AIMC Topic: Soil

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Soil geochemistry and contamination zoning in Northeastern Ghana: insights from the Bongo and Talensi districts.

Environmental geochemistry and health
Reliable geochemical baselines are largely absent for northern Ghana, limiting efforts to distinguish natural element variability from human-induced contamination. This study addresses that gap by evaluating soil geochemical compositions in the Bongo...

Dual niche modeling with GEE and SHAP for predicting habitat shifts of Haloxylon ammodendron and Cistanche deserticola under climate change.

PloS one
Haloxylon ammodendron, a keystone woody species, and its parasitic plant, Cistanche deserticola, play critical roles in sustaining arid ecosystems and supporting regional economies. However, their distribution is increasingly threatened by global cli...

Biochar Lifecycle Contribution to Carbon Neutrality: Key Factors and Regulatory Mechanisms.

Environmental science & technology
Biochar, a carbon-enriched material derived from pyrolyzed biomass, has evolved from an ancient farming practice into a mature carbon sequestration technology, emerging as a pivotal strategy for achieving carbon neutrality. Nevertheless, heterogeneou...

Interpretable artificial intelligence modeling of pre-emergence herbicide bioactivity in weakly weathered soils for optimized dose recommendations, Part I: Diclosulam.

The Science of the total environment
Conventional herbicide recommendations seldom consider soil physicochemical attributes beyond texture, overlooking key factors that govern bioavailability and environmental fate. This study presents an integrated framework for optimizing the doses of...

Land use change and soil salinization in the Sundarbans: a machine-learning based analysis of long-term transformation and future projections.

Environmental monitoring and assessment
Quantitative data on coastal land use changes are essential for effective resource management and sustainable development. In this study, we examined land use and land cover (LULC) changes, along with erosion and accretion, in the climate-sensitive S...

Predicting plant stress using SAM-L: novel self-adaptive-meta learner with XAI based on soil moisture and chlorophyll analysis.

Scientific reports
Recent advancements in precision agriculture have introduced innovative approaches to addressing plant stress, a critical factor influencing crop productivity and agricultural sustainability. Accurate, real-time prediction of plant stress has become ...

Deep learning-driven investigation of nanoplastic impacts on soil protist behavior in soil chips.

Environmental pollution (Barking, Essex : 1987)
Nanoplastics are emerging environmental contaminants that increasingly threaten soil ecosystems, yet their effects on microbial behavior remain poorly understood. This is mainly due to the lack of experimental tools capable of directly observing micr...

Explainable AI-driven interpretation of environmental drivers of tomato fruit expansion in smart greenhouses using IoT sensing.

Scientific reports
Tomato fruit expansion is a key physiological process that determines fruit size, marketability, and yield, yet its quantitative and threshold-based response to microclimatic factors in smart greenhouses has been insufficiently studied. This study de...

Global Meta-Analysis Integrated with Machine Learning Assesses Context-Dependent Microplastic Effects on Soil Microbial Biomass Carbon and Nitrogen.

Environmental science & technology
Microplastics (MPs) in soil can paradoxically stimulate microbial biomass in a highly context-dependent manner, potentially inducing decomposition and affecting carbon and nitrogen cycles. We conducted a global meta-analysis with 90 studies (710 obse...

Machine learning-based prediction of deep soil metal(loid) contamination in industrial areas: Role of surface environmental factors.

Environmental pollution (Barking, Essex : 1987)
Predicting the distribution of soil contamination is crucial for targeted remediation efforts and risk prevention, especially considering the high costs associated with in-situ contamination surveys. This study proposes a random forest (RF)-based app...