Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms.

peer reviewedKnowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points

Selection of the most appropriate predictors to improve the robustness of dairy cows’ bodyweight prediction by using lactation stage, parity, milk yield, and milk mid-infrared spectra.

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Predictions of dairy related phenotypes using milk mid infrared spectral data under different degree of information availability

Through this thesis work, we built equations from milk mid-infrared (MIR) spectra to assess 3 phenotypes estimated at cow level (bodyweight (BW), bodyweight change (BWC), and dry matter intake (DMI)) as well as 2 phenotypes estimated on a herd basis (grazing intensity (GRASS) and gradient of production intensification (GPI)). Predictive models have limitations as they cannot fully explain reference variability. Factors influencing the model's predictive capacities include the choice of algorithm, model calibration, and data quality and variability. In addition, care must be taken to ensure that data is not corrupted during collection, processing, transformation, or transfer. In our case, the reference data was a crucial aspect that required special attention as some of it was missing or suspected of being unrepresentative. This limitation prompted us to develop approaches beyond traditional supervised modeling methods. To address this issue, we categorized the equations into three groups based on their degree of information availability: complete, semi-complete, and information shortage references. Each category implied specific directions in the modeling strategy. We had enough reference records to use algorithms with supervised learning for the complete category in which we had the BW, BWC, and DMI phenotypes. This pipeline allowed obtaining a prediction error of around 50kg for BW and around 3 kg for DMI. The information shortage category included the GRASS phenotype. We had no reference sample for modeling, requiring unsupervised processes as the learning could not be based on optimizing a cost function. Moreover, the algorithm's direction had to be externally validated to ensure consistency with observations made elsewhere, as the model could not rely on a structure of knowledge based on observations. The obtained predictions observed the grazing evolution managed by the farmers. For the semi-complete category, which included the GPI phenotype, we had under-representative reference samples regarding the potential spanning of the population, requiring a combination of supervised and unsupervised techniques to train and validate the model. However, this approach helped overcome the limitation of a lack of reference values and allowed for the development of more robust and accurate predictive models. The GPI phenotype distinguished extensive and intensive farmers with moderate accuracy. In conclusion, this thesis showed the possibility of designing a different model framework following the degree of reference information. The built equations developed in this thesis will also be helpful for farmers to help them in their daily management and breeding decisions

Prediction of dry matter intake of dairy cows across multiple countries and production systems

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Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass expressed as compressed sward height (CSH) from readily accessible meteorological, optical (Sentinel-2) and radar satellite data (Sentinel-1). This study assumed that combining heterogeneous data sources, data transformations and machine learning methods would improve the robustness and the accuracy of the developed models. A total of 72,795 records of CSH with a spatial positioning, collected in 2018 and 2019, were used and aggregated according to a pixel-like pattern. The resulting dataset was split into a training one with 11,625 pixellated records and an independent validation one with 4952 pixellated records. The models were trained with a 19-fold cross-validation. A wide range of performances was observed (with mean root mean square error (RMSE) of cross-validation ranging from 22.84 mm of CSH to infinite-like values), and the four best-performing models were a cubist, a glmnet, a neural network and a random forest. These models had an RMSE of independent validation lower than 20 mm of CSH at the pixel-level. To simulate the behavior of the model in a decision support system, performances at the paddock level were also studied. These were computed according to two scenarios: either the predictions were made at a sub-parcel level and then aggregated, or the data were aggregated at the parcel level and the predictions were made for these aggregated data. The results obtained in this study were more accurate than those found in the literature concerning pasture budgeting and grassland biomass evaluation. The training of the 124 models resulting from the described framework was part of the realization of a decision support system to help farmers in their daily decision making.Roadste

Creation of a Walloon Pasture Monitoring Platform Based on Machine Learning Models and Remote Sensing

The use of remote sensing data and the implementation of machine learning (ML) algorithms is growing in pasture management. In this study, ML models predicting the available compressed sward height (CSH) in Walloon pastures based on Sentinel-1, Sentinel-2, and meteorological data were developed to be integrated into a decision support system (DSS). Given the area covered (>4000 km2 of pastures of 100 m2 pixels), the consequent challenge of computation time and power requirements was overcome by the development of a platform predicting CSH throughout Wallonia. Four grazing seasons were covered in the current study (between April and October from 2018 to 2021, the mean predicted CSH per parcel per date ranged from 48.6 to 67.2 mm, and the coefficient of variation from 0 to 312%, suggesting a strong heterogeneity of variability of CSH between parcels. Further exploration included the number of predictions expected per grazing season and the search for temporal and spatial patterns and consistency. The second challenge tackled is the poor data availability for concurrent acquisition, which was overcome through the inclusion of up to 4-day-old data to fill data gaps up to the present time point. For this gap filling methodology, relevancy decreased as the time window width increased, although data with 4-day time lag values represented less than 4% of the total data. Overall, two models stood out, and further studies should either be based on the random forest model if they need prediction quality or on the cubist model if they need continuity. Further studies should focus on developing the DSS and on the conversion of CSH to actual forage allowance

Harvesting Insights from the Sky: Satellite-Powered Automation for Detecting Mowing Based on Predicted Compressed Sward Heights

peer reviewedThe 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

Harvesting Insights from the Sky: Satellite-Powered Automation for Detecting Mowing Based on Predicted Compressed Sward Heights

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

Le voyage initiatique (activation et devenir des habitudes d'héritiers migrants issus de la grande bourgeoisie turque)

Ce travail a pour enjeu de décrypter, dans un système économique et scolaire globalisé, les modalités de la reconversion des ressources d'une grande bourgeoisie cosmopolite. L'analyse porte sur les mécanismes de transmission et les rites d'initiation spécifiques d'un collectif de familles partageant des référents d'ancienneté et une même volonté de mobiliser les ressources offertes par le champ international, et ce dans un contexte inédit : la Turquie. Le voyage initiatique des héritiers de la grande bourgeoisie turque est un voyage au sens littéral. Il s'articule autour d'un cycle migratoire complexe comportant plusieurs départs et plusieurs retours. Mais le retour définitif étant projeté en même temps que le premier départ, l'équation de leur migration présente une inconnue de moins que celle du reste des migrants. Cette recherche montre que la dimension internationale des rites de passage en milieu bourgeois est désormais l'élément central d'un modèle de transmission déterminant l'accès aux formations d'excellence et aux positions les plus sélectives, tant dans le champ international que dans le milieu d'origine. L'originalité de l'approche proposée réside dans la mise en évidence de combinaisons individualisantes et de situations contradictoires, à partir de l'étude détaillée des cadres socialisateurs et des choix relationnels des héritiers. Les coûts du voyage initiatique se révèlent ainsi très élevés. Le moment de la prise de rôle est déterminé par la gestion des périodes critiques d'intensification de la réflexivité. En particulier, la durée et l'impact des décalages ressentis lors du retour définitif en Turquie viennent contredire l'idée selon laquelle les héritiers ont achevé, une fois validées les épreuves successives de la formation, l'essentiel de leur socialisation. L'issue du processus de transmission est dès lors subordonnée aux logiques de compromis traduisant la renégociation permanente, par l'héritier, de sa position et des rôles qui lui sont assignés.The premise of this work is to explore how a cosmopolitan bourgeoisie converts its social resources in the context of a globalized education and a globalized economy, undertaking a novel case study: Turkey. It analyses the transmission process and the specific rites of initiation in a group of families ail possessing an extensive social capital accumulated over time and sharing a common agenda to mobilize the resources offered by the international field . The initiatory journey of the heirs of the Turkish bourgeoisie is a journey in the literal sense, as it involves a complex migratory cycle with several departures and returns. However, since the first departure and the final return are planned at the same time, their migration presents one less unknown factor when compared to other migration patterns. This research shows that the international dimension of the rites of initiation is the central element of a model of transmission determining access to prestigious educational institutions and to the most selective working positions, both on the international market and in the country of origin. This approach is original in that it focuses on the contradictory situations through a detailed study of socializ ing contexts and relationship choices. The costs of the initiatory journey appear then to be very high. The unexpected readjustment crisis experienced in the first years after final return to Turkey is in direct contradiction to the established idea that the heirs have completed their socialization when the successive challenges of their formation have been conquered. The success of transmission process depends on the ability to compromise, as there is a continuing renegotiation of the heirs' position and roles they perceive as assigned to them.PARIS3-BU (751052102) / SudocSudocFranceF