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Different feed efficiency modeling approaches for the prediction of genomic breeding values in lactating dairy cows
Chegini, Arash; Negussie, Enyew; Kokkonen, T.; Lidauer, Martin H.
Frontiers in genetics (Frontiers Media S.A., 2026)
Feed is the main cost of production in dairy farming. Any improvement in feed efficiency (FE) would increase marginal profit and sustainability and mitigate the environmental impact of dairy farming. In this study, we applied single-step genomic best linear unbiased prediction to different feed-efficiency metrics using records collected from Nordic Red dairy cattle (RDC). The main objective was to compare different metrics in terms of their effectiveness in selecting more feed-efficient animals. Weekly observations (n = 22,071) of dry-matter intake records from 791 RDC cows collected from 1998 to 2021 were used in this study. The pedigree consisted of 5,604 individuals, of which 1,489 animals were genotyped. Different modeling approaches, including conventional residual feed intake (RFI), regression on expected feed intake (ReFI), two multi-trait residual feed efficiency indices (RFIIndex and RZFE), and energy conversion efficiency (ECE) were analyzed. For the ReFI approach, two alternatives for predicting the expected feed intake, namely, a prediction equation tailored to the RDC data and a prediction equation based on Holstein dairy cow data proposed by the National Academies of Sciences, Engineering, and Medicine (NRC 2021), were compared. First, a BLUP model was developed, and the necessary variance components were estimated for each approach. Then, pedigree-based and genomic-enhanced breeding values (PEBV and GEBV, respectively) were estimated using either reduced or full datasets. For model validation, PEBV and GEBV estimated using the full dataset were regressed on PEBV and GEBV estimated using the reduced dataset, respectively, to measure bias, dispersion, and prediction accuracy (PAC). The heritability estimates of different residual metrics ranged from 0.23 for RFI to 0.30 for ReFINRC2021, and the repeatability estimates ranged from 0.48 to 0.52. The estimated heritability and repeatability of ECE were 0.23 and 0.56, respectively. For all metrics, the use of genomic information increased PAC. However, there were discrepancies between the metrics in terms of the magnitude of PAC, with the PAC being the highest for ReFIRDC and the lowest for RFIIndex. Similarly, ReFIRDC had the lowest bias, while the highest bias was estimated for RFIIndex. In addition, RZFE and ReFIRDC showed lower dispersion. The correlations between GEBV of the residual metrics and the GEBV of ECE were lowest for RFINRC2021 and RFI and highest for ReFIRDC. Among the metrics compared, ReFIRDC and RFIIndex showed the highest effectiveness in selecting efficient cows. This indicates that the use of appropriate partial regression coefficients and the type of modeling are vital in breeding programs aimed at enhancing FE.
Power dynamics and (intergenerational) values : What influences the forest ownership of young translocal forest owners in Finland?
Runko, Panu; Mustalahti, Irmeli
Forest policy and economics (Elsevier, 2026)
Forests and trees have diverse meanings in Finland that are claimed for many different purposes. Urbanisation has and will continue to increase the number of translocal forest owners, leading to the diversification of forest ownership, values, utilisation, and human-forest relationships. It is important for translocal forest owners, the generation of future forest owners, to gauge the values and meaningfulness of translocal forest ownership and to understand the possible future changes in forest management and ownership structures. In-depth interviews were gathered from ten forest owners aged 18–30 living in the greater Helsinki area, six of whom later participated in a World Café workshop, to understand their values and ability to conduct values-based decision making. Value conflicts were identified in both the interviews and the group discussion, and going against the prevailing norms of commercial forest ownership was viewed as difficult and requiring courage. Deviation from these norms was made difficult not only by disciplinary and neoliberal governance structures but also by intergenerational ties and social pressure from older family members. These factors reduced the autonomous decision making of young forest owners, leading them to internalise the Finnish forestry sector's prevailing norms. Thus, young forest owners could end up shaping their forests according to the values of those who wielded more power rather than their own. Nevertheless, there was little intention to transfer the legal ownership outside of the owners' family, because the forests, and their intergenerational and social ties often provided sentimental value, leading to the intention to retain ownership control.
Dynamic quantification of methane emissions at facility scale using laser tomography : demonstration of a farm deployment
Scheel, Kenneth; Vänskä, Elias; Weidmann, Damien; Ursin, Aku
Atmospheric measurement techniques : 7 (Copernicus Publications, 2026)
Detecting and quantifying greenhouse gas (GHG) emissions is essential for understanding global GHG budgets, updating emission inventories, and evaluating climate change mitigation efforts. Most anthropogenic emissions occur at the scale of facilities, and emission distribution in time and space relates to facility operations. This paper presents a novel GHG monitoring technique for facility-scale, dynamic emission quantification under complex wind conditions, referred to as laser dispersion tomography (LDT), which integrates laser dispersion spectroscopy (LDS) with Bayesian inversion methods. It uses sequential multi-beam open-path LDS measurements and wind data to infer dynamic GHG concentration and source maps at facility scale. In this work, the use of LDT for monitoring methane emissions in agriculture is demonstrated by deploying it on an operational farm. For this aim, computational methods used in data analysis of LDT are also further developed. Particularly, we introduce spatial constraints to the tomographic reconstruction based on prior knowledge on potential source locations – information often available in facility-scale GHG monitoring applications. We investigate numerically whether such constraints could improve the tolerance of LDT to misrepresentations induced by complex wind fields caused by building effects, and/or presence of interfering external emission sources, both highly likely to characterize a real-world farm environment. The results of numerical studies indicate that including spatial constraints reduces the uncertainty and improves the reliability of source quantification in such conditions, with one simulation case showing an average reduction in posterior uncertainty of 36.2 %. In the experimental study, dynamic emission patterns caused by various operations in the farm, such as slurry and dry manure management, are well captured, both temporally and spatially. The results support the feasibility of LDT as a tool for robust quantification of GHG mass emission rates at farms, especially when the spatial constraining of sources is possible. Owing to the fine spatial and temporal resolution of LDT, we foresee its use in improving GHG emission inventories through fine parametrization, and also its extension to other GHGs and other sectors contributing to global emissions.
Projecting the potential distribution of Setaria palmifolia in China in response to climate change
Xiang, Yangzhou; Li, Suhang; Liu, Ying; Yang, Qiong; Zhang, Jinxin; Yao, Bin; Li, Yuan
Environmental and sustainability indicators (Elsevier, 2026)
Accurately predicting the potential distribution of Setaria palmifolia in China is crucial for assessing its climatic adaptability as a forage resource and for developing sustainable utilization strategies. This study systematically integrated 927 species distribution records while employing a comprehensive set of multidimensional environmental variables, including climate, topography, vegetation, and human activity. Using an optimized Maximum Entropy (MaxEnt) model, we simulated the potential geographic distribution of S. palmifolia under both current conditions and future scenarios (2050s, 2070s, 2090s) across three Shared Socioeconomic Pathways (SSP126, SSP370, SSP585). The results identified the key environmental factors influencing its distribution: precipitation of the warmest quarter, precipitation of the coldest quarter, and temperature diurnal range. These factors collectively contributed 93.4% to the model. Under current climatic conditions, the total potentially suitable habitat for S. palmifolia is approximately 177.22 × 104 km2, primarily concentrated in southern China. Future projections indicate that the total suitable area will remain relatively stable across all scenarios (ranging from 177.22 × 104 to 196.80 × 104 km2). However, its internal structure will undergo significant reorganization, with highly suitable habitat expanding from 12.20 × 104 km2 (current) to between 29.69 × 104 and 50.39 × 104 km2 depending on emission scenarios. By the 2070s, this expansion is projected to reach 35.80 × 104 to 50.39 × 104 km2. Meanwhile, the distribution centroid shows complex, non-linear migration trajectories. This internal suitability reorganization pattern suggests that S. palmifolia responds to climate change through local adaptation rather than simple range shifts. Our findings provide critical scientific evidence and spatial decision-support for regional introduction planning, priority conservation area identification, and climate change adaptive management of S. palmifolia as a forage resource.
Suomen Kalakirjasto : hiljainen monumentti suomalaiselle kalastuskulttuurille
Savikko, Ari
Bibliophilos : 1 (Bibliofiilien seura, 2026)