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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 i 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 valueL’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 PortoYield 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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Smith, Leanne. "Predicting yield and profit losses from multispecies weed competition". Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388471.
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Huang, Di. "Predicting Recessions in the U.S. with Yield Curve Spread". Thesis, North Dakota State University, 2013. https://hdl.handle.net/10365/27126.
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This paper proposes a hidden Markov model for the signal of U.S. recessions. The model uses the spread of interest rate between 10-year Treasury bond and 3-month Treasury bill, together with other financial indicators which are the real M2 growth, the change in the Standard and Poor's 500 index of stock prices, and difference between the 6-month commercial paper and 6-month Treasury bill rates as predictors. The hidden Markov model considers temporal dependence between the recession signals and provides an estimate of the long-term probability of recessions. The empirical results indicate the hidden Markov model well predict the signal of recessions in the U.S.
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Hollinger, David L. "Crop Condition and Yield Prediction at the Field Scale with Geospatial and Artificial Neural Network Applications". Kent State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=kent1310493197.
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Goodwin, Richard Philip. "Crop yield prediction in the UK using the reflected solar radiation". Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312374.
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Carrillo, Salazar Jose Alfredo. "An examination of the prediction of yield from two potato models". Thesis, University of Nottingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342033.
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Sairi, Maryam. "Prediction and experimental validation of the char yield of crosslinked polybenzoxazines". Thesis, University of Surrey, 2018. http://epubs.surrey.ac.uk/849422/.
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This study uses Molecular Operating Environment (MOE) to generate models to calculate the char yield of polybenzoxazines. A series of benzoxazine monomers were constructed to which a variety of parameters relating to the structure (e.g. water accessible surface, negative van der Waals surface area and hydrophobic volume, etc.) were obtained and a quantitative structure property relationships (QSPR) model was generated. The model was used to generate data for a new benzoxazine monomer and a comparison was made of predictions based on the QSPR models with the experimental data. This study shows the quality of predictive models and confirms how useful computational screening is prior to synthesis. In order to do that, the QSPR models were tested over a series of internal and external validation tests to explore their internal and external predictivity, prior to experimental validations which were performed later and reported in Chapter 7. The internal and external validations found out that the discrepancy in the general model (GM) which was initially thought to be a drawback to the model’s performance was actually not, as it does not compromise the model’s prediction accuracy, both internally and externally. The validation process also found that one of the structure-specific models, Ph-M (aniline-based benzoxazines) is externally predictive whilst another structure-specific model, the Ace-M (acetylenic-based polybenzoxazines) is not internally and externally predictive due to the too small training set that affects its predictivity performance. An acetylenic-based polybenzoxazine, poly(BA-apa) and a benzylamine-based polybenzoxazine, poly(BO-ba) have been successfully synthesised in this work. Both materials have been characterised using Fourier Transform – Infra Red Spectroscopy (FT-IR), Nuclear Magnetic Resonance (NMR) spectroscopy (both 1H and 13C) and Liquid Chromatography-Mass Spectrometry (LC-MS) to confirm their structures. These materials were analysed using Differential Scanning Calorimetry (DSC) to study their polymerisation behaviour and were later cured and taken further to Thermogravimetric Analysis (TGA) in order to investigate their thermal properties and the amount of char yield formed upon heating at 800 oC under an inert (nitrogen) atmosphere – which then will be used for experimental validation of the QSPR models. The study of DSC thermograms showed that both polymers exhibit a distinct polymerisation behaviour e.g. BA-apa went through two polymerisation reactions simultaneously (the oxazine ring opening polymerisation and the acetylene addition reaction) whilst BO-ba only polymerised via the ring opening reaction from the oxazine rings. It was also found that BA-apa has a lower polymerisation activation energy, consistent to its lower polymerisation temperature in comparison to the BO-ba. TGA analysis revealed that poly(BO-ba) formed an average of 44.35 % char yield and poly(BA-apa) on the other hand formed approximately 10 % higher char which is 56.28 %. The analysis also discovered that poly(BA-apa) synthesised in this work formed 15 % less char yield than previously reported in the literature (56.28 % vs. 71 %1) due to the shorter curing schedule. The final QSPR validation which is the experimental validation found that the char yield of poly(BO-ba) was predicted very well within 5-7 % error by both GM model and Ph-M. Ace-M which was reported earlier as not internally and externally predictive, has made a nearly accurate prediction towards the char yield of poly(BA-apa), close to the literature value of 71 %. The GM model has also made a close prediction to the Ace-M model, but these predictions deviated 15-17 % from the experimental poly(BA-apa) char yield measured in this work.
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Trinh, Stephen. "Component-derived manufacturing yield prediction in circuit card design and assembly". Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85774.
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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2013. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 51).
Circuit card manufacturing can be a highly risky and volatile proposition due to the placement of hundreds of small, high value components. Operator mistakes, design errors, and defective parts lead to thousands of dollars in troubleshooting and rework costs per product. Raytheon Integrated Defense Systems (IDS) Circuit Card Assembly (CCA) manufactures highly complex circuit cards at a high mix / low volume scale for various purposes. Due to the high input variability and small production lot sizes of this level of circuit card manufacturing, historical trending and defect mitigation is difficult, causing a significant portion of CCA's manufacturing costs to be attributed to troubleshooting defects and rework. To mitigate these costs, yield prediction analysis software is utilized to predict potential manufacturing defect rates and first pass yields of new designs. This thesis describes the creation and testing of a new data analysis model for yield prediction. By gathering and processing data at an individual component level, the model can predict defect rates of designs at an assembly level. Collecting data at the individual component level drives more comprehensive component-based calculations, greatly improving yield prediction accuracy and thereby allowing cost effective circuit card designs to be created. The increase in prediction accuracy translates to a potential $250,000 saved annually for Raytheon CCA from early defect identification and removal. Updated data retrieval and calculation methods also allow for much easier model maintenance, thereby increasing the relevance of yield prediction. This model can be easily incorporated into other design software as a next step in creating comprehensive concurrent engineering tools.
by Stephen Trinh.
M.B.A.
S.M.
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Qaddoum, Kefaya. "Intelligent real-time decision support systems for tomato yield prediction management". Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58333/.
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This thesis describes the research and development of a decision support system for tomato yield prediction. Greenhouse horticulture such as tomato growing offers an interesting test bed for comparing and refining different predictive modelling techniques. The ability to accurately predict future yields, even for as little as days ahead has considerable commercial value to growers. There are several (measurable) causal variables. Some such as temperature are under the grower's control, while others are not. Modern predictive techniques, based on data mining and self-calibrating models, may be able to forecast future yields per unit area of greenhouse better than the biological causal models implicitly now used by growers. Over the past few decades, it has been possible to use the recorded daily environmental conditions in a greenhouse to predict future crop yields. Existing models fail to accurately predict the weekly fluctuations of yield, yet predicting future yields is becoming desperately required especially with weather change. This research project used data collected during seasonal tomato life cycle to develop a decision support system that would assist growers to adjust crops to meet demand, and to alter marketing strategies. The three main objectives are: firstly, to research and utilize intelligent systems techniques for analysing greenhouse environmental variables to identify the variable or variables that most effect yield fluctuations, and Secondly, to research the use of these techniques for predicting tomato yields and produce handy rules for growers to use in decision-making. Finally, to combine some existing techniques to form a hybrid technique that achieves lower prediction errors and more confident results. There are a range of intelligent systems (IS), which are used to process environment data, including artificial neural networks (ANNs), genetic algorithms (GA) and fuzzy logic (FL). A model providing more accurate yield prediction was developed and tested using industrial data from growers. The author develops and investigates the application of an intelligent decision support system for yield management, and to provide an improved prediction model using intelligent systems (IS). Using real-world data, the intelligent system employs a combination of FL, NN and GA. The thesis presents a modified hybrid adaptive neural network with revised adaptive error smoothing, which is based on genetic algorithm to build a learning system for complex problem solving in yield prediction. This system can closely predict weekly yield values of a tomato crop. The proposed learning system is constructed as an intelligent technique and then further optimized. The method is evaluated using real-world data. The results show comparatively good accuracy.Use was made of existing algorithms, such as self-organizing maps (SOMs), and principal component analysis (PCA), to analyse our datasets and identify the critical input variables. The primary conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic algorithm, and fuzzy inference systems, can be successfully applied to the creation of tomato yield predictions, these predictions were better and hence support growers’ decisions. All of these techniques are benchmarked against published existing models, such as GNMM, and RBF.
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Wang, Guangyao (Sam), Mario Gutierrez, Michael J. Ottman i Kelly Thorp. "Durum wheat yield prediction at flowering stage for late N management". College of Agriculture, University of Arizona (Tucson, AZ), 2010. http://hdl.handle.net/10150/203775.
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Managing late N application effectively in durum wheat is important to reach desirable protein content. Yield prediction at anathesis is needed to estimate N requirement for the crop and N application rate. In this project, we use canopy reflectance and image processing, measured at anthesis, to estimate yield at harvest. Our results of the growing season 2009-2010 suggested that the canopy reflectance index ‘NWI-4’ and the spike pixel size obtained from image processing at anthesis are potential approaches to predict durum wheat yield at harvest. The final goal of this research is to find a simple and rapid method to manage late N fertilizer to reach desirable grain protein content.
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Gillett, A. G. "Modelling the response of winter wheat to different environments : a parsimonious approach". Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339658.
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Trigg, Deborah Anne. "The application of remote sensing to the prediction of sugar beet yield". Thesis, University of Nottingham, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.278790.
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Wagner, Nicole Catherine. "Wheat yield prediction modeling for localized optimization of fertilizer and herbicide application". Diss., Montana State University, 2004. http://etd.lib.montana.edu/etd/2004/wagner/WagnerN0805.pdf.
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Mitchell, Hal Lee. "Predicting Pallet Part Yields From Hardwood Cants". Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/41288.
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Pallet cant quality directly impacts pallet part processing and material costs. By knowing the quality of the cants being processed, pallet manufacturers can predict costs to attain better value from their raw materials and more accurately price their pallets. The study objectives were 1) to develop a procedure for accurately predicting hardwood pallet part yield as a function of raw material geometry and grade, processing equipment, and pallet part geometry, 2) to develop a model for accurately predicting raw material costs for hardwood pallet parts as a function of yield, 3) to examine current pallet industry methods of determining hardwood cant quality, and 4) to develop and evaluate hardwood cant grading rules for use in the pallet industry.
Yield studies were necessary to accurately quantify the relationship between yield and cant quality. Thirty-one yield studies were conducted throughout the Eastern United States at pallet mills producing pallet parts from hardwood cants. 47, 258 board feet of hardwood cants were graded, and the usable pallet part yield and yield losses were determined for each grade.
Yield losses were separated into three components: kerf loss, dimension loss, and defect loss. Kerf and dimension losses are a function of raw material and part geometry and were calculated without regard to cant quality. Defect loss is dependant on cant quality and was calculated for each cant grade as a function of total yield, kerf loss, and dimension loss.
Mathematical models were developed from twenty-eight mill studies to predict each yield loss component as a function of cant dimensions, grade, and orientation, cutting bill parameters, pallet part dimensions, and kerf. Dimension and kerf losses were predicted geometrically. Regression analysis was used to predict defect loss. Results indicated that these models accurately predicted the total yield of usable pallet parts and pallet part material costs as a function of cant quality and price.
Results also indicated that the pallet industry's current method of counting the number of "bad" ends per cant bundle to determine cant quality is not adequate. The
effectiveness of the proposed cant grading rules was determined by grading cants and analyzing the cant grade distributions and corresponding pallet part yields. The grade rules produced statistically different quality divisions between grades. However, a more practical single cant grade based on the minimum quality for the proposed grade 2 rules is recommended.Master of Science
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Martins, João Duarte Victor Franco. "Previsão da produção na casta Sauvignon Blanc com base nas componentes do rendimento". Master's thesis, ISA/UTL, 2011. http://hdl.handle.net/10400.5/4131.
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Mestrado em Viticultura e Enologia - Instituto Superior de AgronomiaWith the objective of finding estimated yield models, it was collected data from yield components of a Sauvignon Blanc vineyard variety from Quinta do Pinto, Merceana, Denomination of Origin Alenquer, Lisboa wine region. The method was based on the sampling of 32 spaces that correspond to 192 vines in four different phenological stages (separated flowers, berry pea-size, veraison and ripe bunch). The data obtained was then submitted to an statistical analysis in order to find the correlation coefficients between the several variables and yield. The results indicate significant allometric relations between the inflorescences and bunches variables measured. The number of bunches explains most of the production variability (58%). Based on a stepwise regression, two models were obtained for yield estimation: One based on three variables measured during the berry pea-size stage which explains 57% of the yield variability and other based on two variables measured during the veraison that explain 76%. The model produced at veraison seems to be the more robust and includes fast and easy to determine on the field variables. These models, when adjusted, tend to be an useful tool, allowing early previsions in an easy and economical way.
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Lawson, Elizabeth Anne. "Autologous Stem Cell Transplant: Factors Predicting the Yield of CD34+ Cells". Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1144.pdf.
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Ibrahim, Razi. "Predicting Potato Yield Loss Due to Metribuzin Sensitivity in North Dakota". Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28860.
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A linear-log model to predict yield loss due to metribuzin injury was established by Love et. al. in 1993. Two experiments were conducted in 2016 and 2017 to evaluate and improve this model for application in North Dakota (ND). Metribuzin was applied (1.12 a.i./ha) when potato plants were 20-30 cm tall at Inkster, ND. The model did not accurately predict yield loss in 2016 but performed better in 2017. Foliar injury was more correlated with yield reduction than relative plant height. Results also indicated that new models that used foliar injury at 21 days after treatment (DAT) data and at 7 DAT data, most accurately predicted total yield loss and marketable yield loss, respectively. The new model performed similar to the previous model, but unlike previous model it can predict yield loss very early in growing season (21 DAT).USDA Specialty Block Grant Program
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Hutcheson, Ryan Mitchell. "Quantitative prediction of dye fluorescence quantum yields in proteins". Thesis, Montana State University, 2009. http://etd.lib.montana.edu/etd/2009/hutcheson/HutchesonR0509.pdf.
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The application of a method previously developed by Callis et al. to predict the quantum yields of Trp fluorescence has been successfully applied to the fluorescence of fluorescein and flavins in proteins. The calculated lifetime range of 2 ps - 4 ns is in agreement with experiment. The fluctuations in the electron transfer rate are shown to be dictated by the fluctuations in the density of states. This is evident by the comparison of the fractional deviation of the interaction, density of states and the rate. Here the fluctuations in the density of states is an order of magnitude larger than the fluctuations in the interactions and is nearly the same as that of the kET fluctuations. This demonstrates that the fluorescence lifetime variability is controlled by the electrostatic environment and not the distance dependence of the interaction.
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Pickering, Andrew Mark. "Coal liquefaction : prediction of yields and behaviour of blends". Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339558.
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Subedi, Keshab. "Sugarbeet Yield and Quality Response to Nitrogen Fertilizer Rate and In-Season Prediction of Yield and Quality Using Active-Optical Sensor". Thesis, North Dakota State University, 2016. https://hdl.handle.net/10365/27958.
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Nitrogen (N) management is one of the important factors in sugarbeet production. Under-application of N-fertilizer results in lower root yield while over-application of N-fertilizer can result in decreased sugar concentration and recoverable sugar. In recent years active-optical sensors have been investigated for in-season prediction of sugarbeet yield and quality and to make N management decisions. This study was conducted at four sites in the Red River Valley to determine the sugarbeet yield and quality response to N fertilizer rates and to determine the relationship of NDVI with sugarbeet yield and quality. The yield response to N fertilizer rates was significantly quadratic, however, sugar concentration did not show response to N fertilizer rates. In-season NDVI readings were strongly related with root yield and sugar yield. Active sensing during the growing seasons shows promise as a means to predict sugarbeet root yield and sugar yield.
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Awano, Hiromitsu. "Variability in BTI-Induced Device Degradation: from Silicon Measurement to SRAM Yield Prediction". 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/215689.
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Yun, Ilgu. "Reliability modeling and parametric yield prediction of GaAs multiple quantum well avalanche photodiodes". Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/12999.
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Adeyemi, Rasheed Alani. "Empirical statistical modelling for crop yields predictions: bayesian and uncertainty approaches". Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15533.
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Includes bibliographical referencesThis thesis explores uncertainty statistics to model agricultural crop yields, in a situation where there are neither sampling observations nor historical record. The Bayesian approach to a linear regression model is useful for predict ion of crop yield when there are quantity data issue s and the model structure uncertainty and the regression model involves a large number of explanatory variables. Data quantity issues might occur when a farmer is cultivating a new crop variety, moving to a new farming location or when introducing a new farming technology, where the situation may warrant a change in the current farming practice. The first part of this thesis involved the collection of data from experts' domain and the elicitation of the probability distributions. Uncertainty statistics, the foundation of uncertainty theory and the data gathering procedures were discussed in detail. We proposed an estimation procedure for the estimation of uncertainty distributions. The procedure was then implemented on agricultural data to fit some uncertainty distributions to five cereal crop yields. A Delphi method was introduced and used to fit uncertainty distributions for multiple experts' data of sesame seed yield. The thesis defined an uncertainty distance and derived a distance for a difference between two uncertainty distributions. We lastly estimated the distance between a hypothesized distribution and an uncertainty normal distribution. Although, the applicability of uncertainty statistics is limited to one sample model, the approach provides a fast approach to establish a standard for process parameters. Where no sampling observation exists or it is very expensive to acquire, the approach provides an opportunity to engage experts and come up with a model for guiding decision making. In the second part, we fitted a full dataset obtained from an agricultural survey of small-scale farmers to a linear regression model using direct Markov Chain Monte Carlo (MCMC), Bayesian estimation (with uniform prior) and maximum likelihood estimation (MLE) method. The results obtained from the three procedures yielded similar mean estimates, but the credible intervals were found to be narrower in Bayesian estimates than confidence intervals in MLE method. The predictive outcome of the estimated model was then assessed using simulated data for a set of covariates. Furthermore, the dataset was then randomly split into two data sets. The informative prior was later estimated from one-half called the "old data" using Ordinary Least Squares (OLS) method. Three models were then fitted onto the second half called the "new data": General Linear Model (GLM) (M1), Bayesian model with a non-informative prior (M2) and Bayesian model with informative prior (M3). A leave-one-outcross validation (LOOCV) method was used to compare the predictive performance of these models. It was found that the Bayesian models showed better predictive performance than M1. M3 (with a prior) had moderate average Cross Validation (CV) error and Cross Validation (CV) standard error. GLM performed worst with least average CV error and highest (CV) standard error among the models. In Model M3 (expert prior), the predictor variables were found to be significant at 95% credible intervals. In contrast, most variables were not significant under models M1 and M2. Also, The model with informative prior had narrower credible intervals compared to the non-information prior and GLM model. The results indicated that variability and uncertainty in the data was reasonably reduced due to the incorporation of expert prior / information prior. We lastly investigated the residual plots of these models to assess their prediction performance. Bayesian Model Average (BMA) was later introduced to address the issue of model structure uncertainty of a single model. BMA allows the computation of weighted average over possible model combinations of predictors. An approximate AIC weight was then proposed for model selection instead of frequentist alternative hypothesis testing (or models comparison in a set of competing candidate models). The method is flexible and easy to interpret instead of raw AIC or Bayesian information criterion (BIC), which approximates the Bayes factor. Zellner's g-prior was considered appropriate as it has widely been used in linear models. It preserves the correlation structure among predictors in its prior covariance. The method also yields closed-form marginal likelihoods which lead to huge computational savings by avoiding sampling in the parameter space as in BMA. We lastly determined a single optimal model from all possible combination of models and also computed the log-likelihood of each model.
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Ward, Brian Phillip. "Genomic Prediction and Genetic Dissection of Yield-Related Traits in Soft Red Winter Wheat". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/85503.
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In multiple species, genome-wide association (GWA) studies have become an increasingly
prevalent method of identifying the quantitative trait loci (QTLs) that underlie complex traits.
Despite this, relatively few GWA analyses using high-density genomic markers have been
carried out on highly quantitative traits in wheat. We utilized single-nucleotide polymorphism
(SNP) data generated via a genotyping-by-sequencing (GBS) protocol to perform GWA on
multiple yield-related traits using a panel of 329 soft red winter wheat genotypes grown in four
environments. In addition, the SNP data was used to examine linkage disequilibrium and
population structure within the testing panel. The results indicated that an alien translocation
from the species Triticum timopheevii was responsible for the majority of observed population
structure. In addition, a total of 50 significant marker-trait associations were identified. However,
a subsequent study cast some doubt upon the reproducibility and reliability of plant QTLs
identified via GWA analyses. We used two highly-related panels of different genotypes grown in
different sets of environments to attempt to identify highly stable QTLs. No QTLs were shared
across panels for any trait, suggesting that QTL-by-environment and QTL-by-genetic
background interaction effects are significant, even when testing across many environments. In
light of the challenges involved in QTL mapping, prediction of phenotypes using whole-genome
marker data is an attractive alternative. However, many evaluations of genomic prediction in
crop species have utilized univariate models adapted from animal breeding. These models cannot
directly account for genotype-by-environment interaction, and hence are often not suitable for
use with lower-heritability traits assessed in multiple environments. We sought to test genomic
prediction models capable of more ad-hoc analyses, utilizing highly unbalanced experimental
designs consisting of individuals with varying degrees of relatedness. The results suggest that
these designs can successfully be used to generate reasonably accurate phenotypic predictions. In addition, multivariate models can dramatically increase predictive accuracy for some traits,
though this depends upon the quantity and characteristics of genotype-by-environment
interaction.Ph. D.
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Williams, Sheryl R. "Site-specific energy prediction for photovoltaic devices". Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/28317.
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This thesis presents an energy prediction tool for photovoltaic (PV) modules, based on the measure-correlate-predict principle. The tool allows quantification of the impact of the different environmental factors influencing PV device efficiency for different sites as they deviate from standardised test conditions and combines their effects for energy yield prediction of different module technologies operating in different climates. Amongst these environmental influences, the impact of angle of incidence has been particularly under-researched. In this work, a systematic investigation of the influence of angle of incidence on PV module performance is realised. This is achieved using both short-term module characterisation and long-term energy yield measurement campaigns. A customised purpose built dual axis tracker for mounting paired sets of modules on a fixed south-facing, 45-degree tilted rack is used to investigate the differences in module performance. The quality and quantity of the composition of the incident irradiance is described for various sky conditions at high latitude locations. Furthermore, an understanding of the entangled effects on photocurrent of both the angle of incidence and spectral variation is presented. This is achieved by analysing data from a system developed especially in this work which integrates an instantaneous all-sky mapping of irradiance from a monochromatic CCD camera with precision measurements of small-aperture normal irradiance from a collimated pyranometer in the short-term measurement campaign. The proposed energy prediction tool is validated using long-term datasets from several locations and is compared to other current methods. This was conducted under the European-funded PV-Catapult and IP Performance projects. The tool's prediction uncertainty falls within the ±5% for crystalline and ±10% for thin films, which is the same accuracy as other methods and within the measurement uncertainty of outdoor measurements.
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Xu, Chang. "Index-Based Insurance, Informal Risk Sharing, and Agricultural Yields Prediction". The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1529794733186832.
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Stanford, Kim. "Prediction of lamb carcass composition and classification of Canadian lambs by saleable meat yield". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq29113.pdf.
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Al-Shammari, Dhahi Turki Jadah. "Remote sensing applications for crop type mapping and crop yield prediction for digital agriculture". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29771.
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This thesis addresses important topics in agricultural modelling research. Chapter 1 describes the importance of land productivity and the pressure on the agricultural sector to provide food. In chapter 2, a summer crop type mapping model has been developed to map major cotton fields in-season in the Murray Darling Basin (MDB) in Australia. In chapter 3, a robust crop classification model has been designed to classify two major crops (cereals and canola) in the MDB in Australia. chapter 4 focused on exploring changes in prediction quality with changes in the spatial resolution of predictors and the predictions. More specifically, this study investigated whether inputs should be resampled prior to modelling, or the modelling implemented first with the aggregation of predictions happening as a final step. In chapter 5, a new vegetation index is proposed that exploits the three red-edge bands provided by the Sentinel-2 satellite to capture changes in the transition region between the photosynthetically affected region (red region) and the Near-Infrared region (NIR region) affected by cell structure and leaf layers. Chapter 6 was conducted to test the potential of integration of two mechanistic-type model products (biomass and soil moisture) in the DDMs models. Chapter 7 was dedicated to discussing each technique used in this thesis and the outcomes of each technique, and the relationships between these outcomes. This thesis addressed the topics and questioned asked at the beginning of this research and the outcomes are listed in each chapter.
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Johnson, Andrew. "A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed". OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1238.
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Climate change will have a significant impact on the productivity of agricultural lands and ecosystem services in the coming decades. Variability in temperature and precipitation will alter many central U.S. watersheds. Simulation models such as the Soil and Water Assessment Tool (SWAT) offer the ability to model changes in watersheds by varying inputs. Unfortunately, SWAT requires a large number of input parameters and computation time to process the output data. Regression metamodels offer an alternative that seeks to replace the simulation model with a regression equation. This research created a linear regression metamodel to approximate SWAT in crop yield prediction. Results show that regression models can account for 45-84 percent of variance in yields for corn, soybean, alfalfa, switchgrass, and cotton in Big Creek Watershed. The coefficient of variation for each of these models ranged from 13 to 41 percent. These metamodels were able to reduce simulation time from hours to minutes. The tradeoff for utilizing metamodels is computation time versus accuracy. The results of this research indicate that the considerable reduction in computation time coupled with a moderate degree of accuracy in predicting crop yields necessitates the use of metamodels over SWAT. Regression coefficients for each metamodel can reveal how various weather and farm management techniques impact crop yields. These metamodels will be utilized by the Agent Based Model to determine how farmers will respond to future economic policies and crop prices based on a series of climate scenarios.
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Daniel, Jean-Baptiste. "Dynamic prediction of milk yield and composition responses to dietary changes in dairy cows". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLA009/document.
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Afin de répondre à la diversification des objectifs de la filière laitière (ex. efficacité alimentaire, santé et longévité des animaux, etc.), et ceci dans un contexte de forte volatilité des prix du lait et des intrants, la quantification des réponses multiples aux changements de régimes alimentaires représente un intérêt afin d’aider les producteurs laitiers à optimiser la ration des animaux. Le principal objectif de ce travail a consisté à développer et évaluer un modèle de prédiction des réponses de l’ingestion, de la quantité et composition du lait aux changements de régimes alimentaires chez la vache laitière. Une méta-analyse a ainsi été appliquée sur une grande base de données de la littérature afin de quantifier la réponse de l’ingestion aux régimes, et les réponses laitières (quantité totale, sécrétion des composants du lait et composition du lait) aux changements d’énergie nette (UFL) et protéines métabolisables (PDI) de la ration. Un élément clé dans le développement de ces équations de réponses était qu’elles soient applicables à travers différent potentiel laitier. Ceci a pu être atteint en exprimant les apports PDI et UFL par rapport à une situation nutritionnel pivot, qui correspond à une efficacité d’utilisation des PDI de 67% et une efficacité d’utilisation des UFL par lait de 100% (équivalente à un bilan énergétique nul). Construite à partir des efficacités PDI et UFL, une approche a été proposée pour estimer les apports PDI et UFL à la situation pivot, à partir desquels les équations de réponses peuvent s’appliquer. Evalué sur deux jeux de données indépendants, cette approche a permis de prédire les réponses de production laitière, productions de matières grasses, lactose et protéines du lait aux changements d’apports PDI et UFL avec une bonne précision pour des rations considérablement différentes, et à travers différents stades de lactation. Dans un autre modèle, les effets du stade physiologique (stade de lactation, stade de gestation, croissance) sur les performances animal, i.e. production laitière, productions de matières grasses, lactose et protéines du lait, changement de composition corporelle et ingestion, ont été quantifiés à travers des animaux de potentiel laitier différent. Il a été constaté que la structure du modèle était adéquate pour simuler les performances de différentes races laitières (Holstein, Rouge Danoise et Jersiaise). Afin de prédire les conséquences d’un changement alimentaire sur le long terme, les équations de réponses, centrées sur la situation nutritionnel pivot, ont été intégrées au sein du modèle dynamique. Cette intégration a pu se faire en appliquant le concept du pivot au modèle dynamique afin d’obtenir des courbes pivot, à partir desquelles les lois de réponses s’appliquent. Le modèle construit est le premier à intégrer les deux grands types de régulation biologique (homéostase et l’homéorhèse) chez la vaches laitière permettant de prédire les performances animales à partir d’une définition précise du potentiel laitierIn order to better cope with the increasing diversity of objective in dairy production (e.g. feed efficiency, animal health, animal longevity, etc.) in a context of high volatility of feed and milk prices, quantification of animal’s multiple responses to dietary changes is of particular interest to help dairy farmers in optimizing the diet. The main aim of the present study was to develop and evaluate a model to predict the responses in dry-matter intake, milk yield, milk component yields and contents to changes in dietary composition in dairy cows. A meta-analysis of the literature was conducted to quantify dry-matter intake response to changes in diet composition, and milk responses (yield, milk component yields and milk composition) to changes in dietary net energy (NEL) and metabolizable protein (MP) in dairy cows. A key point in the development of these response equations was that they could be apply on animals of varying production potential. This was achieved by expressing MP and NEL supply relative to a pivot nutritional status, defined as the supply of MP and NEL resulting to MP efficiency of 0.67 and NEL efficiency of 1. Based on MP and NEL efficiency, an approach was proposed to estimate the pivot MP and NEL supplies, around which the response equations can be applied. Evaluated with two independent datasets, this approach predicted milk yield and milk component yields responses to change in MP and NEL supply with a good accuracy for diets that are substantially different, and across all stages of lactation. In another model, the effect of physiological status (lactation stage, gestation, growth) on animal performance, i.e. milk yield, milk component yields, body composition change and dry-matter intake, were quantified across a range of animal potential. It was found that the model structure was adequate to simulate performance of different dairy breeds (Holstein, Danish Red and Jersey). To predict the long-term consequences of a dietary change, response equations, centred on the pivot nutritional status, were integrated into the dynamic model. This integration has been possible by applying the pivot concept into the dynamic model. This way, lactation pivot curves were calculated, from which response equations are applied. The model built is the first to integrate the two major biological regulations (homeostasis and homeorhesis) in dairy cows that predicts animal performance using a precise definition of milk potential
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Zaytsev, Michael. "Predicting Enrollment Decisions of Students Admitted to Claremont McKenna College". Scholarship @ Claremont, 2011. http://scholarship.claremont.edu/cmc_theses/107.
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College admission has become increasingly competitive in the internet era. This is especially true for the highest caliber of students and institutions. College admission is a process filled with asymmetric information. One of the biggest asymmetries occurs when schools admit students not knowing whether or not students will actually enroll. This uncertainty is economically costly to schools. As national rankings become more and more influential, schools are more sensitive to their rank and the statistics that determine them. One of these is yield, the percentage of admitted students who enroll. This paper examines data on admitted students to Claremont McKenna College and uses a probit regression to predict their enrollment decision. By successfully predicting enrollment decisions schools can eliminate some information asymmetry and therefore raise their yield.
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Liu, Guozheng [Verfasser]. "Genome-wide prediction of hybrid performance and yield stability analysis in winter wheat / Guozheng Liu". Halle, 2018. http://d-nb.info/116051447X/34.
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Pasman, Egbert Jan. "The development of control and prediction systems for milk yield and mastitis from field data". Thesis, University of Reading, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359436.
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Van, Acker Rene C. "Multiple-weed species interference in broadleaved crops : evaluation of yield loss prediction and competition models". Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308562.
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Jenni, Sylvie. "Predicting yield and development of muskmelon, Cucumis melo L., under mulch and rowcover management". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0011/NQ30303.pdf.
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Jenni, Sylvie. "Predicting yield and development of muskmelon (Cucumis melo L.) under mulch and rowcover management". Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=42061.
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Field experiments were undertaken to predict the yield of 'Earligold' muskmelon grown with plastic mulches, rowcovers and thermal water tubes. Mulches were either black, photoselective or clear. Rowcover systems included a clear perforated polyethylene, a polypropylene agrotextile, or unperforated polyethylenes (standard or infra-red treated) with a water-filled tube. In all tunnel types, the photoselective mulch had an intermediate effect between clear and black mulch on air and soil temperatures, chilling injury and days to flowering. Plants with a clear mulch and an infrared or standard unperforated tunnel with a thermal tube survived chilling temperatures between 1.6-5.8C for seven days, flowered first, had the heaviest biomass at anthesis and had the highest early yields. Plant dry weight from transplanting to anthesis was predicted from a multiple linear regression based on heat unit formulas using air (base temperature of 14C, maximum threshold of 40C) and soil temperatures (base temperature of 12C). The base temperature for predicting developmental time to anthesis of perfect flowers was 6.8C. In order to study fruit growth, a rapid and non-destructive method for estimating volume of ovaries was established. Fruit phenology was described as six classes for flower development and seven for fruit development. Flower abortion was studied in relation to each class. Plants displayed either one or two fruit growth cycles. The second cycle was triggered as the absolute growth rate of the first cycle fruits decreased. Developmental time of individual fruits was predicted from a heat unit formula with a base temperature of 15C when temperatures were recorded from a meteorological station. Another heat unit formula was proposed for air temperature at 7.5 cm. Fruit growth from the second cycle had a 60-degree day lag. Fruit volume proportion from blooming to maturity of first cycle fruits was described by a common Richards function. Although 65% of the plants produc
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Poole, Warren J., B. Raeisinia, X. Wang i D. J. Lloyd. "A model for predicting the yield stress of AA6111 after multi-step heat treatments". Springer, 2006. http://hdl.handle.net/2429/398.
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A model has been developed to predict the yield stress of the aluminum alloy AA6111
after multi-step heat treatments which involve combinations of ambient temperature
ageing and high temperature artificial ageing. The model framework follows the internal state variable framework where the two principal state variables are i) the volume fraction of clusters which form at ambient temperature and ii) the volume fraction of metastable phases which form during high temperature ageing. The evolution of the these state variables has modeled using a set of coupled differential equations. The mechanical response (the yield stress) is then formulated in terms of the state variables through an appropriate flow stress addition law. To test the model predictions a series of experiments were conducted which examined two scenarios for multi-step heat treatments. In general, good agreement was observed between the model predictions and the experimental results. However, for the case where a short thermal excursion at 250oC was applied immediately after the solution treatment, the results were not satisfactory. This can be understood in terms of the importance of the temperature dependence for the nucleation density of metastable precipitates.
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MACHADO, URSULLA MONTEIRO DA SILVA BELLOTE. "A HIERARCHICAL FACTOR MODEL FOR THE JOINT PREDICTION OF CORPORATE BOND YIELDS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19535@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOO objetivo deste trabalho é a construção de um modelo integrado para previsão da estrutura a termo da taxa de juros, referentes a títulos corporativos americanos para diferentes níveis de risco. A metodologia é baseada no modelo de Nelson e Siegel (1987), com extensões propostas por Diebold e Li (2006) e Diebold, Li e Yue (2008). Modelamos a estrutura a termo para 14 níveis de risco e estimamos conjuntamente os fatores latentes de nível e inclinação que governam a dinâmica das taxas, para a posterior estimação de dois super fatores, que por sua vez, conduzem a trajetória de cada fator, onde está centrada a nossa principal inovação. A previsão da curva de juros é então construída a partir da previsão dos super fatores, modelados por processos auto-regressivos, como sugere Diebold e Li (2006). Através dos super fatores extrapolados da amostra reconstruímos, na forma da previsão, os fatores latentes e a própria taxa de juros. Além da previsão fora da amostra, comparamos a eficiência do modelo proposto com o modelo mais tradicional da literatura, o passeio aleatório. Pela comparação, não obtivemos ganhos significativos em relação a esse competidor, principalmente na previsão um passo a frente. Resultados melhores foram obtidos aumentando o horizonte de previsão, mas não sendo capaz de superar o passeio aleatório.
This dissertation constructs an integrated model for interest rate term structure forecast for American corporate bonds associated with different risk levels. Our methodology is primarily based on Nelson and Siegel (1987) and presents extensions proposed in Diebold and Li (2006) and Diebold, Li and Yue (2008). We model the term structure for 14 risk levels and we jointly estimate the level and slope latent factors that drive interest rates dynamics. These factors are then used in the estimation of two super factors which is our main innovation. The yield curve forecast is then determinate from the forecast of the super factors, described by autoregressive processes, as suggested by Diebold and Li (2006). Through the super factors forecast, reconstructed in the form of forecasting the latent factors and their own interest rate. Our results focus on the model’s out of sample forecast and efficiency compared with the random walk model, considered the benchmark model in this type of literature. Our results provide evidence that the proposed models shows no significant gains in relation to the benchmark, especially in predicting one month ahead. Better results were obtained by increasing the forecast horizon, but not being able to overcome the random walk.
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Oliver, Rebecca Joy. "Predicting the yield and water-use of poplar short rotation coppice under a future climate". Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/188255/.
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Under the current climate there is significant spatial variation in the yield and water-use of bioenergy crops such as poplar short rotation coppice (SRC). Marked changes in patterns of precipitation and temperature are predicted globally as a result of anthropogenic climate change. This is likely to significantly impact on the yield and transpiration of poplar SRC. The response of poplar SRC to future climate change is unknown and represents a significant knowledge gap in the path to a sustainable future. This thesis used a land-surface scheme, JULES, to investigate the response of poplar SRC yield and transpiration to the interaction between changes in atmospheric CO2 concentration and changes in climate. Empirical work generated poplar SRC specific parameter values for use JULES. It was found that Vmax, a key model photosynthetic parameter, was significantly lower when estimated under the assumption of infinite leaf internal conductance to CO2. This invalidated the assumption that internal CO2 transfer has a negligible impact on the drawdown of CO2 from ci to cc. The photosynthesis model in JULES is based on this assumption; however, inclusion of this additional CO2 transfer pathway in the model did not impact on the accuracy of the simulated carbon assimilation, because the value of Vmax used in the model compensated for the presence/absence of this pathway. It was concluded that, given the model’s high sensitivity to Vmax, it is essential to calibrate the model with a parameter value estimated under assumptions appropriate for the model. Further modification, calibration and validation enabled JULES to simulate the dynamic growth and water-use of poplar under a managed SRC cycle, which is a novel application for the model. Changes in climate were simulated using an ensemble of GCM anomalies and atmospheric CO2 concentration was simulated using the SRES A1B emissions scenario. Results of this work highlighted the influence of climate in modifying the yield and transpiration responses to elevated concentrations of atmospheric CO2. Additionally, for a future climate scenario, these simulations indicated higher yields but also higher water-use of poplar SRC, although the magnitude and direction of response was highly spatially variable.
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Munyaradzi, Sipho Musevenzo Ward Andrew D. "Predicting soil water deficits and crop yields for Seneca County 1988 /". Connect to resource, 1991. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1145449951.
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Munyaradzi, Sipho Musevenzo. "Predicting soil water deficits and crop yields for Seneca County 1988". The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1145449951.
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