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Using only bivariate copulas as building blocks, regular vine copulas constitute a flexible class of high-dimensional dependency models. However, the flexibility comes along with an exponentially increasing complexity in larger dimensions. In order to counteract this problem, we propose using statistical model selection techniques to either truncate or simplify a regular vine copula. As a special case, we consider the simplification of a canonical vine copula using a multivariate copula as previously treated by Heinen & Valdesogo (2009) and Valdesogo (2009). We validate the proposed approaches by extensive simulation studies and use them to investigate a 19-dimensional financial data set of Norwegian and international market variables. En utilisant uniquement des copules bidimensionnelles comme unités de base, les copules en arborescence régulière constituent une classe flexible pour modéliser la dépendance pour les grandes dimensions. Toutefois, en grandes dimensions, la flexibilité s'accompagne d'une croissance exponentielle de la complexité. Pour contrecarrer ce problème, nous proposons l'utilisation des techniques de sélection de modèles statistiques afin de tronquer ou encore de simplifier la copule en arborescence régulière. Comme cas particulier, nous considérons la simplification de la copule en arborescence canonique par l'utilisation d'une copule multidimensionnelle telle que présentée dans Heinen et Valdesogo (2009) et Valdesogo (2009). Nous validons les approches proposées par de vastes études de simulation et nous les utilisons pour analyser un jeu de données financières de dimension 19 sur des variables des marchés norvégien et internationaux.

We introduce an extension of R-vine copula models to allow for spatial dependencies and model based prediction at unobserved locations. The proposed spatial R-vine model combines the flexibility of vine copulas with the classical geostatistical idea of modeling spatial dependencies using the distances between the variable locations. In particular, the model is able to capture non-Gaussian spatial dependencies. To develop and illustrate our approach, we consider daily mean temperature data observed at 54 monitoring stations in Germany. We identify relationships between the vine copula parameters and the station distances and exploit these in order to reduce the huge number of parameters needed to parametrize a 54-dimensional R-vine model fitted to the data. The new distance based model parametrization results in a distinct reduction in the number of parameters and makes parameter estimation and prediction at unobserved locations feasible. The prediction capabilities are validated using adequate scoring techniques, showing a better performance of the spatial R-vine copula model compared to a Gaussian spatial model.

IntroductionReducing feed costs and promoting sustainability are key challenges in yak production. This study aimed to comprehensively evaluate the potential of four locally available agricultural by-products—pumpkin vines (PV), sweet potato vines (SPV), grape vines (GV), and pepper straw (PS)—as alternative feed resources for yaks.MethodsFirst, the chemical composition (dry matter-DM, crude protein, neutral detergent fiber-NDF, acid detergent fiber-ADF) of each by-product was analyzed. Subsequently, a 72-hour in vitro ruminal fermentation experiment was conducted to evaluate fermentation parameters. Finally, high-throughput sequencing was used to analyze associated shifts in the rumen bacterial microbiota, and Spearman correlation analysis was performed to link key microbial genera with fermentation outcomes.Results and Discussion1) GV and PV had a higher crude protein content, while PS had the highest levels of dry matter (DM), neutral detergent fiber (NDF) and acid detergent fiber (ADF, P< 0.001); 2) After 72 hours of in vitro fermentation, PV and SPV had superior gas production and nutrient degradability (DM, NDF and ADF, P< 0.001); 3) The fermentation parameters showed that SPV and GV promoted more efficient fermentation, characterized by a lower pH and a lower acetate-to-propionate ratio, but higher microbial protein (MCP) levels (P< 0.001). PV yielded the highest concentrations of volatile fatty acids (P< 0.001). 4) Rumen microbiota analysis identified distinct, diet-specific enrichments of bacterial genera (P< 0.05), including: g_Fusobacterium and g_Basfia in PV; g_Rikenellaceae_RC9_gut_group and g_Streptococcus in SPV; g_norank_f:p-251-o5 and g_Butyricicoccus in PS; g_Lachnospira and g_Pseudobutyrivibrio in GV Critically, Spearman correlation analysis linked these microbial shifts to fermentation outcomes: Genera such as g_Fusobacterium and g_Basfia were found to be positively correlated with MCP (P< 0.05), while g_Rikenellaceae_RC9_gut_group and g_Butyricicoccus were positively associated with total volatile fatty acids (P< 0.05). In conclusion, while all four by-products show potential as alternative feeds, SPV and GV show better overall feeding value for yaks, supported by their balanced nutrient composition, improved fermentability, and positive associations with rumen microbiota. This integrated assessment provides a strong basis for utilizing them to enhance the sustainability of yak production.

Abstract Significant anaerobic fermentation occurs in silage through the action of anaerobic bacteria. The objective of this study was to evaluate the effects of cellulase and Lactobacillus plantarum on the fermentation quality and bacterial community of whole-plant corn and peanut vine mixed silage. Mixed silage was tested with no addition (CK), addition of Lactobacillus plantarum (LP), addition of cellulase (CE), and the simultaneous addition of Lactobacillus plantarum and cellulase (LPCE). LPCE samples exhibited decreased pH; decreased content of acetic acid, propionic acid, and butyric acid; and increased content of lactic acid. LP and LPCE had better effects on chemical composition than CK and CE, especially in decreasing acid detergent fiber and neutral detergent fiber content. High-throughput sequencing identified Lactobacillus, Klebsiella, Serratia, and Weissella as the main microorganisms. LP and CE increased the abundance of Acetobacter, and LPCE decreased the abundance of Acetobacter. All additives decreased the abundance of Weissella, Leuconostoc, and Lactococcus, and increased the abundance of Pantoea. Overall, simultaneous addition of cellulase and Lactobacillus plantarum helped to improve the quality of mixed silage of whole-plant corn and peanut vines.

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

Lianas constitute a diverse polyphyletic plant group that is advancing our understanding of ecological theory. Specifically, lianas are providing newinsights into the mechanisms that control plant distribution and diversity maintenance. For example, there is now evidence that a single, scalable mechanism may explain local, regional, and pan-tropical distribution of lianas, as well as the maintenance of liana species diversity. The ability to outcompete trees under dry, stressful conditions in seasonal forests provides lianas a growth advantage that, over time, results in relatively high abundance in seasonal forests and low abundance in aseasonal forests. Lianas may also gain a similar growth advantage following disturbance, thus explaining why liana density and diversity peak following disturbance at the local, forest scale. The study of ecology, however, is more than the effect of the environment on organisms; it also includes the effects of organisms on the environment. Considerable empirical evidence now indicates that lianas substantially alter their environment by consuming resources, suppressing tree performance, and influencing emergent properties of forests, such as ecosystem functioning, plant and animal diversity, and community composition. These recent studies using lianas are transcending classical tropical ecology research and are now providing novel insights into fundamental ecological theory.

We provide an overview of research on climbing plants from Charles Darwin to the present day. Following Darwin's interests, this review will focus on functional perspectives including attachment mechanisms and stem structure and function. We draw attention to a number of unsolved problems inviting future research. These include the mechanism for establishment of the twining habit, a quantitative description following the development of a tissue element through space and time, the chemistry of sticky exudates, the microstructure of xylem and the capacity for water storage, the vulnerability to embolism, and the mechanism for embolism repair. In conclusion we cite evidence that, in response to increasing CO2 concentration, anthropic perturbation and/ or increasing forest fragmentation, lianas are increasing relative to tree species. In the 21st century, we are returning to the multiscale, multidisciplinary approach taken by Darwin to understand natural history.

Vines or climbing plants exhibit vigorous vegetative shoot extension. GA have long been recognized as an important signal for seasonal stem elongation and flowering in many woody perennials. However, less is explored as how GA pathway is involved in the regulation of shoot extension in woody vines. Here, we investigated the role of GA and its signaling components in shoot elongation in Jasminum sambac. We found high accumulation of GA4 in the elongating internode, in contrast to a depletion of GAs in the floral differentiating shoot, which in turn featured a higher zeatin content, and a lower IAA and JA concentrations. This GA accumulation was coincident with the strong expression of JsGA20ox1 and JsGAS1 in the leaves, as well as of the JsGA2ox3 in the internode. Treatment of GA biosynthesis inhibitor reduced elongation while stimulated the terminal flowering. Remarkably, three B-type GA-receptor genes were abundantly expressed in both internodes and leaves of the extending shoots, which could enhance GA responsiveness in heterologous transgenic Arabidopsis. Furthermore, these JsGID1s showed distinct GA-dependent interaction with the JsDELLA in a yeast-two-hybrid assay. Taken together, our results establish a reciprocal relationship between vine elongation and flowering, and reveal that GA is a positive signal for stem elogation but a negative regulator of flowering in this species.

Strategic design decisions regarding wine label, plant material, vine age renewal, and planting choices are crucial for winegrowers when planning their future vineyards that will influence grapevine yield in the long term. However, the repercussions of these choices have mostly been studied in experimental vineyards and small datasets. Therefore, we are missing a comprehensive analysis of a large diversity of vineyard situations that can robustly provide avenues for improving vineyard design and ensure sustainable wine production. To fill this research gap, we analyzed a big sample of vineyards using random plot data (n = 3507) from surveys conducted among winegrowers of the Languedoc-Roussillon viticultural region. We carried out a data analysis that focused on examining the relationship between grapevine yield and (i) diverse vineyard management frameworks (wine label, organic management, irrigation), (ii) plant material (varieties, rootstocks), and (iii) planting choices (planting density, vine age as a proxy for vine lifespan and renewal). Our findings indicate that wine label greatly affected yield; in conjunction with vine age, they explained up to 40% of the total yield variance. Most cultivated varieties exhibited similar yield levels for the same type of wine label. Notably, SO4 rootstock displayed the best yield performance across multiple cultivated varieties. We observed an adverse effect of vine aging on grapevine yield, particularly in highly productive vineyards. In contrast, the impact of vine age on the yield of low-yield plots was almost negligible. Plots under organic farming presented lower yields, although they were scarce in the sample. Unexpectedly, planting density did not significantly affect yield. Overall, these results underscore the significance of conducting big data analysis from winegrowers at a regional level, when it comes to assessing the influence of vineyard design and plant material on yield.