Environmental and Earth Sciences

Abstract: We present a new whole-rock geochemical dataset for intrusive rocks of the late Variscan Serre batholith (Calabria, southern Italy), a well-exposed section of tilted con-tinental crust emplaced between ~305 and 292 Ma. The dataset includes major, trace and rare earth element (REE) analyses for 74 samples collected from the main plutonic units, ranging from tonalites and quartz-diorites at deeper structural levels to peraluminous granites at shallower levels, as well as leucosomes from associated migmatitic metase-diments. Analytical data were obtained using X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). The dataset integrates new and previously published geochemical data into a con-sistent and reusable format, including sample locations (WGS84), lithological classification and stratigraphic attribution. Sampling sites are also provided as a downloadable geo-spatial (.kmz) file for visualization in GIS platforms. The dataset is made available as a supplementary Excel file. These data are intended to support a wide range of applications, including petro-genetic studies of granitoid magmatism and investigations of water–rock interaction processes in crystalline aquifers. The dataset represents a valuable resource for both fundamental and applied geoscientific research.

Abstract: Schistosomiasis intermediate host snails’ data in the Democratic Republic of the Congo are limited and geographically dispersed. The objective of this study was to characterize snail habitats and identify environmental determinants of their presence. Monthly malacological surveys were conducted at 72 water contact sites. The morphological identification of the snails was complemented by the sequencing of the mitochondrial cox1 gene in order to guarantee confirmation of the species. The physicochemical parameters of the water, as well as human activities on the site, were recorded. The associations between environmental characteristics and snail presence were evaluated using generalized estimating equation models to account for repeated measurements. A total of 172,491 snails were collected, including 4,899 Schistosoma intermediate hosts (Bulinus spp., n = 3,812; Biomphalaria spp., n = 1,087). Biomphalaria pfeifferi, Biomphalaria sudanica, Bulinus truncatus, and Bulinus forskalii were identified. Biomphalaria species were detected in stagnant or slow-flowing waters; however, they occupied distinct habitats. The presence of snails was found to be independently associated with stagnant water and inversely associated with cassava retting, dishwashing/laundry, and river crossing. These findings provide baseline evidence on the distribution and ecological determinants of the Schistosoma intermediate host in Kimpese, supporting targeted malacological surveillance and integrated control strategies.

Abstract: Background: The char regions of Bangladesh — temporary riverine islands — experience compound climate vulnerability intensified by chronic structural poverty, yet sustainable financing models for community-based adaptation remain underdeveloped. Aim: This study diagnoses the capacity–commitment gap between households’ expressed willingness to support climate adaptation and their actual financial capacity, and proposes an evidence-based blended finance instrument. Methods: Using the Contingent Valuation Method (CVM) with a payment-card format and an open-ended follow-up, we surveyed 400 households across two char sites (Bahadurpur and Vasarpara). Probit models estimate the binary decision to contribute; Tobit models estimate the determinants of the contribution amount conditional on willingness. Results: Willingness to pay is high (65% of households), but capacity is sharply constrained: 90% of willing households pledge ≤ 400 BDT/month (mean = 244.5 BDT, median = 220 BDT). Probit and Tobit estimates show that education (β = 1.46, p < 0.001; β = 101.39, p < 0.001) and direct disaster experience (β = 1.49, p < 0.001; β = 153.85, p < 0.001) are three-to-eight times more influential than income (β = 0.49, p < 0.001; β = 19.33, p = 0.034). An institutional-trust paradox emerges: lower trust in government effectiveness is weakly associated with higher stated contributions (Tobit β = −17.88, p = 0.066), consistent with compensatory self-reliance. Near-universal clustering of WTP in the lowest payment class across seven adaptation strategies (89.7–100%) indicates a binding affordability ceiling rather than strategy-specific variation in valuation. Conclusions: These findings invalidate user-pays models for char populations and reframe household WTP as a signal of prioritised demand under a structural affordability ceiling. We translate this diagnostic into the Char Resilience Bond — a blended-finance instrument that securitises formalised in-kind community co-investments (labour, local knowledge, materials) to credit-enhance and leverage external capital, offering a replicable template for financing adaptation public goods in subsistence economies.

Abstract: Climate change is intensifying droughts, heatwaves, and hydrological extremes, increasing crop vulnerability and threatening global food security. This study analyzes the scientific evolution of research on remote sensing-based crop climate vulnerability, with emphasis on temporal, geographical, and thematic patterns. A quantitative, exploratory, descriptive, longitudinal, and retrospective bibliometric approach was applied to 2,343 documents indexed in Scopus between 1985 and 2026. The dataset was processed using Bibliometrix 5.1.1 and VOSviewer 1.6.20 to evaluate productivity, impact, collaboration, and intellectual structure, including Reference Publication Year Spectroscopy (RPYS). Results show a sustained annual growth rate of 4%, with 627 sources, 10,408 authors, an average of 5.17 co-authors per document, 35.3% international collaboration, and 19.93 citations per document. China, the United States, and India lead scientific production, while key journals concentrate dissemination. Thematic analysis highlights the dominance of drought-related studies and the increasing importance of machine learning and cloud-based platforms such as Google Earth Engine. The findings indicate that the field has reached a stage of scientific and technological maturity, transitioning from descriptive monitoring toward predictive and operational geospatial intelligence. However, challenges remain in methodological integration, geographical representation, and the translation of scientific outputs into decision-oriented tools for agricultural adaptation.

Abstract: This paper reviews how far national laws in Africa acknowledge the communal lands of communities as their property, as compared to family house and farm plots. This is examined in context of the role which commons ownership by communities could and should play in lessening rural poverty. These include rangelands, forest/bushlands, marshlands and other off-farm resource lands, often with lucrative extractive and non-extractive potentials. These are generally developed without community partnership. Findings suggest resistance to acknowledgement of communal lands as lawfully owned in over half of all 55 states. In effect, governments sustain their colonial designation as unowned wastelands, albeit lawfully used. Yet the one quarter of African states which do now acknowledge community ownership of resource commons also suggest the tide may be turning. There is need to promote this lawful possession is entrenched as a sustained stake in new takings and exploitation of these lands, not merely for fairer compensation for losses incurred, but towards adoption of economic growth path which are more directly inclusive of the rural poor. Inter alia, it is timely for post-2030 sustainable development goals to promote this.

Abstract: Global water scarcity due to climate change coupled with environmental pollution from artificial fertilizers threatens sustainable agricultural productivity. To address these challenges, innovative circular and sustainable agricultural practices are being sought after globally. Two promising sustainable approaches are the utilization of biochar coupled treated wastewater (TWW). In our experiment, maize biochar (MB) was locally produced by pyrolysis of maize stover waste in a Top-Lit-Up-Draft (TLUD) biochar kiln and characterized using FTIR, SEM, AAS, UV-vis and adsorption isotherms. Then, the performance of beetroots (Beta vulgaris) in terms of the germination rate, growth rate and yield in the cultivation soils fertilized with MB under drip irrigation with TWW was investigated. Post-hoc analysis (Bonferroni correction, α = 0.01667) revealed that cultivation soils fertilized with MB produced beetroots with significantly longer leaves and larger-heavier bulbs than the control. However, doubling MB fertilization dosage from 10 g to 20 g in the cultivation soils did not yield statistically significant improvement in the average leave height, mass and circumference of beetroot bulbs. Therefore, the findings demonstrated that combining MB with TWW enhanced beetroot growth and yield, highlighting a sustainable and circular agricultural system.

Abstract: Coastal basins are highly dynamic systems susceptible to flooding and erosion, processes intensified by extreme cyclonic events. This study aims to develop a physical-geographic framework for analyzing the multi-hazard geomorphological dynamics of the La Sabana River basin in southern Mexico. The methodology integrates the analysis of the basin's natural and anthropogenic components with morphometric evaluation and multivariate analysis (PCA) at the sub-basin level. The results show a highly efficient drainage network (3.8-5.4 km/km²) and short concentration times (0.98–2.75), which favor a rapid hydrological response and high susceptibility to flooding and erosion. PCA explained 65.8% of the total variance, identifying basin size, drainage organization, and system shape as dominant controls. Critical sub-basins with rapid hydrological response (Tc ≤ 1.5 h) were identified, coinciding with areas of high anthropogenic exposure. It is concluded that integrating morphometric indices through multivariate approaches provides a robust, replicable basis for risk governance and territorial planning in coastal basins.

Abstract: The Jiaduoling area is located in the northern segment of the Southwest Sanjiang Metallogenic Belt, a region characterized by complex geological structures and abundant mineral resources. This study systematically identifies the spatial correlation between subsurface magnetic bodies and tectonic structures by utilizing 1:50,000 high-precision aeromagnetic data. Advanced processing techniques—including upward continuation, vertical derivatives, total gradient modulus, and Euler deconvolution—were integrated to refine the structural framework and clarify the mechanisms of fault-controlled mineralization.The results indicate that the aeromagnetic anomaly pattern is predominantly governed by NW-trending faults. Specifically, the deep-seated major fault F1 (with a calculated depth exceeding 3 km) served as the primary migration channel for ore-forming fluids, while secondary faults created localized ore-hosting spaces. Physical property analysis reveals a significant magnetic contrast, where Mesozoic intermediate-acid magmatic rocks act as the essential source for mineralization, providing both material and thermal energy for the formation of porphyrite-type iron deposits.Based on these findings, a three-dimensional "aeromagnetic anomaly-structural framework-mineralization" correlation model was established. Finally, two high-potential metallogenic prospective zones (P1 and P2) were delineated, providing precise geophysical evidence and strategic guidance for regional mineral exploration and the targeting of concealed ore bodies.

Abstract: Tropical forests are facing escalating deforestation, while forest degradation, driven by a complex interplay of human-induced factors, emerges as an additional and compounding threat. In this context, regulated selective logging persists as an alternative to conciliate forest protection and economic development. This study synthesizes current knowledge on the impacts of logging, focusing on research trends, geographic distribution, ecological topics, and key variables like logging intensity, time since logging, and number of logging cycles. Since the 1970s, 641 papers listed on the Scopus platform have demonstrated a sharp increase in publication activity over the past five years, followed by a tendency toward stabilization. Papers were concentrated in Brazil and Malaysia, with few papers coming from other countries, particularly from Africa. Notably, 47% of the studies did not report logging intensity, and one-third focused almost exclusively on its impacts on forest physical structure, damage, or biomass—leaving a wide range of other topics largely unexplored until 2022. We refer to 13 topics with less than 20 studies in total, such as nutrient cycling, non-timber forest products, biological invasion, and key biological taxa. Herbs, epiphytes, fish and amphibians were among the least investigated taxa across the regions. Furthermore, when controlling variables like region and logging intensity, most ecological topics had fewer than five dedicated studies. Research remains largely restricted to similar scenarios: first-cycle logging in old-growth forests, leaving substantial knowledge gaps. As logging operations are expected to increase, we argue for a (1) mandatory long-term monitoring in logging regulations; (2) public access to monitoring data, reports and information related to regulated logging; (3) a global platform to exchange experience as long-term monitoring, better practices, silvicultural approaches and sustainability assessment; (4) alignment among regulatory and certification agencies on sustainability standards; (5) capacity building initiatives; and (6) long-term experiments devoted to logging sustainability and better practices.

Abstract: Transitioning to a circular bioeconomy in agro-environmental systems requires decision-support approaches able to address interdependencies across water, energy, food, and ecosystems (WEFE), especially in Mediterranean regions affected by climate variability, water scarcity, land degradation, and fragmented governance. However, the practical operationalization of the WEFE Nexus remains limited by methodological constraints and insufficient integration of dynamic analysis. This study presents an AI-enabled decision-support tool designed to support the implementation of circular bioeconomy solutions within the WEFE Nexus. The framework integrates participatory multi-criteria assessment, compensation mechanisms, and artificial intelligence-based scenario analysis within the NECADA digital twin environment, enabling the assessment of elements under uncertainty. Developed and applied within the “Ensuring fair NEXUS transition for climate change adaptation and sustainable development implementation based on coupled nature-based systems and bioeconomy (SureNexus)” project, the tool was used to assess two circular bioeconomy solutions, biochar and agroforestry, across Mediterranean agro-environmental contexts. Results show complementary performance profiles: biochar provides targeted benefits for soil restoration, water regulation, and climate mitigation, whereas agroforestry generates broader system-level effects that enhance ecosystem services, resilience, and long-term sustainability. These findings highlight the value of context-specific solution portfolios and show that AI-enabled WEFE tools can support evidence-based policy and planning for sustainability transitions.

Abstract: This study presents a sodium alginate/chitosan/activated carbon (SA/CS/AC) gel microspheres loaded with citrus peel allelochemicals for continuous inhibition of Microcystis aeruginosa by controlled release. Preparation parameters were optimized via response surface methodology (RSM) for improved algal inhibition, yielding an optimal formulation: 1.97% SA, 0.76% CS, 0.31% AC. The optimized gel microspheres showed a 7-day inhibition rate of 85.17 ± 2.49%, consistent with the predicted 85.29%. Characterization revealed that AC enriched the gel’s porous structure and surface functionality, increasing allelochemical adsorption sites, enhancing loading efficiency, and sustaining long-term release with a 25-day cumulative release of 70%. Algal inhibition declined slightly from day 7 to 30 due to allelochemical depletion but remained 76.27%, versus 30.58% for the blank SA/CS/AC carrier and 52.81% for the allelochemical-loaded SA/CS gel microspheres. AC thus synergistically strengthens algal inhibition by elevating allelochemical loading and prolonging activity, providing a feasible strategy for sustainable cyanobacterial bloom control.

Abstract: Rockfall from slope unstable rock masses, a typical geological hazard induced by brittle failure, is characterized by abrupt occurrence, negligible macroscopic deformation prior to failure, and extremely short lead time for early warning, posing a severe threat to the safety of mountainous transportation systems, water conservancy and hydro-power projects, and urban settlements. Conventional static analysis methods have sig-nificant limitations in real-time acquisition of damage evolution of structural planes and dynamic assessment of stability changes, which can hardly meet the practical re-quirements of early warning for unstable rock masses. The dynamic evaluation method for the stability state of unstable rock masses, based on the principles of structural dy-namics, establishes a correlation model between dynamic parameters (natural fre-quency, damping ratio, mode shape, etc.) and the damage degree of structural planes, providing a new paradigm for dynamic identification and quantitative evaluation of the stability of unstable rock masses. This paper systematically reviews the dynamic behavior mechanism and theoretical evaluation framework of slope unstable rock masses, and elaborates on the damage evolution of structural planes, the disturbance effect of environmental dynamic loads, and the key dynamic parameter system. The single-degree-of-freedom dynamic models and their theoretical derivation for three typical types of unstable rock masses (sliding-type, toppling-type, and falling-type) are thoroughly analyzed, and the cutting-edge advances such as multi-block chain collapse model and data-physics dual-driven surrogate model are reviewed. Meanwhile, the contact and non-contact monitoring methods based on Micro-Electro-Mechanical System (MEMS) and Laser Doppler Vibrometer (LDV) techniques, as well as the de-velopment status of cloud-edge collaborative intelligent early warning architecture, are systematically summarized. On this basis, the core challenges are pointed out, includ-ing the long-term evolution under multi-field coupling, high-fidelity inversion calcu-lation for large-scale rock masses, and the scientific correlation between early warning thresholds and failure probability. The full-life-cycle dynamic simulation based on digital twin is also prospected. The research results provide a systematic reference for the improvement of the theoretical system of dynamic evaluation of slope unstable rock masses and the engineering practice of disaster prevention and mitigation.

Abstract: This technical note discusses the structural limitations of current climate-related Artificial Intelligence (AI) applications due to the lack of standardized and geographically representative in situ monitoring networks. Drawing on the experience of the Salado River Basin monitoring system in Buenos Aires Province, Argentina, the document highlights the risks of training AI models with non-representative data and the urgent need for multilateral investment in physical infrastructure for satellite validation and environmental monitoring. The note argues that AI will only be as reliable as the measurements that support it, emphasizing the importance of certified, continuous, and well-maintained networks to ensure climate resilience and evidence-based water management.

Abstract: This paper focuses on the problem of anticipating the local occurrence of future large earthquakes. "Local" is defined as the probability of a large earthquake occurring with a defined circle of arbitrary radius surrounding a point of interest. The main (and for that matter, the only) assumption for all these works is that the Gutenberg-Richter (GR) magnitude-frequency relation holds. Here we describe a method for computing calendar time forecasts in a local area for large earthquakes of a target magnitude MT using a count small earthquakes MS < MT in the area. Using the idea that the GR relation is valid throughout the surrounding region, we define an ensemble of earthquakes in larger surrounding regions to be used in computing the forecast. What follows is simple data mining. The method has significant skill, as defined by the Receiver Operating Characteristic (ROC) test, which improves as time since the last major earthquake increases. The probability is conditioned on the number of small earthquakes n(t) that have occurred since the last large earthquake. The probability is computed directly as the Positive Predictive Value (PPV) associated with the ROC curve. The method is validated by comparison to the UCERF3 forecasts for the UCERF3-defined geographic boxes centered on Los Angeles and San Francisco. The method is then applied to a 125-KM radius circular area around Los Angeles, California, following the January 17, 1994 magnitude M6.7 Northridge earthquake, and short term forecasts (1 year and 5 year ) are computed.

Abstract: Recently, the Japanese government has introduced ambitious policies for agricultural sustainability, specifically the MIDORI Strategy, aimed at reducing chemical fertilizer use, expanding organic farmland, and increasing calorie-based food self-sufficiency. To evaluate the feasibility of these goals, this study quantified nitrogen and phosphorus flows within the 2021 food and feed system using a normalized “Nutrient Index.” A scenario analysis was conducted using policy targets as parameters, where currently non-circulated waste streams were modeled as potential sources for domestic nutrient recovery. The results indicate that Scenario A (a 30% reduction of chemical fertilizers) is the most feasible, achieving significant improvements in circulation ratios through recovery of nutrients from sewage and livestock waste. While Scenario B (increasing organic farmland) shows similar trends, its success depends on technological advancements to mitigate the yield gap between organic and conventional systems. Scenario C (increasing calorie-based food self-sufficiency) presents the greatest challenge: maintaining current dietary patterns requires a 20% expansion of farmland and total nutrient recovery from waste. However, shifting dietary habits toward higher domestic rice consumption (Scenario C-2) significantly mitigates land and fertilizer demand. Achieving these targets requires a holistic approach that integrates technological infrastructure with socio-political shifts in land use planning and consumer behavior.

Abstract: The origin of zongzi — rice and millet wrapped in leaves — has long been debated owing to a millennium-long gap between its legendary association with Qu Yuan (c. 340–278 BC) and the earliest unequivocal evidence, which dates only to the Song Dynasty. Here we present well-dated archaeological evidence of plant bundles (c. 2200 cal. BP) from the Wuwangdun Site, the first well-excavated tomb of King Kaolie of Chu. These plant bundles share the essential characteristics of modern zongzi and overlap both temporally and geographically with the Qu Yuan legend. They consist of Quercus dentata leaves containing rice (Oryza sativa, 43.6%), broomcorn millet (Panicum miliaceum, 26.1%) and foxtail millet (Setaria italica, 28.5%), secured with cords made from Panicoideae and Chloridoideae plants — a wrapping practice structurally identical to that of modern zongzi. Most seeds were preserved as whole husks, indicating that the bundles were used as funerary offerings rather than for consumption, which aligns with the legend that zongzi were thrown into the river to commemorate Qu Yuan. Notably, Q. dentata leaves are still used to wrap zongzi in regions that once formed the northern border of the Chu Kingdom. Our findings demonstrate that the plant bundles from the Wuwangdun Site most likely represent the prototype of zongzi, bridging the gap between legend and material culture and showing that the tradition of wrapping grains for ritual purposes began in the Chu Kingdom no later than 2200 years ago.

Abstract: To improve the efficiency of AD for tetracycline-containing wastewater, this study systematically investigated the effects of ZVI and PAC on pollutant removal, VFAs metabolism, biogas production, and sludge physicochemical characteristics. RSM was employed to optimize the co-dosing conditions of ZVI and PAC. The results demonstrated that ZVI and PAC could significantly facilitate COD degradation and TC removal, with the TC removal rate exceeding 90%. The optimal dosages for COD removal were 1000 mg/L ZVI and 2000 mg/L PAC with removal efficiency of 61.03% and 56.9%, respectively. Both additives effectively accelerated the catabolism of typical VFAs, thereby mitigating the accumulation of intermediate metabolites that may cause AD system instability. In terms of biogas production, 1000 mg/L ZVI and 2000 mg/L PAC enhanced methane yield by 55.9% and 35.0% compared to the control group, with ZVI exhibiting a more prominent enhancement effect. Mechanistic analysis revealed that ZVI and PAC reinforced the AD process through multiple synergistic pathways: enhancing the electrical conductivity of the AD system, facilitating DIET between functional microorganisms, and stimulating the secretion of EPS by anaerobic microbes. RSM optimization yielded the optimal co-dosing parameters: 1000 mg/L ZVI and 1200 mg/L PAC. Under these conditions, the methane yield was increased by 71.18% relative to the control group, and the model validation accuracy reached 97.94%. This study provides a viable technical strategy and theoretical basis for enhancing the efficiency of anaerobic treatment of tetracycline-containing wastewater.

Abstract: Research and Development (R&D) represents a strategic pillar of the petroleum industry, where technological innovation drives competitiveness, and the transition toward sustainable and cleaner energy systems. However, measuring the performance of R&D projects remains a complex challenge because their outcomes are often intangible, uncertain, and multidimensional. Traditional Key Performance Indicators (KPIs)—such as cost, time, and number of deliverables—provide only a partial view of effectiveness. R&D performance assessment must therefore consider the intrinsic nature of the activity. Reverse engineering emphasizes replication and adaptation of existing technologies, while innovation-driven R&D seeks to create novel solutions. Accordingly, the selection of performance indicators must differ across these categories. To avoid biased evaluation, the framework integrates B. Roy’s (1996) Multi-Criteria Decision Analysis (MCDA) approach, enabling prioritization of criteria aligned with each project’s objectives and complexity (Martinsuo et al., 2022). Moreover, in R&D environments, traditional indicators such as cost and time act as strategic signals rather than mere management metrics. Cost data guide managerial decisions on partnerships, external funding, and open innovation when internal resources are limited. Similarly, adherence to schedule directly influences technological relevance—delays may result in obsolescence, missed market windows, or loss of first-mover advantage (Tsinopoulos & Al-Zu’bi, 2023). To move beyond simple cost and time metrics, this study revisits the meaning of “performance” in R&D and explores multi-dimensional evaluation tools capable of capturing both tangible and intangible value creation, by integrating five novel dimensions: knowledge creation and diffusion, innovation velocity, dynamic strategic alignment, team and organizational health, and resilience under uncertainty. Beyond its conceptual formulation, the framework has been numerically applied to a portfolio of 10 ongoing R&D projects spanning renewable energy, digitalization of upstream processes, advanced materials, and industrial decarbonization. Each project was scored on a standardized 0–10 scale across the five dimensions, allowing for fine-grained benchmarking and identification of strengths and gaps. For example, Projects 3 and 7 achieved high innovation velocity scores (≥ 9) but lagged in resilience metrics (< 5), indicating exposure to external risks. Conversely, Projects 5 and 9 showed strong knowledge diffusion and team health (scores of 8–10) but slower strategic alignment (< 6). The analysis demonstrates how the proposed framework can generate actionable dashboards for managers, enabling more balanced resource allocation, improved project selection, and proactive mitigation of weaknesses. Applications in industry, academia, and public R&D contexts are also explored, illustrating how this systemic, ecosystem-aware approach moves performance management beyond a narrow project-level perspective to a dynamic, portfolio-wide view. The results provide both theoretical contributions and practical tools for R&D managers seeking to measure and enhance the multidimensional value of their projects.

Abstract:

The Analytic Hierarchy Process (AHP) was applied to six agronomic scenarios for durum wheat (Triticum durum Desf.) in the Capitanata plain (Apulia, southern Italy), combining three sowing dates (15 October, 1 November, 15 November) with two water regimes (rainfed; supplemental irrigation at flowering, 13–41 mm season⁻¹). Five performance indicators — grain yield (4983–5722 kg ha⁻¹), CO₂-equivalent emissions (CO₂_eq, 1190–1214 kg ha⁻¹), carbon footprint (CFP, 0.19–0.24 kg CO₂ eq kg⁻¹), total water consumption (TotW, 5555–6387 m³ ha⁻¹) and water footprint (WFP, 1.08–1.18 m³ kg⁻¹) — were derived from AquaCrop-GIS simulations coupled with cradle-to-gate life cycle assessment. A symmetric weight scheme (w = 0.60 for the dominant criterion, w = 0.10 for each of the remaining four) defined six decision profiles, with all pairwise comparison matrices perfectly consistent (CI = 0, CR = 0). The rankings revealed a systematic inversion between absolute indicators (CO₂_eq, TotW) and ratio indicators (CFP, WFP): under absolute-metric profiles, the lowest-yielding scenario (4983 kg ha⁻¹) paradoxically ranked first because reduced productivity mechanically lowered per-hectare resource consumption. Under ratio-metric and balanced profiles, early-November sowing consistently led the rankings, combining the lowest carbon footprint (0.19 kg CO₂ eq kg⁻¹) and the lowest water footprint (1.08 m³ kg⁻¹) among the six scenarios. Switching point analyses quantified the weight thresholds at which leadership changed: w₁ = 0.20 (S1 → S2) and w₁ = 0.60 (S2 → S5) along the Yield axis, w₃ = 0.60 (S2 → S5) along the CFP axis, with rainfed early-November sowing retaining leadership across the full range of WFP weights. The AHP procedure was also applied to the 72 simulation replicates spanning all combinations of soil profile, climatic cell and cropping year in the 2013–2023 dataset, providing the empirical rank distribution for each scenario under each profile and extending the mean-based analysis to the full pedo-climatic variability of the region.

Abstract: Accurate soil organic carbon (SOC) estimation is vital for analyzing the global carbon cycle. Currently the bare soil compositing approaches for multi-temporal images are widely used, however the optimized length of compositing period and influence of different indicators on SOC estimaiton for both bare soil and crop cover conditions is unknown. In this study, a time series of Landsat 8 Operational Land Imager multitemporal images was obtained from 2013–2018, with the aim of generating datasets that represent SOC changes across single dates, single years, and multiple years. Soil properties (S), terrain attributes (T), vegetation conditions (V), and farm management practices (F) were employed to predict the spatial distribution of SOC by using the random forest model for both bare soil and crop cover conditions. The results revealed that multi-temporal images from three years and longer produced accurate SOC predictions, with coefficients of determination (R2) and root mean squared errors (RMSEs) of 0.94-0.95 and 1.75-1.77 g kg-1, respectively. The four types of indicator combinations (S+T+V+F) achieved the best model performance, followed by the T+V+F, S+V+F, and V+F combinations for the bare soil condition in 2016-2018 period. This study provides a possible way for obtaining farmland SOC sequestration under crop cover conditions.