Download or read book Estimation of Corn Yield Response to Selected Weather Variables in West Tennessee written by Garey Banks Perkins and published by . This book was released on 1971 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book American Doctoral Dissertations written by and published by . This book was released on 1990 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Multivariate Model of Corn Yield Response to Climate for the North Central United States written by Edward Christopher Velie and published by . This book was released on 1976 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Effect of Weather and Technology on Corn Yields in the Corn Belt 1929 62 written by Lawrence Hugh Shaw and published by . This book was released on 1965 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advanced Degrees Granted written by University of Tennessee, Knoxville. Graduate School and published by . This book was released on 1971 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Estimating Non linear Weather Impacts on Corn Yield written by Tian Yu (Ph.D.) and published by . This book was released on 2011 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: We estimate impacts of rainfall and temperature on corn yields by fitting a linear spline model with endogenous thresholds. Using Gibbs sampling and the Metropolis - Hastings algorithm, we simultaneously estimate the thresholds and other model parameters. A hierarchical structure is applied to capture county-specific factors determining corn yields. Results indicate that impacts of both rainfall and temperature are nonlinear and asymmetric in most states. Yield is concave in both weather variables. Corn yield decreases significantly when temperature increases beyond a certain threshold, and when the amount of rainfall decreases below a certain threshold. Flooding is another source of yield loss in some states. A moderate amount of heat is beneficial to corn yield in northern states, but not in other states. Both the levels of the thresholds and the magnitudes of the weather effects are estimated to be different across states in the Corn Belt.
Download or read book The University of Tennessee Record written by University of Tennessee and published by . This book was released on 1982 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Comprehensive Dissertation Index written by and published by . This book was released on 1973 with total page 838 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Comprehensive Dissertation Index 1861 1972 written by Xerox University Microfilms and published by . This book was released on 1973 with total page 814 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Corn Yield in Relation to Soil Management and Weather Variables in Western Iowa written by Benjamin Valeriano Pena-Olvera and published by . This book was released on 1979 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book County level Corn Yield Prediction with Deep Learning written by Yuchi Ma and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the world's leading corn producer, the United States supplies more than 30% of the global corn production. Accurate and timely estimation of corn yield is therefore essential for commodity trading and global food security. Recently, machine learning (ML) and deep learning (DL) models have been explored for corn yield prediction. Despite the success, there are still two major limitations of existing ML-based crop yield prediction models. First, most existing models mainly focus on predicting the crop yield without providing any information about the uncertainty which is important to provide quantified confidence interval of the prediction to users for their knowledgeable decision makings. Second, data-driven DL models require a large amount of reference data samples (i.e., yield records) for model training and tend to have low spatial transferability due to domain shifts between different regions. In this dissertation, we focused on addressing these two major limitations using Bayesian learning and unsupervised domain adaptation (UDA) for corn yield prediction. Specifically, to address the first limitation, this dissertation proposed Bayesian neural networks (BNN) for corn yield prediction and uncertainty analysis. By applying Bayesian inference, the proposed BNN model can provide not only accurate yield prediction but also the corresponding predictive uncertainty. Feature variables were collected from multiple data sources, including remote sensing (RS) imagery, weather variables, soil properties, and historical average yield. Using preceding years since 2001 for model training, the developed BNN model achieved an average coefficient of determination (R2) of 0.77 for late-season prediction across the U.S. corn belt in testing years 2010-2019 and outperformed five other state-of-the-art ML models. Evaluation results of in-season yield prediction showed that the BNN model achieved the optimal prediction results by the middle of August, which is about two months before the harvest. We also assessed the predictive uncertainty and found that more than 84% of the observed yield records were successfully enveloped in the 95% confidence interval of the predictive yield distribution. Uncertainties in yield prediction were mainly induced by the observation noise and related to the inter-annual and seasonal variabilities of environmental stress such as heat stress and water stress. To address the second limitation, this dissertation utilized the UDA strategy to reduce the domain shift between the source domain and the target domain with the aim of accurately predicting corn yield in the target domain without using labeled data from the target domain. We first proposed two single-source UDA models for county-level corn yield prediction based on RS images and weather variables. The proposed adaptive domain adversarial neural network (ADANN) and Bayesian domain adversarial neural network (BDANN) have been proven to have better spatial transferability and outperformed other supervised learning models and DANN in transfer experiments across two ecoregions in the U.S. corn belt. Furthermore, we proposed a multi-source UDA method named multi-source maximum predictor discrepancy (MMPD) to address the remaining issues of single-source domain adaptation methods. First, the multi-source UDA strategy was adopted in MMPD to avoid negative interference among source samples from heterogeneous regions. Also, by using the maximum predictor discrepancy (MPD), MMPD was trained to align source and target domains by considering crop yield response in the target domain based on task-specific regression models. Experiments on three UDA scenarios in the U.S. corn belt and Argentina have been conducted to evaluate the model performance. It was observed that MMPD outperformed representative single-source and multi-source UDA methods. In summary, this dissertation introduced Bayesian inference and UDA to county-level corn yield prediction based on RS and weather variables. Novel solutions have been provided for quantifying predictive uncertainty in crop yield prediction and improving spatial transferability for deep learning-based crop yield prediction models. This dissertation provides a robust framework for the in-season prediction of crop yield and highlights the need for a deeper understanding of the impact of environmental stress on agricultural productivity and crop yield. Moreover, this dissertation applied the UDA for crop yield prediction and demonstrated the effectiveness of adversarial learning for improving the transferability of DL models on crop yield prediction.
Download or read book Comprehensive Dissertation Index 1861 1972 Business and economics written by Xerox University Microfilms and published by . This book was released on 1973 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Journal of Agricultural and Applied Economics written by and published by . This book was released on 2002 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Selected Water Resources Abstracts written by and published by . This book was released on 1987 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Comprehensive Dissertation Index 1861 1972 Author index written by Xerox University Microfilms and published by . This book was released on 1973 with total page 1118 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Precision Farming written by K. R. Krishna and published by CRC Press. This book was released on 2016-04-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precision farming involves soil fertility and crop growth monitoring, electronic equipment, remote sensing, global information and positioning systems, computer models, decision support systems, variable-rate technology, and accurate recordkeeping. This book on precision techniques provides valuable information on instrumentation and methodology. I
Download or read book Cereal Diseases Nanobiotechnological Approaches for Diagnosis and Management written by Kamel A. Abd-Elsalam and published by Springer Nature. This book was released on 2022-10-25 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: New ways to improve cereal crops against fungal, bacterial, and viral diseases are covered in this book that was put together by a group of experts. These include genetics, genome editing systems, and nano-biotechnological tools. Cereal crops are mainly the world's leading food crops and feed a large share of the world population. However, external factors, such as pathogens, have often threatened their productivity. Like wheat, rice, maize, oats, barley, millet and storage, etiology, epidemiology, and diseases in cereal crop management. In addition, the importance of crop genetics and genomics in combating pathogens has been discussed. This book offers up-to-date information on new methods, such as the potential of the genome editing system for crop improvement, in particular the CRISPR-Cas system. The current volume also talks about identification, plant breeding, genome editing, and nanotechnology tools that can be used to fight disease in cereal crops. This book is good for students, teachers, and researchers who study biotic stress in cereals, as well as scientists who study nanotechnology, disease resistance, pathogen biology, genome editing, agriculture sciences, and future biotechnology.
