Search Results (5,823)

The increasing variability of climatic conditions poses significant challenges for agricultural and livestock systems worldwide. In regions with warm climates, such as northern Colombia, the effects of changing temperature, precipitation, and evapotranspiration are particularly pronounced, influencing the productivity and sustainability of native pastures. To address these challenges, modeling tools provide a valuable means of understanding and predicting forage production dynamics under diverse climatic scenarios, enabling farmers to make informed decisions that enhance resilience and sustainability. This research was conducted in Córdoba, Colombia, with the objective of evaluating the impact of climatic variations in temperature, precipitation, and evapotranspiration on forage production in native pastures in hot climates in northern Colombia. Modeling tools were used to assess the potential yield of pastures based on climate conditions, enabling the understanding and addressing of challenges associated with climatic fluctuations in estimated production. To plan animal grazing, climate variability from 2018 to 2021, a period influenced by the El Niño–Southern Oscillation (ENSO) phenomenon, was analyzed. This type of integrated analysis, which combines meteorological data, soil, crops, and evaluation of animal load per unit area, is an ideal and practical approach to addressing productivity challenges associated with climatic variability in livestock production in the warm climate of Colombia. The results confirmed the significant impact of climatic conditions on forage production, leading to the conclusion that simulation tools for water use in Bothriochloa “Colosuana” pastures are relevant for efficient water resource management, particularly during the dry season and drought events. This allows for anticipating the impacts of climate change on agriculture and livestock, facilitating timely and sustainable decision-making by farmers. Full article

The application of reflective vegetation indices is crucial for advancing precision agriculture, particularly in monitoring crop growth and development. Among these indices, the Normalized Difference Vegetation Index (NDVI) is the most widely used due to its reliability in capturing vegetation dynamics. This study focuses on the applicability and temporal dynamics of the NDVI in monitoring winter wheat (Triticum aestivum) under the specific climatic conditions of Southern Dobrudja, Bulgaria. Using a Survey3W Camera RGN mounted on DJI unmanned aerial vehicles (Phantom 4 Pro and Mavic 2 Pro) at an altitude of 100 m, NDVI data were collected over a five-year period (2019–2024). Results reveal distinct NDVI trends, with maximum values reaching 0.56 during favorable conditions, and sharp declines during late spring frosts or drought periods. These NDVI variations correlate strongly with environmental factors, including precipitation and temperature fluctuations. For instance, during the 2019–2020 season, the NDVI decreased by 30% due to severe drought and high winter temperatures. In this study, vegetation indices, including the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), were utilized to compare the results with the NDVI. The high-resolution UAV methodology demonstrated in this study proves highly effective for breeding and agronomic applications, offering precise data for optimizing wheat cultivation under variable agro-climatic conditions. These findings highlight the NDVI’s potential to enhance crop monitoring, yield prediction, and stress response management in winter wheat. Full article

Polymer-coated controlled-release fertilizers’ (CRFs) unique nutrient release mechanism has the potential to mitigate the leaching of mobile soil nutrients, such as nitrate-nitrogen (NO₃-N). The study aimed to evaluate the capacity of a polymer-coated CRFs to maintain maize (Zea mays L.) crop growth/health indicators and production goals, while reducing NO₃-N leaching risks compared to conventional (CONV) fertilizers in North Florida. Four CRF rates (168, 224, 280, 336 kg N ha⁻¹) were assessed against a no nitrogen (N) application and the current University of Florida Institute for Food and Agricultural Sciences (UF/IFAS) recommended CONV (269 kg N ha⁻¹) fertilizer rate. All CRF treatments, even the lowest CRF rate (168 kg N ha⁻¹), produced yields, leaf tissue N concentrations, plant heights, aboveground biomasses (AGB), and leaf area index (LAI) significantly (p < 0.05) greater than or similar to the CONV fertilizer treatment. Additionally, in 2022, the CONV fertilizer treatment resulted in increases in late-season movement of soil NO₃-N into highly leachable areas of the soil profile (60–120 cm), while none of the CRF treatments did. However, back-to-back leaching rainfall (>76.2 mm over three days) events in the 2023 growing season masked any trends as NO₃-N was likely completely flushed from the system. The results of this two-year study suggest that polymer-coated CRFs can achieve desirable crop growth, crop health, and production goals, while also having the potential to reduce the late-season leaching potential of NO₃-N; however, more research is needed to fully capture and quantify the movement of NO₃-N through the soil profile. Correlation and Principal Component Analysis (PCA) revealed that CRF performance was significantly influenced by environmental factors such as rainfall and temperature. In 2022, temperature-driven nitrogen release aligned with crop uptake, supporting higher yields and minimizing NO₃-N movement. In 2023, however, rainfall-driven variability led to an increase in NO₃-N leaching and masked the benefits of CRF treatments. These analyses provided critical insights into the relationships between environmental factors and CRF performance, emphasizing the importance of adaptive fertilizer management under varying climatic conditions. Full article

Water resources are essential for human survival and sustainable development. However, the global freshwater scarcity, exacerbated by climate change, presents significant sustainability challenges. Using the SWAT model, we simulated the spatiotemporal distribution of total nitrogen (TN) in the Hangjiang River Basin from 2005 to 2020. The average TN concentration was 2.16 mg/L, with the soil nitrogen pool contributing 92.78% of emissions, highlighting the need to address the soil nitrogen legacy. Sampling showed average concentrations of TN, nitrate, ammonium, nitrite, and dissolved organic nitrogen at 3.01 mg/L, 1.66 mg/L, 0.21 mg/L, 0.02 mg/L, and 1.11 mg/L, respectively. Precipitation accounted for 61.4% of nitrogen emission variability, indicating that water resource sustainability will be significantly influenced by climate change. Projections indicated that from 2020 to 2050, climate change will increase runoff by 6.19 m³/s and reduce TN concentration by 0.004 mg/L annually, potentially enhancing the overall sustainability of water resources. It’s necessary to continue strengthening the prevention and control of agricultural non-point source pollution and reduce nitrogen discharge to further enhance water resource security for the Beijing–Tianjin–Hebei development. The findings provide critical insights to inform policies aimed at protecting water sources and ensuring public water safety. Full article

Sacha inchi (Plukenetia volubilis), or the Inca peanut, is a promising functional food and sustainable alternative to traditional oilseed crops like soybean. Its seeds are rich in omega-3, omega-6, and omega-9 fatty acids, high-quality protein, and bioactive compounds, offering significant nutritional and health benefits. Moreover, sacha inchi cultivation thrives on degraded soils with minimal agrochemical input, supporting biodiversity and reducing environmental impacts. Despite its potential, its large-scale cultivation faces challenges such as genetic variability, low seed viability, and susceptibility to pests and diseases, resulting in inconsistent yields and plant quality. In vitro propagation presents a viable solution, enabling the production of genetically uniform, disease-free seedlings under controlled conditions. Successful in vitro cultivation depends on factors like explant selection, plant growth regulator combinations, medium composition, and environmental control. Advances in these techniques have improved propagation outcomes in other oilseed crops, such as enhanced germination, oil yield, and genetic stability, and offer similar opportunities for sacha inchi. By integrating in vitro and field techniques, this review highlights the potential of sacha inchi as a nutritionally rich, sustainable agricultural solution. These findings provide a foundation for advancing its cultivation, ensuring enhanced productivity, improved oil quality, and greater accessibility to its health benefits around the world. Full article

Biochar, produced from biomass, has become recognized as a sustainable soil amendment that has the potential to improve soil quality and agricultural production. This review focuses on production processes and properties of biochar derived from different types of biomass, including the synergistic interactions between biochar and soil microorganisms, emphasizing their influence on overall soil quality and crop production, particularly in cultivation of Brassica crops. It additionally addresses the potential benefits and limitations of biochar and microbial application. Biomass is a renewable and abundant resource and can be converted through pyrolysis into biochar, which has high porosity, abundant surface functionalities, and the capacity to retain nutrients. These characteristics provide optimal conditions for beneficial microbial communities that increase nutrient cycling, reduce pathogens, and improve soil structure. The information indicates that the use of biochar in Brassica crops can result in improved plant growth, yield, nutrient uptake, and stress mitigation. This review includes information about biochar properties such as pH, elemental composition, ash content, and yield, which can be affected by the different types of biomass used as well as pyrolysis conditions like temperature. Understanding these variables is essential for optimizing biochar for agricultural use. Moreover, the information on the limitations of biochar and microbes emphasizes the importance of their benefits with potential constraints. Therefore, sustainable agriculture methods can possibly be achieved by integrating biochar with microbial management measurements, resulting in higher productivity and adaptability in Brassica or other plant crop cultivation systems. This review aims to provide a comprehensive understanding of biochar’s role in supporting sustainable Brassica farming and its potential to address contemporary agricultural challenges. Full article

This study had two primary objectives: (1) to determine relative differences in soil infiltration capacity between native grasslands and thicketized oak woodlands and (2) to compare the effectiveness of three infiltration measurement techniques—rainfall simulation, an automated Simplified Steady Beerkan Infiltration (SSBI) method, and the Saturo dual-head infiltrometer. The study was conducted at three sites with clay, loamy sand, and sandy soils. Rainfall simulation captured significant infiltration differences between vegetation covers at all three sites, while SSBI did so at two sites, and Saturo failed to detect significant differences. Consistent with past studies, rainfall simulation results showed significantly higher infiltration capacity in thicketized woodlands compared to adjacent grasslands, with mean infiltration capacity an order of magnitude greater in clay soils (67 mm h⁻¹ vs. 7.5 mm h⁻¹) and more than twice as high in sandy (144.5 mm h⁻¹ vs. 69 mm h⁻¹) and loamy sand (106 mm h⁻¹ vs. 49 mm h⁻¹) soils. Across sites, rainfall simulation and SSBI showed strong positive correlations between infiltration capacity and dead biomass (R² = 0.74 and 0.46, respectively; p < 0.001 for both), as well as significant negative correlations with live biomass and bulk density. In contrast, the Saturo method exhibited higher variability, overestimating infiltration capacity by an average of 34.3 mm h⁻¹ compared to rainfall simulation, and did not capture significant relationships with biomass or bulk density. Our findings have twofold importance: first, they demonstrate that thicketization of oak savannahs results in higher soil infiltration capacity; and second, they show that for determining soil infiltration capacity, the SSBI methodology is an accurate and practical alternative to the labor-intensive rainfall simulation. Full article

The evaluation of ecological environment quality (EEQ) is an important method to determine regional eco-environment status, and topography, as one of the key factors affecting eco-environment, has an impact on the EEQ by influencing hydrothermal conditions. However, research on the effect of topography on the EEQ still needs to be strengthened, especially in the red soil region of southern China. Therefore, based on the evaluation of the EEQ for Changting County using the remote sensing ecological index (RSEI) combined with Landsat images from 2000 to 2019, the effects of topography on the EEQ were analyzed further. The main findings indicated, firstly, that the average values of topographic factors increased as the EEQ grade raised; secondly, the distribution of the EEQ gradually moved to the lower terrain factor categories as the EEQ grade declined for each study period on the whole; thirdly, the coupling effect of any two topographic factors on the EEQ was greater than the effect of a single topographic factor, and the coupling effect of the aspect with the elevation and topographic position index (TPI) on the EEQ was the most prominent. The main findings of the research can enhance the understanding of the variability of the EEQ and the effects of topography on the EEQ. Full article

The variation in crop growth provides critical insights for yield estimation, crop health diagnosis, precision field management, and variable-rate fertilization. This study constructs key monitoring indicators (KMIs) for corn growth based on satellite remote sensing data, along with inversion models for these growth indicators. Initially, the leaf area index (LAI) and plant height were integrated into the KMI by calculating their respective weights using the entropy weight method. Eight vegetation indices derived from Sentinel-2A satellite remote sensing data were then selected: the Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Soil-Adjusted Vegetation Index (SAVI), Red-Edge Inflection Point (REIP), Inverted Red-Edge Chlorophyll Index (IRECI), Pigment Specific Simple Ratio (PSSRa), Terrestrial Chlorophyll Index (MTCI), and Modified Chlorophyll Absorption Ratio Index (MCARI). A comparative analysis was conducted to assess the correlation of these indices in estimating corn plant height and LAI. Through recursive feature elimination, the most highly correlated indices, REIP and IRECI, were selected as the optimal dual red-edge vegetation indices. A deep neural network (DNN) model was established for estimating corn plant height, achieving optimal performance with an R² of 0.978 and a root mean square error (RMSE) of 2.709. For LAI estimation, a DNN model optimized using particle swarm optimization (PSO) was developed, yielding an R² of 0.931 and an RMSE of 0.130. KMI enables farmers and agronomists to monitor crop growth more accurately and in real-time. Finally, this study calculated the KMI by integrating the inversion results for plant height and LAI, providing an effective framework for crop growth assessment using satellite remote sensing data. This successfully enables remote sensing-based growth monitoring for the 2023 experimental field in Haicheng, making the precise monitoring and management of crop growth possible. Full article

Understanding the mechanical behaviour of carbonate sand-fine mixtures is crucial for modelling geotechnical-related gas exploitation problems in potential gas hydrate reservoirs. This paper presents a laboratory investigation of the mechanical behaviour of carbonate sand-fine mixtures with a carbonate sand content of no more than 40%. By means of computer tomography, the internal porosity of carbonate sand grains was measured, and the interfine void ratio was modified. For carbonate sand-fine mixtures with a similar void ratio, all mixtures exhibited strain-softening behaviour, and the peak strength decreased with increasing carbonate sand content. Compared to the mixture whose density was controlled by the void ratio, the change rule of mechanical behaviour influenced by the carbonate sand content was precisely the opposite for that controlled by the interfine void ratio. The mixture with the smallest modified interfine void ratio exhibited the highest peak strength and the strongest dilative volumetric behaviour. By adopting the modified interfine void ratio as a state variable, a unique critical state line can be identified with a carbonate sand content of no more than 40%, leading to a framework that coherently characterises the mechanical behaviour of these gap-graded soil mixtures. Full article

In this paper, a new definition of a structural parameter for soil is given to characterize the mechanical properties of soils and their changing patterns. The soil structural parameter is a quantitative descriptor of soil structural properties. Structural parameters are related not only to the grain size, density, and moisture content of the soil material composition and state, but also to the spatial arrangement of soil particles in the soil skeleton structure and the characteristics of intergranular associations. The new definition of the structural parameter, established from the comparison of loess structural stability and variability, is defined as the ratio of the shear strength of undisturbed loess to that of remodeled saturated loess. The patterns of moisture content, confining pressure, and dry density on structural properties were analyzed and the degrees of influence of each factor on structural properties were quantified. By analyzing the change rule of the structural parameter of loess with the difference of moisture content and plastic limit, its change rule with plastic limit and liquid limit, and the change rule of the structural parameter with the liquidity index, the essential relationship between the structural parameter and grain size, density, and moisture is revealed. The essential relationship between the structural parameter and grain size, density, and moisture were also revealed. Full article

Climate warming has led to changes in floods in snow-packed mountain areas, but how snowmelt contributes to floods in the high-altitude Tibetan Plateau remains to be studied. To solve this problem, we propose a more reasonable method for evaluating snowmelt’s contributions to floods. We use a distributed hydrological model with the capability to track snowmelt paths in different media, such as snowpack, soil, and groundwater, to assess snowmelt’s contribution to peak discharge. The study area, the Xiying River basin, is located northeast of the Tibetan Plateau. Our results show that in the past 40 years, the average annual air temperature in the basin has increased significantly at a rate of 0.76 °C/10a. The annual precipitation (precipitation is the sum of rainfall and snowfall) decreased at a rate of 5.59 mm/10a, while the annual rainfall increased at a rate of 11.01 mm/10a. These trends were not obvious. The annual snowfall showed a significant decrease, at a rate of 14.41 mm/10a. The contribution of snowmelt to snowmelt-driven floods is 85.78%, and that of snowmelt to rainfall-driven floods is 10.70%. Under the influence of climate change, the frequency of snowmelt-driven floods decreased significantly, and flood time advanced notably, while the intensity and frequency of rainfall-driven floods slowly decreased in the basin. The causes of the change in snowmelt-driven floods are the significant increase in air temperature and the noticeable decrease in snowfall and snowmelt runoff depth. The contribution of snowmelt to rainfall-driven floods slowly weakened, resulting in a slight decrease in the intensity and frequency of rainfall-driven floods. The results also indicate that rising air temperature could decrease snowmelt-driven floods. In snow-packed mountain areas, rainfall and snowmelt together promote the formation of and change in floods. While rainfall dominates peak discharge, snowpack and snowmelt play a significant role in the formation and variability of rainfall-driven floods. The contributions of snowmelt and rainfall to floods have changed under the influence of climate change, which is the main cause of flood variability. The changed snowmelt adds to the uncertainties and could even decrease the size and frequency of floods in snow-packed high mountain areas. This study can help us understand the contributions of snowmelt to floods and assess the flood risk in the Tibetan Plateau under the influence of climate change. Full article

The smoke tree (Cotinus coggygria Scop.) is a woody species mainly distributed in the Mediterranean region and East Asia, known for its high ecological and ornamental value. Investigation of changes in suitable habitats under different conditions can provide valuable insights with implications for predicting the distribution of C. coggygria. In this study, we employed a MaxEnt model to simulate the current, historical, and future suitable habitat of C. coggygria using distribution records and environmental variables. The results indicated that climatic variables had a much stronger impact on the suitable habitat of this species compared with soil and topographic variables, and bio11 (mean temperature of the coldest quarter) and bio12 (annual precipitation) played particularly important roles in determining the suitable habitat. The core distribution of C. coggygria exhibited an East Asian–Tethyan disjunction. During the glacial period (Last Glacial Maximum), C. coggygria in Europe was concentrated in the glacial refugia in southern Europe; its range was substantially smaller during the glacial period than during interglacial periods (mid-Holocene). In contrast, C. coggygria in East Asia survived in regions similar to those of the interglacial period. Future climate change led to a gradual northward expansion of suitable habitats for C. coggygria, and the area of suitable habitat was substantially larger in Europe than in East Asia. There were significant differences among the four climate scenarios in Europe, with minimal variation in East Asia. Our findings provide valuable insights into the contrasting effects of climate change on European and East Asian populations of C. coggygria, which enhances our understanding of Eurasian species with discontinuous distributions. Full article

Rapidly obtaining information on the content and spatial distribution of soil organic carbon (SOC) in farmland is crucial for evaluating regional soil quality, land degradation, and crop yield. This study focuses on mountain soils in various crop cultivation areas in Shangzhou District, Shangluo City, Southern Shaanxi, utilizing ZY1-02D hyperspectral satellite imagery, field-measured hyperspectral data, and field sampling data to achieve precise inversion and spatial mapping of the SOC content. First, to address spectral bias caused by environmental factors, the Spectral Space Transformation (SST) algorithm was employed to establish a transfer relationship between measured and satellite image spectra, enabling systematic correction of the image spectra. Subsequently, multiple spectral transformation methods, including continuous wavelet transform (CWT), reciprocal, first-order derivative, second-order derivative, and continuum removal, were applied to the corrected spectral data to enhance their spectral response characteristics. For feature band selection, three methods were utilized: Variable Importance Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Stepwise Projection Algorithm (SPA). SOC content prediction was conducted using three models: partial least squares regression (PLSR), stepwise multiple linear regression (Step-MLR), and random forest (RF). Finally, leave-one-out cross-validation was employed to optimize the L4-CARS-RF model, which was selected for SOC spatial distribution mapping. The model achieved a coefficient of determination (R²) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg⁻¹, and a mean absolute error (MAE) of 1.37 g kg⁻¹. The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy; (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy; and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. These findings provide scientific methods and technical support for SOC monitoring and management in mountainous areas and offer valuable insights for assessing the long-term impacts of different crops on soil ecosystems. Full article

Human-induced environmental changes threaten the functional stability of natural forest ecosystems. Understanding the dominant factors influencing both functional space and stability in extremely heterogeneous environments is crucial for elucidating the stability of heterogeneous forest ecosystems. Here, 30 forest dynamic plots were established along the successional pathway in Maolan National Nature Reserve in Southwest China. By measuring 15,725 stems across 286 distinct species’ six key plant functional traits, we constructed the key plant functional traits for functional space and quantified functional redundancy (FR) and functional vulnerability (FV) to represent functional stability, and we further utilized the line model and multiple linear regression model to explore the key biotic/abiotic indicators influencing functional stability along the successional pathway of degraded karst forests. Additionally, as the successional pathway unfolded, the contribution of the six plant traits to the overall functional space increased, from 59.85% to 66.64%. These traits included specific leaf area (SLA), leaf dry matter content (LDMC), leaf thickness (LT) and leaf nitrogen content (LNC), which played a crucial role in driving functional space. With the increasing species richness (FR), functional entities (p < 0.001) and FR (p < 0.001) increased, while FV (p < 0.01) decreased. The results also demonstrated a higher FR in degraded karst forests (FR > 2). However, over 51% of FEs consisted of a single species, with the majority of species clustered into a few functional entities (FEs), indicating an elevated level of FV in karst forests. Soil nutrient availability significantly influences the ecosystem’s functional stability, explaining 87% of FR variability and 100% of FV variability. Finally, the rich SR of karst forests could provide sufficient insurance effects; soil pH and available potassium (AK) enhance resilience, and exchangeable calcium (Eca), total phosphorus (TP) and total potassium (TK) indicate the resistance of functional stability in degraded karst forests. This study highlights the complex mechanisms of functional stability in extreme habitat conditions, thereby deepening our understanding of ecosystem function maintenance. Full article