Search Results (479)

Sustainable food systems are increasingly challenged by climate change, resource constraints, market volatility, and growing food demand, making accurate forecasting of food production essential for food security and long-term sustainability. Despite the growing use of machine learning in agricultural forecasting, studies directly modeling the Food Production Index (FPI) within a sustainable food systems framework remain limited, particularly in emerging economies. This study addresses this gap by forecasting Türkiye’s Food Production Index using agricultural, macroeconomic, and trade-related indicators covering the period 1962–2023. Seven predictive approaches, including Multiple Linear Regression (MLR), Bayesian Ridge Regression, Support Vector Regression (SVR), Random Forest, Gradient Boosting, Artificial Neural Networks (ANNs), and K-Nearest Neighbors (KNN), were comparatively evaluated using R², RMSE, and MAE metrics. The results demonstrate that Bayesian Ridge Regression (R² = 0.968) and MLR (R² = 0.918) significantly outperform more complex machine learning algorithms, indicating that model–data compatibility is more critical than algorithmic complexity in long-term food production forecasting. The findings reveal that economic growth, agricultural inputs, and structural transformation processes play a decisive role in shaping food production dynamics. By integrating machine learning with sustainability-oriented food system analysis, this study provides a robust evidence base for supporting food security strategies, resource-efficient agricultural planning, and resilient food system governance. The proposed framework offers macro-level decision-support insights for policymakers engaged in long-term food system planning, strategic risk monitoring, and evidence-based policy evaluation. Full article

Balancing food production, ecosystem service value, and agricultural irrigation water consumption is a major challenge for sustainable agricultural development in China. However, quantitative evidence at the national scale remains limited on whether crop planting structure optimization derived from models can effectively achieve coordination among these three objectives. Existing studies mainly focus on individual crops or localized regions and rarely integrate the spatiotemporal evolution, influencing factors, and multi-objective optimization of major staple crops within a unified framework. This study developed a progressive framework integrating spatiotemporal evolution analysis, influencing factor identification, and planting structure optimization for wheat, rice, and maize. Spatial autocorrelation analysis, center-of-gravity shift analysis, and pixel-based image differencing were applied to reveal crop evolution patterns across China from 2000 to 2025. A five-dimensional indicator system comprising 17 quantitative indicators was developed through multiple experiments using four large language models, and the Random Forest algorithm was employed to identify key influencing factors and their relative importance. Based on these factors, optimization constraints were constructed, and a multi-objective fuzzy linear programming model combined with the NSGA-II algorithm was used to determine optimal crop area allocation across 28 provincial-level regions. The three staple crops exhibited a significant pattern of northward shift, eastward expansion, and southern contraction. Precipitation and market accessibility were common core influencing factors, ranking among the top five factors in all nine Random Forest models. Crop-specific factors, including soil available phosphorus for rice, accumulated active temperature for wheat, and soil pH for maize, explained differences in spatial responses among crops and provided a scientific basis for optimization modeling and coordinated improvement of food production, ecosystem service value, and irrigation water consumption. The optimized scheme increased total grain output by 3.6%, improved ecosystem service value by 11.0%, and reduced irrigation water consumption by 45.7% compared with the actual planting structure, all 28 provinces achieved improvement or stability in the three indicators simultaneously. Based on optimized crop allocation patterns, national planting structures were summarized into regional models, including a rice–maize dual-core system in Northeast China, wheat–maize rotation in the Huang-Huai-Hai Plain, rice-dominated systems in the middle and lower Yangtze River Basin and South China, water-efficient dryland farming in Northwest China, and a diversified balanced system in Southwest China. These findings provide a quantitative reference for optimizing China’s staple crop planting structure. Full article

Myanmar has developed a range of legality assurance and traceability mechanisms to support legal timber production and respond to evolving international market requirements. This study examines the institutional development of timber legality assurance and traceability in Myanmar’s forest supply chains and its implications for verification, market access, and trade. A qualitative research design was employed, combining thematic analysis of 200 publicly available documents collected between January and May 2026 with descriptive analysis of timber production and trade statistics from successive editions of the International Tropical Timber Organization (ITTO) Biennial Review and Assessment of the World Timber Situation. The trade analysis used annual production, import, export, and domestic consumption data for 2010–2024 together with direction-of-trade statistics for 2013–2023. The findings show that Myanmar has established formal institutional arrangements for timber legality assurance through the Myanmar Timber Legality Assurance System (MTLAS), the Myanmar Timber Chain of Custody (CoC) Process, the Myanmar Forest Certification Scheme (MFCS), and the Digital Timber Traceability System (DTTS). Documentary evidence indicates progressive institutional development while providing comparatively limited evidence regarding operational effectiveness. Descriptive trade analysis identified structural changes in timber exports and increasing orientation towards regional Asian markets. The review demonstrates that the principal challenge lies not only in establishing legality assurance mechanisms but also in maintaining transparent, reliable, and independently verifiable information capable of supporting evolving international due-diligence requirements. Full article

Forest-product certification now operates at the intersection of private sustainability governance, market access, and mandatory due diligence. This structured narrative review asks how Forest Stewardship Council (FSC) and Programme for the Endorsement of Forest Certification (PEFC) certification, together with the EU Deforestation Regulation (EUDR), affect competitiveness and the distribution of compliance costs in Polish business-to-business and export-oriented wood-product supply chains. A documented revision-stage search and screening procedure produced an evidence base of 55 peer-reviewed, regulatory, statistical, and sectoral sources. The analytical framework combines private-governance theory, stakeholder conflict analysis, and relationship marketing to examine information asymmetry, bargaining power, cost allocation, and market access. The Polish case is characterised by a large publicly owned forest resource, extensive but overlapping FSC and PEFC coverage, and strongly export-oriented downstream industries. Certification can reduce buyer verification costs, support supplier qualification, improve traceability, and protect access to demanding markets. However, fixed audit, documentation, digitalisation, and certified-material costs are borne disproportionately by small and medium-sized enterprises, especially where lead buyers do not share adaptation costs. EUDR strengthens these asymmetries because certified status can support, but does not replace, legal due diligence. The review contributes a governance-based explanation of why the same sustainability requirements can generate resilience and market access for digitally mature exporters while creating entry barriers, supplier exclusion, and concentration risks for smaller firms. Policy support should therefore combine clear demand signals, group certification, shared traceability infrastructure, advisory services, and buyer–supplier cost-sharing arrangements. Full article

Skeletal deformities remain a major challenge in marine fish hatcheries, affecting larval quality, animal welfare, production efficiency, and market value. In European seabass (Dicentrarchus labrax), saddleback syndrome (SBS) is a relevant skeletal abnormality that develops during larval ontogeny and has been associated with defects of the primordial marginal finfold around the flexion stage. This study investigated whether gene expression markers, combined with machine learning, could provide a stage-specific molecular approach for assessing SBS-associated larval batch quality. Larval populations from commercial hatcheries were classified as GOOD or POOR according to SBS incidence at mid-metamorphosis. Gene expression was analyzed at first feeding, flexion, post-flexion, and mid-metamorphosis, targeting genes involved in osteogenesis, myogenesis, metabolism, oxidative phosphorylation, and stress response. Stage-specific random forest models were used to classify gene expression profiles derived from larval populations with contrasting SBS incidence and to identify candidate informative genes. The models showed cross-validated area under the receiver operating characteristic curve (ROC AUC) values ranging from 0.83 to 0.962, with the highest performance at flexion. Reduced models based on the three most informative genes retained comparable internal cross-validation performance. Key candidate genes were mainly related to mitochondrial energy production, iron metabolism, stress response, muscle development, and extracellular matrix formation. These findings suggest that gene expression profiling combined with machine learning may support stage-aware discrimination of larval populations with contrasting SBS incidence, although validation in larger independent datasets is required before hatchery application. Full article

Mixed crop–livestock systems remain central to sustainable intensification because they reconnect feed, biomass, soil cover, livestock productivity, and household livelihoods. However, forage integration benefits are neither automatic nor uniformly transferable across regions. This structured critical integrative review asks when forage integration generates net system-level gains. It also asks when integration merely shifts costs among production, labor, water, nutrients, emissions, household risk, or territorial nutrient balances. The review synthesizes evidence on forage legumes, tropical and temperate grasses, dual-purpose crops, grazed cover crops, pasture rotations, silvopastoral arrangements, and beyond-farm feed–manure exchanges. Examples from the Brazilian Cerrado, western São Paulo, and Minas Gerais illustrate tropical pathways involving Urochloa/Brachiaria integration, crop–pasture rotations, crop–livestock–forest systems, and habitat-mediated pest regulation. These examples are interpreted alongside evidence from African, Asian, European, and temperate systems to maintain regional balance. The review contributes a diagnostic framework for assessing system design, evidence strength, and scaling feasibility. The framework links forage portfolios to integration niches, management quality, resource constraints, gendered labor implications, external-input dependency, and adaptive scaling pathways. Forage integration can improve feed quality, animal performance, soil cover, nutrient cycling, biodiversity functions, and emission intensity. These gains require careful management of establishment, grazing, manure distribution, phosphorus and potassium balances, water demand, labor allocation, markets, and governance. Future research should move beyond short-term demonstrations toward causal inference, longitudinal whole-system accounting, transparent evidence-quality grading, and documentation of failed, partial, or discontinued interventions. Full article

The Brazilian Amazon is a contested frontier, shaped by destructive and conservationist forces. Most forest clearing is driven by agro-extractivism, an agrarian pathway based on raw commodity production. The socio-biodiversity economy (SBE) has emerged in response, widely regarded as a transformative agrarian pathway capable of reconciling environmental conservation and rural livelihoods. However, recent research suggests that, as socio-biodiversity products scale up, agro-extractivist dynamics can be reproduced within the SBE. We examine this tension in the cocoa frontier of the Transamazon, where cocoa is institutionally promoted as an SBE alternative. We conduct an exploratory, mixed-methods study combining a geospatial analysis of land use and land cover (LULC) change (2020–2025, random forest classification) with a qualitative analysis drawing on participatory mapping and 87 semi-structured interviews with farmers, cooperatives, buyers, and institutional actors. Using a gradient framework, we read LULC transitions and farmers’ land use practices along an agro-extractivism–SBE continuum. The cocoa frontier emerges as a hybrid geography. The landscape is predominantly stable but internally reorganizing: anthropogenic forest declines while full-sun monoculture expands over pasture, with intensification concentrated in peri-urban areas and restoration in remote ones. Land use practices form five recurring configurations, two firmly anchored at the socio-biodiversity or agro-extractivist poles and three whose alignment with the SBE depends on access to markets, knowledge, and institutions. We argue that the frontier contestation unfolds not only between distinct economies but within the cocoa economy itself, and we identify the policy areas relevant to sustaining SBE-oriented practices in the Transamazon. More broadly, this study suggests that the classification of Amazonian forest-based economies as inherent alternatives to agro-extractivism should be treated as an empirical question rather than an assumption. Full article

Forest certification has become an important market-based tool for promoting environmental sustainability, biodiversity conservation, and improved governance across agricultural and forest production systems. This state-of-the-art (SotA) review examines forest and cocoa certification schemes in Brazil, focusing on cocoa-based agroforestry systems in southern Bahia. Major programs, including the Forest Stewardship Council (FSC), Brazilian Forest Certification Program (Cerflor), Instituto Biodinâmico (IBD), UTZ Certified (UTZ), Rainforest Alliance (RA), and Geographical Indication (GI) systems, were analyzed for implementation, sustainability outcomes, and accessibility across producer groups. This SotA review identifies persistent barriers to certification adoption, including high costs, complex regulations, limited technical capacity, and unequal access among producers. Certification adoption varies substantially among producer groups, with cooperatives playing a critical role as intermediaries that reduce barriers and facilitate market access. Finally, this SotA review proposes a conceptual classification of cocoa producers and an AI-enabled perspective for deforestation-free certification in biodiversity hotspots such as southern Bahia, offering new insights into sustainable cocoa production and forest conservation. Full article

This study evaluates the economic efficiency of the timber-production function across 24 forest stands in Czech Republic, representing monocultures, low-diversity mixed stands, mixed stands, and structurally differentiated stands, in the context of the profound changes that have affected forestry in the Czech Republic in recent years. Bark beetle outbreaks, climatic extremes, and the degradation of Norway spruce monocultures have increased concerns about their long-term production reliability and economic stability, highlighting the need to identify more resilient and sustainable management approaches. Mixed and structurally diversified stands, owing to their species diversity and higher ecological stability, represent a potential alternative; however, their management and economic assessment require more complex planning and interpretation. The study analyses the volume production of selected stands, timber market prices by assortments and tree species recalculated on a per-hectare basis and compares silvicultural and harvesting costs. Economic efficiency is expressed using the cost coefficient (Kn) and the efficiency coefficient (Ke), which quantify both direct production costs and the economic return of individual stand types. Results show that monoculture stands, especially those with a high share of valuable assortments, achieved the highest economic efficiency under the applied static cost–revenue assessment. This finding reflects the observed assortment structure, realized timber prices, and selected management costs. In the broader Central European forestry context, however, previous studies indicate that even-aged conifer monocultures may be more exposed to biotic and abiotic disturbance risks, which can affect their long-term production reliability and economic stability. Stands with higher species and structural diversity exhibit an economic profile that differs substantially from that of monocultures. Based on aggregated price and cost inputs for the reference period 2020–2024, low-diversity mixed and mixed stands reach intermediate values of cost intensity and efficiency, whereas structurally differentiated stands display the highest cost intensity and the lowest efficiency. Monocultures, by contrast, achieve the highest economic efficiency, primarily due to a greater share of high-quality timber assortments (classes I–III). Diversified stand structures (mixed and structurally differentiated stands) broaden the assortment composition and produce a more even distribution of monetization across quality classes. Diversification, therefore, did not maximize immediate economic efficiency in the static assessment; rather, it was associated with broader assortment composition and a less concentrated revenue structure across quality classes. Full article

Mushrooms, forest fruits and game meat represent three major categories of forest foods in Czechia. This study compares their acquisition mechanisms, market visibility and value-chain positions and provides reference-year, category-specific physical estimates and stage-specific indicative economic values. The analysis integrates pooled national survey data on mushrooms and forest fruits from 2021 to 2025 (N = 5025), a 2022 survey extension on game meat (N = 1000), qualitative interviews with 12 stakeholders in the Czech game-meat value chain conducted by the research team between 2023 and 2024, and official hunting statistics. In the 2024 reference year, mushrooms and forest fruits were estimated through household-collected quantities, whereas game meat was estimated as gross carcass-weight equivalent at the primary procurement stage. The three categories together represented an indicative stage-specific economic value of approximately EUR 324.3 million, but their physical quantities are interpreted as product-specific estimates rather than as directly equivalent units of provisioning value. Mushrooms showed the strongest household-collection profile: 70.4% of respondents reported collection and 20.1% reported purchase. Forest fruits displayed a more mixed acquisition pattern, with particularly high purchase shares for blueberries and raspberries. Collection and purchase were largely independent for mushrooms, whereas complementary relationships prevailed among forest fruits. Game meat had an indicative primary procurement value of EUR 33.57 million and reflected a regulated hunting-based value chain. The findings identify a differentiated forest-food system in which socio-economic significance is shaped by product-specific relationships among household acquisition, market access, value-chain organisation and stage-specific value creation. Full article

The intensification of agriculture and large-scale rural development policies present major challenges to the sustainability of agroecosystems worldwide. In China, the ‘Rural Revitalization Strategy’ has reshaped rural landscapes, yet its net impact on the interplay between agricultural production, ecosystem health, and human well-being remains poorly quantified at a national scale. This study investigates the spatiotemporal dynamics of Rural Ecological Livability (REL)—an integrated measure of agroecosystem health and quality of life—and identifies its key agro-ecological and socioeconomic drivers across 31 Chinese provinces from 2005 to 2020. We employed an innovative evaluation framework and a nested-weight spatial Durbin model to analyze these complex interactions. Our findings reveal a significant, yet spatially uneven, improvement in REL, with a persistent gradient between developed coastal regions and inland agricultural provinces. Forest coverage and moderate climatic conditions were key positive local drivers of REL. Crucially, our model uncovers significant negative spatial spillovers, demonstrating that intensified agricultural mechanization in one province can degrade the ecological livability of neighboring agroecosystems, likely through increased market competition and resource pressure. These results reveal a critical tension between local agricultural modernization and regional environmental sustainability. They underscore the necessity for agri-environmental policies that transcend administrative boundaries and manage landscape-level spillovers to achieve truly sustainable agroecosystem management. Full article

Forest ecological product value realization (FEPVR) seeks to convert forest ecosystem services into identifiable, accountable, compensable, tradable, and financeable value returns through institutional and market arrangements. Existing studies have mainly emphasized aggregate evaluation or conversion efficiency, with less attention to the structural relationship between ecological supply capacity and value-capture capacity. This study develops a dual-dimensional framework of use-value realization (UVR) and exchange-value realization (EVR), and constructs a city-level panel of 147 policy-practice sample cities (prefecture-level and above) in China over 2019–2023. An entropy-weighted composite index and an entropy-weighted TOPSIS model are applied to measure FEPVR mechanism development, structural configurations, and relative closeness to the sample-defined ideal state. The results show that the mean composite score increased from 0.194 in 2019 to 0.337 in 2023, while the coefficient of variation declined from 0.561 to 0.398, indicating overall improvement and narrowing intercity disparities. Global Moran’s I remains positive and significant throughout the study period, indicating significant and positive spatial autocorrelation in FEPVR mechanism development. The UVR–EVR decomposition reveals substantial structural divergence: the HH, HL, LH, and LL configurations include 29, 33, 25, and 60 cities, respectively. TOPSIS results further show that EVR relative closeness increased markedly, whereas UVR relative closeness declined slightly, indicating that institutional and market-based value-capture capacity expanded faster than the ecological supply base. Robustness checks suggest that the rise in EVR is strongly associated with institutional-entry indicators, and should therefore be interpreted as the expansion of value-capture instruments rather than direct evidence of realized market performance or ecological improvement. The findings provide a descriptive evaluation of FEPVR mechanism development in cities with documented policy or practice foundations, and should not be generalized as the average condition of all Chinese cities. Full article

Thunderstorm disasters are one of the major meteorological disasters in China, causing significant human casualties and economic losses each year. Traditional loss compensation insurance is confronted with difficulties such as inspection and assessing, causing low claim processing efficiency, while index insurance can effectively overcome these deficiencies by triggering payment through objective indices. This paper is based on satellite remote sensing monitoring data, using a combination of principal component analysis, random forests, and fuzzy mathematical theory to construct a lightning risk index and design a complete index insurance product. Experimental validation based on historical satellite monitoring data has shown that the risk indices constructed in this paper can effectively capture the temporal and spatial variability of lightning activity. Random forest models have a relatively low fitting error of training labels, and the SHAP values reveal a characteristic weight of importance consistent with physical perception. The insurance product has a reasonable distribution of amount and compensation, and premium pricing balances actuarial fairness with market acceptability. The present methodology provides a transportable design path to monitor and transfer the lightning risk using multi-source remote sensing data, with some outreach value in the field of lightning and other natural disasters. Full article

Intermittent sales patterns, prevalent in fast-fashion retail, pose a critical challenge for conventional forecasting methods. This study empirically compares one-stage and two-stage machine learning (ML) frameworks with classical benchmarks (Croston, SBA). The two-stage approach uses a Random Forest classifier for demand occurrence, followed by regression models (RF, GBM, XGBoost, LightGBM) for magnitude. Models are evaluated using weekly sales data from an Iraqi fashion retailer, incorporating rich exogenous features like product attributes, pricing, weather, and special events across 64 unique attribute-defined product group time series. Performance is assessed via a fixed 13-week holdout and rolling-origin cross-validation, with LSTM and Temporal Fusion Transformer (TFT) serving as deep learning benchmarks. Empirical findings show that machine learning configurations achieve superior WRMSSE accuracy, with two-stage models often outperforming one-stage counterparts, and both significantly surpassing classical and deep learning baselines. The Two-Stage XGBoost yielded the lowest WRMSSE, establishing the feature-engineered two-stage framework as the strongest overall for this intermittent retail setting. Furthermore, a detailed SHAP analysis elucidated the distinct feature contributions to demand occurrence versus demand magnitude, providing actionable insights for inventory management. This rigorous benchmarking analysis offers practical implications for inventory planning and demand management in volatile markets, highlighting the effectiveness of explicit demand occurrence modeling. Full article

Shaded Arabica coffee production in agroforestry systems, as opposed to full-sun production, is a nature-based solution improving soil water balance, reducing heat exposure of coffee plants, and supporting sustainable forest management as opposed to deforestation. For this coffee production system in Mexico, which is dominated by smallholders as the largest group of coffee producers, we herein analyze current and estimate future yields. For the first time, to our best knowledge, this is done with a process-based coffee agroforestry model CAF2014 that we adapted for geo-spatial applications and named CAF2014-Rhaobi. Modeling of smallholders’ representative management is based on tree thinning, pruning frequency, and nitrogen supply through fertilizer and litter from nitrogen-fixing shade trees. Modeled historical yields generally agree with the reported numbers; however, there are discrepancies explained by modeling assumptions and simplifications. While shade trees help sustain coffee production, the projected drop in yields under present management is about 30% at the end of the century compared to the present as estimated using an ensemble of CMIP6 SSP5-8.5 climate projections. Economic analysis for three typologies of Mexican small coffee producers (conventional low, high-efficiency, and organic) reveals the major role of farmer associations and organic coffee price premiums in making production economically sustainable. This emphasizes the need for innovative marketing approaches and policies supporting farmers opting for certified production. Full article