Toward precision soil health: A regional framework for site-specific management across Missouri
- URL: http://arxiv.org/abs/2510.26815v1
- Date: Tue, 28 Oct 2025 03:46:24 GMT
- Title: Toward precision soil health: A regional framework for site-specific management across Missouri
- Abstract summary: Missouri's diverse landscapes limit the effectiveness of broad generalized management recommendations.<n>A regional soil clustering framework designed to support precision soil health management strategies across the state.
- Score: 0.21056212639738645
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Effective soil health management is crucial for sustaining agriculture, adopting ecosystem resilience, and preserving water quality. However, Missouri's diverse landscapes limit the effectiveness of broad generalized management recommendations. The lack of resolution in existing soil grouping systems necessitates data driven, site specific insights to guide tailored interventions. To address these critical challenges, a regional soil clustering framework designed to support precision soil health management strategies across the state. The methodology leveraged high resolution SSURGO dataset, explicitly processing soil properties aggregated across the 0 to 30 cm root zone. Multivariate analysis incorporating a variational autoencoder and KMeans clustering was used to group soils with similar properties. The derived clusters were validated using statistical metrics, including silhouette scores and checks against existing taxonomic units, to confirm their spatial coherence. This approach enabled us to delineate soil groups that capture textures, hydraulic properties, chemical fertility, and biological indicators unique to Missouri's diverse agroecological regions. The clustering map identified ten distinct soil health management zones. This alignment of 10 clusters was selected as optimal because it was sufficiently large to capture inherited soil patterns while remaining manageable for practical statewide application. Rooting depth limitation and saturated hydraulic conductivity emerged as principal variables driving soil differentiation. Each management zone is defined by a unique combination of clay, organic matter, pH, and available water capacity. This framework bridges sophisticated data analysis with actionable, site targeted recommendations, enabling conservation planners, and agronomists to optimize management practices and enhance resource efficiency statewide.
Related papers
- GreenSeg: Ground Segmentation Algorithm for Agricultural Robots in Mediterranean Greenhouses using RGB-D Point Clouds [41.99844472131922]
GreenSeg is a robust perception framework for autonomous navigation using RGB-D sensing.<n>Results show that GreenSeg consistently outperforms benchmark segmentation methods.
arXiv Detail & Related papers (2026-05-24T22:19:38Z) - GeoDecider: A Coarse-to-Fine Agentic Workflow for Explainable Lithology Classification [52.5232577739543]
Lithology classification aims to infer subsurface rock types from well-logging signals.<n>Existing methods still treat lithology classification as a single-pass classification task.<n>We propose GeoDecider, a coarse-to-fine agentic workflow that enables accurate and explainable lithology classification.
arXiv Detail & Related papers (2026-05-05T05:42:51Z) - Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings [48.54230513143053]
This study examines whether AlphaEarth Foundation embeddings offer a more informative way to describe basin characteristics.<n>We find that models using them achieve higher accuracy when predicting flows in basins not used for training.
arXiv Detail & Related papers (2026-01-04T15:14:16Z) - Seeing Soil from Space: Towards Robust and Scalable Remote Soil Nutrient Analysis [0.0]
This study presents a robust and scalable modeling system for estimating soil properties in croplands.<n>The system employs a hybrid modeling approach, combining the indirect methods of modeling soil through proxies and drivers with direct spectral modeling.<n>We validate the system on a harmonized dataset that covers Europes cropland soils across diverse pedoclimatic zones.
arXiv Detail & Related papers (2025-12-10T12:08:55Z) - Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning [1.4786253394033289]
High-resolution soil maps are essential for characterizing permafrost distribution, identifying vulnerable areas, and informing adaptation strategies.<n>We present MISO, a vision-based machine learning (ML) model to produce statewide fine-scale soil maps for near-surface permafrost and soil taxonomy.<n>We compare MISO with Random Forest (RF), a traditional ML model that has been widely used in soil mapping applications.
arXiv Detail & Related papers (2025-06-17T22:09:48Z) - Adaptive Per-Tree Canopy Volume Estimation Using Mobile LiDAR in Structured and Unstructured Orchards [42.32889225423819]
We present a real-time system for per-tree canopy volume estimation using mobile LiDAR data collected during routine robotic navigation.<n>We evaluate the system across two commercial orchards, one pistachio orchard with regular spacing and one almond orchard with dense, overlapping crowns.
arXiv Detail & Related papers (2025-06-09T08:40:28Z) - Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
arXiv Detail & Related papers (2025-05-21T03:57:10Z) - Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning [53.527506374566485]
We propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN.<n>We show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.
arXiv Detail & Related papers (2025-05-07T11:37:23Z) - Unveiling 3D Ocean Biogeochemical Provinces in the North Atlantic: A Systematic Comparison and Validation of Clustering Methods [1.2932412290302258]
Aim was to objectively define regions of the North Atlantic through systematic comparison of clustering methods.<n>About 300 million measured salinity, temperature, and oxygen, nitrate, phosphate and silicate concentration values served as input.<n>Results showed that UMAP-DBSCAN best represented the data.
arXiv Detail & Related papers (2025-04-25T08:55:40Z) - Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning [4.414885369283509]
Given inputs of diverse soil characteristics and climate data, we aimed to build a model to predict accurate land emissions.<n>SDSA-KGML models achieve higher local accuracy for the specified states in the Midwest Region.
arXiv Detail & Related papers (2025-02-20T18:52:24Z) - Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.<n>Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.<n>Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
arXiv Detail & Related papers (2024-10-14T17:22:55Z) - Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation [9.99241578238986]
Wetlands are important to communities, offering benefits ranging from water purification to flood protection to recreation and tourism.
Data-driven solutions are feasible, but are complicated by significant data sparsity due to the low proportion of wetlands in many areas of interest in the southwestern US.
This makes it hard to develop data-driven models that can help guide the identification of additional wetland areas.
arXiv Detail & Related papers (2024-06-08T21:25:24Z) - Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation [42.39035033967183]
Service robots need a real-time perception system that understands their surroundings and identifies their targets in the wild.
Existing methods, however, often fall short in generalizing to new crops and environmental conditions.
We propose a novel approach to enhance domain generalization using knowledge distillation.
arXiv Detail & Related papers (2023-04-03T14:28:29Z) - Jalisco's multiclass land cover analysis and classification using a
novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis.
In this work, we combine three real-world open data sources to obtain 13 channels.
Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
arXiv Detail & Related papers (2022-01-26T14:58:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.