Search Results (968)

Compost amendments are widely recognized as an effective strategy for improving soil quality, modulating enzyme activities, and enhancing nitrogen cycling. Urease, a key enzyme in nitrogen transformation, is characterized by kinetic parameters such as the maximum reaction rate (V_max) and Michaelis constant (K_m), as well as thermodynamic attributes including temperature sensitivity (Q₁₀), activation energy (E_a), enthalpy change (ΔH), Gibbs free energy change (ΔG), and entropy change (ΔS). However, how different compost sources regulate urease kinetics, thermodynamics, and nitrogen availability remains poorly understood. In this study, we evaluated the effects of three compost amendments—mushroom residue (MR), mushroom residue–straw mixture (MSM), and leaf litter (LL)—on urease kinetics and thermodynamics in a temperate agroecosystem. The MSM treatment significantly enhanced urea hydrolysis capacity and catalytic efficiency. In contrast, LL treatment resulted in the highest Km value, indicating a substantially lower enzyme-substrate affinity. Furthermore, MSM reduced the E_a and increased the thermal stability of urease, thereby supporting enzymatic performance under fluctuating temperatures. Collectively, our findings highlight that compost composition is a critical determinant of urease function and nitrogen turnover. By elucidating the coupled kinetic and thermodynamic responses of urease to compost inputs, this study provides mechanistic insights to guide optimized soil management and sustainable nitrogen utilization in temperate agricultural systems. Full article

The climate-smart agriculture (CSA) approach, a sustainable alternative to conventional practices in agriculture, supports three main pillars: increasing productivity, resilience, and greenhouse gas (GHG) mitigation through the adoption of climate-smart practices (CSPs). Effective CSA assessment tools are needed to evaluate the impact of and support the broader adoption of CSPs. This study addresses this need by developing the RAPTURE (Resilient Agricultural Practices for Transforming Uncertain and Resource-Scarce Environments) tool. The RAPTURE tool was developed through five steps, which included collecting data on CSA definitions, existing practices and classifications, climatic conditions of the study areas, and the mathematical equations used to assess CSPs—all of which were stored in databases. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was adopted to guide the selection and inclusion of 222 studies from the Web of Science database, forming the basis for the development of the RAPTURE tool. The first step of RAPTURE synthesizes simple and complex definitions of CSA from the database of 35 definitions. For the second and third steps, an updated classification of the CSPs was developed using a database with 78 CSPs, and a weather conditions database created from areas where CSPs have been studied and implemented was also provided, respectively. The fourth step of the RAPTURE tool includes a database containing the input and output variables necessary for the assessment of CSPs’ impacts, which is essential for the selection of an assessment method. The fifth and last step of the tool contains the assessment methods available, including 24 mathematical methods documented and synthesized. An application of RAPTURE using agricultural data from Florida in 2022 and 2023, and considering an increase of 20% with the implementation of CSPs, showed better productivity and rain-use efficiency. While previous studies have shown that adopting CSPs in agriculture provides several benefits, such as better agricultural production, higher carbon sequestration, the application of the RAPTURE tool in assessing CSPs also demonstrates their ability to increase productivity and resource-use efficiency. Full article

The integration of beneficial microorganisms with sensor technologies represents a transformative advancement toward sustainable smart agriculture. This review synthesizes recent progress in combining microbial bioinoculants with sensor-based monitoring systems to enhance crop productivity, resource-use efficiency, and environmental resilience. Beneficial bacteria and fungi improve nutrient cycling, stress tolerance, and soil fertility thereby reducing the reliance on chemical fertilizers and pesticides. In parallel, sensor networks—including soil moisture, nutrient, environmental, and remote-sensing platforms—enable real-time, data-driven management of agroecosystems. Integrated microbe–sensor approaches have demonstrated 10–25% yield increases and up to 30% reductions in agrochemical inputs under optimized field conditions. We propose an integrative Microbe–Sensor Closed Loop (MSCL) framework in which microbial activity and sensor feedback interact dynamically to optimize inputs, monitor plant–soil interactions, and sustain productivity. Key applications include precision fertilization, stress diagnostics, and early detection of nutrient or pathogen imbalances. The review also highlights barriers to large-scale adoption, such as variable field performance of inoculants, high sensor costs, and limited interoperability of data systems. Addressing these challenges through standardization, cross-disciplinary collaboration, and farmer training will accelerate the transition toward climate-smart, self-regulating agricultural systems. Collectively, the integration of biological and technological innovations provides a clear pathway toward resilient, resource-efficient, and ecologically sound food production. Full article

The slaughterhouse-treated effluent, enriched with nitrogen, phosphorus, and organic matter, presents a promising alternative for water and nutrient reuse in irrigated crop systems. This study assessed the chemical composition of the effluent, nutrient dynamics in the soil, and agronomic performance of soybean (Glycine max (L.) Merr) and sorghum (Sorghum bicolor (L.) Moench) under fertigation. A randomized block design was used, with five treatments (tap water—control—and four effluent levels: 25%, 50%, 75%, and 100%) applied to two crop species, with four replications. The effluent exhibited elevated concentrations of ammoniacal nitrogen (43.9 ± 18.7 mg L⁻¹), and potassium (13.1 ± 3.8 mg L⁻¹), confirming its potential as a nutrient source. No significant differences were observed in soybean plant height across treatments, whereas early-stage sorghum growth showed only slight variation. Irrigation with treated effluent successfully replaced 100% of tap water in both soybean and sorghum, with no significant differences in productivity across concentrations. These results demonstrate the agronomic feasibility of using treated effluent as a substitute for tap water and synthetic fertilizers. Moreover, they highlight its potential as a sustainable input for fertigation, contributing to resource efficiency and promoting more integrated and environmentally conscious agricultural practices. Full article

In this study, we investigated the spatial and temporal dynamics of pork carcass prices across European Union Member States, focusing on the influence of wheat prices and population income levels between 2014 and 2023. Our analysis revealed that both input costs (reflected by wheat price fluctuations) and income-driven demand factors exert significant and spatially correlated effects on pork carcass prices. The results demonstrate the existence of spatial interdependencies among neighboring countries, indicating that price changes in one region may propagate through the broader European market. By integrating spatial econometric techniques within a panel data framework, this research provides empirical evidence of the interconnected nature of EU agricultural markets, advancing the existing literature by demonstrating how input markets and consumer income dynamics jointly shape price behavior within an integrated regional economy. Our findings contribute to a deeper understanding of price transmission mechanisms in the livestock sector and offer valuable insights for policymakers seeking to enhance market efficiency and resilience within the Common Agricultural Policy context. Full article

This study examines the relationship between energy efficiency in agricultural production and its determinants, considering technological, economic, political, and social factors. The aim was to determine the impact of the CAP on the energy efficiency of agricultural production, as well as technological, market, and social changes. The impact of time effects was also taken into account. The study focuses on the four Visegrad Group countries over the 2004–2023 period. Both fixed-effects and dynamic panel models were employed to capture structural changes over time. The significance of agriculture, as a result of structural transformations, is relatively small and hovers around 3% in these countries. The CAP was found to have a significant impact on the energy efficiency of agricultural production. However, it was not the amount of support but rather its structure that played a crucial role, particularly environmental support (0.04). The inertia effect was also of fundamental importance (0.41—elasticity in the inertia model). The total value of transfers, especially in the long term, proved to be a discouraging factor for this process. Market conditions, including energy prices (0.456), structural changes in farms (0.016), and labor input (−0.04), were also significant factors. However, it was not so much the size of support but rather the structure of support that was crucial. The total value of transfers, especially in the long term, was a demotivator for this process. Market conditions, including energy prices, structural changes on farms, and labor inputs, were also important factors. A key recommendation for agricultural financial support policy is to focus support more on environmental and low-emission issues, which are linked to improving the energy efficiency of production while maintaining its growth. Transfers related to the growing importance of renewable energy sources and support for rural development, which do not yield beneficial effects in the considered scope, require increased conditionality. Full article

This study presents a comprehensive sustainability assessment of open field strawberry production in Batman, Türkiye, utilizing thermodynamic-based analyses. The inputs used during the production phase were obtained through survey data collected from local farms over the past few growing seasons. This study is the first to provide an integrated energy, exergy, and environmental assessment of open-field strawberry production in the Batman region, offering novel insights into resource efficiency and sustainability evaluation in agricultural systems. The evaluation was conducted for a functional unit of one ton of strawberries by analysing the cumulative energy consumption (CEnC), cumulative exergy consumption (CExC), and cumulative carbon dioxide emissions (CCO₂E). The total CEnC was calculated to be 1207.8 MJ/ton, with nitrogen fertilizer (390.91 MJ/ton) and diesel fuel (229.67 MJ/ton) being the most significant inputs. The total CExC was 1919.82 MJ/ton, where farmyard manure (1170.19 MJ/ton) was the dominant contributor, accounting for approximately 61% of the total. The total carbon footprint (CCO₂E) was determined to be 41.43 kg/ton, with irrigation water (10.19 kg/ton) and farmyard manure (10.14 kg/ton) being the primary sources. Key sustainability indicators, the Cumulative Degree of Perfection (CDP) and Renewability Indicator (RI), were calculated as 1.34 and 0.26, respectively. The CDP value is notably higher than that of other open field strawberry studies, while the low RI value indicates a heavy reliance on non-renewable resources. The findings highlight that improving sustainability requires a focus on efficient manure management and reducing fossil fuel-based energy consumption. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article

Balancing the development of agricultural cold chain logistics with ecological conservation remains a critical challenge for green cold chain logistics in China’s three northeastern provinces. This study evaluates the efficiency of green cold chain logistics to promote synergy between logistics development and ecological sustainability. Using CiteSpace for keyword co-occurrence analysis and literature extraction, an evaluation index system comprising eight input and output indicators was constructed. The super-efficiency Slacks-Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index were employed to assess efficiency from static and dynamic perspectives, respectively. Kernel density estimation was used to examine spatial distribution patterns, and the Dagum Gini coefficient was applied to decompose regional disparities. The results indicate that (1) overall efficiency remains relatively low, with ML index changes primarily driven by technological progress; (2) substantial regional differences exist among the three provinces in terms of distribution location, shape, and degree of polarization; and (3) inter-regional disparities are the main source of variation. A Tobit model further identified the key influencing factors, indicating that the level of economic development, growth of the tertiary industry, and informatization are the main drivers. These findings provide valuable insights for optimizing regional green cold chain logistics and promoting sustainable agricultural development. Full article

In order to alleviate the constraints of global warming and sustainable development, digitalization has made significant contributions to promoting agricultural carbon reduction through resources, technology, and platforms. Under this situation, China insists on developing agricultural scale management. However, what impact will scale management in agricultural digital emission reduction have on mechanisms and pathways? Based on three rounds of follow-up surveys conducted by the Digital Countryside Research Institute of Nanjing Agricultural University in Jiangsu Province from 2022 to 2024, in this study a total of 258 valid questionnaires on the rice and wheat industry were collected. Methods such as member checking and audit trail were employed to ensure data reliability and validity. Using econometric approaches including Tobit, mediation, and moderation models, this study quantified the Scale Management Level (SML), examined the mechanism pathways of digital emission reduction in a scaled environment, further demonstrated the impact of scale management on digital emission reduction, and verified the mediating and moderating effects of internal and external scale management. We found that: (1) In scale and carbon reduction, the SBM-DEA model calculates that the scale of agricultural land in Jiangsu showed an “inverted S” trend with SML and an “inverted W” trend with the overall agricultural green production efficiency (AGPE), and the highest agricultural green production efficiency is 0.814 in the moderate scale range of 20–36.667 hm². (2) In digitalization and carbon reduction, the Tobit regression model results indicate that Network Platform Empowerment (NPE) significantly promotes carbon reduction (p < 1%), but its squared terms exhibit an inverted U-shaped relationship with agricultural green production efficiency (p < 1%), and SML is significant at the 5% level. From a local regression perspective, the strength of SML’s impact on the three core variables is: NPE > DRE > DTE. (3) Adding scale in agricultural digital emission reduction, the intermediary mechanism results showed that the significant intensity (p < 5%) of the mediating role of Agricultural Mechanization Level (AML) is NPE > DTE > DRE, and that of the Employment of Labor (EOL) is DRE > NPE > DTE. (4) Adding scale in agricultural digital emission reduction, the regulatory effect results showed that the Organized Management Level (OML) and Social Service System (SSS) significantly positively regulate the inhibitory effect of DRE and DTE on AGPE. Finally, we suggest controlling the scale of land management reasonably and developing moderate agricultural scale management according to local conditions, enhancing the digital literacy and agricultural machinery training of scale entities while encouraging the improvement of organizational level and social service innovation, and reasonably reducing labor and mechanization inputs in order to standardize the digital emission reduction effect of agriculture under the background of scale. Full article

This paper evaluates the eco-efficiency of crop production in the European Union (EU) and the Republic of Serbia for the period 2015–2023, using a stochastic frontier analysis (SFA) model based on panel data. Eco-efficiency was assessed as the ratio of agricultural output to key environmental pressures, with expenditures on fertilizers, plant protection products, and energy serving as proxies for ecological burden. The analysis shows that the average eco-efficiency score (Total EE) across the sample is 59.26%, implying that nearly 41% of inputs could be reduced without decreasing output. Decomposition reveals high residual eco-efficiency (93.62%) and lower persistent eco-efficiency (63.30%), suggesting that systematic inefficiencies dominate and are primarily linked to internal farm-level factors such as management practices, organizational structures, and technology adoption. Serbia’s total eco-efficiency score of 63.0% places it close to the EU average, confirming structural similarities with Southern and Eastern European countries. Eco-efficiency scores exhibit notable cross-country variation, ranging from approximately 35% to 96%. About 59% of countries fall within the 50–75% interval, while roughly 11% exceed 75%, indicating considerable scope for further improvement. Cluster analysis further indicates that while Serbia belongs to the lower-intensity group, it has significant potential to converge toward EU frontrunners through farm-level improvements. The findings highlight the importance of targeting internal determinants of efficiency, while recognizing that policy measures can provide enabling conditions and long-term incentives for the green transition. A coherent policy for the green transition should prioritize farm-level structural upgrades, such as technology adoption, advisory and knowledge transfer, and sustainable nutrient and soil management, supported by enabling CAP instruments (eco-schemes and GAEC) and IPARD measures to accelerate improvements in resource efficiency and environmental performance. Full article

Efficient pesticide application remains a critical resource-management challenge in modern agriculture, where limited spray penetration reduces treatment efficacy, wastes chemical inputs, and increases environmental losses. This study quantified the effect of air-assisted spraying (AAS) on droplet deposition in two contrasting field crops, oilseed rape and wheat. Field trials were conducted using a sprayer equipped with an adjustable airflow module, and spray coverage was measured with water-sensitive papers at multiple canopy heights and orientations. In oilseed rape, AAS improved deposition on front-facing and top surfaces in the lower canopy, for example, increasing top-surface coverage at 90 cm from 53.4% to 65.5% at 6 km∙h⁻¹, indicating more uniform distribution and enhanced penetration. In wheat, which typically exhibits a more open canopy structure compared to oilseed rape, AAS effects were smaller and less consistent, with the greatest gain on front-facing lower surfaces (from 13.3% to 21.9% at 7 km∙h⁻¹). Although drift was not measured in this experiment, previous studies using the same sprayer prototype demonstrated measurable reductions, supporting the environmental relevance of improved deposition. These results highlight the role of canopy architecture in determining AAS performance and underscore the technology’s potential to reduce pesticide inputs, minimize off-target losses, and improve the resource efficiency of crop protection in line with EU Farm to Fork objectives. Full article

Efficient water and nitrogen management is essential for maintaining soil fertility and achieving sustainable agricultural production, especially in arid oasis regions where soil degradation and nutrient loss are common challenges. However, the interactions between irrigation regimes, nitrogen application, and soil biological processes in such environments remain insufficiently understood. This study investigated the effects of water and nitrogen management on the sustainability of sandy soil nutrients within the context of the sustainable development goals during silage maize cultivation in the oasis irrigation area of the Hexi Corridor, Northwest China. Four irrigation regimes and five nitrogen management regimes were tested. The results indicate that ammonium nitrogen (NH₄⁺-N) varied significantly during the jointing stage (W4 treatment), ranging from 3.52 to 16.38 mg/kg (p < 0.05). Nitrate nitrogen (NO₃⁻-N) exhibited significant differences during the tasseling stage (W1 treatment), with a range of 6.16–21.58 mg/kg (p < 0.05). Soil total phosphorus (STP) gradually declined from early to late growth stages, ranging from 0.20 to 0.97 g/kg. Regarding enzyme activity, alkaline phosphatase (ALP) increased progressively throughout the growth period, with a range of 0.02–0.14 mg/g/d, while urease (URE) showed a declining trend, ranging from 0.25 to 0.66 mg/g/d. Water management exerted a significant negative effect on soil enzyme activity (p < 0.05), while nitrogen fertilization had a minimal impact on soil microbial communities (p > 0.05). Growth stage and irrigation regime are key regulators of the soil–microbe–enzyme activity system. The crop’s nutrient demand cycles and microbially mediated nutrient transformations exhibited strong dependence on growth stage. Enzyme activity is notably and positively affected by nitrogen inputs and plant developmental stages, while microbial biomass is mainly regulated by soil C, N, and P contents and enzyme activities. These findings provide a scientific basis for implementing water-saving irrigation and high-efficiency fertilization strategies in oasis agricultural systems. Full article

Energy consumption and environmental pollution pose significant challenges to sustainable development. This study develops a comprehensive coupled framework model that advances the quantitative integration of carbon (C), nitrogen (N), and phosphorus (P) cycles driven by multiple anthropogenic pollution sources. This paper used Panjin city as a case study to analyze the dynamic changes and interconnections among C, N, and P. Results indicated that net anthropogenic carbon inputs (NAI_C) increased by 33% from 2016–2020, while net anthropogenic nitrogen inputs (NAI_N) and net anthropogenic phosphorus inputs (NAI_P) decreased by 14% and 28%, respectively. The primary driver of NAI_C was energy consumption, while wetlands were the dominant carbon sequestration sink. Agricultural production was identified as the primary source of NAI_N and NAI_P, and approximately 4.5% of NAI_N and 2.9% of NAI_P were discharged into receiving water bodies. We demonstrate that human activities and natural processes exhibit dual attributes, producing positive and negative environmental effects. The increase in carbon emissions drives economic growth and industrial restructuring; however, the enhanced economic capacity also strengthens the ability to mitigate pollution through environmental protection measures. Similarly, natural ecosystems, including forests and grasslands, contribute to carbon sequestration and the release of non-point source pollution. The comprehensive environmental impact assessment of C, N, and P revealed that the comprehensive environmental index for Panjin city exhibited an improved trend. The factors of energy structure, energy efficiency, and economic scale promoted NAI_C growth, with the economic scale factor alone accounting for 93% of the total increment. Environmental efficiency factor and population size factor were the primary drivers in reducing NAI_N and NAI_P discharges into the receiving water bodies. We propose a novel management model, ecological restoration, clean energy utilization, resource recycling, and pollution source reduction to achieve systemic governance of C, N, and P inputs. Full article

Ecological security and sustainable utilization of cultivated land are the fundamental guarantee for agricultural production and a key link in maintaining ecological balance. Based on the emergy analysis theory, this study adopted the modified emergy–ecological footprint model, taking counties as the evaluation unit, to analyze the spatiotemporal differentiation characteristics of ecological security and sustainable utilization of cultivated land in Sichuan Province from 2010 to 2020. The results indicated that (1) in hilly regions, emergy output increased the most, despite a decrease in emergy input. Overall, both emergy input and output of cultivated land in Sichuan Province showed an increasing trend, with average growth rates of 6.3% and 32.6%, respectively. (2) The overall ecological security of cultivated land in the province was at risk. The at-risk area was mainly concentrated in plain, hilly and peripheral mountainous regions. The spatial pattern presented an evolutionary characteristic where the safe area contracted northwestward while the at-risk area expanded northwestward–southwestward. (3) The overall sustainable utilization of cultivated land in the province degraded from strong to weak. The spatial pattern showed an evolutionary characteristic where plain, hilly and peripheral mountainous regions tended to stabilize, while southwestern mountainous regions and northwestern plateau regions degraded. The ecological security and sustainable utilization of cultivated land in Sichuan Province both show a degradation trend. It is necessary to optimize the input structure of cultivated land systems, improve agricultural production efficiency, and formulate targeted optimization and regulation measures in combination with the actual conditions of each region. Full article