The use of mid-infrared spectrometry to predict body energy status of Holstein cows

Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations, to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application

Genetic parameters of stearoyl coenzyme-A desaturase 9 activity estimated by test-day model

peer reviewedDairy and beef products account for a large part of fat intake in human nutrition and therefore can be linked to dietary diseases. The stearoyl Coenzyme-A desaturase 9 (delta-9) gene was identified as a potential functional candidate gene affecting milk fat composition in dairy cattle. The objective of this research was to estimate the genetic parameters of delta-9 activity indicator traits and to study the relationship between delta-9 activity as described by these indicator traits and common milk production traits. A total of 126,331 test-day records were obtained from 14,259 Holstein (> 84% Holstein gene) heifers belonging to 105 herds. The studied traits were milk yield, percentages of fat and protein, content of monounsaturated fatty acids, and 3 ratios reflecting the delta-9 activity (C14:1/ C14:0; C16:1/C16:0 and C18:1/C18). The used model was a multiple-trait random regressions test-day model and included as fixed effects: herd x date of test, class of age, and month x year. Random effects were herd x year of calving, permanent environmental, additive genetic, and residual effects. The fatty acid contents were estimated by mid-infrared spectrometry. Delta-9 activity varied within year and lactation. The obtained heritability estimates of delta-9 as well as the genetic and phenotypic correlation varied also through lactation. This study suggests potential improvements of delta-9 activity and subsequently milk fat composition can be achieved by animal management but also by breeding and animal selection

La spectrométrie du moyen-infrarouge dans le lait peut-elle améliorer la production laitière et sa transformation

peer reviewedMany research developments have been done with milk Fourier-transform mid-infrared (FT-MIR) spectrometry during the last 20 years, but only few applications have been implemented in the field. ExtraMIR will try to solve the intrinsic and extrinsic constraints leading to this situation

Using fatty acid contents in milk to improve fertility of dairy cows?

Improving dairy cow fertility by means of genetic selection has become increasingly important over the last years in order to overcome the declining cow fertility. This study investigated whether the fatty acids profile in milk could be used as an early predictor of genetic merit for fertility. Genetic covariances among 17 fatty acid contents in milk and the number of days from calving to conception were estimated from 29,792 first-parity Holstein cows. Results substantiated the unfavorable relationship among fertility and body fat mobilization in early lactation. Also, about 75% of the genetic variability of fertility was explained by the variability in milk fatty acids profile over the lactation indicating that these traits could be used to supplement genetic evaluations for fertility

Deployment of models predicting compressed sward height on Wallonia: quality and validity of the predictions

Currently, there is a high interest to integrate remote sensed data and machine learning algorithms to develop pastures management tools. In this context, over the past two years, we published models predicting the available compressed sward height (CSH) in pasture using Sentinel-1, Sentinel-2, and meteorological data. Those scalable models were developed to be the basis of a decision support system (DSS) available for Walloon farmers. A platform predicting CSH over Wallonia was developed and this presentation aims to provide some insights in its prediction capabilities by detailing the values and their variability at the parcel level for the year 2021. The first prospect was the distribution of the predictions without considering a mean per parcel: 2% of the predicted values were out of the [0:250] mm of CSH range on which model were trained and the median was around 56mm. If we were to consider a mean per parcel, the values fall back in training range and the median is around 60mm and coefficient of variation (cv) of the prediction within each parcel, which indicated the variability, ranged from 0 to 986. These cv values indicated that the predictions might be too variable and that further training data are required to get more stable outputs. However, given that the range of predictions respects the training CSH range, the predicting platform was considered mature enough to be ready for being a data provider for a future DSS, the main interest in this future tool being to provide everyday food availability to the farmer in order to help him manage his feed wedge and therefore improving cattle welfare.ROADSTE

Validation of a workflow based on Sentinel-2, Sentinel-1 and meteorological data predicting biomass on pastures

This study develops the validation of the four best promising models resulting from a workflow processing Sentinel-1, Sentinel-2 and meteorological data through 13 different machine learning algorithms that led to 124 models predicting biomass under the form of compressed sward height on square sub-samples of paddocks (i.e., pixel-based estimation with a resolution of 10 m). The training and validation data were acquired in 2018 and 2019 in the Walloon Region of Belgium with a rising platemeter equipped with a GPS. The cubist, perceptron, random forest and general linear models had a validation root mean square error (RMSE) around 20 mm of CSH. However, the information relevant for the farmer and for integration in a decision support system is the amount of biomass available on the whole pasture. Therefore, those models were also validated at a paddock-scale using data from another farm (117 CSH records acquired with a different rising platemeter) based on input variables expressed at paddock scale or predictions aggregated at paddock scale. The resulting RMSE were higher than before. To improve the quality of prediction, a combination of the outputs of the models might be needed

Deployment of models predicting compressed sward height on Wallonia: results and feedback

peer reviewedThere is currently high interest in integrating data linked to remote sensing and methods from the machine-learning domain to develop tools to support pasture management. In this context, over the past two years, we have published models predicting the available compressed sward height (CSH) in pastures using Sentinel-1, Sentinel-2, and meteorological data. These scalable models could provide the basis of a decision support system (DSS) available for Walloon farmers. A platform performing the CSH prediction was developed and this paper aims to provide some insights in its prediction capabilities and tackle the challenge of using data acquired at different moments in time. Predictions were made from the beginning of January until the end of October 2021 using our most promising published models. After data cleaning, the coefficient of variation of CSH predictions, calculated for each studied date (n=35) and parcel (n=192,862), ranged from 0 to 986. This extreme variation suggests some prediction imperfections. Before the integration of the platform in a DSS, the main task to solve is the issue of missing or non-operational S1 or S2 data. Indeed, even if a gap filling method was applied, only 62% of potentially exploitable dates were usable.ROADSTE

Deployment of models predicting compressed sward height on Wallonia: confrontation to ground truth

peer reviewedCurrently, pasture management is of interest for economical or ecological reasons. The use of remote sensed data and the implementation of machine learning algorithms is growing. So, over the past two years, models predicting the available compressed sward height (CSH) in Walloon pastures using Sentinel-1, Sentinel-2, and meteorological data were published. Those models were developed to be integrated in a decision support system (DSS). A platform predicting CSH over Wallonia was therefore developed. The variability of the predicted CSH within parcels ranged from 0 to 287.7% once the non-finite values and the values out of the training range were discarded. Concerning the CSH values, the five developed models predicted CSH below 75 mm more than 75% of the time. These values were compared with an independent dataset including a total of 122 average measures of CSH were available and concerned 5 different parcels, grazed in 2019. These reference values ranged from 45 to 212.5 mm of CSH with a mean of 83,8 ± 31.2 mm. The estimated root mean square error values estimated between predicted and reference values varied between 20 and 35 mm of CSH. The coefficient of determination ranged from 0.6 to 0.8 depending on the model and the parcel considered. The poorest performances were recorded on parcels that were split in sub-parcels managed differently during the year. So, there is a need for including flexibility in the parcel definition for the future DSS, the visual support and their corresponding analysis

Overview of possibilities and challenges of the use of infrared spectrometry in cattle breeding

peer reviewedNear or mid-infrared (NIR or MIR) spectrometry is a versatile and cost-efficient technology used in cattle production to trace the chemical composition of gases, liquids and solid matters. Recent research showed the potential of MIR spectrometry in milk to predict many different milk components but also status and well-being of the cows, quality of their products, their efficiency and their environmental impact. Under changing socio-economic circumstances, novels traits could help to select for enlarged breeding objectives. But the following challenges need to be overcome: (1) access to and harmonization of MIR data; (2) availability of reference values representing the variability to be described, also highlighting the importance of international collaborations; (3) difficulties to obtain, but also to transfer prediction equations between instruments; (4) modeling of the massive longitudinal data generated; (5) estimation of parameters to assess phenotypic and genetic variability and links with other traits leading to the; (6) assessment of the position of novel traits in breeding objectives. Recent research reported how to address these issues for traits close to routine use including fatty acids and methane. Expected future developments include direct use of MIR data and multivariate modeling of novel traits. Similarly, genomic prediction for novel traits, which are limited by the availability of phenotyped reference populations, will also benefit from the use of correlated, MIR predicted, traits. Currently, MIR instruments can only be used in the frame of milk recording and not on-farm. But recent research showed that NIR is closing the gap thereby allowing advances in precise on-farm phenotyping and giving new opportunities for breeding, but also management. Possibilities for the use of infrared technologies for other trait groups such as meat composition and quality should allow cross-fostering of developments