Agronomy

Soil degradation driven by intensive management practices has become of increasing concern for olive cultivation, as trends for desertification and loss of arable land have emerged across the Mediterranean basin. Agroecological management practices, such as mulching made from olive tree pruning remains, have shown potential for improving soil structure, nutrient retention and biodiversity. This study aimed to enhance the understanding of how soil management influences soil properties and arthropod diversity in small-scale olive orchards in a heterogeneous landscape in south-west Greece. Soil was sampled from 11 orchards managed under one of two systems: conventional (herbicide use, tillage, mowing) and agroecological (cover cropping, mulching), encompassing a diversity of management practices. Physicochemical properties were measured alongside soil arthropod abundance and diversity, allowing for comparisons at two levels: between management systems and among practices nested within each system. When compared across broader systems, the agroecological orchards, compared to conventional orchards, had greater porosity (56.38% and 48.75%), and soil organic matter (8.99% and 6.87%), though differences in soil composition likely accounted for some of the variation. Additionally, metrics for arthropod diversity were improved under agroecological management, with 21% higher Shannon diversity and 16.8% greater evenness compared to conventional management. Ordination analysis and generalized linear models further supported these findings illustrating the relationship between agroecological management, soil health and arthropod diversity. These results support a growing body of research which illustrate the potential of agroecological management in enhancing soil health and biodiversity in olive orchards and contributing to the development of more resilient agroecosystems within the Mediterranean basin.

The fragile Karst landscapes of southwest China face persistent challenges of soil degradation and rocky desertification. While sustainable land use such as mulberry plantation can support ecological restoration, the dynamics of soil organic carbon (SOC) and its driving mechanisms across contrasting soil types remain poorly understood, limiting the development of targeted pedogenically aware carbon management strategies. A comparative field study was conducted in central Guizhou, China, over an eight-month mulberry growing season (April to November). We monitored SOC, physicochemical properties, GRSP, and enzyme activities in plantations established on two contrasting limestone-derived soils (Calcisols and Chromic Luvisols). This study aimed to clarify the relationships between SOC and key soil parameters within each soil type and to identify their dominant driving factors. Soil type significantly influenced SOC concentration, dynamics, and its regulatory mechanisms. SOC was significantly higher and exhibited greater seasonal variability in Calcisols (31.51–39.71 g·kg⁻¹) than in Chromic Luvisols (22.50–28.51 g·kg⁻¹), with Calcisols maintaining 1.28–1.57 times the SOC concentration of Chromic Luvisols. Regression analysis revealed that SOC was significantly positively correlated with TN, AN, AK, and GRSP, but significantly negatively correlated with AP. Random forest modeling further identified distinct key correlated factors in each soil type as follows: TN, T-GRSP, and Urease were primary in Calcisols, whereas TN, T-GRSP, and pH dominated in Chromic Luvisols. Partial least squares path modeling confirmed that soil type does not directly associate with SOC but exerts an indirect effect by modulating core biochemical mediators specifically (Alkaline protease, T-GRSP, and TN); The model also indicated that pH and TN exert direct positive effects on SOC accumulation. In Karst mulberry systems, pedogenically distinct soils (Calcisols vs. Chromic Luvisols) shape SOC storage, stability, and regulatory mechanisms through divergent biogeochemical pathways. SOC management should therefore be soil-type-specific, prioritizing nitrogen synergy in Calcisols and pH-mediated stabilization in Chromic Luvisols, rather than applying uniform strategies. This study thereby establishes a mechanistic framework for understanding and managing SOC in heterogeneous Karst landscapes, providing a critical foundation for developing targeted, soil-specific carbon sequestration practices in ecologically vulnerable regions.

Analyzing three-dimensional (3D) phenotypic parameters of maize seedlings is of significant importance for maize cultivation and selection. However, existing methods often struggle to balance cost, efficiency, and accuracy, particularly when capturing the complex morphology of seedlings characterized by slender stems. To address these issues, this study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras. The pipeline initiates with Instant-NGP to rapidly reconstruct dense point clouds, establishing the 3D data foundation for phenotypic extraction. Subsequently, we formulate a directed topological graph-based mechanism. By mathematically defining bifurcation constraints via vector analysis, this mechanism guides a depth-first traversal strategy to explicitly disentangle stem and leaf skeletons. Building upon these decoupled skeletons, organ-level point cloud segmentation is achieved through constraint-based expansion, followed by density-based spatial clustering (DBSCAN) to detect individual leaves. Algorithms combining point cloud geometry with 3D Euclidean distance are also implemented to calculate key phenotypes including plant height and stem width. Finally, single-leaf skeleton fitting is used to estimate leaf length, and principal component analysis (PCA) is adopted to determine the stem–leaf angle, realizing the comprehensive automatic extraction of maize seedling phenotypes. Experiments show that the proposed method achieves high accuracy in extracting key phenotypic parameters. The mean relative errors for plant height, stem width, leaf length, stem-leaf angle, and leaf area are 0.76%, 2.93%, 1.26%, 2.13%, and 3.33%, respectively. Compared with existing methods as far as we know, the proposed method significantly improves extraction efficiency by reducing the processing time per plant to within 5 min while maintaining such high accuracy.