Relevant bibliographies by topics / Yield predictions

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Academic literature on the topic 'Yield predictions'

Author: Grafiati
Published: 9 May 2025

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Journal articles on the topic "Yield predictions"

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Yadav, Kamini, and Hatim M. E. Geli. "Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period." Land 10, no. 12 (December 15, 2021): 1389. http://dx.doi.org/10.3390/land10121389.

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Agricultural production systems in New Mexico (NM) are under increased pressure due to climate change, drought, increased temperature, and variable precipitation, which can affect crop yields, feeds, and livestock grazing. Developing more sustainable production systems requires long-term measurements and assessment of climate change impacts on yields, especially over such a vulnerable region. Providing accurate yield predictions plays a key role in addressing a critical sustainability gap. The goal of this study is the development of effective crop yield predictions to allow for a better-informed cropland management and future production potential, and to develop climate-smart adaptation strategies for increased food security. The objectives were to (1) identify the most important climate variables that significantly influence and can be used to effectively predict yield, (2) evaluate the advantage of using remotely sensed data alone and in combination with climate variables for yield prediction, and (3) determine the significance of using short compared to long historical data records for yield prediction. This study focused on yield prediction for corn, sorghum, alfalfa, and wheat using climate and remotely sensed data for the 1920–2019 period. The results indicated that the use of normalized difference vegetation index (NDVI) alone is less accurate in predicting crop yields. The combination of climate and NDVI variables provided better predictions compared to the use of NDVI only to predict wheat, sorghum, and corn yields. However, the use of a climate only model performed better in predicting alfalfa yield. Yield predictions can be more accurate with the use of shorter data periods that are based on region-specific trends. The identification of the most important climate variables and accurate yield prediction pertaining to New Mexico’s agricultural systems can aid the state in developing climate change mitigation and adaptation strategies to enhance the sustainability of these systems.
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Mia, Md Suruj, Ryoya Tanabe, Luthfan Nur Habibi, Naoyuki Hashimoto, Koki Homma, Masayasu Maki, Tsutomu Matsui, and Takashi S. T. Tanaka. "Multimodal Deep Learning for Rice Yield Prediction Using UAV-Based Multispectral Imagery and Weather Data." Remote Sensing 15, no. 10 (May 10, 2023): 2511. http://dx.doi.org/10.3390/rs15102511.

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Precise yield predictions are useful for implementing precision agriculture technologies and making better decisions in crop management. Convolutional neural networks (CNNs) have recently been used to predict crop yields in unmanned aerial vehicle (UAV)-based remote sensing studies, but weather data have not been considered in modeling. The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading stage, along with weather data. The effects of the CNN architectures, layer depths, and weather data integration methods on the prediction accuracy were evaluated. Overall, the multimodal deep learning model integrating UAV-based multispectral imagery and weather data had the potential to develop more precise rice yield predictions. The best models were those trained with weekly weather data. A simple CNN feature extractor for UAV-based multispectral image input data might be sufficient to predict crop yields accurately. However, the spatial patterns of the predicted yield maps differed from model to model, although the prediction accuracy was almost the same. The results indicated that not only the prediction accuracies, but also the robustness of within-field yield predictions, should be assessed in further studies.
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Chatterjee, Sabyasachi, Swarup Kumar Mondal, Anupam Datta, and Hritik Kumar Gupta. "Enhancing Feature Optimization for Crop Yield Prediction Models." Current Agriculture Research Journal 12, no. 2 (September 10, 2024): 739–49. http://dx.doi.org/10.12944/carj.12.2.19.

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The growth of the world population is leading to an increased demand for food production. Crop yield prediction models are vital for agricultural planning and decision-making, providing forecasts that can significantly impact resource management and food security. This paper focuses on the importance and benefits of feature optimization in enhancing the performance of crop yield prediction models. By reducing noise and complexity, optimized features allow the prediction models to concentrate on the critical factors affecting crop yield, leading to more precise predictions and lesser computation times. This work utilizes an enhanced genetic algorithm to optimize feature selection and model parameters, outperforming the performance of standard genetic algorithms. Comparative analysis showed significant improvement in the accuracy of yield predictions by optimizing the selection of relevant features. The minimal error between actual and predicted yields on both the training and testing datasets highlights the effectiveness of the enhanced genetic algorithm. Enhanced feature optimization not only improves the robustness and adaptability of yield prediction models but also contributes to more sustainable and efficient agricultural management.
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Ulfa, Fathiyya, Thomas G. Orton, Yash P. Dang, and Neal W. Menzies. "Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models." Agronomy 12, no. 2 (February 3, 2022): 384. http://dx.doi.org/10.3390/agronomy12020384.

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One important issue faced by wheat producers is temporal and spatial yield variation management at a within-field scale. Vegetation indices derived from remote-sensing platforms, such as Landsat, can provide vital information characterising this variability and allow crop yield indicators development to map productivity. However, the most appropriate vegetation index and crop growth stage for use in yield mapping is often unclear. This study considered vegetation indices and growth stages selection and built and tested models to predict within-field yield variation. We used 48 wheat yield monitor maps to build linear-mixed models for predicting yield that were tested using leave-one-field-out cross-validation. It was found that some of the simplest models were not improved upon (by more complex models) for the prediction of the spatial pattern of the high and low yielding areas (the within-field yield ranking). In addition, predictions of longer-term average yields were generally more accurate than predictions of yield for single years. Therefore, the predictions over multiple years are valuable for revealing consistent spatial patterns in yield. The results demonstrate the potential and limitations of tools based on remote-sensing data that might provide growers with better knowledge of within-field variation to make more informed management decisions.
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Lutman, Peter J. W., Ruth Risiott, and H. Peter Ostermann. "Investigations into Alternative Methods to Predict the Competitive Effects of Weeds on Crop Yields." Weed Science 44, no. 2 (June 1996): 290–97. http://dx.doi.org/10.1017/s0043174500093917.

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Sixteen experiments have investigated alternative methods of predicting the competitive effects of a simulated weed (oats) on the yields of spring barley, spring oilseed rape (canola), peas, spring field (faba) beans and flax. The experiments were designed to discover whether early postemergence assessments of crop and weed vigor would achieve more reliable prediction of yield loss than weed density. Weed density (plants m−2) was a very variable predictor of yield loss. The standardized ranges (range/mean) of values over 3 to 4 years of data for the five crops, in the densities causing 5% yield loss, were between 1.14 and 2.59. Predictions based on the relative dry weight of crop and oats (oat dwt/(oat dwt + crop dwt)), assessed while the plants were still small, achieved more reliable predictions, as the standardized ranges were between 0.10 and 1.86. In three of the experiments, predictions based on relative dry weights were compared to similarly timed predictions based on measurements of relative leaf area and of ground cover, assessed subjectively (by eye) and photographically. Subjective and objective (photographic) assessments of cover achieved similar predictions of yield loss, indicating that visual assessments could be a viable tool to assess the potential competitive effects of weeds.
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Yan, Zhangpeng, Weimin Zhai, and Chao Li. "A novel motherboard test item yield prediction model based on parallel feature extraction." Journal of Physics: Conference Series 2816, no. 1 (August 1, 2024): 012078. http://dx.doi.org/10.1088/1742-6596/2816/1/012078.

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Abstract Functional testing of motherboards in Surface Mount Technology (SMT) assembly lines is crucial. Accurate yield prediction for each test item optimizes testing strategies, reduces costs, and ensures test coverage. Manual estimation of test item yields remains common, hindering accurate on-site predictions. Existing research on motherboard yield lacks predictions for individual test items and ignores temporal correlations during placement. This paper introduces a method, a convolutional bidirectional long short-term memory attention network (CBA-Net), which combines a convolutional neural network and a bidirectional long short-term memory network with an attention mechanism for parallel processing. It preprocesses historical test data, leveraging both networks to identify key features and extract temporal correlations. The attention mechanism optimizes yield predictions by assigning weights to information at different time steps. Experimental validation using actual production data demonstrates that the proposed method performs better compared to traditional models.
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Grzesiak, W., R. Lacroix, J. Wójcik, and P. Blaszczyk. "A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records." Canadian Journal of Animal Science 83, no. 2 (June 1, 2003): 307–10. http://dx.doi.org/10.4141/a02-002.

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Milk yield predictions based on artificial neural etworks and multiple regression were studied. The 305-d lactation yield predictions were based on milk yield of the first 4 test days. Average 305-d milk production of the herd, number of days in milk and month of calving. The predictions made with either the neural network or the multiple regression model did not differ (P > 0.05) from the values estimated with the current Polish dairy cattle evaluation system. The neural network model may be alternative method of predicting these traits. Key words: Artificial neural networks, multiple linear regression, milk yield prediction, test day data
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Vishwajeet Singh, Med Ram Verma, and Subhash Kumar Yadav. "PREDICTIVE MODELLING FOR SUGARCANE PRODUCTION: A COMPREHENSIVE COMPARISON OF ARIMA AND MACHINE LEARNING ALGORITHMS." Applied Biological Research 26, no. 2 (May 30, 2024): 199–209. http://dx.doi.org/10.48165/abr.2024.26.01.23.

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Accurate prediction of sugarcane yield is essential for trade, economic planning, and sustainable agriculture in India. This study addressed the challenge of forecasting sugarcane yield by evaluating the effectiveness of time series modelling and machine learning algorithms. Leveraging data spanning from 2001 to 2020, the research focuses on predicting the sugarcane yield for the subsequent years. The problem statement revolves around the need for precise yield predictions to inform decision-making in the agricultural sector. Methods employed included the utilization of Autoregressive Integrated Moving Average (ARIMA) for time series analysis and machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM). The analysis encompassed sugarcane yield data spanning multiple years, with predictions extending for a specified duration. Through analysis of temporal patterns and dependencies within the sugarcane yield time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), the study optimized the predictive models. Results indicated that ARIMA outperformed machine learning algorithms, exhibiting superior performance with a root meansquare error of 36700.68 anda minimumAICvalue of 456.7. The study emphasizes the significance of accurate yield predictions for agricultural planning and decision-making, highlighting the implications for sustainable crop management and the fortification of Indian sugar industry.The study affirms the importance of informed decisions facilitated by accurate yield predictions in resilient agricultural sector. Overall, this study contributes to the advancement of sugarcane yield prediction, offers practical insights for stakeholders and policymakers in India's agricultural landscape.
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Engen, Martin, Erik Sandø, Benjamin Lucas Oscar Sjølander, Simon Arenberg, Rashmi Gupta, and Morten Goodwin. "Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks." Agronomy 11, no. 12 (December 18, 2021): 2576. http://dx.doi.org/10.3390/agronomy11122576.

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Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weather data, farm data, grain delivery data, and cadastre-specific data. We introduce a deep hybrid neural network model to train this multi-temporal data. This model combines the features of convolutional layers and recurrent neural networks to predict farm-scale crop yield production across Norway. The proposed model could efficiently make the target predictions with the mean absolute error of 76 kg per 1000 m2. In conclusion, the reusable farm-scale multi-temporal crop yield dataset and the proposed novel model could meet the actual requirements for the prediction targets in this paper, providing further valuable insights for the research community.
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Semenov, Mikhail A., Rowan A. C. Mitchell, Andrew P. Whitmore, Malcolm J. Hawkesford, Martin A. J. Parry, and Peter R. Shewry. "Shortcomings in wheat yield predictions." Nature Climate Change 2, no. 6 (April 11, 2012): 380–82. http://dx.doi.org/10.1038/nclimate1511.

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Dissertations / Theses on the topic "Yield predictions"

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Vagh, Yunous. "Mining climate data for shire level wheat yield predictions in Western Australia." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2013. https://ro.ecu.edu.au/theses/695.

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Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production. Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations. This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields. The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures. The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively. The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification. The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations.
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Yildirim, Sibel [Verfasser], Urs [Akademischer Betreuer] Schmidhalter, Eckart [Gutachter] Priesack, and Urs [Gutachter] Schmidhalter. "Wheat and maize yield development in Bavaria until 2045 : Usage of statistical models for predictions / Sibel Yildirim ; Gutachter: Eckart Priesack, Urs Schmidhalter ; Betreuer: Urs Schmidhalter." München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/122807318X/34.

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Ferragina, Alessandro. "New phenotypes predictions obtained by innovative infrared spectroscopy calibrations and their genetic analysis in dairy cattle populations." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424294.

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The main objective of this thesis was to assess the infrared spectroscopy for the prediction at individual level of “new phenotypes” related to the technological properties of the cow milk, testing classic and innovative statistical approaches and evaluating the genetic parameters for a possible inclusion of the predicted traits in the selection indices as indirect selection method. A total of 1,264 individual milk samples were used for an individual model cheese making procedure and 7 new cheese-making related traits were obtained: 3 measures of cheese yield as percentage of processed milk (%CYs; fresh cheese yield, total solids cheese yield, water retained in the curd) and 4 measures of milk nutrients retained in the curd or lost in the whey (%RECs; fat, protein, total solids and energy). The traditional milk coagulation properties (rennet coagulation time, RCT; curd firming time, k20; curd firmness at 30 and 45 min, a30 and a45 respectively ) were also measured using a Formagraph (Foss Electric A/S, Hillerød, Denmark) in a curd firmness (CF) testing time of 90 min. Using all the 360 information of the CF test recorded for each sample over the 90 min, some new modeled parameters were also obtained (modeled rennet coagulation time, RCTeq; asymptotical potential value of CF at an infinite time, CFP; curd-firming rate constant, kCF; curd-syneresis rate constant, kSR; maximum level of CF, CFmax; time at which CF attains the maximum level, tmax;). For each sample two Fourier-transform infrared (FTIR) spectra were collected with a MilkoScan FT6000 (Foss Electric, Hillerød, Denmark) over the spectral range from 5,000 to 900 wavenumber × cm-1, and averaged before data analysis. A first chemometric process was carried out, using the WinISI II software (Infrasoft International LLC, State College, PA) in which the partial least square regression (PLS) models are implemented, for the prediction of %CYs and %RECs. High prediction accuracies were found except for the fat recovery. In order to improve the prediction accuracy, Bayesian models, commonly used for genomic data, were tested and compared with PLS models. The results have shown that for those traits that are difficult to be predicted, the Bayesian models perform better than PLS. Using an external validation procedure, the PLS was used for the prediction of %CYs and %RECs, while the BayesB model was used for the prediction of MCP and CF modeled parameters. In both cases the prediction accuracy found in validation, ranged from low to moderate. The genetic parameters of the predicted were estimated through a bivariate Bayesian analysis and linear models. Despite the low-moderate prediction accuracy in validation, the heritabilities of the predicted values were similar or higher than those of the corresponding measured values. The indirect selection of the studied traits was assessed through the genetic correlations between measured and predicted values, and the results shown that even when the coefficient of determination for the validation was moderate, the genetic correlations between predicted and measured values were always higher than the phenotypic correlations, and in the majority of cases near or higher than 90%. The calibrations developed for the %CYs and %RECs have been used to obtain the predictions on a population data set consisting of about 200,000 spectra of individual milk samples of Holstein, Brown Swiss and Simmental dairy cows. The genetic parameters of the predicted traits were estimated and the heritability values were comparable to those of the measured traits. The genetic correlations of %CYs and %RECs with milk production and composition provide evidence that the current selection paradigm used in dairy cattle may have a limited effects on the technological parameters. Milk protein and fat content do not explain all the genetic variations of %CYs and (in particular) %RECs, thus, these traits could be directly selected to improve the cheese making aptitude of milk and its correlated economic value
L’obiettivo principale di questa tesi è stato quello di valutare l’efficienza della spettroscopia a infrarosso per la predizione, a livello individuale, di “nuovi fenotipi” che descrivono le proprietà tecnologiche del latte bovino. Sono stati testati approcci statistici di calibrazione classici e innovativi, e sono stati inoltre stimati e valutati i parametri genetici delle predizioni ottenute per verificarne la possibile inclusione negli indici di selezione come metodo indiretto. Su un totale di 1,264 campioni di latte individuale, sono state effettuate le analisi che hanno previsto l’impiego di una procedura standard di micro-caseificazione per la misura di 7 caratteri relativi alla trasformazione casearia, in particolare sono state rilevate 3 misure di resa espresse come percentuale del latte lavorato, (%CYs; resa a fresco, resa in solidi totali, acqua ritenuta nella cagliata) e 4 misure di recupero di nutrienti nella cagliata o persi nel siero (%RECs; grasso, proteina, solidi totali ed energia). Le proprietà di coagulazione tradizionali (tempo di coagulazione, RCT; tempo di rassodamento, k20; consistenza del coagulo a 30 e 45 minuti dall’aggiunta del caglio, a30 e a45 rispettivamente) sono state misurate con un Formagraph (Foss Electric A/S, Hillerød, Denmark) in un test della consistenza del coagulo (CF) di 90 min. Utilizzando tutte le 360 informazioni di CF per campione registrate nei 90 min, sono stati inoltre ricavati, attraverso un modello matematico, dei nuovi parametri (tempo di coagulazione modellizzato, RCTeq; valore asintotico potenziale di CF per un tempo infinito, CFP; costante di rassodamento, kCF; costante di sineresi, kSR; valore massimo di CF, CFmax; tempo necessario affinché CF raggiunga il livello massimo, tmax). Per ogni campione sono stati raccolti due spettri a infrarosso in trasformata di Fourier (FTIR), utilizzando un MilkoScan FT6000 (Foss Electric, Hillerød, Denmark) nel range spettrale compreso tra 5,000 e 900 onde × cm-1, i due spettri sono stati mediati prima delle analisi. Un primo processo di calibrazione è stato effettuato per la predizione di %CYs e %RECs, utilizzando il software WinISI II (Infrasoft International LLC, State College, PA) in cui sono implementati dei modelli basati sulla partial least square regression (PLS). I risultati ottenuti hanno mostrato ottime accuratezze di predizione tranne che per il recupero di grasso. Per migliorare le accuratezze di predizione, sono stati testati dei modelli Bayesiani, comunemente usati in genomica, e confrontati con la PLS. Dai risultati ottenuti, per alcuni caratteri difficili da predire, si è visto che i modelli Bayesiani hanno delle prestazioni migliori. Utilizzando una procedura di validazione esterna come metodo di valutazione delle prestazioni di calibrazione, la PLS è stata utilizzata per la predizione di %CYs e %RECs, mentre i modelli Bayesiani sono stati utilizzati per la predizione delle proprietà di coagulazione e per i parametri derivanti dalla modellizzazione della consistenza del coagulo. In entrambi i casi i risultati ottenuti, relativi all’accuratezza di predizione, hanno mostrato un’efficienza medio bassa. Inoltre, sono stati stimati i parametri genetici dei valori predetti nel processo di validazione e nonostante la medio-bassa accuratezza delle predizioni, le ereditabilità dei valori predetti sono state simili o più alte dei corrispondenti valori misurati. L’impiego dei valori predetti come metodo di selezione indiretta è stato valutato attraverso la stima delle correlazioni genetiche tra valori predetti e misurati. I risultati hanno dimostrato, anche in questo caso che le correlazioni genetiche erano sempre superiori a quelle fenotipiche e nella maggior parte dei casi vicine o superiori al 90%. Infine, le equazioni di predizione sviluppate per %CYs e %RECs, sono state impiegate per la predizione di questi fenotipi su un set di dati costituito da circa 200,000 spettri di campioni individuali di latte di vacche di razza Frisona, Bruna e Pezzata Rossa italiane. I parametri genetici delle predizioni ottenute per ogni carattere sono stati stimati, dimostrando di essere ereditabili, con valori di ereditabilità simili a quelli dei valori misurati. Le correlazioni genetiche tra i valori predetti di %CYs e %RECs, e quelli relativi ai dati produttivi e di composizione del latte, hanno dimostrato che i modelli di selezione in uso hanno un effetto limitato sul miglioramento dei parametri tecnologici. Proteina e grasso del latte non spiegano tutta la variabilità genetica di %CYs e, in particolare, di %RECs, quindi per il miglioramento dell’attitudine casearia e conseguente valorizzazione economica del latte, questi caratteri andrebbero selezionati direttamente
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Bayazit, Dervis. "Yield Curve Estimation And Prediction With Vasicek Model." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605126/index.pdf.

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The scope of this study is to estimate the zero-coupon yield curve of tomorrow by using Vasicek yield curve model with the zero-coupon bond yield data of today. The raw data of this study is the yearly simple spot rates of the Turkish zero-coupon bonds with different maturities of each day from July 1, 1999 to March 17, 2004. We completed the missing data by using Nelson-Siegel yield curve model and we estimated tomorrow yield cuve with the discretized Vasicek yield curve model.
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Misailidis, Nikiforos. "Understanding and predicting alcohol yield from wheat." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/understanding-and-predicting-alcohol-yield-from-wheat(845cbadd-5825-488e-94e7-160c60b2ef0d).html.

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Bioethanol is a promising renewable biofuel and wheat is currently the main candidate asthe feedstock for its production in the UK context. The quality of the numerous varieties ofwheat developed in the past by plant breeders has been well examined in terms of bread, biscuitand pasta producing industries. In general, the end-use quality determination of wheat in termsof alcohol yield is less investigated. This work focused on understanding and predicting thealcohol yield from wheat according to its physical, physicochemical and chemicalcharacteristics. The research ran alongside the GREEN Grain project and utilised its wheatsamples, which consist of a range of wheat varieties, agronomic regimes and growing sitesfrom four harvests years 2005-2008. The combined dataset consists of a diverse range ofchemical, physicochemical and physical characteristics of the GREEN Grain wheats. An initial multivariate analysis (PCA) indicated that the first principal component, whichexplains most of the variability of the wheat characteristics, is related with the classification ofwheat as hard or soft. High alcohol yielding wheats typically have high starch, mealiness andalbumin+globulin fraction, and also low protein, gliadin fraction and hardness. They also havelarger and more spherical kernels. Analysis of Variance (ANOVA) was applied in order to identify differences between thevarieties, the sites and the application or not of N fertiliser. The ANOVA showed that theapplication of N fertiliser increases all the protein components, although it increases the Gliadinand the LMW glutenins more. N fertiliser also yields smaller (TGW, width, depth) and moreelongated kernels. High alcohol yielding varieties tend to be softer with lower protein andlarger and more spherical kernels. This consistent variability allowed prediction of the alcoholyield based on easily measured parameters. The following model, based on the SKCS reportedvalues plus protein, could predict the alcohol yield with an R2 of about 78%:Alcohol yield = 466.62 - 5.07 × Protein - 0.21 × hardness + 11.6 × diameter ±6.94 l/dry tonIt is frequently hypothesised that larger and more rounded kernels produce more alcoholbecause they have a smaller relative amount of the unfermentable outer layers. In an effort totest this hypothesis, the pericarp thicknesses and the crease characteristics of the wheat sampleswere measured. It was found that pericarp thickness and crease dimensions vary with kernelsize, with significant differences between varieties. A physical model was developed thatconsiders these differences and calculates the endosperm to non-endosperm ratio. None of thevariables obtained by the physical model could be related to alcohol yield. The SKCS fundamental data were further analysed in an effort to improve the alcoholyield predictability. It was found that the averaged Crush Response Profiles are morereproducible than the hardness index itself. It was shown that the initial peak does not occurbecause of the "shell" (i.e. the bran layers) as suggested in the literature, but because of thecrease. Examination of the effects of moisture content on the aCRPs showed that their 1stquarter is equivalent to the stress-strain plots of dedicated rheological tests. The remaining partsof the curve relate to the post-failure behaviour of the kernels and with hardness as used incereal science. The aCRP parameters could improve the alcohol yield predictability of theGREEN Grain wheats to an R2 of about 82.3% and a standard error of the regression of6.3 l/dry ton. Further standardisation and calibration with respect to the moisture content and tothe size of the kernels could improve the predictions even further. Textural testing of cereals is constrained by the complexity of the wheat kernel structureand exacerbated by the between-kernel variation. The current work has demonstrated howSKCS data can be interpreted more insightfully in order to improve end-use quality predictions. The aCRP parameters clearly contain rheological information about wheats. Further research toestablish their examination by more standardised methodologies will allow effectiveinvestigation of connections between the rheological properties, chemical characteristics,processing behaviour and end-use quality prediction of wheat.
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Varadan, Sridhar. "Efficient vlsi yield prediction with consideration of partial correlations." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2503.

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Lima, Isabel Maria Sarmento de Beires de Abreu e. "Previsão de produção da casta Touriga Franca na Região do Douro com base nas componentes de rendimento." Master's thesis, ISA, 2014. http://hdl.handle.net/10400.5/6801.

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Mestrado em Viticultura e Enologia - Instituto Superior de Agronomia / Faculdade de Ciências. Universidade do Porto
Yield predicting is a great advantage for winegrowers' competitiveness. To determine which variables explain most of the yield's variation at harvest in a Touriga Franca parcel at Quinta do Vale D. Maria (Douro), a sample of 98 grapevines was selected and its yield components studied through2013. Using an 18 grapevine subsample, 3 yield predicting models were achieved. The first uses "bunch number/vine", "average stem weight/bunch", "berry weight/spur" and "average berry number/ bunch" and explains 92% of yield variation per vine with the smallest statistical deviation measures, offering the best quality estimate. The second uses "fertility index/spur" and "bunch number" to explain 73% of yield variation per vine, with intermediate deviation measures. The third model allows a yield estimate through bunch number per vine, with a R2 of 0,72 , but higher deviation measures than the previous one. The last two models are determined through observation, avoiding bunch destruction. The choice of which model to use depends on the quality of the estimate and the practicality desired by the winegrower. This work showed good results relating to yield predicting for Touriga Franca, a poorly studied variety despite its importance in Douro. The implementing of these procedures will enable production control in the biggest parcel in Quinta do Vale D. Maria.
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Iqbal, Muhammad Mutahir. "Analysis of long-term experiment on cotton using a blend of theoretical and new graphical methods to study treatment effects over time." Thesis, University of Kent, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298101.

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Msadala, V. P. "Sediment yield prediction based on analytical methods and mathematical modelling." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/2863.

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Thesis (MScEng (Civil Engineering)--University of Stellenbosch, 2009.
ENGLISH ABSTRACT: A study of the state of reservoir sedimentation in South Africa based on reservoir sediment deposit data, has shown that a considerable number of reservoirs have serious sedimentation problems. The analysis of the reservoir sediment deposit data showed that almost 25% of the total number of reservoirs have lost between 10 to 30% of their original storage capacity. The average storage loss due to sedimentation in South African reservoirs is approximately 0.3% per year while the average annual storage loss for all the reservoirs in the world is 0.8%. The aim of this research was to develop sediment yield prediction methods based on analytical approaches and mathematical modelling. The sediment yield prediction methods can be used in planning and management of water resources particularly in reservoir sedimentation control. The catchment erosion and sediment yield modelling methods can be applied in temporal and spatial analysis of sediment yields which results are essential for detailed design of water resources, particularly in the identification of critical erosion areas, sediment sources and formulation of catchment management strategies. Current analytical methods for the prediction of sediment yield have been reviewed. Nine sediment yield regions have been demarcated based on the observed sediment yields and catchment characteristics. Empirical and probabilistic approaches were investigated. The probabilistic approach is based on analysis of the observed sediment yields that were calculated from reservoir sediment deposit, river suspended sediment sampling data and soil erodibility data. The empirical equations have been derived from regression analysis of the variables that were envisaged to have a significant effect on erosion and sediment yields in South Africa. Empirical equations have been developed and shown to have accurate and reliable predictive capability in six of the nine regions. The probabilistic approach has been recommended for the prediction of sediment yields in the remaining three regions where reliable regression equations could not be derived. The predictive accuracy of both the probabilistic and empirical approaches was checked and verified using the discrepancy ratio and graphs of the observed and calculated data. While the analytical methods are needed to predict the sediment yield for the whole catchment, mathematical modelling to predict sediment yields is applied for more detailed analysis of sediment yield within the catchment. An evaluation of available catchment sediment yield mathematical modelling systems was carried out. The main criteria for the choice of a numerical model to be adopted for detailed evaluation was based on the following considerations: the model’s capabilities, user requirements and its application. The SHETRAN model (Ewen et al., 2000) was therefore specifically chosen because of its ability to simulate relatively larger catchment areas (it can handle catchment scales from less than 1km2 to 2500km2), its ability to simulate erosion in channels, gullies and landslides, its applicability to a wide range of land-use types and ability to simulate land use changes. Another model, ACRU (Smithers et al., 2002) was also reviewed. The aim of the model evaluation was to provide a conceptual understanding of catchment sediment yield modelling processes comprising model set up, calibration, validation and simulation. The detailed evaluation of the SHETRAN model was done through a case study of Glenmaggie Dam in Australia. The flow was calibrated and validated using data from 1975 to 1984, and 1996 to 2006 respectively. The results for both the calibration and validation were reasonable and reliable. The sediment load was validated against turbidity derived sediment load data from 1996 to 2006. The model was used to identify sources of sediment and areas of higher sediment yield. The land use of a selected sub-catchment was altered to analyse the impact of land use and vegetative cover on the sediment yield. Based on the results, the SHETRAN model was confirmed to be a reliable model for catchment sediment yield modelling including simulation of different land uses.
AFRIKAANSE OPSOMMING: ‘n Studie van die stand van damtoeslikking in Suid-Afrika toon dat daar ernstige toeslikkingsprobleme by baie reservoirs bestaan. ’n Ontleding van die toeslikkingsyfers gegrond op damkomopmetings toon dat omtrent 25% van die totale getal reservoirs tussen 10 en 30% van hulle oorspronklike opgaarvermoë verloor het. Die gemiddelde tempo van damtoeslikking in Suid-Afrika is 0.3%/jaar, wat laer is as die wêreld gemiddeld van 0.8%/jaar. Die oogmerk met hierdie navorsing was om sedimentlewering voorspellingsmetodes te ontwikkel deur gebruik te maak van analitiese metodes en wiskundige modellering. Die sedimentlewering voorspellingsmetodes kan gebruik word vir die beplanning en bestuur van waterbronne en veral vir damtoeslikking beheer. Die opvangsgebied erosie en die sedimentlewering modelleringsmetodes kan toegepas word in tydveranderlike en ruimtelike ontleding van sedimentlewering. Hierdie inligting word benodig vir die detail ontwerp van waterhulpbronne en veral vir die identifisering van kritiese erosiegebiede, bronne van sediment en die formulering van opvangsgebied-bestuur strategië. ‘n Literatuuroorsig oor die huidige metodes vir die voorspelling van erosie en sedimentlewering is gedoen. Nege sedimentasie streke is afgebaken in Suid-Afrika, gegrond op waargenome damtoeslikkingsdata en opvangsgebied-eienskappe. Proefondervindelike en waarskynlikheidsbenaderinge is ondersoek. Die waarskynlikheidsbenadering is gegrond op die ontleding van waargenome damtoeslikking wat bereken is uit reservoir opmeting data en rivier gesuspendeerde sediment data, asook data oor gronderosie. Die proefondervindelike metode se vergelykings is afgelei vanuit regressie ontleding van die veranderlikes wat ‘n belangrike invloed het op die erosie en sedimentlewering in Suid-Afrika. Daar is bevestig dat die ontwikkelde proefondervindelike (empiriese) vergelykings ‘n akkurate en betroubare voorspellingsvermoë in ses van die nege streke het. Die waarskynlikheidsbenadering is aanbeveel vir die voorspelling van sedimentlewering in die ander drie streke, waar betroubare regressie vergelykings nie afgelei kon word nie. Die voorspellingsakkuraatheid van albei metodes is nagegaan en bevestig deur gebruik te maak van die teenstrydigheidsverhouding en grafieke van die waargenome en berekende data. Analitiese metodes van sedimentleweringsvoorspelling is nodig vir ‘n volle opvangsgebied, terwyl wiskundige modellering om sedimentlewerings te voorspel gebruik kan word om ‘n meer in diepte ontleding van die sedimentlewering binne ‘n opvanggebied te doen. ‘n Evaluasie van beskikbare wiskundige modelle wat opvangsgebied sedimentlewering kan voorspel, is gedoen. Die hoofkriteria vir die keuse van ‘n model vir gebruik by gedetailleerde ontleding is gegrond op die volgende: die vermoëns van die model, wat verbruikers benodig en die aanwending van die model. Die SHETRAN model (Ewen et al., 2000) is spesifiek gekies weens sy vermoë om relatief groter opvangsgebiede te simuleer (dit kan opvangsgebiede van 1km2 tot 2500km2 wees) asook om erosie in kanale, dongas en grondverskuiwing simuleer. Dit kan toegepas word op ‘n wye reeks grondtipes en kan ook die gevolge simuleer as die gebruik van die grond verander. ‘n Ander model, ACRU (Smithers et al., 2002) is ook ondersoek. Die doel van die modelevaluering was om ‘n konseptuele begrip te kry van sedimentlewering modelleringsprosesse wat die opstelling, kalibrasie, toetsing en simulasies insluit. Die volledige evaluasie van SHETRAN is gedoen deur middel van ‘n gevalle-studie van die Glenmaggiedam in Australia. Die riviervloei is gekalibreer en getoets deur gebruik te maak van data wat strek van 1975 tot 1984, en van 1996 tot 2006 onderskeidelik. Die resultate van beide die kalibrasie en die toetswas redelik en betroubaar. Die sedimentlading is gekalibreer teen velddata van 1996 tot 2006. Die model is gebruik om bronne van sediment te identifiseer, asook gebiede met ‘n hoër sedimentlewering. Die gebruik van die grond op ‘n gekose sub-opvangsgebied is verander om die impak van grondgebruik en plantbedekking op sedimentlewering te ontleed. Die resultate bewys dat die SHETRAN model ‘n betroubare model is vir groot opvangsgebied sedimentlewering modellering, asook vir die simulasie van verskillende grondgebruike.
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Grennstam, Nancy. "On Predicting Milk Yield and Detection of Ill Cows." Thesis, KTH, Reglerteknik, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107531.

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The fully automated milking system VMS has different functions which complements the actual milking of cows. This master thesis presents a method to improve the calculation of milk yield in dairy cows for the VMS. This report also investigates if it is possible to improve the algorithm for finding cows with mastitis (udder inflammation). The correctness of the prediction of milk yield is important for a couple of actions in the VMS. For example, valuable time can be saved if teatcups are attached first to high yielding teats. Only cows with an attained minimum level of predicted yield should be allowed to enter the VMS and get milked. Milking has traditionally been an event to monitor the condition of the cows. Therefore methods that determine the condition are demanded for any automatic milking systems. Mastitis is a costly illness and a working test for ill cows should be implemented in the VMS in order to know which cows that are ill. The goal of this thesis work is to develop two new algorithms for the VMS. First, an improved algorithm for the prediction of secretion rate is presented. The improved algorithm uses a Kalman-filter to update the secretion-rate. The improved method has a lower total prediction in most cases. The Kalman-filter was tested and developed for five farms and was verified on one farm. Second, this report investigates if a cusum test can be used to detect ill cows. The method turns out to be slightly better than the current algorithm. A test for cows which are milked on three or two teats is evaluated. In this test the number of milkings with high conductivity and low secretion rate are weighted together. This algorithm is slightly better than the current algorithm used for detection of ill cows.
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Books on the topic "Yield predictions"

1

J, Zarnoch Stanley, and Southern Forest Experiment Station (New Orleans, La.), eds. Growth and yield predictions for thinned and unthinned slash pine plantations on cutover sites in the west Gulf Region. New Orleans, La: U.S. Dept. of Agriculture, Forest Service, Southern Forest Experiment Station, 1992.

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International, Symposium on Stocks Assessment and Yield Prediction (1985 Quetico Centre Ontario). International Symposium on Stocks Assessment and Yield Prediction. Ottawa: Department of Fisheries and Oceans, 1987.

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B, Yang, Outcalt Kenneth W, and United States. Forest Service. Southern Research Station, eds. Stand-yield prediction for managed Ocala sand pine. [Asheville, NC]: U.S. Dept. of Agriculture, Forest Service, Southern Research Station, 1997.

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Dennington, Roger W. New loblolly pine growth and yield prediction system. Atlanta, Ga: U.S. Dept. of Agriculture, Forest Service, Cooperative Forestry, 1988.

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B, Yang, Outcalt Kenneth W, and United States. Forest Service. Southern Research Station, eds. Stand-yield prediction for managed Ocala sand pine. [Asheville, NC]: U.S. Dept. of Agriculture, Forest Service, Southern Research Station, 1997.

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B, Yang, Outcalt Kenneth W, and United States. Forest Service. Southern Research Station., eds. Stand-yield prediction for managed Ocala sand pine. [Asheville, NC]: U.S. Dept. of Agriculture, Forest Service, Southern Research Station, 1997.

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Rockwood, D. L. Stand-yield prediction for managed Ocala sand pine. Ashville, NC: U.S. Dept. of Agriculture, Forest Service, Southern Research Station, 1997.

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Rockwood, D. L. Stand-yield prediction for managed Ocala sand pine. Ashville, NC: U.S. Dept. of Agriculture, Forest Service, Southern Research Station, 1997.

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International Symposium on Stocks Assessment and Yield Prediction (1985 Quetico Centre, Ont.). International Symposium on Stocks Assessment and Yield Prediction [proceedings]. Ottawa: Fisheries and Oceans, Information and Publications Branch, 1987.

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José de Jesús Pineda de Gyvez. IC defect-sensitivity: Theory and computational models for yield prediction. [s.l.]: [s.n.], 1991.

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Book chapters on the topic "Yield predictions"

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Lipping, Tarmo, and Petteri Ranta. "Digital Yield Predictions." In Digital Agriculture, 369–87. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43548-5_12.

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Arunya, K. G., and M. Krishnaveni. "Crop Yield Predictions Based on Machine Learning." In Lecture Notes in Mechanical Engineering, 355–66. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6009-1_33.

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Gupta, Anshika, Mohit Soni, and Kalpana Katiyar. "A Perusal of Machine-Learning Algorithms in Crop-Yield Predictions." In Data-Driven Farming, 101–25. Boca Raton: Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781003485179-6.

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Rambal, Serge. "Fire and Water Yield: A Survey and Predictions for Global Change." In Ecological Studies, 96–116. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4613-8395-6_6.

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Marapelli, Bhaskar, Lokeshwari Anamalamudi, Chandra Srinivas Potluri, Anil Carie, and Satish Anamalamudi. "Enhancing Agricultural Decision-Making Through Machine Learning-Based Crop Yield Predictions." In Data Science and Network Engineering, 209–24. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6755-1_16.

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Habyarimana, Ephrem, and Sofia Michailidou. "Genomic Prediction and Selection in Support of Sorghum Value Chains." In Big Data in Bioeconomy, 207–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_16.

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AbstractGenomic prediction and selection models (GS) were deployed as part of DataBio project infrastructure and solutions. The work addressed end-user requirements, i.e., the need for cost-effectiveness of the implemented technologies, simplified breeding schemes, and shortening the time to cultivar development by selecting for genetic merit. Our solutions applied genomic modelling in order to sustainably improve productivity and profits. GS models were implemented in sorghum crop for several breeding scenarios. We fitted the best linear unbiased predictions data using Bayesian ridge regression, genomic best linear unbiased predictions, Bayesian least absolute shrinkage and selection operator, and BayesB algorithms. The performance of the models was evaluated using Monte Carlo cross-validation with 70% and 30%, respectively, as training and validation sets. Our results show that genomic models perform comparably with traditional methods under single environments. Under multiple environments, predicting non-field evaluated lines benefits from borrowing information from lines that were evaluated in other environments. Accounting for environmental noise and other factors, also this model gave comparable accuracy with traditional methods, but higher compared to the single environment model. The GS accuracy was comparable in genomic selection index, aboveground dry biomass yield and plant height, while it was lower for the dry mass fraction of the fresh weight. The genomic selection model performances obtained in our pilots are high enough to sustain sorghum breeding for several traits including antioxidants production and allow important genetic gains per unit of time and cost.
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Plevris, Vagelis, Alejandro Jiménez Rios, and Usama A. Ebead. "Exploring the Predictive Performance of Simple Regression Models and ANN in 2D Truss Analysis." In Lecture Notes in Civil Engineering, 1473–85. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69626-8_123.

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AbstractThis research investigates the performance of various regression models in predicting critical structural parameters within a plane truss model. The study encompasses linear, second- and third-degree polynomial, and artificial neural network (ANN) regression models, which are evaluated for their accuracy in estimating the maximum displacement, maximum (tensile) stress, and minimum (compressive) stress of the truss under specific loading conditions. The findings unequivocally establish the superiority of the ANN model, showcasing its ability to capture complex nonlinear relationships within the data. Moreover, the research explores the influence of model complexity, demonstrating that the transition from simpler to more intricate models enhances predictive performance. The implications of this study extend to diverse engineering applications, offering insights into the selection of appropriate regression models for structural analysis and design. Beyond improved predictive accuracy, the ANN’s predictions provide potential for reducing computational demands, making them valuable tools in structural optimization and similar contexts. However, the study underscores the importance of cautious interpretation, as certain scenarios may yield outlier predictions. Overall, this research contributes to the understanding of regression modeling in engineering and provides a foundation for informed decision-making in structural analysis and design.
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Takeyama, Tomohide, Thirapong Pipatpongsa, Atsushi Iizuka, and Hideki Ohta. "Stress–Strain Relationship for the Singular Point on the Yield Surface of the Elasto-Plastic Constitutive Model and Quantification of Metastability." In Geotechnical Predictions and Practice in Dealing with Geohazards, 229–39. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5675-5_15.

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Jayashree, T. R., N. V. Subba Reddy, and U. Dinesh Acharya. "Application of Ensemble Machine Learning Techniques in Yield Predictions of Major and Commercial Crops." In Communication and Intelligent Systems, 451–61. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2100-3_35.

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Stenner, A. Jackson, William P. Fisher, Mark H. Stone, and Donald Burdick. "Causal Rasch Models." In Explanatory Models, Unit Standards, and Personalized Learning in Educational Measurement, 223–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3747-7_18.

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AbstractRasch’s unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.
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Conference papers on the topic "Yield predictions"

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Awad, Abdalaziz, Cyrus Behroozi, and Andreas Erdmann. "Integrated mask process modeling for better yield predictions." In Photomask Technology 2024, edited by Lawrence S. Melvin and Seong-Sue Kim, 63. SPIE, 2024. http://dx.doi.org/10.1117/12.3035191.

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Ramachandran, A. Ganesh, S. K. Saravanan, M. Bhanumathi, M. Sangeetha, and F. Mary Harin Fernandez. "Computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET), 1–5. IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10895915.

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Lewis, Mandy R., Victoria Jancowski, Christopher E. Valdivia, and Karin Hinzer. "Importance of Spectral Effects in Energy Yield Predictions for High Latitude Locations." In 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC), 0842. IEEE, 2024. http://dx.doi.org/10.1109/pvsc57443.2024.10749076.

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N, Chandiraprakash, A. Chinnasamy, and M. Ashok. "Enhancing Agricultural Yield Predictions with Real-Time IoT Sensor Data and Machine Learning Integration." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), 335–41. IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823110.

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Gowsic, K., A. Ashwini, M. Arul Sankar, and R. Balamurugan. "Enhanced Crop Yield Predictions Amid Climate Change to Improve Agriculture With SwinTrans-Att Based Model." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV), 830–35. IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10960928.

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Sikarwar, Shailendra Singh, Sudha Pandey, S. Arun Kumar, Pramod Kumar, Praveen Kumar Sahu, and Beerpal Singh. "Optimizing Crop Yield Predictions Using K-Nearest Neighbors Regression: An Analysis of Temperature, Rainfall and Soil pH Influences." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 1336–41. IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10894947.

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Duijvestijn, M. C. "Fission Yield Predictions with TALYS." In INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND TECHNOLOGY. AIP, 2005. http://dx.doi.org/10.1063/1.1945228.

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Peters, Ian Marius, Haohui Liu, and Tonio Buonassisi. "Photovoltaic energy yield predictions using satellite data." In Photonics for Solar Energy Systems VIII, edited by Jan Christoph Goldschmidt, Alexander N. Sprafke, and Gregory Pandraud. SPIE, 2020. http://dx.doi.org/10.1117/12.2557375.

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Zach, Franz X., Srividya Cancheepuram, Kaushik Sah, Roel Gronheid, and Fatima Anis. "Multi-metrology: towards parametric yield predictions beyond EPE." In Metrology, Inspection, and Process Control XXXVII, edited by John C. Robinson and Matthew J. Sendelbach. SPIE, 2023. http://dx.doi.org/10.1117/12.2658042.

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Xu, Siguang, and K. J. Weinmann. "Comparison of Hill's Yield Criteria in Forming Limit Predictions." In International Congress & Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1999. http://dx.doi.org/10.4271/1999-01-0999.

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Reports on the topic "Yield predictions"

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Dahl, Travis A., Anthony D. Kendall, and David W. Hyndman. Climate and Hydrologic Ensembling Lead to Differing Streamflow and and Sediment Yield Predictions. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39760.

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This record contains datasets related to the journal article "Climate and Hydrologic Ensembling Lead to Differing Streamflow and Sediment Yield Predictions", which is currently undergoing submission to the journal Climatic Change.
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Zarnoch, Stanley J., Donald P. Feduccia, V. Clark Baldwin, and Tommy R. Dell. Growth and Yield Predictions for Thinned and Unthinned Slash Pine Plantations on Cutover Sites in the West Gulf Region. New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1991. http://dx.doi.org/10.2737/so-rp-264.

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ARCHIVE, Ryan Milligan. PR328-223813-R02 In Ditch Material Verification for Fittings and Seamless Pipe. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2024. http://dx.doi.org/10.55274/r0000070.

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This report compares destructive yield and tensile strength data for fittings and seamless line pipe against predictions from four in-ditch technology providers. Numerous in-ditch material verification studies have been conducted using welded line pipe, but minimal data exists in the public domain for fittings and seamless line pipe. The results from the blind tests were analyzed and compared to show each vendor's relative performance. -Public version
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ARCHIVE, Ryan Milligan, and Ravi Krishnamurthy. PR328-223813-R01 In-Ditch Material Verification for Seamless Pipe and Fittings. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2024. http://dx.doi.org/10.55274/r0000064.

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This report compares destructive yield and tensile strength data for fittings and seamless line pipe against predictions from four in-ditch technology providers. Numerous in-ditch material verification studies have been conducted using welded line pipe, but minimal data exists in the public domain for fittings and seamless line pipe. The results from the blind tests were analyzed and compared to show each vendor's relative performance. Unblinded Copy For Members Only
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Tenney, Craig M., Kevin Nicholas Long, and Jamie Michael Kropka. Predictions of Yield Strength Evolution Due to Physical Aging of 828 DGEBA/DEA using the Simplified Potential Energy Clock Model. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1498246.

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Chauhan, Vinod. L52294 Corrosion Assessment Guidance for High Strength Steels. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2009. http://dx.doi.org/10.55274/r0010319.

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For the burst tests on high strength line pipe investigated in this report, standard assessment methods used by the pipeline industry generally give conservative failure predictions. For a small number of test points the ASME B31G, Modified ASME B31G and the LPC-1 methods gave non-conservative failure predictions when used to assess defect depths greater than 50% of the pipe wall. However, for machined defects, particularly those that are rectangular flat bottomed patches the use of ASME B31G and Modified ASME B31G to predict failure pressures may be inappropriate because the area of metal loss can be underestimated. Therefore the results need to be treatedwith caution. The RSTRENG method is the most reliable and conservative method forpredicting the failure pressure of corroded pipelines. RSTRENG predicts conservative failure pressures for defect depths up to 80% of the pipe wall in line pipe of strength grades up to X100. Modifying the flow stress to equal the arithmetic mean of the specified minimum yield strength and the ultimate tensile strength adds conservatism tothe calculated failure predictions. The non-linear FE method gives failure predictions within a scatter band of ~�10%, although in a number of cases the failure predictions are nonconservative. This level of scatter is typical. More accurate modeling of the geometry and material properties, to take into account of any through wall variation, should reduce the observed scatter.
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7

Levy Yeyati, Eduardo, and Martín González Rozada. Global Factors and Emerging Market Spreads. Inter-American Development Bank, May 2006. http://dx.doi.org/10.18235/0010852.

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This paper shows that a large fraction of the variability of emerging market bond spreads is explained by the evolution of global factors such as risk appetite (as reflected in the spread of high yield corporate bonds in developed markets), global liquidity (measured by the international interest rates) and contagion (from systemic events like the Russian default). This link has remained relatively stable over the history of the emerging market class, is robust to the inclusion of country-specific factors, and helps provide accurate long-run predictions. Overall, the results highlight the critical role played by exogenous factors in the evolution of the borrowing cost faced by emerging economies.
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Zhu, Xian-Kui, Brian Leis, and Tom McGaughy. PR-185-173600-R01 Reference Stress for Metal-loss Assessment of Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2018. http://dx.doi.org/10.55274/r0011516.

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This project focused on quantifying the reference stress to be used in predictive models for assessing the effects of metal loss on pipeline integrity. The results of this project will work in concert with the outcomes of project EC-2-7 that examined sources of scatter in metal-loss predictions with respect to the metal-loss defect geometry. The methodology for developing a new reference stress included empirical and finite element analyses along with comparison of full-scale experimental results that indicate the failure behavior of defect-free pipe has dependence on the strain hardening rate, n, of the pipe steel. Since the strain hardening rate is often unreported in qualification test records and mill certification reports, the development of a new reference stress will seek to include the utilization of the ratio of yield-to-tensile strength (Y/T) as a surrogate for n. This approach ideally would be insensitive to pipe grade, and thus, allow broad application of the reference stress without increasing scatter or bias across grade levels. This work also compared the resulting metal-loss criterion with the new reference stress relative to the B31G and Modified B31G models using a dataset of approximately 75 full-scale burst test results for test vessels containing isolated defects. This comparison was performed by C-FER Technologies under sub-contract to EWI and quantified the prediction bias and prediction variability of the new criterion relative to those widely in use.
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Cho, Seonghwan, Tandra Bagchi, Jongmyung Jeon, and John E. Haddock. Material Characterization and Determination of MEPDG Input Parameters for Indiana Superpave 5 Asphalt Mixtures. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317725.

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Superpave 5 (SP 5) has the ability to slow asphalt binder aging in asphalt pavements, which is why the SP 5 mix with optimum asphalt binder content to yield 5% air voids has recently been used in Indiana roads. INDOT also uses the AASHTOWare Pavement ME design software in pavement design, and the current asphalt aging prediction model in Pavement ME was developed based on the conventional Superpave asphalt mixture (design air voids 4%) design method. For the successful use of the SP 5 mixture design method with Pavement ME, the input level and input parameters play a significant role. The objective of this study was to determine pavement performance using the three different input levels (Level 1, 2, and 3) and to recommend the necessary Pavement ME input parameters for SP 5 mixtures for accurate pavement performance prediction. The results show that Levels 2 and 3 are underpredicting or overpredicting the pavements’ distresses. Therefore, to capture the benefit of SP 5 pavement design, the Level 1 inputs (lab test results) were recommended for the Pavement ME. The findings of this research will provide guidance on using accurate input parameters for the Pavement ME design for SP 5 mixtures, resulting in more accurate asphalt pavement performance predictions during the pavement design process. It is anticipated that this will result in longer asphalt pavement service lives, which is a cost-effective benefit for INDOT.
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Robinson, W., Jeremiah Stache, Jeb Tingle, Carlos Gonzalez, Anastasios Ioannides, and James Rushing. Naval expeditionary runway construction criteria : P-8 Poseidon pavement requirements. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46857.

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A full-scale airfield pavement test section was constructed and trafficked by the US Army Engineer Research and Development Center to determine minimum rigid and flexible pavement thickness requirements to support contingency operations of the P-8 Poseidon aircraft. Additionally, airfield damage repair solutions were tested to evaluate the compatibility of those solutions with the P-8 Poseidon. The test items consisted of various material thickness and strengths to yield a range of operations to failure allowing development of performance predictions at a relatively lower number of design operations than are considered in traditional sustainment pavement design scenarios. Test items were trafficked with a dual-wheel P-8 test gear on a heavy-vehicle simulator. Flexible pavement rutting, rigid pavement cracking and spalling, instrumentation response, and falling-weight deflectometer data were monitored at select traffic intervals. The results of the trafficking tests indicated that existing design predictions were generally overconservative. Thus, minimum pavement layer thickness recommendations were made to support a minimum level of contingency operations. The results of full-scale flexible pavement experiment were utilized to support an analytical modeling effort to extend flexible pavement thickness recommendations beyond those evaluated.
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