@misc{PalacioBunnRahnetal., author = {Palacio, Juan Camilo Rivera and Bunn, Christian and Rahn, Eric and Little-Savage, Daisy and Schmidt, Paul G{\"u}nter and Ryo, Masahiro}, title = {Geographic-scale coffee cherry counting with smartphones and deep learning}, series = {Plant Phenomics}, volume = {6}, journal = {Plant Phenomics}, publisher = {American Association for the Advancement of Science (AAAS)}, issn = {2643-6515}, doi = {10.34133/plantphenomics.0165}, pages = {11}, abstract = {Deep learning and computer vision, using remote sensing and drones, are 2 promising nondestructive methods for plant monitoring and phenotyping. However, their applications are infeasible for many crop systems under tree canopies, such as coffee crops, making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low cost. This study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach. The approach uses basic smartphones to take a few pictures of coffee trees; 2,968 trees were investigated with 8,904 pictures in Jun{\´i}n and Piura (Peru), Cauca, and Quind{\´i}o (Colombia) in 2022, with the help of nearly 1,000 smallholder coffee farmers. Then, we trained and validated YOLO (You Only Look Once) v8 for detecting cherries in the dataset in Peru. An average number of cherries per picture was multiplied by the number of branches to estimate the total number of cherries per tree. The model's performance in Peru showed an R2 of 0.59. When the model was tested in Colombia, where different varieties are grown in different biogeoclimatic conditions, the model showed an R2 of 0.71. The overall performance in both countries reached an R2 of 0.72. The results suggest that the method can be applied to much broader scales and is transferable to other varieties, countries, and regions. To our knowledge, this is the first AI-powered method for counting coffee cherries and has the potential for a geographic-scale, multiyear, photo-based phenotypic monitoring for coffee crops in low-income countries worldwide.}, language = {en} } @misc{SchillerJaenickeRecklingetal., author = {Schiller, Josepha and J{\"a}nicke, Clemens and Reckling, Moritz and Ryo, Masahiro}, title = {Higher crop rotational diversity in more simplified agricultural landscapes in Northeastern Germany}, series = {Landscape Ecology}, volume = {39}, journal = {Landscape Ecology}, number = {4}, publisher = {Springer Science and Business Media LLC}, issn = {1572-9761}, doi = {10.1007/s10980-024-01889-x}, pages = {18}, abstract = {Context Both crop rotational diversity and landscape diversity are important for ensuring resilient agricultural production and supporting biodiversity and ecosystem services in agricultural landscapes. However, the relationship between crop rotational diversity and landscape diversity is largely understudied. Objectives We aim to assess how crop rotational diversity is spatially organised in relation to soil, climate, and landscape diversity at a regional scale in Brandenburg, Germany. Methods We used crop rotational richness, Shannon's diversity and evenness indices per field per decade (i.e., crop rotational diversity) as a proxy for agricultural diversity and land use and land cover types and habitat types as proxies for landscape diversity. Soil and climate characteristics and geographical positions were used to identify potential drivers of the diversity facets. All spatial information was aggregated at 10 × 10 km resolution, and statistical associations were explored with interpretable machine learning methods. Results Crop rotational diversity was associated negatively with landscape diversity metrics and positively with soil quality and the proportion of agricultural land use area, even after accounting for the other variables. Conclusion Our study indicates a spatial trade-off between crop and landscape diversity (competition for space), and crop rotations are more diverse in more simplified landscapes that are used for agriculture with good quality of soil conditions. The respective strategies and targets should be tailored to the corresponding local and regional conditions for maintaining or enhancing both crop and landscape diversity jointly to gain their synergistic positive impacts on agricultural production and ecosystem management.}, language = {en} } @misc{DegruneDumackRyoetal., author = {Degrune, Florine and Dumack, Kenneth and Ryo, Masahiro and Garland, Gina and Romdhane, Sana and Sagha{\"i}, Aur{\´e}lien and Banerjee, Samiran and Edlinger, Anna and Herzog, Chantal and Pescador, David S{\´a}nchez and Garc{\´i}a-Palacios, Pablo and Fiore-Donno, Anna Maria and Bonkowski, Michael and Hallin, Sara and Heijden, Marcel G. A. van der and Maestre, Fernando T. and Philippot, Laurent and Glemnitz, Michael and Sieling, Klaus and Rillig, Matthias C.}, title = {The impact of fungi on soil protist communities in European cereal croplands}, series = {Environmental Microbiology}, volume = {26}, journal = {Environmental Microbiology}, number = {7}, publisher = {Wiley}, issn = {1462-2912}, doi = {10.1111/1462-2920.16673}, pages = {9}, abstract = {Protists, a crucial part of the soil food web, are increasingly acknowledged as significant influencers of nutrient cycling and plant performance in farmlands. While topographical and climatic factors are often considered to drive microbial communities on a continental scale, higher trophic levels like heterotrophic protists also rely on their food sources. In this context, bacterivores have received more attention than fungivores. Our study explored the connection between the community composition of protists (specifically Rhizaria and Cercozoa) and fungi across 156 cereal fields in Europe, spanning a latitudinal gradient of 3000 km. We employed a machine-learning approach to measure the significance of fungal communities in comparison to bacterial communities, soil abiotic factors, and climate as determinants of the Cercozoa community composition. Our findings indicate that climatic variables and fungal communities are the primary drivers of cercozoan communities, accounting for 70\% of their community composition. Structural equation modelling (SEM) unveiled indirect climatic effects on the cercozoan communities through a change in the composition of the fungal communities. Our data also imply that fungivory might be more prevalent among protists than generally believed. This study uncovers a hidden facet of the soil food web, suggesting that the benefits of microbial diversity could be more effectively integrated into sustainable agriculture practices.}, language = {en} } @misc{GuoGuoPangetal., author = {Guo, Tongtian and Guo, Meiqi and Pang, Yue and Sun, Xiangyun and Ryo, Masahiro and Liu, Nan and Zhang, Yingjun}, title = {Ungulate herbivores promote beta diversity and drive stochastic plant community assembly by selective defoliation and trampling: From a four-year simulation experiment}, series = {Journal of Ecology}, volume = {112}, journal = {Journal of Ecology}, number = {9}, publisher = {Wiley}, issn = {0022-0477}, doi = {10.1111/1365-2745.14370}, pages = {1992 -- 2006}, abstract = {1. Ungulate herbivores shape grassland plant communities at multiple scales, ultimately affecting ecosystem function. However, ungulates have complex effects on grasslands, including defoliation, trampling, excreta return and their interactions. Moreover, the effects of ungulate density on grasslands are regulated by these three mechanisms. Nevertheless, how these three mechanisms affect biodiversity at multiple scales and community assembly remains poorly understood. 2. Here, we conducted a 4-year novel field experiment to disentangle the effects of defoliation, trampling, and excreta return by ungulates on plant community assembly in a temperate grassland in Inner Mongolia, China. This experiment set two different scenarios: moderate ungulate density (Moderate, characterised by selective defoliation and moderate trampling) and high ungulate density (Intense, characterised by non-selective defoliation and heavy trampling), including different combinations of defoliation, trampling and excreta return in each scenario. 3. We found that defoliation and trampling increased stochasticity in community assembly and promoted alpha and beta diversity under both scenarios. Specifically, defoliation promoted the coexistence of species with multiple resource acquisition strategies (higher functional trait diversity) by reducing interspecific competition; trampling tended to facilitate random species colonisation. Conversely, excreta return favoured grasses, promoting deterministic assembly and impacting species coexistence. Notably, selective defoliation in the Moderate scenario led to a dominance of stochastic processes during community assembly, whereas non-selective defoliation still did not change the dominance of deterministic processes. Further, communities subject to selective defoliation were insensitive to changes in soil properties caused by trampling and excreta return, maintaining a high-level beta diversity and the stochastic of community assembly. 4. Synthesis: Our study provides important insights into the mechanisms by which ungulate herbivores influence plant community assembly, suggesting that defoliation and trampling have the potential to drive stochastic processes, while excreta return plays the opposite role. Our study also suggests that selective foraging by ungulates acts as stronger stochastic forces during community assembly compared to non-selective defoliation. These results imply that considering ungulate feeding preferences and foraging behaviour in grassland management will help prevent biodiversity loss and biotic homogenisation.}, language = {en} } @misc{SafonovaRyo, author = {Safonova, Anastasiia and Ryo, Masahiro}, title = {Deep learning improves point density in PS-InSAR data toward finer-scale land surface displacement detection}, series = {IEEE Access}, volume = {12}, journal = {IEEE Access}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {2169-3536}, doi = {10.1109/ACCESS.2024.3459099}, pages = {132754 -- 132762}, abstract = {The permanent scatterer interferometric aperture radar (PS-InSAR) technique is used to measure and monitor displacements of the Earth's surface over time. While the approach is promising for large-scale deformation, the density of the received PS points is insufficient for localized deformation analysis. In this first work, we aim to improve the technique by increasing the point density of high-precision deformation monitoring in PS-InSAR data by developing a convolutional long short-term memory (ConvLSTM) model that predicts PS points on different land covers, such as forest, urban, natural, water, and combinations among them. The proposed architecture, PS-ConvLSTM, was trained on a temporary dataset with interferograms to classify stable and unstable PS pixels from over 200,000 site images obtained from the city of Barcelona, Spain. The result showed that the trained PS-ConvLSTM model is highly compatible with the method currently used, which requires a large manual effort by an expert (accuracy: 99\%). In addition, the proposed approach increased the point density by 15\%, indicating that ConvLSTM is a promising approach for increasing the point density in PS-InSAR data and thus improving localized deformation analysis.}, language = {en} } @misc{BiLiMeidletal., author = {Bi, Mohan and Li, Huiying and Meidl, Peter and Zhu, Yanjie and Ryo, Masahiro and Rillig, Matthias C.}, title = {Number and dissimilarity of global change factors influences soil properties and functions}, series = {Nature Communications}, volume = {15}, journal = {Nature Communications}, number = {1}, publisher = {Springer Science and Business Media LLC}, issn = {2041-1723}, doi = {10.1038/s41467-024-52511-2}, pages = {14}, abstract = {Soil biota and functions are impacted by various anthropogenic stressors, including climate change, chemical pollution or microplastics. These stressors do not occur in isolation, and soil properties and functions appear to be directionally driven by the number of global change factors acting simultaneously. Building on this insight, we here hypothesize that co-acting factors with more diverse effect mechanisms, or higher dissimilarity, have greater impacts on soil properties and functions. We created a factor pool of 12 factors and calculated dissimilarity indices of randomly-chosen co-acting factors based on the measured responses of soil properties and functions to the single factors. Results show that not only was the number of factors important, but factor dissimilarity was also key for predicting factor joint effects. By analyzing deviations of soil properties and functions from three null model predictions, we demonstrate that higher factor dissimilarity and a larger number of factors could drive larger deviations from null models and trigger more frequent occurrence of synergistic factor net interactions on soil functions (decomposition rate, cellulase, and β-glucosidase activity), which provides mechanistic insights for understanding high-dimensional effects of factors. Our work highlights the importance of considering factor similarity in future research on interacting factors.}, language = {en} } @misc{BreitenbachGerritsDementyevaetal., author = {Breitenbach, Romy and Gerrits, Ruben and Dementyeva, Polina and Knabe, Nicole and Schumacher, Julia and Feldmann, Ines and Radnik, J{\"o}rg and Ryo, Masahiro and Gorbushina, Anna A.}, title = {The role of extracellular polymeric substances of fungal biofilms in mineral attachment and weathering}, series = {npj Materials Degradation}, volume = {6}, journal = {npj Materials Degradation}, number = {1}, publisher = {Nature Publishing Group UK}, issn = {2397-2106}, doi = {10.1038/s41529-022-00253-1}, pages = {11}, abstract = {The roles extracellular polymeric substances (EPS) play in mineral attachment and weathering were studied using genetically modified biofilms of the rock-inhabiting fungus Knufia petricola strain A95. Mutants deficient in melanin and/or carotenoid synthesis were grown as air-exposed biofilms. Extracted EPS were quantified and characterised using a combination of analytical techniques. The absence of melanin affected the quantity and composition of the produced EPS: mutants no longer able to form melanin synthesised more EPS containing fewer pullulan-related glycosidic linkages. Moreover, the melanin-producing strains attached more strongly to the mineral olivine and dissolved it at a higher rate. We hypothesise that the pullulan-related linkages, with their known adhesion functionality, enable fungal attachment and weathering. The released phenolic intermediates of melanin synthesis in the Δ sdh1 mutant might play a role similar to Fe-chelating siderophores, driving olivine dissolution even further. These data demonstrate the need for careful compositional and quantitative analyses of biofilm-created microenvironments.}, language = {en} } @misc{KakhaniTaghizadeh‐MehrjardiOmarzadehetal., author = {Kakhani, Nafiseh and Taghizadeh-Mehrjardi, Ruhollah and Omarzadeh, Davoud and Ryo, Masahiro and Heiden, Uta and Scholten, Thomas}, title = {Towards explainable AI : interpreting soil organic carbon prediction models using a learning-based explanation method}, series = {European Journal of Soil Science}, volume = {76}, journal = {European Journal of Soil Science}, number = {2}, publisher = {Wiley}, issn = {1351-0754}, doi = {10.1111/ejss.70071}, pages = {1 -- 18}, abstract = {An understanding of the key factors and processes influencing the variability of soil organic carbon (SOC) is essential for the development of effective policies aimed at enhancing carbon storage in soils to mitigate climate change. In recent years, complex computational approaches from the field of machine learning (ML) have been developed for modelling and mapping SOC in various ecosystems and over large areas. However, in order to understand the processes that account for SOC variability from ML models and to serve as a basis for new scientific discoveries, the predictions made by these data-driven models must be accurately explained and interpreted. In this research, we introduce a novel explanation approach applicable to any ML model and investigate the significance of environmental features to explain SOC variability across Germany. The methodology employed in this study involves training multiple ML models using SOC content measurements from the LUCAS dataset and incorporating environmental features derived from Google Earth Engine (GEE) as explanatory variables. Thereafter, an explanation model is applied to elucidate what the ML models have learned about the relationship between environmental features and SOC content in a supervised manner. In our approach, a post hoc model is trained to estimate the contribution of specific inputs to the outputs of the trained ML models. The results of this study indicate that different classes of ML models rely on interpretable but distinct environmental features to explain SOC variability. Decision tree-based models, such as random forest (RF) and gradient boosting, highlight the importance of topographic features. Conversely, soil chemical information, particularly pH, is crucial for the performance of neural networks and linear regression models. Therefore, interpreting data-driven studies requires a carefully structured approach, guided by expert knowledge and a deep understanding of the models being analysed.}, language = {en} } @misc{RiveraPalacioBunnRyo, author = {Rivera-Palacio, Juan C. and Bunn, Christian and Ryo, Masahiro}, title = {Factors affecting deep learning model performance in citizen science-based image data collection for agriculture : a case study on coffee crops}, series = {Computers and Electronics in Agriculture}, volume = {232}, journal = {Computers and Electronics in Agriculture}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0168-1699}, doi = {10.1016/j.compag.2025.110096}, pages = {1 -- 13}, abstract = {Citizen science is an effective approach for collecting extensive data scalable for deep learning, although data quality is debatable. However, few studies have determined the factors associated with data collection that affect model performance and potential sampling bias. This study aims to identify the factors that significantly influence the performance of a deep learning object detection model in agricultural prediction tasks. To do so, we analyzed errors in a You Only Look Once (YOLO v8) model trained for counting the number of coffee cherries in mobile pictures. The model was trained with 436 images taken in Colombia and Peru collected by local farmers as a citizen science approach. We analyzed the prediction errors of the model using 637 additional pictures. We then applied a linear mixed model (LMM) and a decision tree machine learning model to regress the model's error against predictor variables related to the following categories: photographer influence, geographic location, mobile phone characteristics, picture characteristics, and coffee varieties. Our results show the strong influence of photographer identity and adherence (whether the image collection protocol was followed or not) on model prediction error. Following the protocol can increase model performance from an R² of 0.48 to 0.73. Additionally, model performance varied significantly depending on photographer identity, with R² ranging from 0.45 to 0.93. In contrast, factors such as mobile phone characteristics (e.g., frontal camera resolution, flash type, and screen size), using the screen behind the branch to obscure other cherries, coffee varieties, and geographic location did not significantly affect prediction error. These findings demonstrate that data quality in citizen science-based data collection for enhancing model prediction can be achieved through straightforward and comprehensive protocols, customized volunteer training, and regular feedback from experts. Such measures collectively support the robust application of deep learning models in agriculture. Furthermore, this study demonstrated that any mobile device with a camera can contribute to citizen science initiatives, underscoring the potential and scalability of this approach in agricultural research.}, language = {en} } @misc{JeltschRoelekeAbdelfattahetal., author = {Jeltsch, Florian and Roeleke, Manuel and Abdelfattah, Ahmed and Arlinghaus, Robert and Berg, Gabriele and Blaum, Niels and De Meester, Luc and Dittmann, Elke and Eccard, Jana Anja and Fournier, Bertrand and Gaedke, Ursula and Gallagher, Cara and Govaert, Lynn and Hauber, Mark and Jeschke, Jonathan M. and Kramer-Schadt, Stephanie and Linst{\"a}dter, Anja and Lucke, Ulrike and Mazza, Valeria and Metzler, Ralf and Nendel, Claas and Radchuk, Viktoriia and Rillig, Matthias C. and Ryo, Masahiro and Scheiter, Katharina and Tiedemann, Ralph and Tietjen, Britta and Voigt, Christian C. and Weithoff, Guntram and Wolinska, Justyna and Zurell, Damaris}, title = {The need for an individual-based global change ecology}, series = {Individual-based ecology}, volume = {1}, journal = {Individual-based ecology}, publisher = {Pensoft Publishers}, address = {Sofia}, issn = {3033-0947}, doi = {10.3897/ibe.1.148200}, pages = {1 -- 18}, abstract = {Biodiversity loss and widespread ecosystem degradation are among the most pressing challenges of our time, requiring urgent action. Yet our understanding of their causes remains limited because prevailing ecological concepts and approaches often overlook the underlying complex interactions of individuals of the same or different species, interacting with each other and with their environment. We propose a paradigm shift in ecological science, moving from simplifying frameworks that use species, population or community averages to an integrative approach that recognizes individual organisms as fundamental agents of ecological change. The urgency of the biodiversity crisis requires such a paradigm shift to advance ecology towards a predictive science by elucidating the causal mechanisms linking individual variation and adaptive behaviour to emergent properties of populations, communities, ecosystems, and ecological interactions with human interventions. Recent advances in computational technologies, sensors, and analytical tools now offer unprecedented opportunities to overcome past challenges and lay the foundation for a truly integrated Individual-Based Global Change Ecology (IBGCE). Unravelling the potential role of individual variability in global change impact analyses will require a systematic combination of empirical, experimental and modelling studies across systems, while taking into account multiple drivers of global change and their interactions. Key priorities include refining theoretical frameworks, developing benchmark models and standardized toolsets, and systematically incorporating individual variation and adaptive behaviour into empirical field work, experiments and predictive models. The emerging synergies between individual-based modelling, big data approaches, and machine learning hold great promise for addressing the inherent complexity of ecosystems. Each step in the development of IBGCE must systematically balance the complexity of the individual perspective with parsimony, computational efficiency, and experimental feasibility. IBGCE aims to unravel and predict the dynamics of biodiversity in the Anthropocene through a comprehensive study of individual organisms, their variability and their interactions. It will provide a critical foundation for considering individual variation and behaviour for future conservation and sustainability management, taking into account individual-to-ecosystem pathways and feedbacks.}, language = {en} } @misc{GuoGuoRyoetal., author = {Guo, Tongtian and Guo, Meiqi and Ryo, Masahiro and Rillig, Matthias C. and Liu, Nan and Zhang, Yingjun}, title = {Ungulate herbivory affects grassland soil biota β-diversity and community assembly via modifying soil properties and plant root traits}, series = {New phytologist : international journal of plant science}, volume = {247}, journal = {New phytologist : international journal of plant science}, number = {1}, publisher = {Wiley}, address = {Oxford}, issn = {0028-646X}, doi = {10.1111/nph.70199}, pages = {281 -- 294}, abstract = {Summary Ungulate herbivory, a widespread and complex disturbance, shapes grassland biodiversity and functions primarily through three mechanisms: defoliation, trampling, and excreta return. However, the specific effects of these mechanisms on soil biodiversity and community assembly remain unclear. We conducted a 4-yr factorial experiment in the Eurasian steppe to investigate how defoliation, trampling, and excreta return influence soil bacterial, fungal, and nematode β-diversity and community assembly under moderate- and high-density ungulate grazing scenarios. Our findings reveal that herbivores affect soil biota through multiple pathways at different grazing intensities. Specifically, selective defoliation in the moderate-density scenario promoted stochastic community assembly of nematodes and fungi by increasing the specific root length of plant communities. Excreta return encouraged stochastic bacterial communities by carbon input, while urine-induced acidification and elevated ammonium levels promoted environmental filtering of bacteria and nematodes. In the high-density scenario, non-selective defoliation and heavy trampling created harsher soil conditions, reducing bacterial and nematode β-diversity via habitat filtering and diminishing association of soil biota with plant roots. This study explored how different components of ungulate behaviour influence soil community assembly and highlighted the crucial role of root traits in mediating soil biota responses, providing insights into the mechanisms of soil biodiversity maintenance under complex disturbances.}, language = {en} } @misc{SchillerStillerRyo, author = {Schiller, Josepha and Stiller, Stefan and Ryo, Masahiro}, title = {Artificial intelligence in environmental and earth system sciences : explainability and trustworthiness}, series = {Artificial intelligence review : an international science and engineering journal}, volume = {58}, journal = {Artificial intelligence review : an international science and engineering journal}, number = {10}, publisher = {Springer Netherlands}, address = {Dordrecht}, issn = {1573-7462}, doi = {10.1007/s10462-025-11165-2}, pages = {1 -- 23}, abstract = {Explainable artificial intelligence (XAI) methods have recently emerged to gain insights into complex machine learning models. XAI can be promising for environmental and Earth system science because high-stakes decision-making for management and planning requires justification based on evidence and systems understanding. However, an overview of XAI applications and trust in AI in environmental and Earth system science is still missing. To close this gap, we reviewed 575 articles. XAI applications are popular in various domains, including ecology, engineering, geology, remote sensing, water resources, meteorology, atmospheric sciences, geochemistry, and geophysics. XAI applications focused primarily on understanding and predicting anthropogenic changes in geospatial patterns and impacts on human society and natural resources, especially biological species distributions, vegetation, air quality, transportation, and climate-water related topics, including risk and management. Among XAI methods, the SHAP and Shapley methods were the most popular (135 articles), followed by feature importance (27), partial dependence plots (22), LIME (21), and saliency maps (15). Although XAI methods are often argued to increase trust in model predictions, only seven studies (1.2\%) addressed trustworthiness as a core research objective. This gap is critical because understanding the relationship between explainability and trust is lacking. While XAI applications continue to grow, they do not necessarily enhance trust. Hence, more studies on how to strengthen trust in AI applications are critically needed. Finally, this review underlines the recommendation of developing a "human-centered" XAI framework that incorporates the distinct views and needs of multiple stakeholder groups to enable trustworthy decision-making.}, language = {en} } @misc{ZhuMeidlLietal., author = {Zhu, Yanjie and Meidl, Peter and Li, Huiying and Bi, Mohan and Ryo, Masahiro and Rillig, Matthias C.}, title = {Concurrent anthropogenic stressors affect plant-soil systems with different plant diversity levels}, series = {New phytologist}, volume = {247}, journal = {New phytologist}, number = {4}, publisher = {Wiley}, address = {Oxford}, issn = {0028-646X}, doi = {10.1111/nph.70275}, pages = {1897 -- 1911}, abstract = {Summary Plant diversity strongly influences ecosystem functioning. Due to human activities, ecosystems are increasingly threatened by the co-occurrence of numerous anthropogenic pressures, but how they respond to this multifaceted phenomenon is poorly documented, and what role plant diversity plays in this process has not been investigated so far. Here, plant-soil systems with different plant diversity levels (3 vs 9 species) were subjected to an increasing number of anthropogenic stressors (0, 1, 2, 5, and 8). Results show that soil properties and functions were directionally driven by stressor number, irrespective of plant diversity level, and plant functional group evenness declined continuously along the stressor number gradient. The impact of stressors on plant-soil systems varied depending on plant diversity, and when plant diversity was higher, concurrent stressors may have interacted more to affect plant-soil systems. Notably, increasing the stressor number tended to diminish the effects of plant diversity. This study represents a first attempt to address the effect of plant diversity under multi-stressor combinations and highlights the importance of emphasizing plant-soil systems in the research field of multifactorial global change. We also suggest that efforts should be made to reduce the number of coacting stressors when managing plant-soil ecosystems.}, language = {en} } @misc{PalkaNendelWeissetal., author = {Palka, Marlene and Nendel, Claas and Weiß, Lucas and Schiller, Josepha and J{\"a}nicke, Clemens and Gaviria, Juliana Arbel{\´a}ez and Ryo, Masahiro}, title = {Cropping history, agronomic rules, and commodity prices shape crop rotations across Central Europe}, series = {Agricultural systems}, volume = {231}, journal = {Agricultural systems}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0308-521X}, doi = {10.1016/j.agsy.2025.104522}, pages = {1 -- 14}, abstract = {Context Crop rotations provide agronomic benefits over monocropping, such as enhanced nitrogen supply, improved weed and pest control, and higher yields. Although the theoretical understanding of optimal rotations has advanced, little is known about their real-world implementation and the factors influencing rotation decisions on large scales. Objective Understanding these factors is key for projecting future cropping patterns, refining agricultural policy, and improving crop models that often oversimplify rotation practices. This study identifies the drivers influencing operational crop rotations across Central Europe and projects future cropping patterns in the region. Methods We analyse over 16 million field-year combinations from Germany, Austria, and the Czech Republic. Using a random forest algorithm, we determine feature importance and apply a novel machine learning approach that incorporates uncertainty in farmers' decision-making to provide a potential outlook on cropping patterns until 2070. Results and Conclusions Historical cropping patterns, agronomic practices, and legume commodity prices significantly shaped crop rotations across the region. Projections indicate a substantial increase in legume cultivation over the coming decades, with implications for nitrogen budgets, dietary transitions, and in-silico upscaling. Significance Rather than optimizing rotations, this study identifies key drivers of operational crop rotations in Central Europe. The findings provide the basis for large-scale simulations that represent cropping patterns more realistically. To the best of our knowledge, the data set compiled here is the most extensive yet analysed in the context of operational crop rotation management.}, language = {en} } @misc{RazaRyoGhazaryanetal., author = {Raza, Ahsan and Ryo, Masahiro and Ghazaryan, Gohar and Baatz, Roland and Main-Knorn, Magdalena and Inforsato, Leonardo and Nendel, Claas}, title = {Predicting regional-scale groundwater levels at high spatial resolution using spatial random forest models}, series = {International journal of applied earth observation and geoinformation}, volume = {144}, journal = {International journal of applied earth observation and geoinformation}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1569-8432}, doi = {10.1016/j.jag.2025.104918}, pages = {1 -- 19}, abstract = {Groundwater is an important resource for sustainable crop growth and agricultural productivity, and groundwater level (GWL) fluctuations directly influence agroecosystems. However, GWL data for most regions is unavailable because of spatial scarcity and discontinuous groundwater observations. In recent studies, machine learning techniques have proven to be a useful tool for producing GWL estimates, but these techniques may not necessarily take into account distances and observations from the nearest points to unmonitored sites as covariates. In this study, we explored the potential of these additional spatial covariates by implementing Random Forest for Spatial Interpolation (RFSI) to generate high-resolution (1 km²) GWL estimates on a monthly scale. To evaluate the effectiveness of the RFSI model, we performed a thorough comparison of the RFSI model with a conventional Random Forest (RF) model, a Random Forest for spatial data (RFsp) and a Support Vector Machine (SVM). The framework was implemented using GWL observation data for the period 2001 to 2022 in Brandenburg, Germany. The RFSI model exhibited the highest predictive accuracy for GWL predictions at testing points, achieving an R² value of 0.86, surpassing the performance of RF, RFsp and SVM with R² values of 0.75, 0.72 and 0.67, respectively. Incorporating nearby-well information substantially improved 1 km GWL predictions: RFSI achieved the lowest RMSE (3.77 m), as compared to RF (4.85 m), RFsp (5.04 m), and SVM (5.59 m). Notably, among the models tested, RF, RFsp, and SVM showed notable inaccuracies, substantially underestimating groundwater levels (GWL) in deeper wells (>10 m) and overestimating levels in shallow wells (1-10 m). In contrast, RFSI performed better, with smaller overestimations (up to ~1.1 m) and less severe underestimations (up to ~-3.9 m). Furthermore, the RFSI model demonstrated significantly higher computational efficiency compared to RFsp and SVM, particularly for large training datasets and high-resolution prediction mapping.}, language = {en} } @misc{Rivera‐PalacioBunnRyo, author = {Rivera-Palacio, Juan C. and Bunn, Christian and Ryo, Masahiro}, title = {Smartphone-based monitoring identifies the importance of farm size and soil type for coffee tree productivity at a large geographic scale}, series = {Journal of sustainable agriculture and environment}, volume = {4}, journal = {Journal of sustainable agriculture and environment}, number = {4}, publisher = {Wiley}, address = {Hoboken, NJ}, issn = {2767-035X}, doi = {10.1002/sae2.70111}, pages = {1 -- 9}, abstract = {Smartphone-based monitoring has been increasingly applied to coffee crops for multiple tasks, such as predicting coffee tree productivity. However, its implementation remains limited to small-scale use, typically at the individual plant level. At larger scales, such as the farm level, its application is largely unexplored. Moreover, it is unclear whether the use of smartphone-based monitoring can help identifying key factors driving coffee tree productivity such as climate, soil, and management characteristics. To address these challenges, we investigate coffee tree productivity at the farm level and its key driving factors using smartphone-based monitoring and explainable artificial intelligence (xAI), and compare the results with those obtained from manual monitoring at the farm level. We used a multimodal data set composed of satellite data (soil and climate), smartphone-based monitoring (coffee tree productivity), and management characteristics (area, shade trees, and farm shape). The results showed that smartphone-based monitoring reached a of R ² = 0.84 in predicting coffee tree productivity at the farm level. The xAI results revealed that both smartphone-based and manual monitoring approaches identified the coffee cultivation area (greater than 13 ha) and soil texture (sandy, clay loam) as the most important variables influencing coffee tree productivity at farm level. The analysis also indicated that shade trees do not significantly affect coffee tree productivity. These findings suggest that smartphone-based monitoring can serve as a reliable and scalable alternative to manual monitoring for evaluating coffee tree productivity at the farm level.}, language = {en} } @misc{SafonovaStillerYordanovetal., author = {Safonova, Anastasiia and Stiller, Stefan and Yordanov, Momchil and Ryo, Masahiro}, title = {Self-supervised learning outperforms supervised learning for crop classification by annotating only 5\% of images}, series = {Precision agriculture}, volume = {27}, journal = {Precision agriculture}, number = {1}, publisher = {Springer Science and Business Media LLC}, address = {Dordrecht}, issn = {1385-2256}, doi = {10.1007/s11119-025-10302-9}, pages = {1 -- 31}, abstract = {Purpose One of the most pervasive Artificial Intelligence (AI) methodologies utilized in the domain of agriculture for image-based classification purposes is Supervised Learning (SL). However, SL depends on a large amount of annotation effort and is susceptible to overfitting to the given prediction task. Self-Supervised Learning (SSL) is a novel training paradigm with the potential to address these issues, while its potential has not been investigated in the agriculture domain. This paper presents the initial experimental investigation and comparison of SL and SSL for the classification of agricultural images in the context of limited samples. Methods We used an agricultural subset of the Land Use and Cover Area Frame Survey (LUCAS) dataset serving as a case study. In total, it comprised 1,000 images for each of the 10 crops: common wheat, barley, oats, maize, potatoes, sugar beet, sunflower, rape and turnip rape, soya, and temporary grassland. For SL, we trained popular and frequently used Convolutional Neural Network (CNN) architectures such as VGG16, Inception, ResNet-18/50, SqueezeNet, ResNeXt-50, MobileNet-V2, ShuffleNet, EfficientNet-V2, and ConvNeXt Tiny with and without data augmentations. For SSL, the best-performing CNN architectures (ResNet-18, ResNet-50, and ResNeXt-50) were further tested. The architectures were pre-trained with the VICReg algorithm (Variance Invariance Covariance Regularization) and fine-tuned successively using supervision for crop type classification. Results Our results demonstrate that the SSL models can distinguish crop types (common wheat, barley, oats, maize, potatoes, sugar beet, sunflower, rape, soya, and grassland) even without labels based solely on morphological features and organize them into three semantically meaningful visual groups: cereal-like and grassland crops, upright broadleaf crops, and low-growing broadleaf crops. The fine-tuned models, particularly ResNeXt-50, achieved superior performance compared to any of the SLs. Notably, we show that the fine-tuned SSL models outperformed the best-performing SL models by using only 5\% of the labeled training data for fine-tuning, corresponding to a small and balanced subset of the training split. Conclusion These findings highlight the potential of SSL for improving classification efficiency and generalization under limited data availability conditions in agriculture applications, providing a viable path toward more efficient agricultural monitoring systems.}, language = {en} }