One-shot technology for three-dimensional imaging of large animals: perspectives for ruminant management.

In numerous systems of animal production, there is increasing interest in the use of three-dimensional (3D)-imaging technology on farms for its ability to easily and safely measure traits of interest in living animals. With this information, it is possible to evaluate multiple morphological indicators of interest, either directly or indirectly, and follow them through time. Several tools for this purpose were developed, but one of their main weaknesses was their sensitivity to light and animal movement, which limited their potential for large-scale application on farms. To address this, a new device, called Deffilait3D and based on depth camera technology, was developed. In tests on 31 Holstein dairy cows and 13 Holstein heifers, the values generated for most measured indicators were highly repeatable and reproducible, with coefficients of variation lower than 4%. A comparison of measurements obtained from both Deffilait3D and the previous validated system, called Morpho3D, revealed a high degree of similarity for most selected traits, e.g., less than 0.2% variation for animal volume and 1.2% for chest depth, with the highest degree of difference (8%) noted for animal surface area. Previously published equations used to estimate body weight with the Morpho3D device were equally valid using Deffilait3D. This new device was able to record 3D images regardless of the movement of animals and it is affected only by direct daylight. The ongoing step is now to develop methods for automated analysis and extraction from images, which should enable the rapid development of new tools and potentially lead to the large-scale adoption of this type of device on commercial farms.

Three-Dimensional (3D) Imaging Technology to Monitor Growth and Development of Holstein Heifers and Estimate Body Weight, a Preliminary Study.

The choice of rearing strategy for dairy cows can have an effect on production yield, at least during the first lactation. For this reason, it is important to closely monitor the growth and development of young heifers. Unfortunately, current methods for evaluation can be costly, time-consuming, and dangerous because of the need to physically manipulate animals, and as a result, this type of monitoring is seldom performed on farms. One potential solution may be the use of tools based on three-dimensional (3D) imaging, which has been studied in adult cows but not yet in growing individuals. In this study, an imaging approach that was previously validated for adult cows was tested on a pilot population of five randomly selected growing Holstein heifers, from 5 weeks of age to the end of the first gestation. Once a month, all heifers were weighed and an individual 3D image was recorded. From these images, we estimated growth trends in morphological traits such as heart girth or withers height (188.1 ± 3.7 cm and 133.5 ± 6.0 cm on average at one year of age, respectively). From other traits, such as body surface area and volume (5.21 ± 0.32 m2 and 0.43 ± 0.05 m3 on average at one year of age, respectively), we estimated body weight based on volume (402.4 ± 37.5 kg at one year of age). Body weight estimates from images were on average 9.7% higher than values recorded by the weighing scale (366.8 ± 47.2 kg), but this difference varied with age (19.1% and 1.8% at 6 and 20 months of age, respectively). To increase accuracy, the predictive model developed for adult cows was adapted and completed with complementary data on young heifers. Using imaging data, it was also possible to analyze changes in the surface-to-volume ratio that occurred as body weight and age increased. In sum, 3D imaging technology is an easy-to-use tool for following the growth and management of heifers and should become increasingly accurate as more data are collected on this population.

A research agenda for scaling up agroecology in European countries.

A profound transformation of agricultural production methods has become unavoidable due to the increase in the world's population, and environmental and climatic challenges. Agroecology is now recognized as a challenging model for agricultural systems, promoting their diversification and adaptation to environmental and socio-economic contexts, with consequences for the entire agri-food system and the development of rural and urban areas. Through a prospective exercise performed at a large interdisciplinary institute, INRAE, a research agenda for agroecology was built that filled a gap through its ambition and interdisciplinarity. It concerned six topics. For genetics, there is a need to study genetic aspects of complex systems (e.g., mixtures of genotypes) and to develop breeding methods for them. For landscapes, challenges lie in effects of heterogeneity at multiple scales, in multifunctionality and in the design of agroecological landscapes. Agricultural equipment and digital technologies show high potential for monitoring dynamics of agroecosystems. For modeling, challenges include approaches to complexity, consideration of spatial and temporal dimensions and representation of the cascade from cropping practices to ecosystem services. The agroecological transition of farms calls for modeling and observational approaches as well as for creating new design methods. Integration of agroecology into food systems raises the issues of product specificity, consumer behavior and organization of markets, standards and public policies. In addition, transversal priorities were identified: (i) generating sets of biological data, through research and participatory mechanisms, that are appropriate for designing agroecological systems and (ii) collecting and using coherent sets of data to enable assessment of vulnerability, resilience and risk in order to evaluate the performance of agroecological systems and to contribute to scaling up. The main lessons learned from this collective exercise can be useful for the entire scientific community engaged in research into agroecology.

Invited review: Disentangling residual feed intake-Insights and approaches to make it more fit for purpose in the modern context.

Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.

Estimation of dairy goat body composition: A direct calibration and comparison of eight methods.

The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2 = 0.95, residual standard deviation, rSD = 0.57 kg), ii) volume of fatty tissues measured by CT (R2 = 0.92, rSD = 0.76 kg), iii) D2OS (R2 = 0.91, rSD = 0.85 kg), and iv) resistance at infinite frequency from BIS (R2 = 0.87, rSD = 1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2 = 0.97, rSD = 0.17 kg), whereas BW alone provided a fair estimate (R2 = 0.92, rSD = 0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2 = 0.80 and 0.95, rSD = 1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2 = 0.74, rSD = 1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2 ≤ 0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness.

Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows.

Mitigation of greenhouse gas emissions is relevant for reducing the environmental impact of ruminant production. In this study, the rumen microbiome from Holstein cows was characterized through a combination of 16S rRNA gene and shotgun metagenomic sequencing. Methane production (CH4 ) and dry matter intake (DMI) were individually measured over 4-6 weeks to calculate the CH4 yield (CH4 y = CH4 /DMI) per cow. We implemented a combination of clustering, multivariate and mixed model analyses to identify a set of operational taxonomic unit (OTU) jointly associated with CH4 y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH4 y. The taxonomic composition on R2 had lower abundance of Succinivibrionaceae and Methanosphaera, and higher abundance of Ruminococcaceae, Christensenellaceae and Lachnospiraceae. Metagenomic data confirmed the lower abundance of Succinivibrionaceae and Methanosphaera in R2 and identified genera (Fibrobacter and unclassified Bacteroidales) not highlighted by metataxonomic analysis. In addition, the functional metagenomic analysis revealed that samples classified in cluster R2 were overrepresented by genes coding for KEGG modules associated with methanogenesis, including a significant relative abundance of the methyl-coenzyme M reductase enzyme. Based on the cluster assignment, we applied a sparse partial least-squares discriminant analysis at the taxonomic and functional levels. In addition, we implemented a sPLS regression model using the phenotypic variation of CH4 y. By combining these two approaches, we identified 86 discriminant bacterial OTUs, notably including families linked to CH4 emission such as Succinivibrionaceae, Ruminococcaceae, Christensenellaceae, Lachnospiraceae and Rikenellaceae. These selected OTUs explained 24% of the CH4 y phenotypic variance, whereas the host genome contribution was ~14%. In summary, we identified rumen microbial biomarkers associated with the methane production of dairy cows; these biomarkers could be used for targeted methane-reduction selection programmes in the dairy cattle industry provided they are heritable.

Early lactation performance in Holstein heifers first calving at 36 months and managed for high or low weight gain during mid- and late gestation.

The effect of weight gain during mid- and late gestation in dairy heifers on performance at the start of first lactation was studied. In this experiment, 47 Holstein heifers with first calving at 36 months of age were used. The plane of nutrition aimed to have a high (900 g/d, H; n = 23) and low (500, L; n = 24) average daily gain (ADG) from the 4th month of gestation until 3 weeks before the expected day of calving, achieved by ad libitum intake of high quality pasture (H) or controlled intake of a total mixed ration (L). Body weight (BW), body condition score (BCS), milking, and reproductive performances were recorded. Concentrations of plasma non-esterified fatty acids (NEFA), glucose, beta-hydroxybutyric acid (BHBA), and urea were characterised at weeks 2, 4, 6 and 8 of lactation. Milk fatty acid composition was determined at weeks 3 and 6. A total of 39 heifers successfully calved and completed first lactation. During feeding treatment the required ADG were achieved. BW and BCS were higher in H heifers at calving compared to L heifers: 707 vs. 640 kg, and 3.91 vs. 3.01 respectively. H heifers lost more weight, BCS and had lower feed intake during the beginning of first lactation (-0.8 kg DM/d/heifer over the first 4 weeks of lactation). Per day of lactation, H heifers produced significantly more milk (29.2 vs. 26.2 kg), fat (1.27 vs. 1.07 kg) and protein (0.84 vs. 0.477 kg) from 0 to 8 weeks of lactation. Concentrations of NEFA, glucose and BHBA were higher in H heifers compared to L heifers, but urea concentration was not affected. Concentration of preformed fatty acids in the milk (C16 and more) was higher. As a result, the calculated daily net energy balance during the first 8 weeks of lactation was -1.53 and -5.95 MJ for L and H heifers, respectively.

The Relationship of Dairy Farm Eco-Efficiency with Intensification and Self-Sufficiency. Evidence from the French Dairy Sector Using Life Cycle Analysis, Data Envelopment Analysis and Partial Least Squares Structural Equation Modelling.

We aimed at quantifying the extent to which agricultural management practices linked to animal production and land use affect environmental outcomes at a larger scale. Two practices closely linked to farm environmental performance at a larger scale are farming intensity, often resulting in greater off-farm environmental impacts (land, non-renewable energy use etc.) associated with the production of imported inputs (e.g. concentrates, fertilizer); and the degree of self-sufficiency, i.e. the farm's capacity to produce goods from its own resources, with higher control over nutrient recycling and thus minimization of losses to the environment, often resulting in greater on-farm impacts (eutrophication, acidification etc.). We explored the relationship of these practices with farm environmental performance for 185 French specialized dairy farms. We used Partial Least Squares Structural Equation Modelling to build, and relate, latent variables of environmental performance, intensification and self-sufficiency. Proxy indicators reflected the latent variables for intensification (milk yield/cow, use of maize silage etc.) and self-sufficiency (home-grown feed/total feed use, on-farm energy/total energy use etc.). Environmental performance was represented by an aggregate 'eco-efficiency' score per farm derived from a Data Envelopment Analysis model fed with LCA and farm output data. The dataset was split into two spatially heterogeneous (bio-physical conditions, production patterns) regions. For both regions, eco-efficiency was significantly negatively related with milk yield/cow and the use of maize silage and imported concentrates. However, these results might not necessarily hold for intensive yet more self-sufficient farms. This requires further investigation with latent variables for intensification and self-sufficiency that do not largely overlap- a modelling challenge that occurred here. We conclude that the environmental 'sustainability' of intensive dairy farming depends on particular farming systems and circumstances, although we note that more self-sufficient farms may be preferable when they may benefit from relatively low land prices and agri-environment schemes aimed at maintaining grasslands.

Effect of a long chain n-3 PUFA-enriched diet on production and reproduction variables in Holstein dairy cows.

The objective of this study was to determine the effect of a rumen-protected fish oil supplement on the production and reproduction variables in postpartum dairy cows. Holstein cows (n=46) were given a basal total mixed diet plus one PUFA supplement: n-3 (n-3; protected fish oil; 1% dry matter intake (DMI); n=23) or control (n-6; toasted soybeans; 1.8% DMI; n=23), in a switchback design over two consecutive lactations. Supplements were added to the diet between calving and 2 months after calving to assess the effect on growth and maturation of ovarian follicles from which ovulation occurred around the day of insemination. Body weight (BW), milk yield (MY) and composition, dry matter intake (DMI), energy balance (EB), subcutaneous fat thickness, plasma fatty acid composition, plasma nonesterified fatty acids (NEFA), glucose and urea concentrations, follicular activity, embryo mortalities and fertility (conception rate after first AI, AI1) were assessed. BW, MY, DMI, plasma NEFA, glucose and urea were unaffected by the diet. There was a trend of an increased number of large follicles (diameter≥10mm) with the n-3 dietary supplementation (P=0.06) and a decrease in infertility or early embryo mortality rate 21 days after AI, 13.5% in the n-3 compared with 38.8% in the n-6 group (P=0.09), with no effect on the conception rate at 35d or 90d after AI1. These data suggest that the effect seen on ovarian variables is not associated with an effect on production and metabolic variables and is specific to n-3 PUFA supplementation. Further studies are necessary to determine whether DHA or EPA enhances fertility in lactating dairy cattle.