Search Results (2,437)

Farmers’ perceptions of improved rice varieties represent a critical initial step in their adoption as climate change adaptation strategies. This study examined farmers’ perceptions by integrating on-farm adaptive research, which compared the agronomic performance of rice varieties, with participatory approaches to capture farmers’ evaluation of improved varieties. A total of 81 farmers from climate-affected areas of Batang Regency were purposively selected as respondents. Data was collected through structured interviews and questionnaires administered during the evaluation of field demonstrations. Farmers’ perception levels were assessed using a Guttman scale and classified into three categories: high, medium, and low. Logistic regression analysis was subsequently employed to examine the relationship between farmers’ socio-demographic characteristics and their acceptance of improved rice varieties. The results indicate that, overall, farmers exhibited a low perception of improved rice varieties. Among the evaluated opinions, Inpari 32 HDB received the highest perception scores across all agronomic attributes. The regression results show that farm size and age significantly influence variety acceptance. The odds ratio for farm size (0.117) suggests that each additional hectare of cultivated land area reduces the likelihood of adopting improved rice varieties by approximately 88.3%, holding other factors constant. In contrast, the odds ratio for age (1.080) indicates that each additional year of age increases the probability of adoption by about 8%. Full article

Five Cornus mas L. genotypes were analysed based on their attractive colour and high productivity. The ‘Bordo’ cultivar stood out, demonstrating the highest berry weight (3.07 g) and yield per plant (8.24 kg). Close behind was the MH-7-17 selection, with an average yield of 7.37 kg per plant. Both the ‘Bordo’ cultivar and the MH-7-17 selection exhibited excellent agronomic potential, making them ideal candidates for large-scale cultivation. UV-Vis absorption spectroscopy was used to quantify the fruits’ levels of sugars, polyphenols, flavonoids, tannins, anthocyanins and carotenoids (lycopene and β-carotene) and to evaluate their antioxidant capacity. The ‘Bordo’ cultivar had the highest carotenoid content (0.88 mg lycopene and 2.47 mg β-carotene per 100 g), while the TG-J-9-17 and TG-J-20-17 selections had the highest total content of sugars, polyphenols, flavonoids, tannins and anthocyanins and the highest antioxidant capacity. According to the correlations analysis, bigger fruit (which correlated to higher yield) had higher carotenoid content, although lower-level tannin (TTC), flavonoid (TFC), anthocyanin (TAC), and sugar (TSC). Also, total phenolic content (TPC) was positively correlated to TTC, TFC, and radical scavenging activity (RSA), while TFC was positively correlated to TTC, TAC, RSA, but also to TSC. Other positive correlations were those found between TTC and RSA and between lycopene and β-carotene. FTIR spectroscopy was used to identify the characteristic vibrations of the biochemical constituents. Processing the FTIR data using chemometric techniques (principal component analysis and hierarchical clustering analysis) revealed consistent clustering patterns between samples with similar characteristics. Full article

Harnessing nature’s ingenuity with microorganisms for industrial production is an attractive solution to today’s climate concerns. Nature’s innate diversity allows the production of many value-added chemicals and can be expanded on through genetic engineering. Although the use of microbial cell factories (MCFs) has been extremely successful at lab scale, the numbers of successful bioprocesses remain limited. High cell densities and long cultivation times lead to reductions in productivity over the course of the cultivation through the effects of genetic and expression instability of the strain. This instability leads to population diversification. In this review, we explore the roots of genetic instability in microorganisms, focusing on prokaryotic bioprocesses, and how organisms cope with this instability. We spotlight single-cell detection methods capable of monitoring populations within the bioprocess both in- and on-line. We also examine different approaches to minimizing population diversification, both through strain development and bioprocess engineering. With this review, we highlight the fact that population-averaged metrics overlook the single-cell stresses driving genetic and functional instability, leading to an overestimation of microbial bioprocess robustness. High-throughput single-cell monitoring in industry-like conditions remains essential to identify and select truly stable microbial cell factories and bioprocesses. Full article

Weeds pose a significant threat to crop yields, both in quantitative and qualitative terms. Modern agriculture relies heavily on herbicides; however, their excessive use can lead to negative environmental impacts. As a result, recent research has increasingly focused on Integrated Weed Management (IWM), which employs multiple complementary strategies to control weeds in a holistic manner. Nevertheless, large-scale adoption of this approach requires a solid understanding of the underlying tactics. This systematic review analyses recent studies (2013–2022) on herbicide alternatives for weed control across major cropping systems in the EU-27 and the UK, providing an overview of current knowledge, the extent to which IWM tactics have been investigated, and the main gaps that help define future research priorities. The review relied on the IWMPRAISE framework, which classifies weed control tactics into five pillars (direct control, field and soil management, cultivar choice and crop establishment, diverse cropping systems, and monitoring and evaluation) and used Scopus as a scientific database. The search yielded a total of 666 entries, and the most represented pillars were Direct Control (193), Diverse Cropping System (183), and Field and Soil Management (172). The type of crop most frequently studied was arable crops (450), and the macro-area where the studies were mostly conducted was Southern Europe (268). The tactics with the highest number of entries were Tillage Type and Cultivation Depth (110), Cover Crops (82), and Biological Control (72), while those with the lowest numbers were Seed Vigor (2) and Sowing Depth (2). Overall, this review identifies research gaps and sets priorities to boost IWM adoption, leading policy and funding to expand sustainable weed management across Europe. Full article

Knowledge and perceptions are prerequisites for contributing to CC mitigation and adaptation. This paper developed a framework and a tool (scale) to capture farmers’ knowledge and perceptions of all aspects of CC. We involved 15 extremely qualified (those with PhD degrees in agriculture and allied disciplines and experience in scale construction and CC research) experts and 83 highly qualified (a minimum of a PhD degree in agriculture and allied fields was the prerequisite criterion for acting as a judge) judges in the construction of this scale. Further, we adopted factor analysis to draw valid conclusions. We proposed 138 items/statements related to 14 dimensions/issues (General, GHGs, Temperature, Rainfall, Agricultural emissions, shifting cultivation, rice cultivation, Mitigation, C-sequestration, Impact on Agriculture, Livestock, Wind, Natural disaster, Impact, and Adaptation) associated with agriculture and CC scenarios. Finally, 102 items/statements were retained with six indicators/dimensions. The results indicate that the scale explains 83% of variance. The scale is highly consistent (Cronbach alpha = 0.985) and widely applicable to future research and policy decisions. Further, the scale was adopted (with 100 respondents) to assess consistency and validity. Finally, the tool (scale) for assessing farmers’ knowledge and perceptions of CC was prepared for further use and replication. The policy and research system may adopt the framework and scale to assess stakeholders’ inclusive knowledge and perceptions of CC. The findings of this study may be helpful for policymakers, researchers, development workers, and extension functionaries. Full article

Amid escalating ecological degradation, social fragmentation, and rising mental health challenges—especially in rural and agricultural communities—there is an urgent need to reimagine systems that support both planetary and human flourishing. This viewpoint examines an emerging paradigm in agriculture that emphasizes the role of farmers’ inner development in fostering practices that enhance ecological health, community well-being, and a resilient food system. A key goal is to draw more academic attention to growing community calls for more holistic, relational, and spiritually grounded approaches to food systems as an important focus for ongoing research. Drawing on diverse case studies from Japan, India, and Europe, we examine how small-scale and natural farming initiatives are integrating inner development, universal human values, and ecological consciousness. These case studies were developed and/or refined through a program led by the Conscious Food Systems Alliance (CoFSA), an initiative of the United Nations Development Programme (UNDP) that seeks to integrate inner transformation with sustainable food systems change. The initiatives are intended as illustrative examples of how agriculture can transcend its conventional, anthropocentric role as a food production system to become a site for cultivating deeper self-awareness, spiritual connection, and regenerative relationships with nature. Participants in these cases reported significant shifts in mindset—from materialistic and extractive worldviews to more relational and value-driven orientations rooted in care, cooperation, and sustainability. Core practices such as mindfulness, experiential learning, and spiritual ecology helped reframe farming as a holistic process that nurtures both land and life. These exploratory case studies suggest that when farmers are supported in aligning with inner values and natural systems, they become empowered as agents of systemic change. By linking personal growth with planetary stewardship, these models offer pathways toward more integrated, life-affirming approaches to agriculture and future academic research. Full article

BslA (Biofilm surface layer protein A), a highly hydrophobic lipoprotein from Bacillus spp., self-assembles at fluid interfaces to form a crystalline film that reduces surface tension. In this study, we selected Pichia pastoris as a eukaryotic system for expressing recombinant BslA identified in Bacillus paralicheniformis BL-1. The secretory expression of recombinant BslA in the P. pastoris GS115 strain under the AOX1 promoter was confirmed in shake-flask cultivation. Next, two fed-batch fermentation strategies, constant dissolved oxygen strategy (DO-stat) and oxygen-limited fed-batch (OLFB) strategy, in a 5 L scale, were compared. The DO-stat process led to late-stage cell death and product degradation, limiting yields. Switching to the OLFB process by removing the glycerol feeding phase mitigated this issue, allowing extended fermentation and increasing the final recombinant BslA concentration to 657 mg/L. This study establishes P. pastoris with an OLFB strategy as an effective system for secreting recombinant BslA protein, providing a basis for future industrial-scale production. Full article

Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article

Orchard crops generate substantial quantities of diverse biomass each year, with grapevines being among the most economically significant species worldwide. Considering the scale of this biomass, there is a growing need to explore rational strategies for its utilisation, for example, for energy production or other value-added applications. Such approaches may contribute to improving resource efficiency and reducing the environmental burden associated with agricultural waste. The aim of this study was to examine the energy potential of woody post-production waste from wine processing, with particular emphasis on grape stems of four cultivars—Chardonnay, Riesling, Merlot, and Zweigelt—grown in two contrasting climatic regions: south-eastern Poland and Moravia (Czech Republic). The results demonstrated that both the grape variety and cultivation site significantly influenced the majority of bunch biometric traits, including bunch and berry weight, berry number, and stem dimensions. A moderately warm climate promoted the development of larger and heavier bunches as well as more robust stems across all examined cultivars. Energy analyses indicated that Zweigelt stems produced under moderately warm conditions and Chardonnay stems from a temperate climate exhibited the most favourable combustion properties. Nonetheless, certain constraints were identified, such as increased ash (12.20%) and moisture content (11.51%) in Chardonnay grown in warmer conditions, and elevated CO and CO₂ emissions observed for Zweigelt (1333.26 kg·mg⁻¹). Overall, the findings confirm that grape stems constitute a promising local source of bioenergy, with their energy performance determined predominantly by varietal characteristics and climatic factors. Their utilisation aligns with circular-economy principles and may help reduce the environmental impacts associated with traditional viticultural waste management. Full article

Two genetically engineered Bacillus subtilis strains, BMV9 and BsB6, were evaluated in terms of culture medium (effect of nutrients on surfactin yield) and potential biotechnological applications of surfactin in agriculture and the petrochemical industry. BMV9 (spo0A3; abrB*; ΔmanPA; sfp+) is, to date, the highest surfactin producer reported scientifically, and BsB6 is a sfp+ laboratory derivative strain that has also demonstrated considerable production potential. To assess their performance, fermentation experiments were conducted in shake flasks using two different culture media, a mineral salt medium and a complex medium, each supplemented with 2% (w/v) glucose. Lipopeptides (surfactin and fengycin) were extracted and quantified at multiple time points (up to 48 h) via high-performance thin-layer chromatography (HPTLC). Optical density, residual glucose, and pH were monitored throughout the cultivation. In parallel, microbial growth in both media were also validated in small-scale cultivation approaches. Antifungal activity of culture supernatants and lipopeptide extracts was tested against two Diaporthe species, key phytopathogens in soybean crops. Given the agricultural relevance of these pathogens, the biocontrol potential of lipopeptides represents a sustainable alternative to conventional chemical fungicides. Additionally, oil displacement tests were performed to evaluate the efficacy of surfactin in enhanced oil recovery (EOR), bioremediation, and related petrochemical processes. High-resolution LC-MS/MS analysis enabled structural characterization and relative quantification of the lipopeptides. Overall, these investigations provide a comprehensive comparison of strain production performance and the associated impact of cultivation media, aiming to define the optimal conditions for economically viable surfactin production and to explore its broader biotechnological applications in agriculture and the petrochemical industry. Full article

This paper presents the design and implementation of an IoT-based automation system for indoor hydroponic plant cultivation using the Nutrient Film Technique. The system employs an ESP32-based controller with multiple sensors and actuators. These enable real-time monitoring and control of pH, TDS, temperature, humidity, light, tank level, and flow conditions. A modular five-layer architecture was developed. It combines the MING stack, which includes MQTT communication, InfluxDB time-series storage, Node-RED flow processing, and Grafana visualization. The system also includes a Flutter-based mobile app for remote access. Key features include temperature-compensated calibration, hysteresis-based control algorithms, dual-mode operation, TLS/ACL security, and automated alarm mechanisms. These features enhance reliability and safety. Experimental results showed stable pH/TDS regulation, dependable actuator and alarm responses, and secure long-term data logging. The proposed open-source and low-cost platform is scalable. It provides a solution for small-scale producers and urban farming, bridging the gap between academic prototypes and production-grade smart agriculture systems. In comparison to related works that mainly focus on monitoring, this study advances the state of the art. It combines continuous time-series logging, secure communication, flow verification, and integrated safety mechanisms to provide a reproducible testbed for future smart agriculture research. Full article

Punicic acid is an uncommon ω-5 conjugated fatty acid with significant biological activity, mainly found in pomegranate seed oil. Due to limited natural availability, heterologous production of punicic acid in oleaginous yeasts offers a sustainable alternative. In this study, Yarrowia lipolytica was engineered for punicic acid biosynthesis by expressing the PgFADX gene from Punica granatum and subsequently modified to evaluate the influence of distinct diacylglycerol acyltransferases on punicic acid accumulation. The effects of seven acyltransferases, originating from P. granatum or Y. lipolytica, were compared under various cultivation conditions. The PgDGAT1 enzyme demonstrated the most favorable balance between total lipid content and punicic acid accumulation. Medium containing crude glycerol as a low-cost carbon source was initially tested in flask experiments with punicic acid accumulation in yeast cells of 129 mg/L. Further optimization of crude glycerol medium and subsequent scale-up experiments confirmed the potential of crude glycerol as an effective substrate, yielding up to 147.8 mg/L of punicic acid. Overall, this work identifies key enzymatic determinants for efficient punicic acid biosynthesis and supports Y. lipolytica as a robust host for the sustainable production of conjugated fatty acids from waste substrates. Full article

This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from $\mu =3.9$ (Challenge phase) to $\mu =4.6$ (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education. Full article

The convergence of food insecurity, water scarcity, and environmental degradation has intensified the global search for sustainable agricultural models. Integrated Microalgal–Aquaponic Systems (IAMS) have emerged as a novel multi-trophic platform that unites aquaculture, hydroponics, and microalgal cultivation into a closed-loop framework for resource-efficient food production and water recovery. This critical review synthesizes empirical findings and engineering advancements published between 2008 and 2024, evaluating IAMS performance relative to traditional agriculture and recirculating aquaculture systems (RAS). Reported under controlled laboratory and pilot-scale conditions, IAMS have achieved nitrogen and phosphorus recovery efficiencies exceeding 95% while potentially reducing water consumption by up to 90% compared to conventional farming. The integration of microalgal photobioreactors enhances nutrient retention, may contribute to internal carbon capture, and enables the generation of diversified co-products, including biofertilizers and protein-rich aquafeeds. Nevertheless, significant barriers to commercial scalability persist, including the biological complexity of maintaining multi-trophic synchrony, high initial capital expenditure (CAPEX), and regulatory ambiguity regarding the safety of waste-derived algal biomass. Technical challenges such as photobioreactor upscaling, biofouling control, and energy optimization are critically discussed. Finally, the review evaluates the alignment of IAMS with UN Sustainable Development Goals 2, 6, and 13, and outlines future research priorities in techno-economic modeling, automation, and policy development to facilitate the transition of IAMS from pilot-scale innovations to viable industrial solutions. Full article

In citrus cultivation practice, regular monitoring of leaf leaf relative water content (RWC) can effectively guide water management, thereby improving fruit quality and yield. When applying hyperspectral technology to citrus leaf moisture monitoring, the precise quantification of RWC still needs to address issues such as data noise and algorithm adaptability. The noise interference and spectral aliasing in RWC sensitive bands lead to a decrease in the accuracy of moisture inversion in hyperspectral data, and the combined sensitive bands of chlorophyll (LCC) in citrus leaves can affect its estimation accuracy. In order to explore the optimal prediction model for RWC of citrus leaves and accurately control irrigation to improve citrus quality and yield, this study is based on 401–2400 nm spectral data and extracts noise robust features through continuous wavelet transform (CWT) multi-scale decomposition. A high-precision estimation model for citrus leaf RWC is established, and the potential of CWT in RWC quantitative inversion is systematically evaluated. This study is based on the multi-scale analysis characteristics of CWT to probe the time–frequency characteristic patterns associated with RWC and LCC in citrus leaf spectra. Pearson correlation analysis is used to evaluate the effectiveness of features at different decomposition scales, and the successive projections algorithm (SPA) is further used to eliminate band collinearity and extract the optimal sensitive band combination. Finally, based on the selected RWC and LCC-sensitive bands, a high-precision predictive model for citrus leaf RWC was established using partial least squares regression (PLSR). The results revealed that (1) CWT preprocessing markedly boosts the estimation accuracy of RWC and LCC relative to the original spectrum (max improvements: 6% and 3%), proving it enhances spectral sensitivity to these two indices in citrus leaves. (2) Combining CWT and SPA, the resulting predictive model showed higher inversion accuracy than the original spectra. (3) Integrating RWC Scale7 and LCC Scale5-2224/2308 features, the CWT-SPA fusion model showed optimal predictive performance (R² = 0.756, RMSE = 0.0214), confirming the value of multi-scale feature joint modeling. Overall, CWT-SPA coupled with LCC spectral traits can boost the spectral response signal of citrus leaf RWC, enhancing its prediction capability and stability. Full article