Records 1 - 20 / 268
Combined use of milk infrared spectra and genotypes can improve prediction of milk fat composition
Wang, Qiuyu ; Bovenhuis, Henk - \ 2020
Journal of Dairy Science 103 (2020)3. - ISSN 0022-0302 - p. 2514 - 2522.
genotypes - milk fat composition - milk infrared spectroscopy - prediction
It has been shown that milk infrared (IR) spectroscopy can be used to predict detailed milk fat composition. In addition, polymorphisms with substantial effects on milk fat composition have been identified. In this study, we investigated the combined use of milk IR spectroscopy and genotypes of dairy cows on the accuracy of predicting milk fat composition. Milk fat composition data based on gas chromatography and milk IR spectra were available for 1,456 Dutch Holstein Friesian cows. In addition, genotypes for the diacylglycerol acyltransferase 1 (DGAT1) K232A and stearoyl-CoA desaturase 1 (SCD1) A293V polymorphisms and a SNP located in an intron of the fatty acid synthase (FASN) gene were available. Adding SCD1 genotypes to the milk IR spectra resulted in a considerable improvement of the prediction accuracy for the unsaturated fatty acids C10:1, C12:1, C14:1 cis-9, and C16:1 cis-9 and their corresponding unsaturation indices. Adding DGAT1 genotypes to the milk IR spectra resulted in an improvement of the prediction accuracy for C16:1 cis-9 and C16 index. Adding genotypes of the FASN SNP to the IR spectra did not improve prediction of milk fat composition. This study demonstrated the potential of combining milk IR spectra with genotypic information from 3 polymorphisms to predict milk fat composition. We hypothesize that prediction accuracy of milk fat composition can be further improved by combining milk IR spectra with genomic breeding values.
Validation strategy can result in an overoptimistic view of the ability of milk infrared spectra to predict methane emission of dairy cattle
Wang, Qiuyu ; Bovenhuis, Henk - \ 2019
Journal of Dairy Science 102 (2019)7. - ISSN 0022-0302 - p. 6288 - 6295.
CH emission - milk infrared spectroscopy - prediction - validation strategy
Because of the environmental impact of methane (CH 4 ), it is of great interest to reduce CH 4 emission of dairy cattle and selective breeding might contribute to this. However, this approach requires a rapid and inexpensive measurement technique that can be used to quantify CH 4 emission for a large number of individual dairy cows. Milk infrared (IR) spectroscopy has been proposed as a predictor for CH 4 emission. In this study, we investigated the feasibility of milk IR spectra to predict breath sensor–measured CH 4 of 801 dairy cows on 10 commercial farms. To evaluate the prediction equation, we used random and block cross validation. Using random cross validation, we found a validation coefficient of determination (R 2 val) of 0.49, which suggests that milk IR spectra are informative in predicting CH 4 emission. However, based on block cross validation, with farms as blocks, a negligible R 2 val of 0.01 was obtained, indicating that milk IR spectra cannot be used to predict CH 4 emission. Random cross validation thus results in an overoptimistic view of the ability of milk IR spectra to predict CH 4 emission of dairy cows. The difference between the validation strategies could be due to the confounding of farm and date of milk IR analysis, which introduces a correlation between batch effects on the IR analyses and farm-average CH 4 . Breath sensor–measured CH 4 is strongly influenced by farm-specific conditions, which magnifies the problem. Milk IR wavenumbers from water absorption regions, which are generally considered uninformative, showed moderate accuracy (R 2 val = 0.25) when based on random cross validation, but not when based on block cross validation (R 2 val = 0.03). These results indicate, therefore, that in the current study, random cross validation results in an overoptimistic view on the ability of milk IR spectra to predict CH 4 emission. We suggest prediction based on wavenumbers from water absorption regions as a negative control to identify potential dependence structures in the data.
Can bacteria in your gut prevent diseases? The health implications of microbiota | WURcast
Belzer, C. - \ 2019
Wageningen : WURcast
gastrointestinal microbiota - human diseases - prediction - disease prevention
What is the role of the model in socio-hydrology? Discussion of “Prediction in a socio-hydrological world”*
Melsen, Lieke Anna ; Vos, Jeroen ; Boelens, Rutgerd - \ 2018
Hydrological Sciences Journal 63 (2018)9. - ISSN 0262-6667 - p. 1435 - 1443.
modelling - prediction - socio-hydrology - socio-natural relationships - transdisciplinarity
Srinivasan et al. provide an interesting overview of the challenges for long-term socio-hydrological predictions. Although agreeing with most of the statements made, we argue for the need to take socio-hydrological analysis a step further and add some fundamental considerations, especially concerning the crucial importance of many (conscious and unconscious) assumptions made upfront of the modelling exercise. Eventual assumptions of technological determinism need correction: Models are not “value-free”, but uncertain, subjective and a product of the society in which they were shaped. It is important to acknowledge this uncertainty and bias when making decisions based on socio-hydrological models, considering also that these models are “social and political actors” in and by themselves. Furthermore, socio-hydrological models require a transdisciplinary approach, since physical water availability is only one of the boundary conditions for society. Last but not least, interaction with stakeholders remains important to enable understanding of what the variable of interest is.
Multimodel ensembles improve predictions of crop–environment–management interactions
Wallach, Daniel ; Martre, Pierre ; Liu, Bing ; Asseng, Senthold ; Ewert, Frank ; Thorburn, Peter J. ; Ittersum, Martin van; Aggarwal, Pramod K. ; Ahmed, Mukhtar ; Basso, Bruno ; Biernath, Christian ; Cammarano, Davide ; Challinor, Andrew J. ; Sanctis, Giacomo De; Dumont, Benjamin ; Eyshi Rezaei, Ehsan ; Fereres, Elias ; Fitzgerald, Glenn J. ; Gao, Y. ; Garcia-Vila, Margarita ; Gayler, Sebastian ; Girousse, Christine ; Hoogenboom, Gerrit ; Horan, Heidi ; Izaurralde, Roberto C. ; Jones, Curtis D. ; Kassie, Belay T. ; Kersebaum, Christian C. ; Klein, Christian ; Koehler, Ann Kristin ; Maiorano, Andrea ; Minoli, Sara ; Müller, Christoph ; Naresh Kumar, Soora ; Nendel, Claas ; O'Leary, Garry J. ; Palosuo, Taru ; Priesack, Eckart ; Ripoche, Dominique ; Rötter, Reimund P. ; Semenov, Mikhail A. ; Stöckle, Claudio ; Stratonovitch, Pierre ; Streck, Thilo ; Supit, Iwan ; Tao, Fulu ; Wolf, Joost ; Zhang, Zhao - \ 2018
Global Change Biology 24 (2018)11. - ISSN 1354-1013 - p. 5072 - 5083.
climate change impact - crop models - ensemble mean - ensemble median - multimodel ensemble - prediction
A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
DNA sequence and shape are predictive for meiotic crossovers throughout the plant kingdom
Demirci, Sevgin ; Peters, Sander A. ; Ridder, Dick de; Dijk, Aalt D.J. van - \ 2018
The Plant Journal 95 (2018)4. - ISSN 0960-7412 - p. 686 - 699.
Arabidopsis thaliana - crossover - DNA shape - genome accessibility - machine learning - maize - meiotic recombination - prediction - rice - tomato
A better understanding of genomic features influencing the location of meiotic crossovers (COs) in plant species is both of fundamental importance and of practical relevance for plant breeding. Using CO positions with sufficiently high resolution from four plant species [Arabidopsis thaliana, Solanum lycopersicum (tomato), Zea mays (maize) and Oryza sativa (rice)] we have trained machine-learning models to predict the susceptibility to CO formation. Our results show that CO occurrence within various plant genomes can be predicted by DNA sequence and shape features. Several features related to genome content and to genomic accessibility were consistently either positively or negatively related to COs in all four species. Other features were found as predictive only in specific species. Gene annotation-related features were especially predictive for maize, whereas in tomato and Arabidopsis propeller twist and helical twist (DNA shape features) and AT/TA dinucleotides were found to be the most important. In rice, high roll (another DNA shape feature) and low CA dinucleotide frequency in particular were found to be associated with CO occurrence. The accuracy of our models was sufficient for Arabidopsis and rice (area under receiver operating characteristic curve, AUROC > 0.5), and was high for tomato and maize (AUROC ≫ 0.5), demonstrating that DNA sequence and shape are predictive for meiotic COs throughout the plant kingdom.
|Postharvest Technology in Tomatoes
Westra, Eelke - \ 2018
postharvest - tomatoes - quality - colour - taste - prediction - vegetables - transport - logistics - postharvest - tomatoes - quality - taste - colour - prediction - vegetables - transport - logistics
The strength of the multivariable associations of major risk factors predicting coronary heart disease mortality is homogeneous across different areas of the Seven Countries Study during 50-year follow-up
Menotti, Alessandro ; Puddu, Paolo Emilio ; Adachi, Hisashi ; Kafatos, Anthony ; Tolonen, Hanna ; Kromhout, Daan - \ 2018
Acta Cardiologica 73 (2018)2. - ISSN 0001-5385 - p. 148 - 154.
coefficients - Coronary heart disease - hazard ratios - homogeneity - mortality - prediction - risk factors
Objectives: To compare the magnitude of multivariable coefficients and hazard ratios of four cardiovascular risk factors across five worldwide regions of the Seven Countries Study in predicting 50-year coronary deaths. Material and methods: A total of 13 cohorts of middle-aged men at entry (40–59 years old) were enrolled in the mid-1900s from five relatively homogeneous groups of cohorts (areas): USA, Finland and Zutphen – the Netherlands, Italy and Greece, Serbia, Japan for a total of 10,368 middle-aged men. The major risk factors measured at baseline were age, number of cigarettes smoked, systolic blood pressure and serum cholesterol. Cox proportional hazards models were solved for 50-year (45 years for Serbia) deaths from coronary heart disease (CHD), and the multivariable coefficients were compared for heterogeneity. Results: The highest levels of risk factors and CHD death rates were found in Finland and Zutphen – the Netherlands and the lowest in Japan. All four risk factors were predictive for long-term CHD mortality in all regions, except serum cholesterol in Japan where the mean levels and CHD events were lowest. Tests of heterogeneity of coefficients for single risk factors in predicting CHD mortality were non-significant across the five areas. The same analyses for the first 25 years of follow-up produced similar findings. Conclusions: The strength of the multivariable associations of four major traditional CHD risk factors with long-term CHD mortality appears to be relatively homogeneous across areas, pending needed further evidence.
Traditional mixed linear modelling versus modern machine learning to estimate cow individual feed intake
Kamphuis, C. ; Riel, J.W. van; Veerkamp, R.F. ; Mol, R.M. de - \ 2017
In: Precision Livestock Farming '17. - - p. 366 - 376.
precision feeding - dairy cows - Big Data - prediction - machine learning
Three modelling approaches were used to estimate cow individual feed intake
(FI) using feeding trial data from a research farm, including weekly recordings
of milk production and composition, live-weight, parity, and total FI.
Additionally, weather data (temperature, humidity) were retrieved from the
Dutch National Weather Service (KNMI). The 2014 data (245 cows; 277
parities) were used for model development. The first model (M1) applied an
existing formula to estimate energy requirement using parity, fat and protein
corrected milk, and live-weight, and assumed this requirement to be equal to
energy intake and thus FI. The second model used ‘traditional’ Mixed Linear
Regression, first using the same variables as in M1 as fixed effects (MLR1), and
then by adding weather data (MLR2). The third model applied Boosted
Regression Tree, a ‘modern’ machine learning technique, again once with the
same variables as M1 (BRT1), and once with weather information added
(BRT2). All models were validated on 2015 data (155 cows; 165 parities) using
correlation between estimated and actual FI to evaluate performance. Both
MLRs had very high correlations (0.91) between actual and estimated FI on 2014
data, much higher than 0.46 for M1, and 0.73 for both BRTs. When validated on
2015 data, correlations dropped to 0.71 for MLR1 and 0.72 for MLR2, and
increased to 0.71 for M1 and 0.76 for both BRTs. FI estimated by BRT1 was, on
average, 0.35kg less (range: -7.61 – 13.32kg) than actual FI compared to 0.52kg
less (range: -11.67 – 19.87kg) for M1. Adding weather data did not improve FI
Hydrological drought and wildfire in the humid tropics
Taufik, Muh - \ 2017
Wageningen University. Promotor(en): R. Uijlenhoet, co-promotor(en): Henny van Lanen. - Wageningen : Wageningen University - ISBN 9789463436359 - 99
wildfires - drought - humid tropics - wetlands - hydrology - prediction - water management - natuurbranden - droogte - humide tropen - wetlands - hydrologie - voorspelling - waterbeheer
Drought is a recurrent hazard, which has happened throughout human history, and it is anticipated to become more severe in multiple regions across the world. Drought occurs in all climate regimes from humid to dry and from hot to cold. Drought is often viewed through its impact on environment and society, including wildfire, which is the topic of this study. The nature of such impacts differs remarkably from region to region. Although drought does not directly cause wildfire, it provides favorable conditions for wildfire ignition and spread. When drought coincides with strong El Niño events in the humid tropics, e.g. Southeast Asia, the impacts worsen through uncontrolled forest fires affecting the global carbon cycle. These include reduction of the carbon stock, intensifying the haze hazard, and other severe socio-economic impacts in Southeast Asia, including areas far away from the burnt area, e.g. Singapore because of fires in Sumatra.
There still remains a serious lack of scientific understanding about the fundamental role of drought in fire-generating processes. Most research, so far, suggests that climate controls wildfire occurrence in the humid tropics. However, this climate-centered approach, which is reflected in contemporary drought-fire related indices, overlooks soil and hydrological processes beneath the surface across the humid tropics. There is also uncertainty about the relative roles of climate variability and human activities in influencing the nature and distribution of drought-related wildfires. Hence, the general objective of this PhD research is to examine how characterization of hydrological drought under natural and human-modified conditions can improve understanding of wildfires in general in the humid tropics.
Chapter 2 discusses the contribution of humans to an increase of hydrological drought severity in the tropical peatland of Southeast Asia. Climate variability induces drought in the region, however, human activities (human-modified drought) may increase its severity. Analyzing long time series of simulated historical groundwater levels from selected regions in Southeast Asia, which were validated against some years with observations, revealed that human interference (through canalization and land-use change) has amplified drought severity. The drought amplification due to human interference was at least double that of climate-induced drought. The amplification is even higher when peatland is converted into acacia plantation. Further, research findings suggest that even if the Paris Agreement target is met, drought risk of peatlands remains high unless sustainable water management receives top priority in the region.
Chapter 3 deals with how an existing, well-known drought-fire related index, i.e. the Keetch-Byram Drought Index (KBDI), is modified to improve applicability in the humid climate environment of Southeast Asia. The improvement includes: (i) adjustment of the drought factor to the local climate, and (ii) addition of the water table depth as a dynamic factor to fine-tune the drought index. The results indicate that the modified Keetch-Byram Drought Index (mKBDI) performed well in predicting fire hazard. Furthermore, the research identified a critical water table depth, which represents maximum fire hazard (0.85 m for the wetland forest of South Sumatra). Below this value hazard does not increase anymore. The mKBDI could be more widely applied, if pedotransfer functions would be developed that link easily-obtainable soil properties to the parameters of the water table factor.
Chapter 4 shows that wetland transformation (i.e. through canalization and land-use change) not only affects hydrological drought (Chapter 2), but also influences fire behaviour. In Southeast Asia, expansion of agricultural cropland and forest plantations has changed the landscape of wetlands. The findings showed that the transformation into acacia plantation has amplified the fire hazard from 4% (under natural conditions) to 17%. An even higher amplification (40% fire hazard) is expected under poor water management, that is, uncontrolled drainage. The findings derived from this observation-based modeling experiment suggest that improved water management (controlled drainage with higher dry season surface water levels) can minimize fire susceptibility.
Chapter 5 explains the importance of hydrology for fire hazard studies. Borneo is selected to investigate the added value of including hydrological variables in fire hazard prediction approaches. More than 300 statistical models were tested, and the results showed that models that include hydrological variables better predict area burnt than those solely based on climate indicators/indices. Further, modelling evidence shows amplifying wildfires and greater area burnt in response to El Niño Southern Oscillation (ENSO) strength, when hydrology is considered. These results highlight the importance of considering hydrological drought for wildfire prediction. I recommend that hydrology should be considered in future studies of the impact of projected ENSO strength, including effects on tropical ecosystems and biodiversity conservation.
The contributions of this thesis research to science are summarized and synthesized in Chapter 6. First, the research identified that fire hazard studies would benefit from adding hydrology, which is reflected in the improved model performance when hydrological variables are integrated. Next, the research revealed that humans play a substantial role in modifying groundwater drought characteristics, hence amplifying the fire hazard in Southeast Asia. Further, the chapter identified several relevant research findings, including the model choice, which should consider the simplicity and the applicability of the model. Another finding demonstrated that controlling canal water level through canal blocking is a practical water management tool to restore degraded wetland. This restored wetland would benefit some endemic species. However, the restored wetland still faces high drought severity. Hence they remain more fire-prone until the un-impacted hydrology condition is achieved. Finally, this research suggest that currently widely-used drought indices (such as FWI) require improvements in their model structure, which means integration of hydrological variables to increase their applicability for fire hazard studies in the humid tropics.
Eindrapportage Veerkracht van Melkvee I : verandering van dynamiek, voorspellende kracht
Dixhoorn, Ingrid van; Mol, Rudi de; Werf, Joop van der; Reenen, Kees van - \ 2016
Wageningen : Wageningen UR Livestock Research (Livestock Research rapport 956) - 94
melkkoeien - melkvee - gustperiode - lactatie - rundveeziekten - diergezondheid - diergedrag - dierfysiologie - gegevens verzamelen - voorspelling - rundveeteelt - dairy cows - dairy cattle - dry period - lactation - cattle diseases - animal health - animal behaviour - animal physiology - data collection - prediction - cattle farming
The transition period is a critical phase in the life of dairy cows. Early identification of cows at risk for disease would allow for early intervention and optimization of the transition period. Based on the theory of resilience of biological systems we hypothesize that the level of vulnerability of an individual cow can be quantified by describing dynamical aspects of continuously measured physiological and behavioural variables. To examine the relationship between the risk to develop diseases early in lactation and dynamic patterns of high-resolution, physiological and behavioural data, were continuously recorded in individual cows before calving. Dynamic, quantitative parameters for high-resolution physiological and behavioural measures, continuously acquired during the dry period have predictive value for the risk of cows to develop diseases during the early lactation period. Our results suggest that quantitative parameters derived from sensor data may reflect the level of resilience of individual cows.
Multi-population genomic prediction
Wientjes, Y.C.J. - \ 2016
Wageningen University. Promotor(en): Roel Veerkamp; Mario Calus. - Wageningen : Wageningen University - ISBN 9789462576193 - 267
cum laude - dairy cattle - genomics - prediction - quantitative trait loci - genetic improvement - breeding value - selective breeding - animal breeding - animal genetics - melkvee - genomica - voorspelling - loci voor kwantitatief kenmerk - genetische verbetering - fokwaarde - selectief fokken - dierveredeling - diergenetica
Cum laude graduation
Data from: Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals
Korpela, K. ; Flint, H.J. ; Johnstone, A.M. ; Lappi, J. ; Poutanen, K. ; Dewulf, E. ; Delzenne, N. ; Vos, Willem de; Salonen, A. - \ 2015
University of Helsinki
CRP - Eubacterium ruminantium - 16S rRNA - obesity - dietary intervention - Clostridium sphenoides - insulin - intestinal microbiota - prediction - bacteria - cholesterol - Clostridium felsineum
Background: Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology: Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings: Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC = 0.77–1; predicted vs. observed correlation = 0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion: This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of microbiota signatures for personalized nutrition.
Using selection index theory to estimate consistency of multi-locus linkage disequilibrium across populations
Wientjes, Y.C.J. ; Veerkamp, R.F. ; Calus, M.P.L. - \ 2015
BMC Genetics 16 (2015). - ISSN 1471-2156
genomic breeding values - genetic-relationship information - quantitative trait loci - dairy-cattle breeds - prediction - accuracy - haplotype - markers - impact - lines
The potential of combining multiple populations in genomic prediction is depending on the consistency of linkage disequilibrium (LD) between SNPs and QTL across populations. We investigated consistency of multi-locus LD across populations using selection index theory and investigated the relationship between consistency of multi-locus LD and accuracy of genomic prediction across different simulated scenarios. In the selection index, QTL genotypes were considered as breeding goal traits and SNP genotypes as index traits, based on LD among SNPs and between SNPs and QTL. The consistency of multi-locus LD across populations was computed as the accuracy of predicting QTL genotypes in selection candidates using a selection index derived in the reference population. Different scenarios of within and across population genomic prediction were evaluated, using all SNPs or only the four neighboring SNPs of a simulated QTL. Phenotypes were simulated using different numbers of QTL underlying the trait. The relationship between the calculated consistency of multi-locus LD and accuracy of genomic prediction using a GBLUP type of model was investigated.
The accuracy of predicting QTL genotypes, i.e. the measure describing consistency of multi-locus LD, was much lower for across population scenarios compared to within population scenarios, and was lower when QTL had a low MAF compared to QTL randomly selected from the SNPs. Consistency of multi-locus LD was highly correlated with the realized accuracy of genomic prediction across different scenarios and the correlation was higher when QTL were weighted according to their effects in the selection index instead of weighting QTL equally. By only considering neighboring SNPs of QTL, accuracy of predicting QTL genotypes within population decreased, but it substantially increased the accuracy across populations.
Consistency of multi-locus LD across populations is a characteristic of the properties of the QTL in the investigated populations and can provide more insight in underlying reasons for a low empirical accuracy of across population genomic prediction. By focusing in genomic prediction models only on neighboring SNPs of QTL, multi-locus LD is more consistent across populations since only short-range LD is considered, and accuracy of predicting QTL genotypes of individuals from another population is increased.
Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications
Gevaert, C. ; Suomalainen, J.M. ; Tang, J. ; Kooistra, L. - \ 2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2015)6. - ISSN 1939-1404 - p. 3140 - 3146.
leaf chlorophyll concentration - remote-sensing data - vegetation indexes - data fusion - reflectance - variability - prediction - landsat
Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral–temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.
In vitro selenium accessibility in pet foods is affected by diet composition and type
Zelst, M. van; Hesta, M. ; Alexander, L.G. ; Gray, K. ; Bosch, G. ; Hendriks, W.H. ; Laing, G. Du; Meulenaer, B. de; Goethals, K. ; Janssens, G. - \ 2015
The British journal of nutrition 113 (2015)12. - ISSN 0007-1145 - p. 1888 - 1894.
nutrient digestion - organic selenium - bioavailability - absorption - dog - bioaccessibility - selenomethionine - metabolism - prediction - fiber
Se bioavailability in commercial pet foods has been shown to be highly variable. The aim of the present study was to identify dietary factors associated with in vitro accessibility of Se (Se Aiv) in pet foods. Se Aiv is defined as the percentage of Se from the diet that is potentially available for absorption after in vitro digestion. Sixty-two diets (dog, n 52; cat, n 10) were in vitro enzymatically digested: fifty-four of them were commercially available (kibble, n 20; pellet, n 8; canned, n 17; raw meat, n 6; steamed meat, n 3) and eight were unprocessed (kibble, n 4; canned, n 4) from the same batch as the corresponding processed diets. The present investigation examined if Se Aiv was affected by diet type, dietary protein, methionine, cysteine, lysine and Se content, DM, organic matter and crude protein (CP) digestibility. Se Aiv differed significantly among diet types (P<0·001). Canned and steamed meat diets had a lower Se Aiv than pelleted and raw meat diets. Se Aiv correlated positively with CP digestibility in extruded diets (kibbles, n 19; r 0·540, P =0·017) and negatively in canned diets (n 16; r - 0·611, P =0·012). Moreover, the canning process (n 4) decreased Se Aiv (P =0·001), whereas extrusion (n 4) revealed no effect on Se Aiv (P =0·297). These differences in Se Aiv between diet types warrant quantification of diet type effects on in vivo Se bioavailability.
The effect of rare alleles on estimated genomic relationships from whole genome sequence data
Eynard, S.E. ; Windig, J.J. ; Leroy, G. ; Binsbergen, R. van; Calus, M.P.L. - \ 2015
BMC Genetics 16 (2015). - ISSN 1471-2156
information - pedigree - conservation - populations - prediction - accuracy - cattle - coefficients - improvement - challenges
Relationships between individuals and inbreeding coefficients are commonly used for breeding decisions, but may be affected by the type of data used for their estimation. The proportion of variants with low Minor Allele Frequency (MAF) is larger in whole genome sequence (WGS) data compared to Single Nucleotide Polymorphism (SNP) chips. Therefore, WGS data provide true relationships between individuals and may influence breeding decisions and prioritisation for conservation of genetic diversity in livestock. This study identifies differences between relationships and inbreeding coefficients estimated using pedigree, SNP or WGS data for 118 Holstein bulls from the 1000 Bull genomes project. To determine the impact of rare alleles on the estimates we compared three scenarios of MAF restrictions: variants with a MAF higher than 5%, variants with a MAF higher than 1% and variants with a MAF between 1% and 5%. Results We observed significant differences between estimated relationships and, although less significantly, inbreeding coefficients from pedigree, SNP or WGS data, and between MAF restriction scenarios. Computed correlations between pedigree and genomic relationships, within groups with similar relationships, ranged from negative to moderate for both estimated relationships and inbreeding coefficients, but were high between estimates from SNP and WGS (0.49 to 0.99). Estimated relationships from genomic information exhibited higher variation than from pedigree. Inbreeding coefficients analysis showed that more complete pedigree records lead to higher correlation between inbreeding coefficients from pedigree and genomic data. Finally, estimates and correlations between additive genetic (A) and genomic (G) relationship matrices were lower, and variances of the relationships were larger when accounting for allele frequencies than without accounting for allele frequencies. Conclusions Using pedigree data or genomic information, and including or excluding variants with a MAF below 5% showed significant differences in relationship and inbreeding coefficient estimates. Estimated relationships and inbreeding coefficients are the basis for selection decisions. Therefore, it can be expected that using WGS instead of SNP can affect selection decision. Inclusion of rare variants will give access to the variation they carry, which is of interest for conservation of genetic diversity.
Design of reference populations for genomic selection in crossbreeding programs
Grevenhof, E.M. van; Werf, J.H.J. van der - \ 2015
Genetics, Selection, Evolution 47 (2015). - ISSN 0999-193X - 9 p.
relationship matrix - genetic evaluation - information - accuracy - performance - prediction - livestock
Background In crossbreeding programs, genomic selection offers the opportunity to make efficient use of information on crossbred (CB) individuals in the selection of purebred (PB) candidates. In such programs, reference populations often contain genotyped PB animals, although the breeding objective is usually more focused on CB performance. The question is what would be the benefit of including a larger proportion of CB individuals in the reference population. MethodsIn a deterministic simulation study, we evaluated the benefit of including various proportions of CB animals in a reference population for genomic selection of PB animals in a crossbreeding program. We used a pig breeding scheme with selection for a moderately heritable trait and a size of 6000 for the reference population. ResultsApplying genomic selection to improve the performance of CB individuals, with a genetic correlation between PB and CB performance (rPC) of 0.7, selection accuracy of PB candidates increased from 0.49 to 0.52 if the reference population consisted of PB individuals, it increased to 0.55 if the reference population consisted of the same number of CB individuals, and to 0.60 if the size of the CB reference population was twice that of the reference population for each PB line. The advantage of using CB rather than PB individuals increased linearly with the proportion of CB individuals in the reference population. This advantage disappeared quickly if rPC was higher or if the breeding objective put some emphasis on PB performance. The benefit of adding CB individuals to an existing PB reference population was limited for high rPC. ConclusionsUsing CB rather than PB individuals in a reference population for genomic selection can provide substantial advantages, but only when correlations between PB and CB performances are not high and PB performance is not part of the breeding objective.
Identification of Spodoptera exigua nucleopolyhedrovirus genes involved in pathogenicity and virulence
Serrano, A. ; Pijlman, G.P. ; Vlak, J.M. ; Muñoz, D. ; Williams, T. ; Caballero, P. - \ 2015
Journal of Invertebrate Pathology 126 (2015). - ISSN 0022-2011 - p. 43 - 50.
in-vitro - multiple nucleopolyhedrovirus - myristoylated proteins - baculovirus - sequence - prediction - deletion - finger - genome - vivo
Genome sequence analysis of seven different Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV) isolates that differed in insecticidal phenotype permitted the identification of genes likely to be involved in pathogenicity of occlusion bodies (OBs) and speed of kill (virulence) of this virus: se4 (hoar), se5 (unknown function), se28 (unknown function), se76 (cg30), se87 (p26) and se129 (p26). To study the role of these genes experimentally on the insecticidal phenotype, a bacmid-based recombination system was constructed to delete selected genes from a SeMNPV isolate, VT-SeAL1, designated as SeBacAL1. All of the knockout viruses were viable and the repair viruses behaved like the wild-type control, vSeBacAL1. Deletion of se4, se5, se76 and se129 resulted in decreased OB pathogenicity compared to vSeBacAL1 OBs. In contrast, deletion of se87 did not significantly affect OB pathogenicity, whereas deletion of se28 resulted in significantly increased OB pathogenicity. Deletion of se4, se28, se76, se87 and se129 did not affect speed of kill compared to the bacmid vSeBacAL1, whereas speed of kill was significantly extended following deletion of se5 and in the wild-type isolate (SeAL1), compared to that of the bacmid. Therefore, biological assays confirmed that several genes had effects on virus insecticidal phenotype. Se5 is an attractive candidate gene for further studies, as it affects both biological parameters of this important biocontrol virus.
Marker-Based Estimation of Heritability in Immortal Populations
Kruijer, W.T. ; Boer, M.P. ; Malosetti, M. ; Flood, P.J. ; Engel, B. ; Kooke, R. ; Keurentjes, J.J.B. ; Eeuwijk, F.A. van - \ 2015
Genetics 199 (2015)2. - ISSN 0016-6731 - p. 379 - 398.
genome-wide association - multi-environment trials - quantitative trait loci - plant-breeding trials - linear mixed models - arabidopsis-thaliana - missing heritability - complex traits - selection - prediction
Heritability is a central parameter in quantitative genetics, both from an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within and between genotype variability. This approach estimates broad-sense heritability, and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here, is to use mixed models at individual plant or plot level. Using statistical arguments, simulations and real data we investigate the feasibility of both approaches, and how these affect genomic prediction with G-BLUP and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For GWAS on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.