Journal Paper Digests 2026 #20
- Propagation and preservation of AI-discovered problem-solving strategies in human culture
- Salt effects on soil thermal conductivity: Database construction and impact quantification
- Dominance of temperature over organic matter composition and mineral protection in priming effect regulation
- High-resolution mapping of compressed vis-NIR spectral data of Danish topsoils
- Impact over Origin: Rethinking management of introduced species in novel ecosystems
- Integrating Landsat-based irrigation mapping and actual evapotranspiration estimates with hydrological modelling to assess regional irrigation dynamics in a large basin experiencing extreme climate variability: Towards a national operational irrigation water accounting system
- Satellite embeddings for crop type classification: a comparative examination
- Indicators of Soil Health and Degradation: Contrasting Farmer and Agricultural Advisors’ Mental Models
- Land-use driven microbial community legacy shapes soil functionality
Land-use driven microbial community legacy shapes soil functionality
Microbial communities are central to soil ecosystem function. However, the extent to which functional diversity is conserved across communities, providing resilience to environmental change, remains uncertain. Here, we investigated how microbial legacy and soil properties shape community assembly and function, by cross-inoculating distinct microbial communities into sterilised soils from agricultural and semi-natural habitats. Over a 10-month incubation, the soil environment drove microbial community convergence at high taxonomic ranks, but fine-scale community composition and functional outcomes remained distinct. Microbial communities showed a ‘home-field advantage’ in soil carbon use that increased cumulative respiration by 16-26% in agricultural soils and by 26-84% in semi-natural soils, demonstrating limited redundancy of broad ecological function between soil communities. Distinct communities also caused significant shifts in soil pH associated with contrasting inorganic nitrogen transformations, exposing limited conservation of specialised metabolic functions. In summary, microbial community legacy had a lasting influence on carbon and nitrogen cycling, and thus, the effects of anthropogenic land use change on soil microbial functional diversity will likely have substantial impacts on these key ecosystem processes.
Indicators of Soil Health and Degradation: Contrasting Farmer and Agricultural Advisors’ Mental Models
Soil degradation threatens crop productivity and rural livelihoods worldwide. Efforts to reverse this trend are more likely to succeed when farmers and agricultural advisors share an understanding of what defines ‘healthy’ or ‘degraded’ soils, since both groups influence soil management decisions. In Nepal’s Terai (lowland plains), the country’s main grain-producing region, such knowledge remains poorly documented. This study investigates which indicators farmers and advisors use to assess soil health and how they perceive local soil fertility trends. Semi-structured interviews with 43 farmers and five advisors were analysed using a mental model approach and visualised as weighted semantic web diagrams. After applying frequency thresholds, 32 indicators were retained and grouped into five categories: sensory signs, agronomic outcomes, biological indicators, management activities and scientific testing. Despite broad agreement that local soils are degrading (79% of farmers; all advisors), each group relied on distinct measures. Farmers mainly used sensory, agronomic and biological indicators, whereas advisors emphasised quantifiable indicators and scientific testing. Only 11 of the 32 indicators overlapped, revealing gaps between advisory frameworks and farmers’ soil assessment practices. Gender also subtly influenced farmers’ soil assessments. These gaps reflect more than divergent vocabularies: each group assesses soil health through the lens of their own experience, shaped by how they manage and interact with the land. Any single-indicator framework, whether farmer-derived or scientific, is therefore inherently partial. Drawing on this diversity of perspectives is essential for understanding and responding to complex environmental challenges, and developing soil management guidance that integrates local experience, gendered knowledge and scientific approaches.
Satellite embeddings for crop type classification: a comparative examination
Embedding datasets encode complex relationships among multiple sources of Earth observation data into a compact format. Here, we evaluated the utility of a 10-m global Satellite Embedding product (SE) for classifying crop types in central California for the year 2020. We compared the classification accuracy of a random forest model based exclusively on the SE layer to an existing random forest model with multiple imagery inputs. Our results showed the SE-based classification had higher agreement with the reference dataset (California Department of Water Resources crop map) than the classification based on Landsat and National Agricultural Imagery Program inputs (94.7% versus. 91.9% overall accuracy, respectively). The performance of individual crop types was consistent across models, ranging from high agreement for rice (98.4% versus 98% accuracy) to lower agreement for pasture, grain, and fallow/young perennial classes (< 65% accuracy in both models). The SE-based workflow used three times less cloud-based computational resources and represented substantial savings of predictor development time. Geospatial embedding products can aid classification efforts by reducing predictor development time and processing demands while maintaining classification accuracy.
Integrating Landsat-based irrigation mapping and actual evapotranspiration estimates with hydrological modelling to assess regional irrigation dynamics in a large basin experiencing extreme climate variability: Towards a national operational irrigation water accounting system
Irrigated agriculture is essential for regional and global food security and poverty alleviation — but growing water scarcity and climate change challenge the balance between agricultural production, communities’ livelihoods and ecosystem sustainability. The Murray-Darling Basin (MDB, ∼1.06 million km2), Australia’s largest river system and primary agricultural region, exemplifies this challenge. This research presents the first multi-decadal (1987–2024), high-resolution (∼30 m) Landsat-based monthly irrigation water-use (IWU) dynamics across the MDB. The assessment is underpinned by a framework using advanced blending and spatiotemporal interpolation that produces ∼30 m monthly, gap-free vegetation indices and actual evapotranspiration (ETa) for the MDB. Spatiotemporal patterns of summer irrigation are identified and predicted by Artificial Neural Networks (ANNs) from the Normalised Difference Vegetation Index (NDVI) and ETa timeseries. Together with soil moisture accounting, they provide timeseries quantifying irrigated areas and estimates of both rainfall-derived and irrigation-derived IWU components. Validation against: (i) independent field ETa; (ii) multi-scale agricultural survey statistics; and (iii) water-use records demonstrated encouraging performance. The annual timeseries of irrigated area and IWU reveal nearly four decades of evolution in irrigated extent, broad irrigation composition, and water-use under shifting climatic and management conditions; offering unprecedented insights into how long-term irrigation dynamics respond to hydroclimate variability and policy reform. Underpinned by operational Earth Observation inputs and an established ETa product, the framework demonstrates how Landsat observations can support systematic irrigation accounting spanning ∼40 years over large regions. While several components remain under development, the framework establishes a scalable and transferable pathway towards a sustained irrigation information product, consistent with emerging principles for operational satellite-based environmental monitoring systems.
Impact over Origin: Rethinking management of introduced species in novel ecosystems
Debates in invasion biology often emphasize species’ places of origin, although humans have moved organisms for millennia and ecological baselines are historically contingent. Many ecosystems now exist in novel states where restoration to presettlement conditions is infeasible and introduced species are embedded in ecological function. Although a global phenomenon, this is especially evident in Florida, USA, where hundreds of introduced taxa coexist with natives amid rapid environmental change. We argue that long-established introduced species, particularly in human-dominated landscapes, should be managed according to feasibility and empirically evaluated ecological, economic, and sociocultural impacts rather than reflexive eradication based on origin alone. This approach does not discount severe harm from some introductions; instead, it advances a research-first framework centered on impact assessment, functional role evaluation, and feasibility-based triage to prioritize limited conservation resources. As ecological novelty becomes widespread, origin-based heuristics grow increasingly misaligned with ecological reality, and invasion governance must evolve accordingly.
High-resolution mapping of compressed vis-NIR spectral data of Danish topsoils
Visible and near-infrared (vis-NIR) spectroscopy enables rapid and cost-effective soil characterization, supporting the estimation of soil chemical, physical, and biological properties. Most applications, however, focus on using spectroscopy to predict individual soil properties and augment conventional laboratory analyses, while the potential of spectral information to study soil variation in space remains largely unexplored. Latent variables derived from the dimensionality reduction of spectral data offer a compact representation of soil variability, capturing multiple soil properties simultaneously. When spatially predicted, these latent variables can provide a means to investigate soil-landscape relationships in a spatially explicit context. This study aimed to (i) model the spatial variation of latent variables derived from compressed topsoil vis-NIR spectral data across Denmark at 10 m resolution using a digital soil mapping approach; (ii) identify the drivers, in relation to SCORPAN factors, controlling their spatial variation; and (iii) examine how the predicted latent variables represent the soil-landscape relationships across Denmark. An earlier study compressed the data using principal component analysis, kernel principal component analysis, shallow autoencoders, and convolutional autoencoders. To predict the latent variables in space, we used 32 predictors comprising harmonized environmental layers representing climate, relief, and parent material. A weighted machine learning algorithm was used to model each latent variable, with bootstrap resampling providing pixel-level uncertainty estimates. Overall, climate was the main driver across all methods, followed by topography and parent material, mainly the extent of clay till deposits. By integrating compressed spectral data with diverse spatial predictors, we captured complex, non-linear soil-landscape relationships. The resulting high-resolution maps offer a spatial description of soil variation in relation to soil-forming factors and spectral signature. Our maps can be used to improve point-based predictions of soil properties or provide enhanced covariates to support digital soil mapping of soil properties and classes.
Dominance of temperature over organic matter composition and mineral protection in priming effect regulation
Consensus is emerging that the turnover of native soil organic matter (SOM) through the priming effect (PE) can be controlled by plant, soil and climate variables. Here, we apply a systematic approach to the quantitative assessment of the relative importance of plant C input quality, SOM persistence and temperature in regulating PE. We conducted a unique laboratory microcosm experiment comparing OM fractions originating from the topsoil and subsoil of a Central European temperate mixed forest incubated at two different temperatures, simulating the effects of climate warming. We repeatedly added 13C-labelled deciduous or coniferous leaf leachates or root exudates and determined PE. Our study emphasizes the critical role of temperature in regulating PE, with warming generally leading to reduced SOM turnover via PE and thus enhancing C storage in soils. Further, our results indicate the importance of OM composition together with mineral protection for PE regulation. Moreover, our findings challenge contemporary concepts that see mineral-associated OM as a stable SOM pool, and provide proof that at least a part of this pool is available with increased temperature. Our results thus provide new perspectives for future modelling and management efforts, and these may help promote enhanced SOM accumulation under future climate scenarios.
Salt effects on soil thermal conductivity: Database construction and impact quantification
Soil thermal conductivity (STC) is a key thermal property that affects transport of heat and water. Although the influences of bulk density, water content, and temperature on STC has been widely reported in previous studies, the effect of various salt types at a wide range of concentrations has been understudied, which leads to unsatisfactory accuracy of many STC predictive models. To quantify the impact of salts on STC, the most comprehensive STC database (to the best of our knowledge) considering salt effects was established in this study. Meanwhile, three machine learning models (i.e., Extreme Gradient Boosting-XGBoost, Random Forest-RF, and Backpropagation Neural Network-BPNN), explainable artificial intelligence (e.g., feature importance ranking and partial dependence plots), and Spearman correlation analysis were employed to quantify the effects of salt types and concentrations on STC. The results show that XGBoost performs best within the full STC range (R2= 0.86, RMSE= 0.16 W m−1 K−1), while BPNN performs better at greater STC range (e.g., >2.0 W m⁻¹ K⁻¹). STC gradually transforms from significantly negative to significantly positive correlation as NaCl concentration increases from 0∼2 mol L−1 to 4∼6 mol L−1, but the opposite trend was observed for CaCl2. In addition, STC increases with increase of positive temperature, bulk density, water content, and decrease of sub-freezing temperatures.
Propagation and preservation of AI-discovered problem-solving strategies in human culture
Intelligent machines have the potential to uncover problem-solving strategies beyond human discovery. Emerging evidence from competitive gameplay, such as Go and chess, demonstrates that AI systems are evolving from mere tools to sources of cultural innovation adopted by humans. However, the conditions under which intelligent machines transition from tools to drivers of persistent cultural change remain unclear. We identify three key dimensions that modulate machine influence on human problem-solving: the discovered strategies must be non-trivial, learnable, and offer a clear advantage. Using a cultural transmission experiment, we demonstrate that when these conditions are met, machine-discovered strategies can be transmitted, understood, and preserved by human populations, leading to enduring cultural shifts. Conversely, using agent-based simulations, we show how machine influence is constrained in the absence of these conditions. These findings provide a framework for understanding how machines can persistently expand human cognitive skills and underscore the need to consider their broader implications for human cognition and cultural evolution.