Sustainable land use requires precise monitoring of soil pollution, yet accurately predicting the spatial distribution of heavy metals often relies on post hoc accuracy comparisons with limited a priori diagnosis. To address the challenge of cost effective environmental monitoring, we conducted a PRISMA guided systematic review (2000–2024) and synthesized 135 studies to develop a mechanism-informed, context aware method selection framework. Evidence revealed three regularities: (i) element–driver coupling is structured (Pb/Cd/Zn predominantly anthropogenic; Cr/Ni geogenic; As/Hg mixed), with dominant influence scales from local to regional; (ii) model performance hinges on alignment between algorithmic assumptions, and context hybrid machine learning models integrating multi-source covariates tend to excel under strong, non-stationary anthropogenic heterogeneity, whereas kriging variants are more robust when geogenic continuity holds; and (iii) applicability is jointly constrained by environmental context, data foundations, and management objectives. Building on these insights, we propose a three-step decision workflow—goal definition, contextual diagnosis, and method matching. This framework serves as a decision support tool that shifts selection from trial and error to a priori alignment, optimizing resource allocation and enhancing the reliability of pollution assessments for sustainable soil remediation and policymaking.

Urban carbon emissions have emerged as a central challenge for sustainable development in China. Existing statistical and machine learning approaches, however, face limitations in capturing spatial heterogeneity and historical dynamics of urban emissions. To overcome these challenges, this study developed Carbon Graph Multi-branch Network (CGMN), a graph-based deep learning framework that integrates spatial relationships, temporal dependencies, and multi-source urban features for urban carbon emissions estimation. Given the scarcity of labeled city-level emissions, a composite loss framework is designed to incorporate both strong and weak supervision, enhancing the model's consistency, generalization, and robustness. We also developed a spatial mismatch index between emissions and Gross Domestic Product (GDP) to investigate the evolving relationship between urban carbon emissions and economic activity. The proposed CGMN achieves robust predictive performance (R & sup2; = 0.79; RMSE = 10.63; MAE = 8.31; WAPE = 27.51), demonstrating its capability to capture the contributions of key driving factors. Feature importance analysis reveals that economic structure is the dominant determinant of urban carbon emissions, accounting for 32% of the total feature contribution. From 2000 to 2021, China's urban carbon emissions increased rapidly until around 2012 before stabilizing, with an average annual growth rate of 5.7%. The spatial mismatch analysis reveals pronounced regional disparities: low-mismatch cities decreased by 53%, mainly in western and central regions, while high-mismatch cities increased by 35%, concentrated in eastern coastal areas. These results provide a scientific basis for understanding the spatiotemporal evolution of China's urban carbon emissions and offer insights for promoting coordinated economic and low-carbon development.

Having the ability to accurately and effectively obtain soil organic carbon (SOC) spatial information is critical for assessing soil carbon sequestration capacity and mitigating climate change. However, there remains a significant research gap in the collaborative application of multi-source data and their impact on model estimation accuracy. This gap limits the ability to assess soil carbon pools accurately. Therefore, we propose a data-model fusion framework that uses three types of multi-source data-environmental variables, optical remote sensing, and synthetic aperture radar (SAR)- along with three machine learning algorithms to predict SOC. We conducted data fusion of SOC field observation data and model simulations using high-accuracy surface modeling (HASM). The results showed that: (1) The data VII combination, which incorporates all three data types, paired with support vector machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost) models, obtained higher prediction accuracy (R2 increased by 4 % - 53 %, and RMSE decreased by 18 % - 25 %) compared to other data combinations. (2) After data fusion using HASM, simulation accuracy improved significantly (R2 increased by 18 % - 22 %, and RMSE decreased by 2 % - 12 %). Additionally, the spatial distribution pattern was more reasonable, with corrections made to previously underestimated and overestimated SOC content. This study demonstrates that multi-source data fusion combined with machine learning techniques can achieve optimal results for SOC prediction. This approach provides an accurate and novel method for estimating SOC at national and global scales and offers scientific guidance for the spatial planning of terrestrial carbon sink strategies.

High-resolution spatiotemporal column-averaged CO2 (XCO2) data is essential for understanding anthropogenic carbon emissions, but current satellite limitations hinder detailed analysis. To address this, we develop the Spatial-Temporal Attention XCO2 Network (STAXN) to improve prediction accuracy by capturing spatialtemporal variability and multiscale influences of auxiliary variables. Monte Carlo validation demonstrates robust performance, with an RMSE of 0.90 ppm and an R2 of 0.97. Using this model, we generate a 1-km resolution daily XCO2 dataset for China (2015-2020) and analyze XCO2 anomaly patterns. Seasonal XCO2 anomalies peak in summer and winter, with nighttime light exhibiting strong positive effects (beta = 0.134, 0.107), and GPP exerting the most substantial adverse influence in winter (beta = -0.200). The centroid trajectories of XCO2 anomalies exhibit consistent seasonal shifts, shaped by regional disparities in carbon efficiency, industrial structure, and emission intensity. These findings offer valuable insights into China's carbon emission dynamics, informing policy and management strategies.

Abstract Extreme precipitation events are intensifying under climate change, driving escalating flood risks in some of the world’s most vulnerable regions. Pakistan is one of the most hydrologically diverse and flood-prone country, previous studies have largely emphasized seasonal or mean rainfall, leaving monthly maximum daily precipitation extremes (Rx1day at monthly resolution) underexplored, despite their direct role in triggering flash floods, landslides, and infrastructure failures. This study fills that gap by analyzing bias-corrected CMIP6 multi-model ensembles to project Rx1day-month precipitation extremes for SSP2-4.5 and SSP5-8.5 across seven hydroclimatic zones. Projections are assessed for the near future (2017–2044), mid-century (2045–2072), and late century (2073–2100), relative to the 1985–2014 baseline. Findings reveal strong spatial heterogeneity. Northern and northwestern highlands exhibit the largest absolute increases, with late-century monsoon monthly maxima of daily precipitation reaching approximately 130–150 mm, nearly double baseline values. Central and southern zones also experience pronounced amplification, intensifying flash-flood, riverine, and urban drainage hazards. By contrast, western arid and coastal regions show a decline in the magnitude of monthly maximum daily precipitation, punctuated by occasional high-intensity events. Intensification is most under SSP5-8.5, where both the magnitude and spatial footprint of extremes expand significantly over time. These high-resolution, zone-specific projections demonstrate that even localized shifts in extreme rainfall can compound hazard exposure, destabilize agriculture, and overwhelm water-management systems. The results provide actionable evidence for strengthening early-warning capacity, guiding resilient infrastructure planning, and informing targeted adaptation in one of the world’s most flood-exposed countries.

To overcome longstanding issues of error propagation and low computational efficiency in geographical information systems (GIS) and computer-aided design (CAD) systems, high-accuracy surface modeling (HASM) methods were developed through the systematic integration of systems theory, optimization cybernetics, and surface theory. Following nearly two decades of numerical experimentation and empirical investigation, a universal fundamental theorem of surface modeling (UFTSM) was formulated. This theorem provides a general theoretical framework applicable to spatial interpolation, upscaling, downscaling, data fusion, and model-data assimilation across a wide range of disciplines, including Earth surface system science, eco-environmental informatics, medical imaging, and computer-aided design. The UFTSM was first successfully applied to the simulation of eco-environmental surfaces. Within the conceptual framework of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), ecoenvironmental components were classified into three categories: nature (including species diversity, ecosystem structure, and geographical features), nature's contributions to people (such as food provision, freshwater supply, and environmental pollution remediation), and drivers of natural change (including climate change, land-use change, policies, and regulations). The term eco-environmental surface is used as a unified concept to represent surfaces describing nature, nature's contributions to people, or drivers of natural change. Numerous studies have demonstrated that intrinsic information (e.g., ground-based observations) and extrinsic information (e.g., satellite observations) provide complementary perspectives, and that neither alone can fully characterize an eco-environmental surface. Instead, such surfaces are governed by the joint influence of intrinsic and extrinsic information, and cannot be adequately understood without considering both. To address the challenge of simulating eco-environmental surfaces through the integration of these two information sources, an iterative differential method for HASM-based machine learning was developed, and a fundamental theorem for eco-environmental surface modeling (FTEEM) was proposed. In addition, a suite of high-efficiency algorithms suitable for classical computing platforms, including a modified conjugate gradient algorithm, a multigrid algorithm, an adaptation algorithm, and adjustment computation, was designed to accelerate eco-environmental surface modeling. Compared with existing surface modeling approaches, HASM-based applications exhibit substantially improved accuracy. However, computational cost remains a major bottleneck for global-scale eco-environmental surface modeling, particularly as spatial resolution becomes increasingly fine. To address the limitations of classical algorithms and hardware, HASM was reformulated as a large sparse linear system using the Lagrange multiplier method. This system was then implemented on both a real quantum computer and a virtual quantum computing platform by employing two widely used quantum linear solvers: the Harrow-Hassidim-Lloyd (HHL) algorithm and the iterative refinement of the variational quantum linear solver (iVQLS). Based on these approaches, two HASM-oriented quantum linear solvers, HASM-HHL and HASM-iVQLS, were developed, enabling the solution of simulation problems that are intractable for classical computers. The respective advantages and limitations of HASM-HHL and HASM-iVQLS, as quantum machine learning algorithms, were systematically evaluated through simulation experiments conducted on both real and virtual quantum computing platforms. The comparative results highlight the urgent need for a universal tool library that supports both classical and quantum intelligent computing. Moreover, the development of a full-stack quantum input-computing-output system is essential for overcoming the principal challenges currently faced by HASM-based quantum machine learning.

Conventional view holds that soil phosphorus (P) availability peaks at near-neutral pH. However, how soil pH governs long-term soil available phosphorus (SAP) dynamics over large spatial scales remains unclear. Here, we conducted a large-scale resampling campaign across China's Sichuan Basin to investigate how soil pH regulates changes in cropland SAP from the 1980s to the 2010s. The results showed that SAP content in croplands across the basin nearly tripled over the past nearly four decades, increasing from an average of 6.87 mg/kg to 18.34 mg/kg. Random forest model revealed that this increase was primarily controlled by soil pH and its temporal changes (Delta pH), which collectively accounted for 23.16% of the relative importance. The largest SAP increment (13.05 mg/kg) occurred in the pH range of 5-6 rather than neutrality, peaking at exactly pH 5.83 based on parabolic fitting, which resulted in the highest SAP levels across the broader pH interval of 5-7 in the 2010s. Moreover, the parabolic relationship between SAP increment and soil pH was observed only under acidification, while alkalization led to a negative correlation. For soils with pH > 7.5, a decrease in soil pH beyond 1 unit did not cause further increases in SAP content. Structural Equation Modeling confirmed that the pH change direction-dependent pattern is mainly dictated by precipitation-induced leaching and intrinsic soil clay buffering rather than carbon-mediated pathways. These findings challenge the conventional paradigm regarding the soil P availability-pH relationship in real-world settings and offer critical insights for optimizing agricultural P management.

The accurate estimation of double-season rice yield is critical for ensuring national food security. To address the limitations of traditional crop models in spatial resolution and accuracy, this study innovatively developed the HASM-APSIM coupled model by integrating High-Accuracy Surface Modeling (HASM) with the Agricultural Production Systems sIMulator (APSIM) to simulate the historical yield of double-season rice in Jiangxi Province from 2000 to 2018. The methodological advancements included the following: the localized parameter optimization of APSIM using the Nelder–Mead simplex algorithm and NSGA-II multi-objective genetic algorithm to adapt to regional rice varieties, enhancing model robustness; coarse-resolution yield simulations (10 km grids) driven by meteorological, soil, and management data; and high-resolution refinement (1 km grids) via HASM, which fused APSIM outputs with station-observed yields as optimization constraints, resolving the trade-off between accuracy and spatial granularity. The results showed that the following: (1) Compared to the APSIM model, the HASM-APSIM model demonstrated higher accuracy and reliability in simulating historical yields of double-season rice. For early rice, the R-value increased by 14.67% (0.75→0.86), RMSE decreased by 34.02% (838.50→553.21 kg/hm2), MAE decreased by 31.43% (670.92→460.03 kg/hm2), and MAPE dropped from 11.03% to 7.65%. For late rice, the R-value improved by 27.42% (0.62→0.79), RMSE decreased by 36.75% (959.0→606.58 kg/hm2), MAE reduced by 26.37% (718.05→528.72 kg/hm2), and MAPE declined from 11.05% to 8.08%. (2) Significant spatiotemporal variations in double-season rice yields were observed in Jiangxi Province. Temporally, the simulated yields of early and late rice aligned with statistical yields in terms of numerical distribution and interannual trends, but simulated yields exhibited greater fluctuations. Spatially, high-yield zones for early rice were concentrated in the eastern and central regions, while late rice high-yield areas were predominantly distributed around Poyang Lake. The 1 km resolution outputs enabled the precise identification of yield heterogeneity, supporting targeted agricultural interventions. (3) The growth rate of double-season rice yield is slowing down. To safeguard food security, the study area needs to boost the development of high-yield and high-quality crop varieties and adopt region-specific strategies. The model proposed in this study offers a novel approach for simulating crop yield at the regional scale. The findings provide a scientific basis for agricultural production planning and decision-making in Jiangxi Province and help promote the sustainable development of the double-season rice industry.

Widespread soil acidification driven by nitrogen (N) fertilization and precipitation challenges the conventional notion of the long-term stability of soil inorganic carbon (SIC) in agroecosystems. However, the changes in SIC with precipitation and N fertilization remain ambiguous. Based on 4,000+ soil samples collected in the 1980s and 2010s and by developing machine learning models to fill the missing SIC of soil samples, this study generated 3,697 paired soil samples between the two periods and then investigated the cropland SIC change and explored its relationship with precipitation and N fertilization across the Sichuan Basin, China. The results showed an overall SIC loss, with a decline of the mean SIC by 15.73%. SIC change varied with initial soil pH and initial SIC and exhibited an exponential relationship with soil pH change, indicating the changing role of carbonates in providing acid-buffering capacity. There was a parabolical relationship between the magnitude of SIC decline and N fertilizer rates, and low N fertilizer rates contributed to a reduction in SIC loss, while SIC loss was promoted by N fertilization occurred when N fertilizing rates exceeded 250 kg ha(-1) yr(-1). The change in SIC showed a sinusoidal variation with precipitation, with 950 mm being the threshold controlling whether SIC increased or decreased. Meanwhile, N fertilization did not alter the sinusoidal relationship between SIC change and precipitation. In areas with rainfall<950 mm, the high N fertilizer rate did not cause SIC loss, while higher precipitation could also cause larger SIC loss in areas with lower N fertilizer rates. These results suggest that SIC dynamics are jointly driven by precipitation and N fertilization and are controlled by acid-buffering mechanisms associated with initial pH and SIC, with precipitation being the predominant driver. These findings emphasize the need for more regional soil observations and in-depth studies of SIC change and its mechanisms for accurately estimating SIC change.

The rapid development and urbanization along the GT roadside has brought severe potentially toxic elements (PTEs) contamination to the soil and may lead to considerable risk to the ecosystem. In this study, the concentrations, spatial distribution along with ecological risk, and health risk assessments of PTEs (Pb, Cr, Cu, and Cd) in the GT roadside soil from Sialkot to Rawalpindi are evaluated, aiming to provide a theoretical understanding of managing and mitigating the PTEs contamination. 200 samples are collected at varying distances from the road's edge across 4 different zones (Sialkot, Gujrat, Jhelum, and Rawalpindi). A non-linear fitting model is applied to analyze the correlation between PTEs and roadside distance and shows PTEs concentration decrease with an increase in the road proximity. As distance from the road increases, ERI decreases with the highest risk at 0 m (265.99) and the lowest risk at 100 m (59.46), highlighting the gradual dissipation of the pollutant. The maximum concentration of PTEs is observed in Sialkot, while the minimum is noted in Jhelum. The mean concentrations of PTEs at 0 m were 34.9, 14.3, 27.1 and 0.66 for Pb, Cu, Cr and Cd, respectively in this descending order: Pb > Cu > Cr > Cd. The mean concentration of ERI of PTEs was 120.84 at Sialkot, 86.61 at Gujrat, 50.03 at Jhelum, and 69.63 at Rawalpindi. Cr and Cu pose a lower ecological risk as compared to Pb and Cd. Roadside soil ranges from “unpolluted” to “moderately polluted” for all metals excluding Cr. Nemerow Integrated Pollution Index (NIPI) value of 1.005 indicates that the contamination level slightly exceeds the acceptable limit. The trend of the average daily dose of PTEs (ADD) in soil via the three pathways is noted in the order of ADDinh ˂ ADDderm ˂ ADDing. Among ADD of soil, ADDderm has maximum value in adults, while ADDing and ADDinh values are maximum for children. The hazard index (HI) for all inspected PTEs in the soil is below 1, demonstrating no considerable health risk for either children or adults. The Pakistani government should prioritize traffic, and industrial-related environmental issues along GT road. This study is helpful to further analyze and assess the health risks associated with exposure to PTEs near highways, including those in industrial areas globally.

The widespread abandonment of croplands poses a severe threat to China's food security. However, the existing knowledge regarding the potential for recultivating abandoned croplands is limited. Previous studies have generally overestimated this potential due to inadequate assessments of their suitability for recultivation. In the present study, an optimized machine learning approach was introduced to assess, for the first time, the suitability of abandoned croplands for recultivation in China. In 2022, the total abandoned croplands in China covered 31.2 million hectares, of which 82.54 % was deemed suitable for recultivation. Recultivating abandoned croplands in China could substantially enhance food security, potentially yielding around 162 million tons annually. Moreover, crop switching can further increase the yield rate by up to 24.95 % or energy supply by up to 43.14 %. The present study offers valuable insights to policymakers in determining cropping strategies, establishing strategic grain reserves, and managing international food trade.

Large sample sizes are crucial for accurately capturing spatial changes in soil properties by spatial interpolation methods. However, soil bulk density (BD) data in historical datasets is often incomplete, and it's uncertain if filled values enhance spatial interpolation accuracy. Using 2,883 cropland soil BD samples from the Sichuan Basin in China, we developed the best prediction models from traditional pedotransfer function (PTF), multiple linear regression (MLR), random forest (RF), and radial basis function neural network (RBFNN) to fill missing BD values for 1,336 samples. We then applied ordinary kriging (OK) and inverse distance weighting (IDW) to map soil BD, incorporating the filled BD as modeling points. The RBFNN model, tailored for each sub-watershed, yielded the highest accuracy in filling missing BD, with an increase in coefficient of determination (R2) by 19.54-37.36% and reductions in mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) by 8.91-14.81%, 9.02-16.22% and 7.71-13.61%, respectively. Incorporating filled BD data reduced the MAE, MRE, and RMSE of OK and IDW by 4.17%, 4.36%, 4.96%, and 6.54%, 6.92%, 8.15%, respectively, significantly lowering spatial interpolation uncertainty. This methodology improves the accuracy of soil property mapping in regions with incomplete historical data.

Accurate prediction of future gross primary productivity (GPP) change in Central Asia is crucial for assessing the health of terrestrial ecosystems in the region and adapting to impending climate change. This study investigates the feasibility of utilizing deep learning (DL) models for forecasting future annual GPP based on historical data. Specifically, we introduce a novel model, LSTM-Transformer-Group model (LTG), tailored to the unique characteristics of GPP prediction in Central Asia. Based on MODIS GPP data, we evaluate the future annual GPP prediction performance of the LTG model and classical DL models, and we further examine the impact of different input time steps on prediction accuracy and carefully investigate the influence of climatic factors on the accuracy of annual GPP predictions. Furthermore, we evaluate the LTG model's long-term forecasting capability. Our experiments reveal several key findings: 1) DL models adeptly capture the temporal dynamics within GPP time series, facilitating direct prediction of future annual GPP based on past data. The proposed LTG model, optimized with an appropriate input time step, achieves superior prediction accuracy, yielding an R-2 of 0.945; 2) GPP prediction performance varies across different climatic zones, but integrating climatic variables as direct inputs to the DL model does not enhance accuracy and may even diminish performance; 3) Over the long term (2019-2023), the LTG model consistently outperforms, exhibiting an average R-2 exceeding 0.95. Our findings offer methodological insights for future GPP prediction endeavors in diverse geographical contexts.

Analyzing the spatial dynamics of China's forest carbon storage under future climate scenarios is crucial for understanding terrestrial carbon sequestration potential, addressing climate change impacts, and formulating optimized carbon management strategies. This study examines its spatiotemporal changes under five Shared Socioeconomic Pathways (SSP) scenarios from the 2020s-2090s, focusing on land cover change impacts and realistic carbon storage pathways constrained by natural and basic human activities. Findings reveal an overall increasing trend in China's forest carbon storage. Growth rates are relatively stable under SSP1-2.6 (24.84 %), SSP2-4.5 (25.09 %), and SSP4-3.4 (24.23 %), with the most significant increase (26.46 %) under SSP3-7.0. Even the high-emission, high-growth SSP5-8.5 scenario sees substantial growth (25.82 %). Spatially, carbon storage varies significantly across longitudes and latitudes. Longitudinal variations (influenced by forest coverage and topography): low/stable in the west, sharp fluctuations with multiple peaks in the center, then fluctuating decline in the east; latitudinally, variations (driven by climate suitability and forest distribution) follow a north-south parabolic pattern-lower at low/high latitudes, peaking at mid-latitudes with larger fluctuations. Land cover affects forest carbon storage in two ways: conversion balance (non-forest to forest net gains boost storage, while forest conversion to other uses may hinder it); and conversion quality (the quality gap between lost high-carbon mature forests and added low-carbon young forests impacts storage). These results suggest future forest management and climate mitigation strategies must adapt to diverse climate scenarios and regional socioeconomic conditions to ensure optimal, effective implementation for forest protection and carbon storage conservation.

High-precision digital soil mapping in complex terrain is challenging. This study proposed a new method using the partitioning around medoids clustering algorithm to partition the study area into distinct habitat patch types. Utilizing multisource data and three machine learning models, we estimated soil organic carbon (SOC) content in southwest China. Results showed higher SOC content (0-15 cm) in the southwestern mountains and the northwestern plateaus of Sichuan, while lower in the Sichuan Basin. The prediction uncertainty exhibited a similar pattern. Topographic and climatic variables played crucial roles in SOC estimation. Among the three machine learning models, RF and XGBoost demonstrated higher simulation accuracy than SVM (R-2 increased by 2.86%-82.35%). Using the RF feature selection (FS) method to select optimal factors as model input variables improved simulation accuracy compared with using all factors or selecting based on Pearson correlation analysis (R-2 increased by 1.75%-64.71%). The study found that a hybrid model based on different habitat patches achieved higher accuracy than the single model for the whole study area (for example, with RF FS method and modeling, R-2 increased by 2.17%-34.78%, and RMSE decreased by 2.19%-28.80%). These findings enhanced the accuracy and refinement of existing mapping in southwest China (compared to SoilGrids 1 km and SoilGrids 250 m products, R-2 increased by 20.45% and 317.73%, and RMSE decreased by 35.51% and 60.49%). Such improvements better characterized the spatial variability of SOC and provided important implications for future soil carbon stock accounts in complex terrain areas.

Understanding how biotic and environmental drivers jointly shape forest carbon dynamics over time is essential for climate-adapted management of subtropical forests. We investigated the long-term interactions between biotic factors, environmental factors, and forest carbon dynamics in the subtropical forests of Jiangxi Province, China, over the period 1989–2019. The High Accuracy Surface Modelling (HASM) multi-source data fusion method integrates ground observation points with area-wide data from remote sensing and existing datasets to simulate the spatial distribution of forest carbon density across the entire study area. In Zixi, forest carbon density increased most rapidly between 1989 and 2009, after which the rate slowed as forest stands matured. Structural Equation Modelling (SEM) disentangled direct and indirect effects of drivers, and identified species richness and community-weighted functional traits as key positive drivers of aboveground carbon density. The influence of environmental factors reversed over the study period. Under ongoing global warming, the combined effects of altitude, temperature, and precipitation shifted from suppressing to reinforcing carbon accumulation in later years, increasingly operating through pathways mediated by functional traits. These findings enhance our understanding of carbon dynamics in subtropical forests and underline the importance of preserving species richness, especially in subtropical mountain forest. This study provides valuable insights for adaptive forest management and climate change mitigation strategies, aiming to improve ecosystem resilience and sustain carbon sequestration efforts in the face of ongoing global warming.

>1. Introduction Malthus (1798) was one of the earliest researchers to propose a coupled human-natural system (CHANS) model, particularly in his analysis of the relationship between population growth and food supply. He argued that while population grows geometrically, food supply increases only arithmetically, suggesting that this imbalance could eventually lead to a food crisis due to diminishing per capita food availability (Malthus, 1798).