Добірка наукової літератури з теми "Crop yields – Methodology"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Crop yields – Methodology".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Crop yields – Methodology"
Shirsath, Paresh B., Vinay Kumar Sehgal, and Pramod K. Aggarwal. "Downscaling Regional Crop Yields to Local Scale Using Remote Sensing." Agriculture 10, no. 3 (March 2, 2020): 58. http://dx.doi.org/10.3390/agriculture10030058.
Повний текст джерелаDmytrenko, V. P., L. P. Odnolyetok, О. О. Kryvoshein, and A. V. Krukivska. "Development of the methodology of estimating of agricultural crop yield potential with consideration of climate and agrophytotechnology impact." Ukrainian hydrometeorological journal, no. 20 (October 29, 2017): 52–60. http://dx.doi.org/10.31481/uhmj.20.2017.06.
Повний текст джерелаNeill, D. E., and G. B. Follas. "Use of crop sensing technology in crop protection research." New Zealand Plant Protection 64 (January 8, 2011): 287. http://dx.doi.org/10.30843/nzpp.2011.64.5993.
Повний текст джерелаNarayan, Kale Jaydeep. "Review of Crop Yield Prediction using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4626–28. http://dx.doi.org/10.22214/ijraset.2021.36058.
Повний текст джерелаKirthiga, S. M., and N. R. Patel. "In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model." AgriEngineering 4, no. 4 (October 30, 2022): 1054–75. http://dx.doi.org/10.3390/agriengineering4040066.
Повний текст джерелаEser, Adnan, Hajnalka Kató, Laura Kempf, and Márton Jolánkai. "Water footprint of yield protein content of twelve field crop species on a Hungarian crop site." Agrokémia és Talajtan 68, Supplement (December 2019): 53–60. http://dx.doi.org/10.1556/0088.2019.00041.
Повний текст джерелаShevchenko, M. S., L. M. Decyatnik, and K. A. Derevenets-Shevchenko. "Modern systems of agriculture and a new interpretation of crop rotation value of agricultural crops." Scientific Journal Grain Crops 4, no. 2 (December 11, 2020): 319–29. http://dx.doi.org/10.31867/2523-4544/0141.
Повний текст джерелаArumugam, Surendran, Ashok K.R., Suren N. Kulshreshtha., Isaac Vellangany, and Ramu Govindasamy. "Yield variability in rainfed crops as influenced by climate variables." International Journal of Climate Change Strategies and Management 7, no. 4 (November 16, 2015): 442–59. http://dx.doi.org/10.1108/ijccsm-08-2013-0096.
Повний текст джерелаDelbridge, Timothy A., and Robert P. King. "How important is the transitional yield (t-yield)? An analysis of reforms to organic crop insurance." Agricultural Finance Review 79, no. 2 (April 1, 2019): 234–54. http://dx.doi.org/10.1108/afr-03-2017-0022.
Повний текст джерелаSHARIFIFAR, Amin, Hadi GHORBANI, and Fereydoon SARMADIAN. "Soil suitability evaluation for crop selection using fuzzy sets methodology." Acta agriculturae Slovenica 107, no. 1 (April 6, 2016): 159. http://dx.doi.org/10.14720/aas.2016.107.1.16.
Повний текст джерелаДисертації з теми "Crop yields – Methodology"
Chen, Xiangtuo. "Statistical Learning Methodology to Leverage the Diversity of Environmental Scenarios in Crop Data : Application to the prediction of crop production at large-scale." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC055.
Повний текст джерелаCrop yield prediction is a paramount issue in agriculture. Considerable research has been performed with this objective relying on various methodologies. Generally, they can be classified into model-driven approaches and data-driven approaches.The model-driven approaches are based on crop mechanistic modelling. They describe crop growth in interaction with their environment as dynamical systems. Since these models are based on the mechanical description of biophysical processes, they potentially imply a large number of state variables and parameters, whose estimation is not straightforward. In particular, the resulting parameter estimation problems are typically non-linear, leading to non-convex optimisation problems in multi-dimensional space. Moreover, data acquisition is very challenging and necessitates heavy specific experimental work in order to obtain the appropriate data for model identification.On the other hand, the data-driven approaches for yield prediction necessitate data from a large number of environmental scenarios, but with data quite easy to obtain: climatic data and final yield. However, the perspectives of this type of models are mostly limited to prediction purposes.An original contribution of this thesis consists in proposing a statistical methodology for the parameterisation of potentially complex mechanistic models, when datasets with different environmental scenarios and large-scale production records are available, named Multi-scenario Parameter Estimation Methodology (MuScPE). The main steps are the following:First, we take advantage of prior knowledge on the parameters to assign them relevant prior distributions and perform a global sensitivity analysis of the model parameters to screen the most important ones that will be estimated in priority;Then, we implement an efficient non-convex optimisation method, the parallel particle swarm optimisation, to search for the MAP (maximum a posterior) estimator of the parameters;Finally, we choose the best configuration regarding the number of estimated parameters by model selection criteria. Because when more parameters are estimated, theoretically, the calibrated model could explain better the variance of the output. Meanwhile, it increases also difficulty for optimization, which leads to uncertainty in calibration.This methodology is first tested with the CORNFLO model, a functional crop model for the corn.A second contribution of the thesis is the comparison of this model-driven method with classical data-driven methods. For this purpose, according to their different methodology in fitting the model complexity, we consider two classes of regression methods: first, Statistical methods derived from generalized linear regression that are good at simplifying the model by dimensional reduction, such as Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression; second, Machine Learning Regression based on re-sampling techniques like Random Forest, k-Nearest Neighbour, Artificial Neural Network and Support Vector Machine (SVM) regression.At last, a weighted regression is applied to large-scale yield prediction. Soft wheat production in France is taken as an example. Model-driven and data-driven approaches have also been compared for their performances in achieving this goal, which could be recognised as the third contribution of this thesis
Книги з теми "Crop yields – Methodology"
Eurostat. Crop Yield Forecasting Methods: Proceedings of the Seminar (Theme 0--Miscellaneous. Series D, Studies and Research). Statistical Office of European Communities, 1997.
Знайти повний текст джерелаStatens planteavlsforsøg (Denmark). Afdeling for arealanvendelse., ed. Yield and farm survey in two agricultural regions in Denmark, 1994. [Vejle]: Ministry of Agriculture and Fisheries, Danish Institute of Plant and Soil Science, Dept. of Land Use, 1995.
Знайти повний текст джерелаЧастини книг з теми "Crop yields – Methodology"
Durner, 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 джерелаChoudhary, Mahendra, Rohit Sartandel, Anish Arun, and Leena ladge. "Crop Recommendation System and Plant Disease Classification using Machine Learning for Precision Agriculture." In Artificial Intelligence and Communication Technologies, 39–49. Soft Computing Research Society, 2022. http://dx.doi.org/10.52458/978-81-955020-5-9-4.
Повний текст джерелаKulyk, Maksym, Dmytro Dʼomin, and Іlona Rozhkо. "RECLAMATION OF MARGINAL LANDS USING RARE ENERGY CROPS." In European vector of development of the modern scientific researches. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-077-3-27.
Повний текст джерелаM. Hatture, Sanjeevakumar, Pallavi V. Yankati, Rashmi Saini, and Rashmi P. Karchi. "Organic Farming for Sustainable Agriculture Using Water and Soil Nutrients." In New Generation of Organic Fertilizers. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100319.
Повний текст джерелаDas, Sripriya, Manoj Kumar Singh, Sneha Kumari, and Manimala Mahato. "Recent Advances in Crop Establishment Methods in Rice-Wheat Cropping System-a Review." In Cereal Grains [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98743.
Повний текст джерелаKulkarni, Arun, and Sara McCaslin. "Fuzzy Neural Network Models for Knowledge Discovery." In Intelligent Data Analysis, 103–19. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-982-3.ch006.
Повний текст джерелаCivan, Peter, Renaud Rincent, Alice Danguy-Des-Deserts, Jean-Michel Elsen, and Sophie Bouchet. "Population Genomics Along With Quantitative Genetics Provides a More Efficient Valorization of Crop Plant Genetic Diversity in Breeding and Pre-breeding Programs." In Population Genomics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/13836_2021_97.
Повний текст джерелаKhyzhnyak, Svitlana, and Volodymyr Voitsitskiy. "BIOTESTING AS A METHOD FOR ASSESSING THE STIMULATING EFFECT OF HUMIC COMPOUNDS ON HIGHER PLANTSBIOTESTING AS A METHOD FOR ASSESSING THE STIMULATING EFFECT OF HUMIC COMPOUNDS ON HIGHER PLANTS." In Science, technology, and innovation: the experience of European countries and prospects for Ukraine. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-190-9-3.
Повний текст джерелаGutsalenko, Liubov, and Tetiana Mulyk. "ANALYTICAL PROVISION OF LAND RESOURCES MANAGEMENT OF THE ENTERPRISE: STATE AND IMPROVEMENT." In Theoretical and practical aspects of the development of modern scientific research. Publishing House “Baltija Publishing”, 2022. http://dx.doi.org/10.30525/978-9934-26-195-4-4.
Повний текст джерелаТези доповідей конференцій з теми "Crop yields – Methodology"
James M McKinion and Jeffrey L Willers. "Development of a Crop Yield Stability Methodology for a Field." In 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.30024.
Повний текст джерелаSoria-Ruiz, Jesus, and Yolanda M. Fernandez-Ordonez. "Methodology to generate yield maps of maize crops." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651696.
Повний текст джерелаZhu, Jinxia, Ke Wang, Jinsong Deng, and Tom Harmon. "Quantifying Nitrogen Status of Rice Using Low Altitude UAV-Mounted System and Object-Oriented Segmentation Methodology." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87107.
Повний текст джерелаSantos, Thiago T., and Luciano Gebler. "A methodology for detection and localization of fruits in apples orchards from aerial images." In Congresso Brasileiro de Agroinformática. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbiagro.2021.18369.
Повний текст джерелаBushueva, Vera Ivanovna, Marina AVRAMENKO, Victoria Volyntseva, and Viktoriya BARDOVSKAYa. "Results of Galega orientalis breeding in the Republic of Belarus." In Multifunctional adaptive fodder production 29 (77). ru: Federal Williams Research Center of Forage Production and Agroecology, 2022. http://dx.doi.org/10.33814/mak-2022-29-77-95-104.
Повний текст джерелаKontokostas, Georgios, Ioannis Goulos, and Anastassios Stamatis. "Techno–Economic Evaluation of Recuperated Gas Turbine Cogeneration Cycles Utilizing Animal Manure and Energy Crops for Biogas Fuel." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-25308.
Повний текст джерелаVIESTURS, Dainis, Nikolajs KOPIKS, and Adolfs RUCINS. "RESEARCH ON THE DEVELOPMENT OF THE TRACTOR AND COMBINE FLEET IN LATVIA." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.183.
Повний текст джерелаЗвіти організацій з теми "Crop yields – Methodology"
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 джерелаMiller, Gad, and Jeffrey F. Harper. Pollen fertility and the role of ROS and Ca signaling in heat stress tolerance. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598150.bard.
Повний текст джерелаAparicio, Gabriela, Vida Bobić, Fernando De Olloqui, María Carmen Fernández Diez, María Paula Gerardino, Oscar A. Mitnik, and Sebastian Vargas Macedo. Liquidity or Capital?: The Impacts of Easing Credit Constraints in Rural Mexico. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003336.
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 джерелаSeginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
Повний текст джерелаMevarech, Moshe, Jeremy Bruenn, and Yigal Koltin. Virus Encoded Toxin of the Corn Smut Ustilago Maydis - Isolation of Receptors and Mapping Functional Domains. United States Department of Agriculture, September 1995. http://dx.doi.org/10.32747/1995.7613022.bard.
Повний текст джерелаNaim, Michael, Gary R. Takeoka, Haim D. Rabinowitch, and Ron G. Buttery. Identification of Impact Aroma Compounds in Tomato: Implications to New Hybrids with Improved Acceptance through Sensory, Chemical, Breeding and Agrotechnical Techniques. United States Department of Agriculture, October 2002. http://dx.doi.org/10.32747/2002.7585204.bard.
Повний текст джерела