Search Results (516)

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region. Full article

Peanut (Arachis hypogaea L.) is a globally important oilseed cash crop, yet its limited genetic diversity and unique reproductive biology present persistent challenges for conventional crossbreeding. Traditional breeding approaches are often time-consuming and inadequate, mitigating the pace of cultivar development. Essential for double fertilization and programmed cell death (PCD), DUF679 membrane proteins (DMPs) represent a membrane protein family unique to plants. In the present study, a comprehensive analysis of the DMP gene family in peanuts was conducted, which included the identification of 21 family members. Based on phylogenetic analysis, these genes were segregated into five distinct clades (I–V), with AhDMP8A, AhDMP8B, AhDMP9A, and AhDMP9B in clade IV exhibiting high homology with known haploid induction genes. These four candidates also displayed significantly elevated expression in floral tissues compared to other organs, supporting their candidacy for haploid induction in peanuts. Subcellular localization prediction, confirmed through co-localization assays, demonstrated that AhDMPs primarily localize to the plasma membrane, consistent with their proposed roles in the reproductive signaling process. Furthermore, chromosomal mapping and synteny analyses revealed that the expansion of the AhDMP gene family is largely driven by whole-genome duplication (WGD) and segmental duplication events, reflecting the evolutionary dynamics of the tetraploid peanut genome. Collectively, these findings establish a foundational understanding of the AhDMP gene family and highlight promising targets for future applications in haploid induction-based breeding strategies in peanuts. Full article

Seedling transplantation, a pivotal component in advancing the cultivation of vegetables and cash crops, significantly bolsters crops’ resilience against drought, cold, pests, and diseases, while substantially enhancing yields. The implementation of transplanting machinery not only remarkably alleviates the labor-intensive nature of transplantation but also elevates the precision and uniformity of the process, thereby facilitating mechanized plant protection and harvesting operations. This article summarizes the research status and development trends of mechanized field transplanting technology and equipment. It also analyzes and summarizes the types and current status of typical representative automatic seedling picking mechanisms. Based on the current research status, the challenges of mechanized transplanting technology were analyzed, mainly the following: insufficient integration of agricultural machinery and agronomy; the standards for each stage of transplanting are not perfect; the adaptability of existing transplanting machines is poor; the level of informatization and intelligence of equipment is low; the lack of innovation in key components, such as seedling picking and transplanting mechanisms; and the proposed solutions to address the issues. Corresponding solutions are proposed, such as the following: strengthening interdisciplinary collaboration; establishing standards for transplanting processes; enhancing transplanter adaptability; accelerating intelligentization and digitalization of transplanters; strengthening the theoretical framework; and performance optimization of transplanting mechanisms. Finally, the development direction of future fully automatic transplanting machines was discussed, including the following: improving the transplanting efficiency and quality of transplanting machines; integrating research and development of testing, planting, and seedling supplementation for transplanting machines; unmanned transplanting operations; and fostering collaborative industrial development. Full article

The Altay oasis, located at the heart of the transnational ecological conservation zone shared by China, Kazakhstan, Russia, and Mongolia, is a region with tremendous potential for water resource utilization. However, with the continued expansion of agriculture, its ecological vulnerability has become increasingly pronounced. Within this fragile balance lies a critical opportunity: efficient water resource management could pave the way for sustainable development across the entire arid oasis regions. This study uses a decision tree model based on a feature threshold to map the spatial distribution of major crops in the Altay Prefecture oasis, assess their water requirements, and identify the coupling relationships between agricultural water and land resources. Furthermore, it proposed optimization planting structure strategies under three scenarios: water-saving irrigation, cash crop orientation, and forage crop orientation. In 2023, the total planting area of major crops in Altay Prefecture was 3368 km², including spring wheat, spring maize, sunflower, and alfalfa, which consumed 2.68 × 10⁹ m³ of water. Although this area accounted for only 2.85% of the land, it consumed 26.23% of regional water resources, with agricultural water use comprising as much as 82.5% of total consumption, highlighting inefficient agricultural water use as a critical barrier to sustainable agricultural development. Micro-irrigation technologies demonstrate significant water-saving potential. The adoption of such technologies could reduce water consumption by 14.5%, thereby significantly enhancing agricultural water-use efficiency. Cropping structure optimization analysis indicates that sunflower-based planting patterns offer notable water-saving benefits. Increasing the area of sunflower cultivation by one unit can unlock a water-saving potential of 25.91%. Forage crop combinations excluding soybean can increase livestock production by 30.2% under the same level of water consumption, demonstrating their superior effectiveness for livestock system expansion. This study provides valuable insights for achieving sustainable agricultural development in arid regions under different development scenarios. Full article

As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5–70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R²) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51–56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques’ data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems. Full article

Eliminating all forms of poverty is a core component of the United Nations’ Sustainable Development Goals. At the household level, poverty and income inequality significantly threaten farmers’ sustainable development and food security. Based on a sample of 1234 kiwi farmers from the Shaanxi and Sichuan provinces in China, this paper empirically examines the impact of participation in agricultural industry organizations (AIOs) on household income and income inequality, as well as the underlying mechanisms. The results indicate the following: (1) Participation in AIOs increased farmers’ average household income by approximately 19,570 yuan while simultaneously reducing the income inequality index by an average of 4.1%. (2) Participation increases household income and mitigates income inequality through three mechanisms: promoting agricultural production, enhancing sales premiums, and improving human capital. (3) After addressing endogeneity concerns, farmers participating in leading agribusiness enterprises experienced an additional average income increase of 21,700 yuan compared to those participating in agricultural cooperatives. Therefore, it is recommended to optimize the farmer–enterprise linkage mechanisms within agricultural industry organizations, enhance technical training programs, and strengthen production–marketing integration and market connection systems, aiming to achieve both increased farmer income and improved income distribution. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

Rubber tree (Hevea brasiliensis Muell. Arg.) is a major tropical cash crop in southern China, with Hainan and Yunnan provinces being the main planting areas. In July 2023, bark cracking and gumming were observed on the trunks of mature rubber trees in Haikou City, Hainan Province, leading to xylem rot, which severely impacted the healthy growth of the rubber trees. The present study was conducted to confirm the pathogenicity of the patho-gen associated with stem gummosis disease, characterize it using morphological and mo-lecular tools, and devise field management strategies. Pathogenicity testing showed that this strain induced symptoms similar to those of natural outdoor infestation. Based on morphological study and molecular analyses of internal transcribed spacer (ITS), transla-tion elongation factor 1 alpha (TEF1-α), and β-tubulin 2 (TUB2) sequences, the causal agent was identified as Lasiodiplodia theobromae. Field trials demonstrated that an inte-grated fungicide approach—combining trunk application of Bordeaux mixture with root irrigation using citric acid–copper 6.4% + chelated copper-ammonium 15% at both 0.1% and 0.2% concentration—effectively suppressed stem gummosis disease incidence in rub-ber trees. To the best of our knowledge, this is the first report of L. theobromae causing stem gummosis on rubber tree in China. The findings of this study can provide valuable infor-mation for the management strategies and understanding of this disease. Full article

Biochar-based fertilizers can improve soil structure and fertility. However, their efficiency is affected by the raw materials of biochar. The effects of biochar-based fertilizers on the soil microenvironment under Fenlong-ridging conditions remain unclear. This study aimed to evaluate the effects of biochar-based fertilizers derived from sugarcane filter mud and rice straw on soil physicochemical properties, microbial communities, and sugarcane yield under Fenlong-ridging in Guangxi’s acidic red soil (Hapludults). A two-year field experiment (2021–2022) was conducted on a clay loam soil classified as Hapludults (USDA Soil Taxonomy) in the same experimental plots using three fertilizer applications—conventional chemical fertilization (CK), straw biochar-based fertilizer (T1), and sugar filter mud biochar-based fertilizer (T2)to determine the responses of soil physicochemical properties and bacterial community diversity to different biochar-based fertilizers and evaluate benefits to the soil environment and sugarcane yield. Soil samples (0–20 cm depth) revealed that T1 and T2 reduced bulk density by 2.31% and increased porosity by 2.00–2.31% versus CK. Notably, T2 exhibited 4.1-fold higher specific surface area than T1, driving stronger soil–bacterial interactions: it enhanced soil moisture (7.17–13.05%) and pH (17.89–24.14% in 2021; 8.68–11.57% in 2022), thereby promoting nutrient availability (N, P, K), organic matter (SOM), and sucrase activity. Microbiome analysis showed T2 enriched Gemmatimonadota and Sphingomonas (beneficial taxa) while suppressing Acidothermus. The results of RDA and Spearman correlation analysis indicated that the bacterial community structure was mainly affected by soil pH, TN, AP, and SOM. Consequently, T2 increased sugarcane yield by 5.63–11.16% over T1 through synergistic soil–microbial improvements. Future studies involving multi-site and long-term experiments are needed to confirm the broader applicability and stability of these findings. This study provides a theoretical basis for the positive regulation of sugar filter mud biochar-based fertilizers in the soil environment, bacterial community structure, and sugarcane yield. Full article

Against the backdrop of global climate change, enhancing farmers’ adaptive capacity to reduce crop production risks has emerged as a critical concern for governments and researchers worldwide. Drawing on social capital theory, this study develops a four-dimensional measurement framework comprising social networks, social trust, social norms, and social participation, utilizing survey data from 1772 households in the Yellow River Basin. We employ factor analysis to construct comprehensive social capital scores and apply ordered Probit models to examine how social capital influences farmers’ climate adaptation behaviors, with particular attention to the moderating roles of agricultural extension interaction and digital literacy. Key findings include: (1) Adoption patterns: Climate adaptation behavior adoption remains low (60%), with technical adaptation measures showing particularly poor uptake (13%); (2) Direct effects: Social capital significantly promotes adaptation behaviors, with social trust (p < 0.01), networks (p < 0.01), and participation (p < 0.05) demonstrating positive effects, while social norms show no significant impact; (3) Heterogeneous effects: Impact mechanisms differ by crop type, with grain producers relying more heavily on social networks (+, p < 0.01) and cash crop producers depending more on social trust (+, p < 0.01); (4) Moderating mechanisms: Agricultural extension interaction exhibits scale-dependent effects, negatively moderating the relationship for large-scale farmers (p < 0.05) while showing no significant effects for smaller operations; digital literacy consistently demonstrates negative moderation, whereby higher literacy levels weaken social capital’s promotional effects (p < 0.01). Policy recommendations: Effective climate adaptation strategies should integrate strengthened rural social organization development, differentiated agricultural extension systems tailored to farm characteristics, and enhanced rural digital infrastructure investment. Full article

Thrips flavus (Thysanoptera: Thripidae) is a Eurasian pest that primarily attacks a variety of cash crops such as soybean. Currently, there is insufficient knowledge of thrips-resistance mechanisms in soybeans and a lack of effective thrips-resistant soybean varieties. The objective of this study was to identify the correlation between the pest thrips, T. flavus, resistance levels and morphological structures of soybean varieties. A total of 41 spring soybean varieties were planted in a field in Northeast China. Observations were made regarding the infestation intensity of T. flavus, the morphological structures (compound leaf shape, leaf length, leaf width, leaf surface humidity, trichome density, length, and color), leaf SPAD value, leaf nitrogen content, etc. Specifically, leaf trichome density (regardless of whether it was on the upper or lower surfaces of the upper, middle, or lower leaves), trichome color, and compound leaf shape all showed significant positive correlations with the amount of T. flavus. Additionally, principal component analysis (PCA) indicated that, during the peak flowering stage, leaf width, trichome length, trichome density, SPAD value, and nitrogen content were key factors for evaluating resistance; meanwhile, during the podding stage, leaf length, SPAD value, nitrogen content, and leaf surface humidity made the most significant contributions. Field resistance screening using the number of T. flavus per meter of double rows, the average number of T. flavus per plant, and hierarchical cluster analysis yielded consistent results. The soybean variety “podless-trichome” is a thrips-resistant variety (high resistance), and “Jinong 29” is a thrips-sensitive variety (high sensitivity). This study provides valuable insights into the occurrence of insect resistance to thrips in soybean varieties. Full article

Soil salinization presents a significant challenge, driven by factors such as inadequate drainage, shallow aquifers, and high evaporation rates, threatening global food security. The sunflower emerges as a key cash crop in such areas, providing the opportunity to convert its straw into biochar, which offers additional agronomic and environmental benefits. This study investigates the effectiveness of biochar interlayers in enhancing salt leaching and suppressing upward salt migration through integrated laboratory and field experiments. The effectiveness of varying biochar interlayer application rates was assessed in promoting salt leaching, decreasing soil electrical conductivity (EC), and enhancing crop performance in saline soils through a systematic approach that combines laboratory and field experiments. The biochar treatments included a control (CK) and different applications of 20 (BL20), 40 (BL40), 60 (BL60), and 80 (BL80) tons of biochar per hectare, all applied below a depth of 20 cm, with each treatment replicated three times. The laboratory and field experimental setups maintained consistency in terms of biochar treatments and interlayer placement methodology. During the laboratory column experiments, the soil columns were treated with deionized water, and their leachates were analyzed for EC and major ionic components. The results showed that columns with biochar interlayers exhibited significantly higher efflux rates compared to those of the control and notably accelerated the time required for the effluent EC to decrease to 2 dS m⁻¹. The CK required 43 days for full discharge and 38 days for EC stabilization below 2 dS m⁻¹. In contrast, biochar treatments notably reduced these times, with BL80 achieving discharge in just 7 days and EC stabilization in 10 days. Elution events occurred 20–36 days earlier in the biochar-treated columns, confirming biochar’s effectiveness in enhancing leaching efficiency in saline soils. The field experiment results supported the laboratory findings, indicating that increased biochar application rates significantly reduced soil EC and ion concentrations at depths of 0–20 cm and 20–40 cm, lowering the EC from 7.12 to 2.25 dS m⁻¹ and from 6.30 to 2.41 dS m⁻¹ in their respective layers. The application of biochar interlayers resulted in significant reductions in Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and HCO₃⁻ concentrations across both soil layers. In the 0–20 cm layer, Na⁺ decreased from 3.44 to 2.75 mg·g⁻¹, K⁺ from 0.24 to 0.11 mg·g⁻¹, Ca²⁺ from 0.35 to 0.20 mg·g⁻¹, Mg²⁺ from 0.31 to 0.24 mg·g⁻¹, Cl⁻ from 1.22 to 0.88 mg·g⁻¹, SO₄²⁻ from 1.91 to 1.30 mg·g⁻¹ and HCO₃⁻ from 0.39 to 0.18 mg·g⁻¹, respectively. Similarly, in the 20–40 cm layer, Na⁺ declined from 3.62 to 3.05 mg·g⁻¹, K⁺ from 0.28 to 0.12 mg·g⁻¹, Ca²⁺ from 0.39 to 0.26 mg·g⁻¹, Mg²⁺ from 0.36 to 0.27 mg·g⁻¹, Cl⁻ from 1.18 to 0.80 mg·g⁻¹, SO₄²⁻ from 1.95 to 1.33 mg·g⁻¹ and HCO₃⁻ from 0.42 to 0.21 mg·g⁻¹ under increasing biochar rates. Moreover, the use of biochar interlayers significantly improved the physiological traits of sunflowers, including their photosynthesis rates, stomatal conductance, and transpiration efficiency, thereby boosting biomass and achene yield. These results highlight the potential of biochar interlayers as a sustainable strategy for soil desalination, water conservation, and enhanced crop productivity. This approach is especially promising for managing salt-affected soils in regions like California, where soil salinization represents a considerable threat to agricultural sustainability. Full article

Angelica sinensis, a highly valued Chinese herb renowned for its medicinal and nutritional properties, occupies a distinctive position in montane agriculture. The remote sensing monitoring of grain crops and their driving factors has been extensively studied, yet research on medicinal cash crops, particularly Angelica sinensis, remains limited. This study employed Landsat imagery and a two-step supervised classification method to map Angelica sinensis cultivation areas in southern Gansu Province while also assessing and projecting climate change impacts on its spatial distribution and yield based on the MaxEnt model and CMIP6 models. The results revealed a pronounced upward altitudinal shift in Angelica sinensis cultivation between 1990 and 2020, with the proportion of cultivation areas above 2400 m increasing from 28.75% to 67.80%. Climate factors explained 59.07% of the spatial distribution of Angelica sinensis, with precipitation, temperature, and altitude identified as the key environmental factors influencing its spatial distribution, yield, and growth. Projections for 2020 to 2060 indicate that Angelica sinensis cultivation areas will continue to shift to higher altitudes, accompanied by overall declines in both suitable area and yield. Under the SSP5-8.5 scenario, nearly all suitable areas are expected to be confined to altitudes above 2400 m by 2060, with 41.46% occurring above 2800 m. By 2060, the yield is expected to decrease to 361–421 kg/mu (down 20–31% from 2020) while the suitable area is projected to shrink to 0.98–1.80 million mu (40–60% smaller than 2040) under different scenarios. This study provides new insights into the protection and sustainable management of Angelica sinensis under changing climatic conditions, offering a scientific basis for the sustainable utilization of this valuable medicinal plant. Full article

This study seeks to identify the factors driving household food insecurity in Cameroon during the COVID-19 pandemic, examine the effects of coping strategies on household resilience, and explore complementarities among these strategies. We used data from the COVID-19 panel surveys conducted by the National Institute of Statistics of Cameroon. Three models are estimated: an ordered logit model for food insecurity factors, a logit model for the impact of coping strategies, and a multivariate probit model for complementarities. The findings reveal that food insecurity is exacerbated by conflict, socio economic shocks (e.g., loss of employment, crop theft), and price hikes. About 28.59% of households are resilient, mainly due to past savings, cash transfers, free food, and in-kind transfers. The study emphasizes the importance of social and governmental support to mitigate food insecurity during crises, and underscores the need for monitoring socio-economic conditions during pandemics and other crises. Full article

Upland fields are a significant source of N₂O emissions. Thus, an accurate estimation of these emissions is essential. This study employed four classical modeling approaches—the Stepwise Regression Model, Decision Tree Regression, Support Vector Machine, and Random Forest (RF)—to simulate soil N₂O emissions from Chinese upland fields. The upland crops considered in this study covered food crops, oil crops, cash crops, sugar crops, fruits, and vegetables, excluding flooded rice. Comparative analysis revealed that the RF algorithm performed the best, with the highest R² at 0.66 and the lowest root mean square error at 0.008 kg N₂O ha⁻¹ day⁻¹. The application rate of mineral nitrogen fertilizers, mean temperature during the growing season, and soil organic carbon content were the key driving factors in the N₂O emission model. Utilizing the RF model, total N₂O emissions from Chinese upland fields in 2020 were estimated at 183 Gg. Future projections under Representative Concentration Pathway (RCP) scenarios indicated a 2.80–5.92% increase in national N₂O emissions by 2050 compared to 2020. The scenario analysis demonstrated that the proposed nitrogen reduction strategies fail to counteract climate-driven emission amplification. Under the combined scenarios of RCP8.5 and nitrogen reduction strategies, a net 4% increase in national N₂O emissions was projected, highlighting the complex interplay between anthropogenic interventions and climate feedback mechanisms. This study proposes that future attention should be paid to the development of nitrogen optimization strategies under the impact of climate change. Full article