability for Louisiana crops. Baton Rouge, La: Dept. of Agricultural Economics and Agribusiness, Louisiana Agricultural Experiment Station, Louisiana State University Agricultural Center, 1985.
Знайти повний текст джерелаSakamoto, Clarence M. The water satisfaction index for estimating crop yield and harvested/planted area ratio in Botswana. [Gaborone, Botswana]: Republic of Botswana, Dept. of Meteorological Services, Ministry of Works, Transport, and Communications, 1990.
Знайти повний текст джерелаG, Gauch Hugh. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Amsterdam: Elsevier, 1992.
Знайти повний текст джерелаManfred, Sievers, ed. Instability in world food production: Statistical analysis, graphical presentation, and interpretation. Kiel, West Germany: Wissenschaftsverlag Vauk Kiel, 1985.
Знайти повний текст джерелаLakshmi, K. R. Statistical methods for tropical tuber crop research. Thiruvananthapuram: Central Tuber Crops Research Institute, 2003.
Знайти повний текст джерелаMaerz, Ulrich. Methods to simulate distributions of crop yields based on farmer interviews. Aleppo, Syria: International Center for Agricultural Research in the Dry Areas, 1987.
Знайти повний текст джерелаM, Cupello James, and Meadows Becki, eds. Managing Six Sigma: A practical guide to understanding, assessing, and implementing the strategy that yields bottom line success. New York: John Wiley, 2001.
Знайти повний текст джерелаMoney and capital markets: Pricing, yields and analysis. 2nd ed. St. Leonards, N.S.W., Australia: Allen & Unwin, 1996.
Знайти повний текст джерелаMaughan, Chris. Field studies on winter milling wheat: Fertiliser nitrogen programmes for yield and quality improvement : evaluation of methods of monitoring crop nitrogen status. Dublin: University College Dublin, 1997.
Знайти повний текст джерелаHarrison, Scott. Productivity differences across New South Wales rice farms: Links to resource quality. Canberra, Australia: ABARE, 1999.
Знайти повний текст джерелаЧастини книг з теми "Crop yields – Statistical methods"
Kawaye, Floney P., and Michael F. Hutchinson. "Maize, Cassava, and Sweet Potato Yield on Monthly Climate in Malawi." In African Handbook of Climate Change Adaptation, 617–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_120.
Повний текст джерелаKusumaningrum, Dian, Rahma Anisa, Valantino Agus Sutomo, and Ken Seng Tan. "Alternative Area Yield Index Based Crop Insurance Policies in Indonesia." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 285–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_42.
Повний текст джерелаDurner, Edward F. "Simple linear regression." In Applied plant science experimental design and statistical analysis using the SAS® OnDemand for Academics, 80–145. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245981.0009.
Повний текст джерелаGrignani, Carlo, Francesco Alluvione, Chiara Bertora, Laura Zavattaro, Massimo Fagnano, Nunzio Fiorentino, Fabrizio Quaglietta Chiarandà, Mariana Amato, Francesco Lupo, and Rocco Bochicchio. "Field Plots and Crop Yields Under Innovative Methods of Carbon Sequestration in Soil." In Carbon Sequestration in Agricultural Soils, 39–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23385-2_3.
Повний текст джерелаCastellani, Marco, and Emanuel A. dos Santos. "Prediction of Long-Term Government Bond Yields Using Statistical and Artificial Intelligence Methods." In Studies in Computational Intelligence, 341–67. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01866-9_11.
Повний текст джерелаAnisa, Rahma, Dian Kusumaningrum, Valantino Agus Sutomo, and Ken Seng Tan. "Potential of Reducing Crop Insurance Subsidy Based on Willingness to Pay and Random Forest Analysis." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 27–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_5.
Повний текст джерелаPassamani, Giuliana. "Time Series Convergence within I(2) Models: the Case of Weekly Long Term Bond Yields in the Four Largest Euro Area Countries." In Advanced Statistical Methods for the Analysis of Large Data-Sets, 217–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21037-2_20.
Повний текст джерелаRowhani, Pedram, Navin Ramankutty, William J. Martin, Ana Iglesias, Thomas W. Hertel, and Syud A. Ahmed. "The Impacts of Climate Change on Crop Yields in Tanzania: Comparing an Empirical and a Process-Based Model." In Economic Tools and Methods for the Analysis of Global Change Impacts on Agriculture and Food Security, 149–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99462-8_9.
Повний текст джерелаMkomwa, Saidi, Amir Kassam, Sjoerd W. Duiker, and Nouhoun Zampaligre. "Livestock integration in conservation agriculture." In Conservation agriculture in Africa: climate smart agricultural development, 215–29. Wallingford: CABI, 2022. http://dx.doi.org/10.1079/9781789245745.0012.
Повний текст джерелаMba, Chikelu, and Hans Dreyer. "The conservation and sustainable use of plant genetic resources for food and agriculture and emerging biotechnologies." In Mutation breeding, genetic diversity and crop adaptation to climate change, 459–68. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249095.0047.
Повний текст джерелаТези доповідей конференцій з теми "Crop yields – Statistical methods"
AMIROV, Marat, Igor SERZHANOV, Farid SHAYKHUTDINOV, and Nicolay SEMUSHKIN. "MAIN DIRECTIONS OF DEVELOPMENT OF SPRING WHEAT PRODUCTION AGRICULTURAL TECHNOLOGIES FOR SUSTAINABLE ARABLE FARMING IN THE FOREST-STEPPE BELT OF THE MIDDLE VOLGA REGION." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.254.
Повний текст джерелаZVIRBULE, Andra, and Raivis ANDERSONS. "FACTORS INFLUENCING CHANGES OF BEEF CATTLE HERD QUANTITY AND SIZE: CASE OF LATVIA." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.147.
Повний текст джерелаJiang, Z. H., J. Zhang, C. H. Yang, Y. Rao, and S. W. Li. "Comparison and Verification of Methods for Multivariate Statistical Analysis and Regression in Crop Modelling." In 2015 International Conference on Electrical, Automation and Mechanical Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/eame-15.2015.163.
Повний текст джерелаKaneko, Daijiro, Peng Yang, and Toshiro Kumakura. "Carbon partitioning as validation methods for crop yields and CO 2 sequestration monitoring in Asia using a photosynthetic-sterility model." In Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2010. http://dx.doi.org/10.1117/12.864887.
Повний текст джерелаSUBIû, Jonel, Biljana GRUJIû, and Svetlana ROLJEVIû NIKOLIû. "ECOLOGICAL AGRICULTURAL PRODUCTION – OPINIONS AND PRACTICES OF PRODUCERS IN SERBIA." In Competitiveness of Agro-Food and Environmental Economy. Editura ASE, 2022. http://dx.doi.org/10.24818/cafee/2019/8/03.
Повний текст джерелаEltaher, Yahia, and Shouxiang Ma. "Carbon/Oxygen Spectral Data Processing, its Affiliation to Scintillation Detector Selectivity & their Impact on Reservoir Saturation Monitoring, Lessons Learnt and Recommended Workflow." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212613-ms.
Повний текст джерелаSingh, Murari P. "Probabilistic HCF Life Estimation of a Mechanical Component." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/pvp-25211.
Повний текст джерелаNelson, Jacob, G. Austin Marrs, Greg Schmidt, Joseph A. Donndelinger, and Robert L. Nagel. "Evaluating Sampling Methods for Reusing Knowledge From Large and Ill-Structured Qualitative Data Sets." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67964.
Повний текст джерелаJaptap, Shubhangi Ramling, Ameeta Ravikumar, Gouri Raut, and Ravi Kumar. "Statistical Optimization of Media for Enhancing Intracellular Lipid Content in Yarrowia Lipolytica NCIM 3589 Grown on Waste Cooking Oil for Biodiesel Production." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/yckc2922.
Повний текст джерелаGURSKIENĖ, Virginija, and Justina JATUŽYTĖ. "LAND USE IN ŽUVINTAS BIOSPHERE RESERVE." In Rural Development 2015. Aleksandras Stulginskis University, 2015. http://dx.doi.org/10.15544/rd.2015.053.
Повний текст джерелаЗвіти організацій з теми "Crop yields – Statistical methods"
Temple, Dorota S., Jason S. Polly, Meghan Hegarty-Craver, James I. Rineer, Daniel Lapidus, Kemen Austin, Katherine P. Woodward, and Robert H. Beach III. The View From Above: Satellites Inform Decision-Making for Food Security. RTI Press, August 2019. http://dx.doi.org/10.3768/rtipress.2019.rb.0021.1908.
Повний текст джерелаCrowley, David E., Dror Minz, and Yitzhak Hadar. Shaping Plant Beneficial Rhizosphere Communities. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7594387.bard.
Повний текст джерелаAgassi, Menahem, Michael J. Singer, Eyal Ben-Dor, Naftaly Goldshleger, Donald Rundquist, Dan Blumberg, and Yoram Benyamini. Developing Remote Sensing Based-Techniques for the Evaluation of Soil Infiltration Rate and Surface Roughness. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7586479.bard.
Повний текст джерелаShani, Uri, Lynn Dudley, Alon Ben-Gal, Menachem Moshelion, and Yajun Wu. Root Conductance, Root-soil Interface Water Potential, Water and Ion Channel Function, and Tissue Expression Profile as Affected by Environmental Conditions. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7592119.bard.
Повний текст джерела