Search Results (2,909)

Using natural plant extracts as food additives is a promising approach for improving food products’ quality, nutritional value, and safety, offering advantages for both consumers and the environment. Therefore, the main goal of this study was to develop a sustainable method for extracting polyphenols and carotenoids from horned melon peel using the cloud point extraction (CPE) technique, intending to utilize it as a natural food additive. CPE is novel promising extraction method for separation and pre-concentration of different compounds while being simple, inexpensive, and low-toxic. Three parameters within the CPE approach, i.e., pH, equilibrium temperature, and equilibrium time, were investigated as independent variables through the implementation of Box–Behnken design and statistical analyses. The optimized conditions for the maximum recovery of both polyphenols and carotenoids, reaching 236.14 mg GAE/100 g and 13.80 mg β carotene/100 g, respectively, were a pH value of 7.32, an equilibrium temperature of 55 °C, and an equilibrium time of 43.03 min. The obtained bioactives’ recovery values under the optimized conditions corresponded to the predicted ones, indicating the suitability of the employed RSM model. These results highlight the effectiveness of CPE in extracting bioactive compounds with varying polarities from agricultural by-products, underscoring its potential for enhancing the value of food waste and advancing sustainable practices in food processing. According to microbiological food safety parameters, the optimal CPE extract is suitable for food applications, while its storage under refrigerated and dark conditions is particularly beneficial. The CPE extract’s enhanced stability under these conditions makes it a more viable option for long-term storage, preserving both safety and quality. Full article

Fractional cointegration in time series data has been explored by several authors, but panel data applications have been largely neglected. A previous study of ours discovered that the Chen and Hurvich fractional cointegration test for time series was fairly robust to a moderate degree of heterogeneity across sections of the six tests considered. Therefore, this paper advances a customized version of the Chen and Hurvich methodology to detect cointegrating connections in panels with unobserved fixed effects. Specifically, we develop a test statistic that accommodates variation in the long-term cointegrating vectors and fractional cointegration parameters across observational units. The behavior of our proposed test is examined through extensive Monte Carlo experiments under various data-generating processes and circumstances. The findings reveal that our modified test performs quite well comparatively and can successfully identify fractional cointegrating relationships in panels, even in the presence of idiosyncratic disturbances unique to each cross-sectional unit. Furthermore, the proposed modified test procedure established the presence of long-run equilibrium between the exchange rate and labor wage of 36 countries’ agricultural markets. Full article

This study comprehensively investigated the synergistic effects and underlying mechanisms of optimized water and fertilizer management on the yield, quality, and lodging resistance of hybrid rice (Oryza sativa), through a two-year field experiment. Two hybrid rice varieties, Xinxiangliangyou 1751 (XXLY1751) and Yueliangyou Meixiang Xinzhan (YLYMXXZ), were subjected to three irrigation methods (W1: wet irrigation, W2: flooding irrigation, W3: shallow-wet-dry irrigation) and four nitrogen fertilizer treatments (F1 to F4 with application rates of 0, 180, 225, and 270 kg ha⁻¹, respectively). Our results revealed that the W1F3 treatment significantly enhanced photosynthetic efficiency and non-structural carbohydrate (NSC) accumulation, laying a robust foundation for high yield and quality. NSC accumulation not only supported rice growth but also directly influenced starch and protein synthesis, ensuring smooth grain filling and significantly improving yield and quality. Moreover, NSC strengthened stem fullness and thickness, converting them into structural carbohydrates such as cellulose and lignin, which substantially increased stem mechanical strength and lodging resistance. Statistical analysis demonstrated that water and fertilizer treatments had significant main and interactive effects on photosynthetic rate, dry matter accumulation, yield, quality parameters, NSC, cellulose, lignin, and stem bending resistance. This study reveals the intricate relationship between water and fertilizer management and NSC dynamics, providing valuable theoretical and practical insights for high-yield and high-quality cultivation of hybrid rice, significantly contributing to the sustainable development of modern agriculture. Full article

Farmers in North Carolina are turning to irrigation to reduce the impacts of droughts and rainfall variability on agricultural production. Droughts, rainfall variability, and the increasing demand for food, feed, fiber, and fuel necessitate the urgent need to provide North Carolina farmers with tools to improve irrigation management and maximize water productivity. This is only possible by understanding the current status of irrigated agriculture in the state and investigating its potential weaknesses and opportunities. Thus, the objective of this study was to perform a comprehensive analysis of the current state of irrigation management in North Carolina based on 15-year data from the Irrigation and Water Management Survey by the United States Department of Agriculture–National Agricultural Statistics Service (USDA-NASS). The results indicated a reduction in irrigation acres in the state. Also, most farms in the state have shifted to efficient sprinkler irrigation systems from gravity-fed surface irrigation systems. However, many farms in North Carolina still rely on traditional irrigation scheduling methods, such as examining crop conditions and the feel of soil in deciding when to irrigate. Hence, there are opportunities for enhancing the adoption of advanced technologies like soil moisture sensors and weather data to optimize irrigation schedules for improving water efficiency and crop production. Precision techniques and data-based solutions empower farmers to make informed, real-time decisions, optimizing water use and resource allocation to match the changing environmental conditions. The insights from this study provide valuable information for policymakers, extension services, and farmers to make informed decisions to optimize agricultural productivity and conserve water resources. Full article

Soybeans are an essential source of food, protein, and oil worldwide, and the nodules on their root systems play a critical role in nitrogen fixation and plant growth. In this study, we tackled the challenge of limited high-resolution image quantities and the constraints on model learning by innovatively employing image segmentation technology for an in-depth analysis of soybean nodule phenomics. Through a meticulously designed segmentation algorithm, we broke down large-resolution images into numerous smaller ones, effectively improving the model’s learning efficiency and significantly increasing the available data volume, thus laying a solid foundation for subsequent analysis. In terms of model selection and optimization, after several rounds of comparison and testing, YOLOX was identified as the optimal model, achieving an accuracy of 91.38% on the test set with an R² of up to 86%, fully demonstrating its efficiency and reliability in nodule counting tasks. Subsequently, we utilized YOLOV5 for instance segmentation, achieving a precision of 93.8% in quickly and accurately extracting key phenotypic indicators such as the area, circumference, length, and width of the nodules, and calculated the statistical properties of these indicators. This provided a wealth of quantitative data for the morphological study of soybean nodules. The research not only enhanced the efficiency and accuracy of obtaining nodule phenotypic data and reduced costs but also provided important scientific evidence for the selection and breeding of soybean materials, highlighting its potential application value in agricultural research and practical production. Full article

Microplastics, plastic particles smaller than 5 mm, pose a significant environmental threat due to their persistence and distribution in aquatic ecosystems. Research on the dynamics of microplastics within freshwater systems, particularly concerning their transport and deposition along river corridors, remains insufficient. This study investigated the occurrence and deposition of microplastics at the water–sediment interface of the White River near Muncie, Indiana. Sediment samples were collected from three sites: White River Woods (upstream), Westside Park (midstream), and Morrow’s Meadow (downstream). The microplastic concentrations varied significantly, with the highest concentration recorded upstream, indicating a strong influence from agricultural runoff. The types of microplastics identified were predominantly fragments (43.1%), fibers (29.6%), and films (27.3%), with fragments being consistently the most abundant at all sampling sites. A polymer analysis with selected particles using Fourier-transform infrared (FTIR) spectroscopy revealed that the most common polymers were polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET). The hydrodynamic conditions played a crucial role in the deposition and transport of microplastics. The statistical analysis demonstrated a strong positive correlation between the microplastic concentration and flow velocity at the downstream site, suggesting that lower flow velocities contribute to the accumulation of finer sediments and microplastics. Conversely, the upstream and midstream sites exhibited weaker correlations, indicating that other environmental and anthropogenic factors, such as land use and the sediment texture, may influence microplastic retention and transport. This study provides valuable insights into the complex interactions between river dynamics, sediment characteristics, and microplastic deposition in freshwater systems. These findings contribute to the growing body of knowledge on freshwater microplastic pollution and can help guide mitigation strategies aimed at reducing microplastic contamination in riverine ecosystems. Full article

Phaseolus lunatus L. is a species of landrace bean widely cultivated in Northeast Brazil. The integration of new technologies in the agricultural sector has highlighted the significance of seed images analysis as a valuable asset in the characterization process. The objective was to assess the morphology of 18 P. lunatus varieties gathered from four states in the Brazilian Northeast. To achieve this, 100 seeds from each variety were utilized, and their images were acquired using a flatbed scanner with a digital resolution of 400 dpi. Subsequently, the images were processed using the ImageJ software package for analyzing seed size, shape and color characteristics. Statistical analyses were performed with SPSS software applying stepwise Linear Discriminant Analysis (LDA). The overall accuracy rate for correct identification was 80.5%. Among the varieties, the lowest classification percentage was attributed to the ‘Coquinho Vermelha’ variety (39%), while the highest rates were observed for ‘Fava Roxa’ and ‘Fava de Moita’ (98%). The morpho-colorimetric classification system successfully discriminated the varieties of P. lunatus produced in the northeastern region of Brazil, highlighting the -+*/high degree of diversity within them. In particular, seeds with uniform coloring or clearly defined secondary color patterns were easier to classify. The varieties showed low correlation, forming distinct groups based on background color, secondary color, or seed size. Full article

This study evaluates the projected impact of climate change on wheat production in Northwest Tunisia, specifically at Medjez El Beb (36.67 m, 9.74°) and Slougia (36.66 m, 9.6°), for the period 2041–2070. Using the CNRM-CM5.1 and GFDL-ESM2M climate models under RCP4.5 and RCP8.5 scenarios, coupled with the AquaCrop and SIMPLE crop growth models, we compared model outputs with observed data from 2016 to 2020 to assess model performance. The objective was to determine how different climate models and scenarios affect wheat yields, biomass, and growth duration. Under RCP4.5, projected average yields are 7.709 q/ha with AquaCrop and 7.703 q/ha with GFDL-ESM2M. Under RCP8.5, yields are 7.765 tons/ha with AquaCrop and 7.198 q/ha with SIMPLE Crop, indicating that reduced emissions could improve wheat growth conditions. Biomass predictions showed significant variation: in Medjez El Beb, average biomass is 17.99 tons/ha with AquaCrop and 18.73 tons/ha with SIMPLE Crop under RCP8.5. In Slougia, average biomass is 18.90 tons/ha with AquaCrop and 19.04 tons/ha with SIMPLE Crop under the same scenario. Growth duration varied, with AquaCrop predicting 175 days in Medjez El Beb and 178 days in Slougia, while SIMPLE Crop predicted 180 days in Medjez El Beb and 182 days in Slougia, with a standard deviation of ±12 days for both models. SIMPLE Crop demonstrated higher accuracy in predicting growth cycle duration and yield, particularly in Slougia, with mean bias errors of −3.6 days and 2.26 q/ha. Conversely, AquaCrop excelled in biomass prediction with an agreement index of 0.97 at Slougia. Statistical analysis revealed significant yield differences based on climate models and emission scenarios, with GFDL-ESM2M under RCP4.5 showing more favorable conditions. These findings emphasize the importance of model selection and calibration for accurately projecting the agricultural impacts of climate change, and they provide insights for enhancing prediction accuracy and informing adaptation strategies for sustainable wheat production in Northwest Tunisia. Full article

Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature. Full article

Plant pathogens are an important agricultural problem, and early and rapid pathogen identification is critical for crop preservation. This work focuses on using fluorescence spectroscopy to characterize and compare healthy and fungal pathogen-infected wheat grains. The excitation–emission matrices of whole wheat grains were measured using a fluorescence spectrometer. The samples included healthy control samples and grains manually infected with Fusarium graminearum and Alternaria alternata fungi. The five distinct zones were identified by analyzing the location of the fluorescence peaks at each measurement. The zone centered at λ_em = 328/λ_ex= 278 nm showed an increase in intensity for grains infected with both pathogens during all periods of the experiment. Another zone with the center λ_em = 480/λ_ex = 400 nm is most interesting from the point of view of early diagnosis of pathogen development. A statistically significant increase of fluorescence for samples with F. graminearum is observed on day 1 after infection; for A. alternata, on day 2, and the fluorescence of both decreases to the control level on day 7. Moreover, shifts in the emission peaks from 444 nm to 452 nm were recorded as early as 2–3 h after infection. These results highlight fluorescence spectroscopy as a promising technique for the early diagnosis of fungal diseases in cereal crops. Full article

The area of Sia Kouanza in the Sahel of southwestern Niger is a potential location for expanding agriculture through irrigation with groundwater. Agriculture is key to supporting smallholders and promoting food security. As plans proceed, questions include how much water is available, how is groundwater replenished, many hectares to develop, and where to locate the wells. While these questions can be addressed with a model, it is difficult to find detailed procedures, especially when data are scarce. How can we use existing information to develop a model of a natural system where groundwater development will take place? We describe an approach that can be employed in data-scarce areas where similar questions are being asked. The approach includes setting details; conceptual model development; water balance; numerical code MODFLOW; model construction, calibration, and statistics; and result interpretation. Conceptual model component estimates are derived from field data: recharge, evapotranspiration, wetlands discharge, existing extraction, and river stages. When field data are not available or scarce, we employ other sources and describe how they are validated with field data or analog sites. The calibrated steady-state model gives a water balance of 22 × 10⁶ m³/yr with inflows (recharge 22 × 10⁶ m³/yr) and outflows (extraction 7.2 × 10⁵ m³/yr, wetlands 5.7 × 10⁶ m³/yr, evapotranspiration 11.9 × 10⁶ m³/yr). The model is a point of departure; approaches for transient and predictive models, which can be used to simulate changes in irrigation pumping volumes and drought, for example, will be described subsequently. Full article

In the context of sustainable agriculture, the search for soil improvers that boost crop growth without harming biodiversity is gaining much attention. Biochar, the solid residue resulting from the pyrolysis of organic material, has recently emerged as a promising bioproduct in enhancing crop yield, but there is a lack of information regarding its effects on arable biodiversity. Thus, in this study, we tested the effect of biochar application on the germination and seedling growth of cornflower (Centaurea cyanus L., Asteraceae), a threatened arable weed, under laboratory conditions. We investigated various parameters, including germination percentage (GP%), mean germination time (MGT), germination rate index (GRI), germination energy (GE%), fresh and dry weight (mg) of seedlings, and radicle length (mm) under biochar treatments at different concentrations: 0% (control), 0.1%, 0.2%, 0.5%, 1%, and 2%. Our findings revealed a significant increase in GP, GE, and GRI at biochar concentrations of 0.5% and 1%. MGT slightly increased at 0.1% biochar. Seedling fresh weight was unaffected by biochar application, whereas seedling dry weight exhibited a significant increase at 0.5% biochar. Radicle length showed a substantial increase under 0.1% biochar on day one, and was significantly higher at 0.2% and 1% biochar on day two. However, by day three, no more statistically significant differences in radicle length were observed between biochar-treated diaspores and controls (i.e., biochar had positive effects only in the first stages). These results suggest that the application of biochar at intermediate concentrations (0.5% and 1%) overall provides the most benefit to the germination and seedling growth of C. cyanus. Full article

This study investigates heavy metal contamination in soils, irrigation water, and agricultural produce (fruits: Vitis vinifera (grape), Cucumis melo var. saccharimus (melon), and Citrullus vulgaris. Schrade (watermelon); vegetables: Lycopersicum esculentum L. (tomato), Cucurbita pepo (zucchini), Daucus carota (carrot), Lactuca sativa (lettuce), Convolvulus Batatas (potato), and Capsicum annuum L. (green pepper)) in the Boumerdes region of Algeria. The concentrations of seven heavy metals (cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), nickel (Ni), lead (Pb), and zinc (Zn)) in soil and food samples were analyzed using atomic absorption spectrometry. Health risks associated with these metals were evaluated through the estimated daily intake (EDI), non-carcinogenic risks (using target hazard quotient (THQ), total target hazard quotient (TTHQ), and hazard index (HI)), and carcinogenic risks (cancer risk factor (CR)). Statistical analyses, including cluster analysis (CA) and Pearson correlation, were conducted to interpret the data. The results revealed the highest metal transfer as follows: Cd was most significantly transferred to tomatoes and watermelons; Cr to carrots; Cu to tomatoes; and Fe, Ni, Pb, and Zn to lettuce. Among fruits, the highest EDI values were for Zn (2.54·10⁻³ mg/day) and Cu (1.17·10⁻³ mg/day), with melons showing the highest Zn levels. For vegetables, the highest EDI values were for Fe (1.68·10⁻² mg/day) and Zn (8.37·10⁻³ mg/day), with potatoes showing the highest Fe levels. Although all heavy metal concentrations were within the World Health Organization’s permissible limits, the HI and TTHQ values indicated potential health risks, particularly from vegetable consumption. These findings suggest the need for ongoing monitoring to ensure food safety and mitigate health risks associated with heavy metal contamination. Full article

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change. Full article

Groundwater quality and availability in coastal aquifers have become a serious concern in recent times due to increased abstraction for domestic, agricultural and industrial purposes. (1) Background: Zhuhai city is selected as a representative coastal aquifer in Southern China to comprehensively evaluate the hydrochemical characteristics, spatial distribution and controlling mechanisms of groundwater. (2) Methods: A detailed study utilizing statistical analyses, a Piper diagram, Gibbs plots, and ion ratios was conducted on 114 surface water samples and 211 groundwater samples. (3) Results: The findings indicate that the pH of most groundwater is from 6.06 to 6.52, indicating a weakly acidic environment. The pH of surface water ranges from 5.35 to 9.86, with most values being weakly alkaline. The acidity in the groundwater may be related to the acidic atmospheric precipitation, an acidic unsaturated zone, oxidation of sulphide minerals and tidal action. The groundwater chemical types are predominantly mixed, followed by Ca-Mg-HCO₃ type. Surface water samples are predominantly Na-Cl-SO₄ type. The NO₃⁻ concentration in groundwater is relatively high, with a mean value of 17.46 mg/L. The NO₂⁻ and NH₄⁺ concentrations in groundwater are relatively low, with mean values of 0.46 mg/L and 7.58 mg/L. (4) Conclusions: The spatial distribution of the principal chemical constituents in the groundwater is related to the landform. The chemical characteristics of groundwater in the study area are mainly controlled by the weathering and dissolution of silicate and sulfate minerals, evaporation, seawater mixing and cation exchange. Nitrate in clastic fissure groundwater, granite fissure groundwater and unconfined pore groundwater primarily originates from atmospheric precipitation, agricultural activities of slope farmland and forest land. Nitrate in confined pore groundwater and karst groundwater primarily originates from domestic sewage and mariculture wastewater. Our findings elucidate the processes characterizing the hydrogeology and surface water interactions in Zhuhai City’s coastal system, which are relevant to other catchments with similar geological characteristics. Full article