Plot Level Research Articles

Adapting agroforestry to upland farming systems: narratives from smallholder farmers in Northwest Vietnam

Fruit tree–based agroforestry has been promoted as an alternative farming practice in upland Northwest Vietnam to replace monocultures of staple crops. Although many studies have focused on evaluating the performance of agroforestry systems at the plot level, research on how farmers perceive and evaluate agroforestry considering whole-farm contexts is limited. We explored the perceptions and reasoned management decisions of agroforestry farmers to uncover challenges that hinder the wider use of agroforestry, and we assessed farmers’ strategies for effective management of adoption challenges. We combined the Q methodology and the systems thinking approach. With the Q methodology, we explored prevalent discourses among the members of the farming community on the impact of agroforestry. Systems thinking elucidated a system-wide understanding of farmers’ adaptive decision-making processes. By combining the two approaches, we uncovered the dynamics that shape farmers’ perceptions and the rationale behind their management of the adoption process. Through the Q method, we identified three distinct discourses among participants. Two of these discourses are in favor of agroforestry, highlighting its beneficial impacts on livelihoods and the environment, e.g., through diversification of household income and through soil erosion control. We also generated a collective development pathway outlining how farmers navigated and adapted agroforestry practices to overcome adoption challenges through a whole-system approach to farm resource management. We identified structural barriers, such as unstable farm-gate prices, that may need high-level interventions. Our study adds a new dimension to the assessment of agroforestry through farmers’ perspectives and contributes to the existing body of research on knowledge systems in agroforestry. Considering farmers’ views and their ways of reasoning during innovation processes may allow tailoring appropriate innovations by accounting for unique farm situations and local farming systems. Such locally generated knowledge will have relevance for real-world contexts and therefore be useful for guiding actions.

Типологические схождения в лирических произведениях В. А. Жуковского «Узник к мотыльку, влетевшему в его темницу» и Дж. Г. Байрона «Шильонский узник»

The aim of the research is to detect typological similarities in the works of V. A. Zhukovsky and G. G. Byron at the level of the images of the main characters, as well as the spatial-temporal organization of the text, with particular attention to the category of “liberation”. The research material includes lyrical works “The Prisoner to the Butterfly That Flew into His Dungeon” (1813) and “The Prisoner of Chillon” (1816). Comparative studies have already noted the contact links and typological similarities between the creative works of V. A. Zhukovsky and G. G. Byron, but a comparative analysis to identify and study parallels on different levels of the text has not been conducted yet, and this in turn determines the research novelty of the present study. For the first time, an analysis of the texts is presented at the level of characters, themes (prison, prisoner, impossibility of liberation), and spatial-temporal characteristics. The article thoroughly examines the interaction of the two lyrical works at the plot and imagery levels. The basis for examination comprises common elements of plots and motifs, similarities and differences in the images of central characters. The result of the research is that, despite the authorship of the authors belonging to the same literary movement (Romanticism), not only similarities in the realization of such images and situations are observed, but also significant differences. The comparative categories will include: the image of the hero, space and time, the image of freedom, liberation, prison. The identified connections and similarities indicate a literary contact between the authors and literatures, revealing the specifics of contact relationships and typological similarities in the works of V. A. Zhukovsky and G. G. Byron.

Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

Soqia: A Responsive Web Geographic Information System Solution for Dynamic Spatio-Temporal Monitoring of Soil Water Status in Arboriculture

The optimization of irrigation in arboriculture holds crucial importance for effectively managing water resources in arid regions. This work introduces the development and implementation of an innovative solution named ‘Soqia’, a responsive WEB-GIS web application designed for real-time monitoring of the water status in arboriculture. This solution integrates meteorological data, remote sensing data, and ground sensor-collected data for precise irrigation management at the agricultural plot level. A range of features has been considered in the development of this WEB -GIS solution, ranging from visualizing vegetation indices to accessing current weather data, thereby contributing to more efficient irrigation management. Compared to other existing applications, ‘Soqia’ provides users with the current amount of water to irrigate, as well as an estimated amount for the next 8 days. Additionally, it offers spatio-temporal tracking of vegetation indices provided as maps and graphs. The importance of the Soqia solution at the national level is justified by the scarcity of water resources due to increasingly frequent and intense drought seasons for the past years. Low rainfall is recorded in all national agricultural areas. The implemented prototype is a first step toward the development of future innovative tools aimed at improving water management in regions facing water challenges. This prototype illustrates the potential of Web-GIS-based precision irrigation systems for the rational use of water in agriculture in general and arboriculture in particular.

Fire facilitates ground layer plant diversity in a Miombo ecosystem.

Little is known about the response of ground layer plant communities to fire in Miombo ecosystems, which is a global blind spot of ecological understanding. We aimed: (1) to assess the impact of three experimentally imposed fire treatments on ground layer species composition and compare it with patterns observed for trees; and (2) to analyse the effect of fire treatments on species richness to assess how responses differ among plant functional groups. At a 60-year-long fire experiment in Zambia, we quantified the richness and diversity of ground layer plants in terms of taxa and functional groups across three experimental fire treatments of late dry-season fire, early dry-season fire and fire exclusion. Data were collected in five repeat surveys from the onset of the wet season to the early dry season. Of the 140 ground layer species recorded across the three treatments, fire-maintained treatments contributed most of the richness and diversity, with the least number of unique species found in the no-fire treatment. The early-fire treatment was more similar in composition to the no-fire treatment than to the late-fire treatment. C4 grass and geoxyle richness were highest in the late-fire treatment, and there were no shared sedge species between the late-fire and other treatments. At a plot level, the average richness in the late-fire treatment was twice that of the fire exclusion treatment. Heterogeneity in fire seasonality and intensity supports diversity of a unique flora by providing a diversity of local environments. African ecosystems face rapid expansion of land- and fire-management schemes for carbon offsetting and sequestration. We demonstrate that analyses of the impacts of such schemes predicated on the tree flora alone are highly likely to underestimate impacts on biodiversity. A research priority must be a new understanding of the Miombo ground layer flora integrated into policy and land management.

Analyzing the environmental risk factors of European spruce bark beetle damage at the local scale

The frequent outbreaks of European spruce bark beetle Ips typographus (L.) have destroyed huge amounts of Norway spruce Picea abies (L.) forests in central and Northern Europe. Identifying the risk factors and estimating the damage level is important for strategic damage control. The risk factors of forest damage by spruce bark beetles have mostly been analyzed on the landscape scale, while the in-stand risk factors have been less investigated. This study aims at exploring the local-scale risk factors in a flat area with spruce-dominated forest in southern Sweden. The investigated factors include four abiotic factors, i.e., soil wetness, solar radiation, slope gradient, and aspect, and three biotic factors, i.e., the number of deciduous trees and trees that died from attacks in previous years that remained (TreesLeft) and removed (TreesRemoved) from the forest stand. We put up 24 pheromone bags in six stands attacked by bark beetle in the previous years, resulting in different numbers of infested trees in each plot. We explored in which microenvironment a pheromone bag resulted in more colonization, the impact radius of each factor, and the necessary factors for a risk model. The environmental factors were obtained from remote sensing-based products and images. A generalized linear model (GLM) was used with the environmental factors as the explanatory variables and the damage levels as the response variables, i.e., the number of attacked trees for the plot scale, and healthy/infested for the single-tree scale. Using 50 m and 15 m radius of the environmental factors resulted in the best fit for the model at plot and individual tree scales, respectively. At those radii, the damage risk increased both at plot and individual tree level when spruce were surrounded by more deciduous trees, surrounded by dead trees that had been removed from the forest, and spruces located at the north and east slopes (315°–135° of aspect, > 2° slope). Soil wetness, solar radiation, and remaining standing dead trees in the surrounding did not significantly impact the damage level in the microenvironment of the study area. The GLM risk model yielded an overall accuracy of 0.69 in predicting individual trees being infested or healthy. Our efforts to investigate the risk factors provide a context for wall-to-wall mapping in-stand infestation risks, using remote sensing-based data.

"THE NIBELUNGENLIED" IN THE CONTEXT OF EUROPEAN EPIC TRADITION

The article considers the German heroic epic "The Nibelungenlied" in the context of the Western European epic (on the example of "Beowulf" and "Song of Roland") and Old Russian byline epic. It is emphasised that the researched epics are characterised by commonality at the plot and thematic level. It is about historical convention; about the opposition of grief and joy, good and evil; about the motives of snake-fighting, betrayal and revenge; about the presence of difficult trials, which the hero is helped to overcome by strength, courage and honour; about material benefits and the hero's attitude to fate, as well as about the hero's death. The author concludes that these features are associated with the presence of elements of oral poetry in the epic genre, which are born from the synthesis of heathen and Christian worldviews. As a result, along with typological similarities, the differences were revealed, connected, on the one hand, with the specificity of culture, and, on the other hand, with the personality of the author of the epic, in particular "The Nibelungenlied".

Mixed-Effects Height Prediction Model for Juniperus procera Trees from a Dry Afromontane Forest in Ethiopia

Tree height is a crucial variable in forestry science. In the current study, an accurate height prediction model for Juniperus procera Hochst. ex Endl. trees were developed, using a nonlinear mixed-effects modeling approach on 1215 observations from 101 randomly established plots in the Chilimo Dry Afromontane Forest in Ethiopia. After comparing 14 nonlinear models, the most appropriate base model was selected and expanded as a mixed-effects model, using the sample plot as a grouping factor, and adding stand-level variables to increase the model’s prediction ability. Using a completely independent dataset of observations, the best sampling alternative for calibration was determined using goodness-of-fit criteria. Our findings revealed that the Michaelis–Menten model outperformed the other models, while the expansion to the mixed-effects model significantly improved the height prediction. On the other hand, incorporating the quadratic mean diameter and the stem density slightly improved the model’s prediction ability. The fixed-effects of the selected model can also be used to predict the mean height of Juniperus procera trees as a marginal solution. The calibration response revealed that a systematic selection of the three largest-diameter trees at the plot level is the most effective for random effect estimation across new plots or stands.

Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs

Timely acquisition of forest structure is crucial for understanding the dynamics of ecosystem functions. Despite the fact that the combination of different quantitative structure models (QSMs) and point cloud sources (ALS and DAP) has shown great potential to characterize tree structure, few studies have addressed their pros and cons in alpine temperate deciduous forests. In this study, different point clouds from UAV-mounted LiDAR and DAP under leaf-off conditions were first processed into individual tree point clouds, and then explicit 3D tree models of the forest were reconstructed using the TreeQSM and AdQSM methods. Structural metrics obtained from the two QSMs were evaluated based on terrestrial LiDAR (TLS)-based surveys. The results showed that ALS-based predictions of forest structure outperformed DAP-based predictions at both plot and tree levels. TreeQSM performed with comparable accuracy to AdQSM for estimating tree height, regardless of ALS (plot level: 0.93 vs. 0.94; tree level: 0.92 vs. 0.92) and DAP (plot level: 0.86 vs. 0.86; tree level: 0.89 vs. 0.90) point clouds. These results provide a robust and efficient workflow that takes advantage of UAV monitoring for estimating forest structural metrics and suggest the effectiveness of LiDAR in temperate deciduous forests.

Predicting lodging severity in dry peas using UAS-mounted RGB, LIDAR, and multispectral sensors

This study investigated the feasibility of using data collected by sensors mounted onto unmanned aerial systems (UASs) and traditional classification methods (Maximum Likelihood, Mahalanobis Distance, Minimum Distance, and Adaptive Coherence Estimator) and four machine learning models (Random Forest [RF], Support Vector Machine [SVM], K-Nearest Neighbor [KNN], and Extreme Gradient Boosting [XGBOOST]) to predict the severity of dry pea lodging. Data were collected in two environments in North Dakota during the 2022 crop season, where lodging was visually estimated at the plot level using a lodging scale range of 0 to 9. Aerial images were collected using two UASs (DJI Matrice 200 and 300) during early, mid, and late stages of growth. Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was used to balance the imbalanced dataset. Different feature selection models (recursive feature elimination, lasso regression, principal component analysis, and correlation-based methods) were used and compared to identify the most important features in the dataset. Our study found that similar channels can be used interchangeably for analyzing lodging without compromising accuracy, simplifying data acquisition, and saving time and resources. The maximum plant height values obtained from LiDAR and RGB sensors showed a strong correlation with actual values. Recursive feature elimination was the most effective feature selection method for handling high-dimensional datasets. The XGBOOST model outperformed all traditional and machine learning classification models, with the lowest variation in precision values. RF showed the most pronounced overfitting among the machine learning models, while Mahalanobis distance showed the least overfitting among all traditional classification models. Overall, the study demonstrated the potential of UAS and machine learning models for predicting lodging severity in dry peas, with XGBOOST being the best-performing model.

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R2 metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.

Natural forest regeneration over a fallow age chronosequence in central African moist forests

AbstractA better understanding of the natural regeneration in tropical forests could help develop more effective restoration strategies. This study examined relationships in structural and diversity attributes of natural regeneration in five forest fallow ages after slash‐and‐burn agriculture (6 months, 2 years, 5 years, 10 years, and 15 years) in the Republic of Congo. For each fallow age, all stems with a diameter ≥1 cm and below 5 cm (height ≥ 130 cm), corresponding to natural forest regeneration, were identified to the species level, and measured (diameter and height) in 12 plots (10 m × 10 m). Three structural attributes (stem density, maximum diameter, and maximum height) and five diversity indices (species richness, Shannon diversity, Simpson diversity, Fisher's alpha and Pielou's evenness) were estimated at the plot level. Our results revealed an increase in structural and diversity attributes with fallow ages, except stem density. The young fallow (6 months, 2 years and 5 years) had greater stem density (61 ± 48 stems), while the old fallow (10 years and 15 years) had higher maximum height (5.39 ± 2.36 m) and Shannon's index (0.94 ± 0.18). These results provided new insights into natural regeneration between young and old secondary forests.

Yield Test of Shade-Tolerant Maize Using the Intercropping Method of Maize-Soybean Under Coconut Trees

Maize is a light-dependent crop, but new varieties have been developed that can tolerate shade. This allows farmers to grow maize as an intercrop between coconut trees, making more efficient use of their land. Farmers can also intercrop with legume plants, which provide additional nitrogen through Rhizobium bacteria, leading to increased maize productivity. The aim of this study was to obtain the best intercropping pattern for maize and soybeans under coconut trees that results high yields in maize. The study was conducted in Padang Pariaman Regency of West Sumatra, at an altitude of approximately 150 meters, from February to July 2022. The experimental design was a Split Plot Design with Randomized Block Design, consisting of three main plot levels (planting time) and four subplot levels (planting model). The results of the study indicate that intercropping maize and soybeans under coconut trees has no effect on the weight of maize cobs, but there is a positive correlation between ear weight, cob length, cob diameter, number of rows per cob, and the weight of 1000 maize seeds.

Environmental filtering and landscape composition drive multifaceted diversity of bird assemblages in vineyards

Abstract Agricultural landscapes are productive areas with high biodiversity potential, but their conversion to monocultures and intensification of practices are recognised drivers of bird biodiversity loss worldwide. This challenges the compatibility of agricultural land use with bird conservation, as landscape composition affects both the diversity of bird species and traits and the assembly of bird communities. We address the underlying mechanisms of avian community assembly in vineyards at the plot and landscape levels in relation to landscape composition in one of the major wine producing regions of France. We jointly analyse how the taxonomic, functional, and phylogenetic facets of bird alpha and beta diversity respond to a gradient of landscape heterogeneity and test for a non‐random assembly rule. We found that birds tended to avoid vineyard monocultures. There was consistent support for the co‐occurrence of both closely related species and similar functional traits within vineyard plots (namely, phylogenetic and functional clustering, respectively). Landscape composition, both within plots and on a large scale (1000 m), was critical in explaining the occurrence of species and traits within plots. Landscape composition determined a nested pattern of functional and phylogenetic beta diversity from vineyard monoculture to more complex landscapes with increasing seminatural land cover. Synthesis and applications. Landscape composition in seminatural vegetation features significantly influenced both the alpha and beta bird diversity in vineyard‐dominated agricultural landscapes. Interspersed shrubs and woodland can help increase bird abundance and species richness in vineyards, while large‐scale land cover diversity might help promote functional and phylogenetic bird diversity.

Forest structure and heterogeneity increase diversity and alter composition of host–parasitoid networks

Abstract Antagonistic host–parasitoid interactions can be quantified using bipartite and metanetworks, which have the potential to reveal how habitat structural elements relate to this important ecosystem function. Here, we analysed the host–parasitoid interactions of cavity‐nesting bees and wasps, as well as their abundance, diversity and species richness with forest structural elements from 127 forest research plots in southwestern Germany. We found that parasitoid abundance, diversity and species richness all increase with host abundance, a potential mediator between parasitoids and forest structure. Both parasitoid abundance and diversity increased with stand structural complexity, possibly mediated by the abundance of hosts. In addition, parasitoid abundance increased with increasing standing deadwood and herb cover. The bipartite networks of host–parasitoid interactions showed higher connectance with increasing standing deadwood, herb cover and host abundance. Analyses of interactions within the host–parasitoid metanetwork revealed that increasing host abundance and decreasing canopy cover diversify the suites of interactions present at the plot level. These results demonstrate that forest structural elements can improve the stability and resilience of host–parasitoid networks by promoting parasitoids and diversifying interactions in ecological networks.

Survey based assessment of sustainable agricultural practices: Evidence from Indian plots

AbstractIn recent years concerns have been raised regarding the environmental consequences of over‐use of nitrogen fertilizers on a global level. However, the balance between sustainability and agricultural productivity, a central concern for policy makers in developing countries, has not been sufficiently addressed. In this paper, I evaluate farmers’ fertilization practices and their effect on yield using unique plot level data from India. I estimate quadratic crop response functions for different crops and cropping systems. To address endogenous input choices, I use input prices and cost shifters from the fertilizer industry as instrumental variables for the fertilization practice. I find that a large share of Indian cultivators overuse nitrogen relative to the other two nutrients, and could benefit from simply reducing the amount of nitrogen used while keeping the other nutrients fixed. This suggests a potential win‐win situation where both productivity and sustainability can be improved by changing fertilizer application. The widespread “nitrogen‐only” fertilization pattern is rejected as optimal in most cases. [EconLit Citations: Q12, Q15, Q16, E23, C26, C14].

GSP-AI: An AI-Powered Platform for Identifying Key Growth Stages and the Vegetative-to-Reproductive Transition in Wheat Using Trilateral Drone Imagery and Meteorological Data.

Wheat (Triticum aestivum) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.

Художня реалізація радянських ідеологем у повісті Олександри Свекли «Надломлені серцем»

The article attempts to analyse the novel «Brokenhearted» in terms of its ideological, thematic, figurative and stylistic dominants in the context of social realism. Social realism as a style was primarily created for political, non-artistic needs, such as the construction of people of the new era, for which art became a tool. That is why in the work of many authors of the period we can observe the development of themes and techniques that meet the objectives of the ruling party. Therefore, the purpose of the study is to examine the elements of Oleksandra Svekla’s textual creation as those that show a strong influence of the ideas of the Soviet era, but are mastered and transformed. The study examines the rejection of the past as a characteristic temporal category for socialist realism works, the separation of private and collective spaces. An attempt is made to describe the types of characters in the story as part of the textual element of a socialist realist work. It is revealed the author’s modelling of the inherent canonical image of the «new man», which is realised through the characters of the Soviet Pantheon of heroes: the warrior hero, the worker hero, the party member hero. Through the image of a new woman, Iryna, who is the centre of the whole story, the author describes the idea of female emancipation, which is realised not only through the rejection of marriage, highly paid female labour, and the desire to raise a child on her own, but also at the plot level: the protagonist’s journey, which is an important part of personality formation. The study revealed a spontaneous ideologisation of the semiosphere. That is, in our opinion, Oleksandra Svekla consciously/unconsciously reproduced ideology in connection with her own political position and an attempt to describe the socio-historical reality.

Harlequinade Grotesque in the Works of Isak Dinesen (Based on the Novellas «The Poet» and «Carnival»)

The article is devoted to the study of the harlequinade grotesque in the novellas by the Danish writer Isak Dinesen (Karen Blixen) «The Poet» and «Carnival». The study proves relevant due to the following factors: 1) the current revival in foreign literary criticism of interest in the aesthetics of commedia dell’arte, as well as the role it played in the art of the 20th century; 2) growing interest in Dinesen’s work in Russian literary criticism. This article is the first to highlight both the presence of the harlequinade grotesque in Dinesen’s work and its originality. The authors of the article show how Dinesen’s passion for Italian popular comedy and harlequinade, typical of Scandinavian literature of her era, grew into a special worldview and writing style that synthesized the tragic and the comic. The article aims at revealing the features of the harlequinade grotesque in Dinesen’s work. The leading method in the study is the method of intertextual analysis: the characters and plots of the short stories are analyzed for their similarity with classical images (Harlequin, Pantaloon, Columbine, etc.) and collisions of the harlequinade (the flight of lovers from oppressive old men). The article reveals the features of the representation of the harlequinade plot and the functions of the harlequinade grotesque that evolve from them. The results presented in the article allow us to say that the harlequinade grotesque in Dinesen’s work was realized at the character and plot level, being the mouthpiece of the writer’s worldview, transmitting her aesthetic principles and moral principles. Also, the presence of the harlequinade grotesque in Dinesen’s prose suggests the presence of a similar technique in the work of other Scandinavian writers and confirms the status of this type of grotesque as a pan-European one.

Effects of shade tree legumes on cacao biomass and bean yields after 20 years of intercropping in Ivory Coast

Summary The choice of tree species planted with cacao trees is essential for ensuring the efficiency and sustainability of cacao farming systems. This raises the question of the long-term impact of associated tree legumes (ATLs) on cacao bean yields and biomass accumulation. This study was carried out in accordance with four-block randomised experimental design in Divo (Ivory Coast, West Africa). The study involved cacao-Albizia lebbeck (Cacao-Alb) and cacao-Acacia mangium (Cacao-Aca) intercrops and unshaded cacao plots (Control). After 20 years of intercropping, we assessed cacao dendrometry (height, circumference, biomass, and biomass C-stock) and production (number of pods per tree and bean yield at the plot level) as well as soil fertility (soil organic matter (SOM) concentration) at various distances from ATLs (D1:0–1.75 m; D2:3.25–5 m; D3:7–9 m). The distance from the ATLs had no significant effect on the measured cacao dendrometric parameters, except for cacao height. In contrast, the Cacao-Aca association had a negative impact on the SOM concentration (−22%), cacao tree height (−6.15%), and productivity parameters (biomass: −12.4%; bean yield: −43%). However, Cacao-Alb and the Control had no significant differences in terms of SOM, tree biomass, or bean production. Intercropping cacao with the tested tree legumes did not enhance cacao productivity and, in some cases, hindered it, depending on the ATL species. This study highlighted the importance of identifying appropriate shade tree legume species that could be promoted in cacao-based agroforestry systems.