Sébastien Franceschini

Summary: The dairy sector is operating in an environment of rapid technological change, driven by advances in big data and artificial intelligence (AI). To remain competitive and sustainable, farms should adopt intelligent, scalable, and privacy-preserving solutions. This paper presents a comprehensive ecosystem that combines multimodal AI, edge computing, and federated learning to enable real-time, data-driven decision making on farms. With emphasis on privacy, local relevance, and research integration, the framework would enable autonomous AI agents to support everyday farm decisions and long-term planning. The graphical abstract depicts a schematic representation of the proposed framework for a future dairy data ecosystem, illustrating interactions among commercial and research farms, edge devices, and the cloud intelligence layer (enterprise and research and development [R&D] clouds). It shows data and model flows, with solid arrows pointing away from farms and dashed arrows pointing toward farms. E = edge layer; FL = federated learning; RAW = raw data.

Summary: This study compared the genetic architectures of 2 mid-infrared (MIR)-predicted proxies of negative energy balance (NEB) in early-lactation Holstein cows: the logit-transformed predicted NEB (LPNEB) and the logittransformed energy deficiency score (LEDS). Using single-step genome-wide association study (ssGWAS) and functional enrichment analysis, the results revealed both overlapping and distinct genomic regions, biological pathways, and candidate genes for the 2 traits. These findings improve our understanding of the genetic background of LPNEB and LEDS, thereby providing new insights into the mechanisms underlying energy balance in dairy cattle. QTL = quantitative trait loci; SCS = somatic cell score.

This article focuses on the creation of a monitoring tool using routinely collected data from milk payment analyses. Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. In this research, these profiles were summarized using 31 fatty acids or groups of fatty acids. The methodology consists of four steps: hierarchical clustering to detect patterns in a Belgian spectral dataset (N = 774,781), interpretation of the identified seven clusters, development of predictive models applied to a Canadian dataset (N = 670,165), and validation using management information collected from Canadian farms. The identified clusters revealed significant relationships with feeding management strategies and temporal evolutions, highlighting the potential to develop automated alert systems that assist farmers and advisors in herd monitoring.

The negative energy balance (NEB) state in dairy cows is a critical factor affecting health, reproduction, and production, particularly during early lactation. Multiple blood and milk biomarkers change when dairy cows are in the NEB state. Direct measurement of NEB is impractical for large-scale use due to costs, necessitating reliance on indirect predictors such as milk mid-infrared (MIR) spectrometry-based predicted biomarkers. However, the genetic relationships between NEB and its potential biomarkers remain unclear. This study aimed to (1) compare measured reference NEB with MIR-predicted NEB (PNEB), a novel energy deficit score (EDS), 15 biomarkers, and 3 production traits; (2) estimate genetic parameters among these traits using a 20-trait repeatability model, quantifying the ability of the 19 other studied traits (logit-transformed EDS (LEDS), 15 biomarkers, and 3 production traits) to genetically predict logit-transformed PNEB (LPNEB); and (3) evaluate the causal effects of LPNEB on the 19 traits through a recursive model. Two datasets were used: dataset I (127 cows, 965 records) provided reference data for objective (1), and dataset II (25,287 first-parity cows, 30,634 records) enabled genetic analysis used for objectives (2) and (3). Traits were analyzed using Pearson correlations, multiple-diagonalization EM-REML-based genetic parameter estimation, and recursive modeling. The studied traits had moderate to moderate-high h² ranging from 0.16 to 0.38. The genetic correlations between LPNEB and the studied traits ranged from -0.60 to 0.85 for LEDS and 0.87 for MIR-predicted blood nonesterified fatty acids (+). Analysis of genetic predictability of LPNEB revealed that the 19 other traits together explained 89% of the genetic variance of LPNEB, with all 15 biomarkers alone contributing the largest fraction with 82%, LEDS alone 65%, nonesterified fatty acids (NEFA) alone 62%, and all traits except LEDS 85%, indicating that LEDS contains useful additional information. Recursive modeling further identified 8 traits, including NEFA and LEDS, as highly dependent on LPNEB, highlighting their potential as robust biomarkers. This study demonstrates the utility of MIR-predicted traits for understanding the genetic mechanisms of NEB and its potential for integration into breeding programs, while emphasizing cautious interpretation of these results due to limitations of MIR-predictions of studied traits to represent directly measured traits.

The issue of consumer acceptance is largely investigated in the literature relative to entomophagy, as edible insects are not a common part of European food habits. This topic was also discussed in the current study, which analysed 1,382 responses to an online survey in Belgium. The first aim of this research was to study the potential influence of personal factors, including field of work, on consumer acceptance. The second objective was to determine the most appropriate context for entomophagy, regarding timing and style of preparation. Gender, nationality and field of work of respondents were identified as influencing factors, while age was identified as a factor that did not impact consumer acceptance. Personal factors that influenced acceptance must be used as discriminating factors to select willing consumers of edible insects. Regarding context, edible insects were not appropriate at breakfast, while they were considered as snacks. Such considerations provide useful information in determining the most appropriate insect-based food for European markets.

The advantage of employing mid-infrared spectrometry for milk analysis in breeding lies in its ability to quickly generate millions of records. However, these records may be biased if the calibration process does not account for their spectral variability when constructing the predictive model. So, this study introduces a novel method for developing a World Representative Spectral Database (WRSD) to reduce the risks of spectral extrapolation when predicting dairy traits in new samples. Utilizing a 2-phase selection procedure that is both efficient and minimizes memory usage, we first generate a decomposition matrix via Principal Component Analysis (PCA) on a data set of 2,324,443 records. The next phase iterates spectral selection based on a location index from PCA scores, calculating spectra occurrence frequency for refined barycenter estimations. The chosen spectra's barycenter closely aligns with the entire data set, proving the efficacy of using just 3 principal components (PCs). Applied to 4 varied data sets, totaling over 21 million records, we select 583,440 spectra to represent spectral diversity, with selection rates between 2.00% and 14.88%. This selection illustrates the spectral variability across different dairy populations and data providers. Demonstrated through a hypothetical calibration set of 71 samples, the WRSD's utility for algorithm developers becomes apparent. This calibration set covers between 91.42 to 98.50% of the WRSD variability, except for the Irish data set (3.50%), indicating a need for additional samples to accurately represent Irish variability and minimize spectral extrapolation. This study offers valuable insights into the representativeness of training sets for capturing spectral variability within targeted dairy populations. While the current WRSD does not fully encompass global milk spectral diversity, its development underscores the importance of gathering more data and standardizing spectral information across spectrometer brands. Ultimately, the WRSD proves crucial not just for trait prediction but also for identifying abnormal milk samples, also marking a significant relevance in dairy science technology.

The extensive identification of mowing events on a territory holds significant potential to help monitor shifts in biodiversity and contribute to assessing the impacts of drought events. Additionally, it provides valuable insights into farming practices and their consequential economic and ecological effects. To overcome challenges in obtaining reference grazing information directly from the field, this study introduces a novel methodology leveraging the compressed sward height (CSH) derived from Sentinel-1, Sentinel-2, and meteorological data, boasting an accuracy of 20 mm. Our central hypothesis posits that the mowing status of a parcel can be automatically discerned by analyzing the distribution and variation of its CSH values. Employing a two-step strategy, we first applied unsupervised algorithms, specifically k-means and isolation forest, and subsequently amalgamated the outcomes with a partial least squares analysis on an extensive dataset encompassing 194,657 pastures spanning the years 2018 to 2021. The culmination of our modeling efforts yielded a validation accuracy of 0.66, as ascertained from a focused dataset of 68 pastures. Depending on the studied year and with a threshold fixed at 0.50, 21% to 57% of all the parcels in the Wallonia dataset were tagged as mown by our model. This study introduces an innovative approach for the automated detection of mown parcels, showcasing its potential to monitor agricultural activities at scale.