Background:Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions,fermentation gases and heat.Heat production may differ among dairy cows despite c...Background:Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions,fermentation gases and heat.Heat production may differ among dairy cows despite comparable milk yield and body weight.Therefore,heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers.The latter is an accurate but time-consuming technique.In contrast,milk Fourier transform mid-infrared(FTIR)spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows.Thus,this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers,milk FTIR spectra and milk yield measurements from dairy cows.Methods:Heat production was computed based on the animal’s consumed oxygen,and produced carbon dioxide and methane in respiration chambers.Heat production data included 16824-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows.Animals were milked twice daily at 07:00 and 16:30 h in the respiration chambers.Milk yield was determined to predict heat production using a linear regression.Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000.The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum.A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares(PLS)regression.Results:Measured heat production of studied animals ranged between 712 and 1470 kJ/kg BW0.75.The coefficient of determination for the linear regression between milk yield and heat production was 0.46,whereas it was 0.23 for the FTIR spectra-based PLS model.The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57%and a root mean square error of prediction of 86.5 kJ/kg BW0.75.The ratio of performance to deviation(RPD)was 1.56.Conclusion:The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.展开更多
Background.Computed X-ray tomography(CTX)is a high-end nondestructive approach for the visual assessment of root architecture in soil.Nevertheless,in order to evaluate high-resolution CTX data of root architectures,ma...Background.Computed X-ray tomography(CTX)is a high-end nondestructive approach for the visual assessment of root architecture in soil.Nevertheless,in order to evaluate high-resolution CTX data of root architectures,manual segmentation of the depicted root systems from large-scale volume data is currently necessary,which is both time consuming and error prone.The duration of such a segmentation is of importance,especially for time-resolved growth analysis,where several instances of a plant need to be segmented and evaluated.Specifically,in our application,the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning.Additionally,the root system itself is expanding in length and in the diameter of individual roots.Objective.For semiautomated and robust root system segmentation from CTX data,we propose the RootForce approach,which is an extension of Frangi’s“multi-scale vesselness”method and integrates a 3D local variance.It allows a precise delineation of roots with diameters down to severalμm in pots with varying diameters.Additionally,RootForce is not limited to the segmentation of small below-ground organs,but is also able to handle storage roots with a diameter larger than 40 voxels.Results.Using CTX volume data of full-grown bean plants as well as time-resolved(3D+time)growth studies of cassava plants,RootForce produces similar(and much faster)results compared to manual segmentation of the regarded root architectures.Furthermore,RootForce enables the user to obtain traits not possible to be calculated before,such as total root volume(Vroot),total root length(Lroot),root volume over depth,root growth angles(θmin,θmean,andθmax),root surrounding soil density Dsoil,or form fraction F.Discussion.The proposed RootForce tool can provide a higher efficiency for the semiautomatic high-throughput assessment of the root architectures of different types of plants from large-scale CTX.Furthermore,for all datasets within a growth experiment,only a single set of parameters is needed.Thus,the proposed tool can be used for a wide range of growth experiments in the field of plant phenotyping.展开更多
基金One part of Experiment 1(Supplementary Table 1)was executed within JPI FACCE program and another part in the optiKuh project,both financially supported by the German Federal Ministry of Food and Agriculture(BMBL)through the Federal Office for Agriculture and Food(BLE),grant number 2814ERA04A and 2817201313,respectivelyExperiment 2 was performed within ERA-GAS program and financially supported by the BMBL through the BLE,grant number 2817ERA09C+2 种基金Experiment 3 was financially supported by the BMBL through the Landwirtschaftliche Rentenbank(LR),grant number 28RZ3P077Experiment 4 received funding from the core budget of the FBNThe authors acknowledge financial support for publication fom the Open Access Fond of the FBN and declare that the aforementioned funding parties had no role in the design of the study or in data collection,analysis,interpretation and writing of the manuscript.
文摘Background:Transformation of feed energy ingested by ruminants into milk is accompanied by energy losses via fecal and urine excretions,fermentation gases and heat.Heat production may differ among dairy cows despite comparable milk yield and body weight.Therefore,heat production can be considered an indicator of metabolic efficiency and directly measured in respiration chambers.The latter is an accurate but time-consuming technique.In contrast,milk Fourier transform mid-infrared(FTIR)spectroscopy is an inexpensive high-throughput method and used to estimate different physiological traits in cows.Thus,this study aimed to develop a heat production prediction model using heat production measurements in respiration chambers,milk FTIR spectra and milk yield measurements from dairy cows.Methods:Heat production was computed based on the animal’s consumed oxygen,and produced carbon dioxide and methane in respiration chambers.Heat production data included 16824-h-observations from 64 German Holstein and 20 dual-purpose Simmental cows.Animals were milked twice daily at 07:00 and 16:30 h in the respiration chambers.Milk yield was determined to predict heat production using a linear regression.Milk samples were collected from each milking and FTIR spectra were obtained with MilkoScan FT 6000.The average or milk yield-weighted average of the absorption spectra from the morning and afternoon milking were calculated to obtain a computed spectrum.A total of 288 wavenumbers per spectrum and the corresponding milk yield were used to develop the heat production model using partial least squares(PLS)regression.Results:Measured heat production of studied animals ranged between 712 and 1470 kJ/kg BW0.75.The coefficient of determination for the linear regression between milk yield and heat production was 0.46,whereas it was 0.23 for the FTIR spectra-based PLS model.The PLS prediction model using weighted average spectra and milk yield resulted in a cross-validation variance of 57%and a root mean square error of prediction of 86.5 kJ/kg BW0.75.The ratio of performance to deviation(RPD)was 1.56.Conclusion:The PLS model using weighted average FTIR spectra and milk yield has higher potential to predict heat production of dairy cows than models applying FTIR spectra or milk yield only.
基金the German Federal Ministry for Educa-tion and Research(BMBF)within the German-Plant-Phenotyping Network(project identification number 031A053)the Fachagentur Nachwachsende Rohstoffe(FNR)within the research project“Verbundvorhaben:Entwicklung von Bio-und Molekularmarkern zur gezielten Züchtung hitzetoleranter Kartoffelsorten”(project identifica-tion number 22010812)the Bill and Melinda Gates Foundation(BMGF)as a subgrant from the Friedrich-Alexander-Universität Erlangen-Nürnberg(FAU)within the project“cassava source sink relations”(CASS).
文摘Background.Computed X-ray tomography(CTX)is a high-end nondestructive approach for the visual assessment of root architecture in soil.Nevertheless,in order to evaluate high-resolution CTX data of root architectures,manual segmentation of the depicted root systems from large-scale volume data is currently necessary,which is both time consuming and error prone.The duration of such a segmentation is of importance,especially for time-resolved growth analysis,where several instances of a plant need to be segmented and evaluated.Specifically,in our application,the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning.Additionally,the root system itself is expanding in length and in the diameter of individual roots.Objective.For semiautomated and robust root system segmentation from CTX data,we propose the RootForce approach,which is an extension of Frangi’s“multi-scale vesselness”method and integrates a 3D local variance.It allows a precise delineation of roots with diameters down to severalμm in pots with varying diameters.Additionally,RootForce is not limited to the segmentation of small below-ground organs,but is also able to handle storage roots with a diameter larger than 40 voxels.Results.Using CTX volume data of full-grown bean plants as well as time-resolved(3D+time)growth studies of cassava plants,RootForce produces similar(and much faster)results compared to manual segmentation of the regarded root architectures.Furthermore,RootForce enables the user to obtain traits not possible to be calculated before,such as total root volume(Vroot),total root length(Lroot),root volume over depth,root growth angles(θmin,θmean,andθmax),root surrounding soil density Dsoil,or form fraction F.Discussion.The proposed RootForce tool can provide a higher efficiency for the semiautomatic high-throughput assessment of the root architectures of different types of plants from large-scale CTX.Furthermore,for all datasets within a growth experiment,only a single set of parameters is needed.Thus,the proposed tool can be used for a wide range of growth experiments in the field of plant phenotyping.
