Untargeted urine metabolomics suggests that ascorbic acid may serve as a promising biomarker for reduced feed intake in rabbits.

Feed restriction is a common nutritional practice in rabbit farming; however, decreased feed intake can also signal potential digestive disorders at an early stage. This study endeavors to investigate the impact of feed restriction on selected productive traits and the urinary metabolome of juvenile rabbits across diverse genetic backgrounds. Our objective is to identify potential biomarkers capable of detecting periods of fasting.

Unravelling the mechanisms underlying the social interaction effects on the feed efficiency of rabbits.

The feed consumption and feeding patterns of group-housed livestock and their productive outputs can be affected by interactions among group members, such as competition for feeder access and aggression. The impact of competition is especially significant when feed restriction is implemented during the fattening stage, a common practice in rabbit farms to reduce mortality from digestive disorders during this period. The overall aim was to quantify the importance of social interaction effects at the genetic and environmental level on feed efficiency, feeding behaviour and social ranking traits in rabbit populations having access to electronic feeders whilst they were housed in groups during fattening.

Causal discovery reveals complex patterns of drought-induced displacement.

The increasing frequency and severity of droughts present a significant risk to vulnerable regions of the globe, potentially leading to substantial human displacement in extreme situations. Drought-induced displacement is a complex and multifaceted issue that can perpetuate cycles of poverty, exacerbate food and water scarcity, and reinforce socio-economic inequalities. However, our understanding of human mobility in drought scenarios is currently limited, inhibiting accurate predictions and effective policy responses.

Integrating computer vision algorithms and RFID system for identification and tracking of group-housed animals: an example with pigs.

Precision livestock farming aims to individually and automatically monitor animal activity to ensure their health, well-being, and productivity. Computer vision has emerged as a promising tool for this purpose. However, accurately tracking individuals using imaging remains challenging, especially in group housing where animals may have similar appearances.

Novel phenotypes of feeding and social behaviour and their relationship with individual rabbit growth and feed efficiency.

Due to the lack of a recording system for individual consumption of group-housed rabbits, published studies about feeding behaviour are based on information recorded at the group- and not at the individual level and periods covering only a few days or, in some cases, only part of a day. Such information could be used to inform rabbit management systems but cannot be used for genetic selection. We aimed to generate and use information from a novel automated feeder for group-housed rabbits to identify new phenotypes for individual animals that could be incorporated into breeding programs to improve feed efficiency and social behaviour under different feeding regimens.

Exploring interactions between socioeconomic context and natural hazards on human population displacement.

Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016-2021 is presented.

Impact of multi-output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms.

Feeding represents the largest economic cost in meat production; therefore, selection to improve traits related to feed efficiency is a goal in most livestock breeding programs. Residual feed intake (RFI), that is, the difference between the actual and the expected feed intake based on animal's requirements, has been used as the selection criteria to improve feed efficiency since it was proposed by Kotch in 1963. In growing pigs, it is computed as the residual of the multiple regression model of daily feed intake (DFI), on average daily gain (ADG), backfat thickness (BFT), and metabolic body weight (MW).

Soil and vegetation water content identify the main terrestrial ecosystem changes.

Environmental change is a consequence of many interrelated factors. How vegetation responds to natural and human activity still needs to be well established, quantified and understood. Recent satellite missions providing hydrologic and ecological indicators enable better monitoring of Earth system changes, yet there is no automatic way to address this issue directly from observations.

Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models.

Background: The effect of the cecal microbiome on growth of rabbits that were fed under different regimes has been studied previously. However, the term "effect" carries a causal meaning that can be confounded because of potential genetic associations between the microbiome and production traits. Structural equation models (SEM) can help disentangle such a complex interplay by decomposing the effect on a production trait into direct host genetics effects and indirect host genetic effects that are exerted through microbiota effects.

Analysis of the causal structure of traits involved in sow lactation feed efficiency.

Background: Feed efficiency during lactation involves a set of phenotypic traits that form a complex system, with some traits exerting causal effects on the others. Information regarding such interrelationships can be used to predict the effect of external interventions on the system, and ultimately to optimize management practices and multi-trait selection strategies. Structural equation models can be used to infer the magnitude of the different causes of such interrelationships.

Influence of atmospheric patterns on soil moisture dynamics in Europe.

Soil moisture (SM) plays a key role in the water cycle, and its variability is intimately linked to coupled land-atmosphere processes. Having a good knowledge of soil-atmospheric interactions is thus essential to assess the impact of climate change on SM; however, many aspects of how water and energy exchanges occur in the soil-atmosphere continuum are still uncertain. In particular, it is known that atmospheric circulation patterns influence climate conditions over Europe but their impact on SM has only rarely been studied.

Use of Bayes factors to evaluate the effects of host genetics, litter and cage on the rabbit cecal microbiota.

Background: The rabbit cecum hosts and interacts with a complex microbial ecosystem that contributes to the variation of traits of economic interest. Although the influence of host genetics on microbial diversity and specific microbial taxa has been studied in several species (e.g.

Genotype by feeding regimen interactions for slaughter traits in rabbit and expected responses under restricted and full feeding.

The interaction between the genotype and feeding regimen (G×FR) for slaughter traits was estimated from data corresponding to 2557 animals under full (FF) and 2424 with restricted feeding (RF). Expected responses to selection under different scenario regarding feeding regimen were also calculated. Body weight at slaughter (SW), carcass weight (CW) and dressing out percentage (DoP) were analysed by using linear animal models in which records obtained under different feeding regimes were treated as different traits.

Inferring causal relations from observational long-term carbon and water fluxes records.

Land, atmosphere and climate interact constantly and at different spatial and temporal scales. In this paper we rely on causal discovery methods to infer spatial patterns of causal relations between several key variables of the carbon and water cycles: gross primary productivity, latent heat energy flux for evaporation, surface air temperature, precipitation, soil moisture and radiation. We introduce a methodology based on the convergent cross-mapping (CCM) technique.

The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes.

Gut microbiota plays an important role in nutrient absorption and could impact rabbit feed efficiency. This study aims at investigating such impact by evaluating the value added by microbial information for predicting individual growth and cage phenotypes related to feed efficiency. The dataset comprised individual average daily gain and cage-average daily feed intake from 425 meat rabbits, in which cecal microbiota was assessed, and their cage mates.

Feature Selection Stability and Accuracy of Prediction Models for Genomic Prediction of Residual Feed Intake in Pigs Using Machine Learning.

Feature selection (FS, i.e., selection of a subset of predictor variables) is essential in high-dimensional datasets to prevent overfitting of prediction/classification models and reduce computation time and resources.

Breeding farm, level of feeding and presence of antibiotics in the feed influence rabbit cecal microbiota.

Background: The effect of the production environment and different management practices in rabbit cecal microbiota remains poorly understood. While previous studies have proved the impact of the age or the feed composition, research in the breeding farm and other animal management aspects, such as the presence of antibiotics in the feed or the level of feeding, is still needed. Characterization of microbial diversity and composition of growing rabbits raised under different conditions could help better understand the role these practices play in cecal microbial communities and how it may result in different animal performance.

Explicit Granger causality in kernel Hilbert spaces.

Granger causality (GC) is undoubtedly the most widely used method to infer cause-effect relations from observational time series. Several nonlinear alternatives to GC have been proposed based on kernel methods. We generalize kernel Granger causality by considering the variables' cross-relations explicitly in Hilbert spaces.

Machine Learning Prediction of Crossbred Pig Feed Efficiency and Growth Rate From Single Nucleotide Polymorphisms.

This research assessed the ability of a Support Vector Machine (SVM) regression model to predict pig crossbred (CB) performance from various sources of phenotypic and genotypic information for improving crossbreeding performance at reduced genotyping cost. Data consisted of average daily gain (ADG) and residual feed intake (RFI) records and genotypes of 5,708 purebred (PB) boars and 5,007 CB pigs. Prediction models were fitted using individual PB genotypes and phenotypes (); genotypes of PB sires and average of CB records per PB sire (); and individual CB genotypes and phenotypes ().

Networks of inbreeding coefficients in a selected population of rabbits.

The correlation between pedigree and genomic-based inbreeding coefficients is usually discussed in the literature. However, some of these correlations could be spurious. Using partial correlations and information theory, it is possible to distinguish a significant association between two variables which is independent from associations with a third variable.

Genome-wide association study for feed efficiency in collective cage-raised rabbits under full and restricted feeding.

Feed efficiency (FE) is one of the most economically and environmentally relevant traits in the animal production sector. The objective of this study was to gain knowledge about the genetic control of FE in rabbits. To this end, GWASs were conducted for individual growth under two feeding regimes (full feeding and restricted) and FE traits collected from cage groups, using 114 604 autosome SNPs segregating in 438 rabbits.

Synergistic integration of optical and microwave satellite data for crop yield estimation.

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches.

Genetic parameters of sow feed efficiency during lactation and its underlying traits in a Duroc population.

As a result of the genetic selection for prolificacy and the improvements in the environment and farms management, litter size has increased in the last few years so that energy requirements of the lactating sow are greater. In addition, selection for feed efficiency of growing pigs is also conducted in maternal lines, and this has led to a decrease in appetite and feed intake that is extended to the lactation period, so the females are not able to obtain the necessary energy and nutrients for milk production and they mobilize their energetic reserves. When this mobilization is excessive, reproductive and health problems occur which ends up in an early sow culling.

Use of group records of feed intake to select for feed efficiency in rabbit.

Models for genetic evaluation of feed efficiency (FE) for animals housed in groups when they are either fed ad libitum (F) or on restricted (R) feeding were implemented. Definitions of FE on F included group records of feed intake ( ) and individual records of growth rate (G ) and metabolic weight (M ). Growth rate (G ) as FE measurement on R was used.