Search Results (13,014)

This study, which began in the 2013/2014 agricultural year, aimed to assess the suitability of two soil tillage systems for wheat cultivation: conventional soil tillage (CS), which involved moldboard plowing to a depth of 28 cm followed by a single pass with a rotary harrow to prepare the seedbed, and no-tillage (NT). It also sought to analyze the impacts of these systems on weed infestation levels and, consequently, on yield. A moderate level of fertilization was applied. The experimental field was established with a three-year crop rotation system: soybean–winter wheat–maize. The total number of weed species was 30 in CS, the representative species being Xanthium strumarium, and in NT there were 29 species, with Xanthium strumarium, Cirsium arvense, Bromus tectorum, and Agropyron repens predominating. There was an increase in the number of perennials (dicots and monocots). The total dry matter of weeds was 35.4 t ha⁻¹ in CS and 38.8 t ha⁻¹ in NT. After 11 agricultural years, it was found that there were no significant differences between the two soil tillage systems in terms of wheat yield (6.55 t ha⁻¹ in CS and 6.46 t ha⁻¹ in NT). The uneven rainfall negatively affected wheat growth and favored the spread of weeds, especially dicotyledonous ones. Full article

Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB^® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector. Full article

Planting patterns and irrigation amounts are key factors affecting maize yield. This study adopted a two-factor experimental design, with planting pattern as the main plot and irrigation amount as the subplot, to investigate the effects of irrigation levels under different planting patterns (including uniform row spacing and alternating wide-narrow row spacing) on spring maize yield and water use efficiency in Xinjiang. Through this approach, the study examined the mechanisms by which planting pattern and irrigation amount influence maize growth, yield formation, and water use efficiency. Experiments conducted at the Agricultural Science Research Institute of the Ninth Division of Xinjiang Production and Construction Corps demonstrated that alternating wide-narrow row spacing combined with moderate irrigation (5400 m³/hm²) significantly optimized maize root distribution, improved water use efficiency, and increased leaf area index and net photosynthetic rate, thereby promoting dry matter accumulation and yield enhancement. In contrast, uniform row spacing under high irrigation levels increased yield but resulted in lower water use efficiency. The study also found that alternating wide-narrow row spacing enhanced maize nutrient absorption from the soil, particularly phosphorus utilization efficiency, by improving canopy structure and root expansion. This pattern exhibited comprehensive advantages in resource utilization, providing a theoretical basis and technical pathway for achieving water-saving and high-yield maize production in arid regions, which holds significant importance for promoting sustainable agricultural development. Full article

Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R² of 0.64, outperforming individual models. The testing R² values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article

This study investigated the impacts of land-use history and an episodic flood event on the soil quality of a riverine floodplain ecosystem, providing long-term and short-term disturbance perspectives. The study took place in the Saint Michael’s College Natural Area, which has over a hundred-year history of land-use change. Based on aerial orthoimagery, three zones (a recently abandoned farm field, a new-growth forest, and an old-growth forest) were selected that reflected different land-use histories. Two plots were selected per zone and pooled soil samples were collected from each before and after a major flooding event. Surface soil quality before flooding was often similar among the new- and old-growth forested areas (1.4 mg-P/g-soil, 6.8% soil organic matter (SOM), 0.79 humification index (HIX), and 13% Peak T) but differed from that found in the recently abandoned farm field, which had higher phosphorus levels (1.6 mg-P/g-soil), lower SOM content (3.9%), more microbial-like SOM (0.65 HIX and 17% Peak T), and drier soils. Flooding caused SOM to better resemble that of a forest rather than an agricultural field, and it lowered phosphorus levels. The results of our study suggest that episodic flooding events could help accelerate the restoration of soil organic matter conditions. Full article

As agricultural land continues to expand, the conversion of forests to farmland has intensified, significantly altering the structure and function of agroecosystems. However, the dynamic ecological responses and their interactions with economic outcomes remain insufficiently modeled. This study proposes an integrated framework that combines a dynamic food web model with the Eco-Economic Benefit and Sustainability (EEBS) model, utilizing empirical data from Brazil and Ghana. A system of ordinary differential equations solved using the fourth-order Runge–Kutta method was employed to simulate species interactions and energy flows under various land management strategies. Reintroducing key species (e.g., the seven-spot ladybird and ragweed) improved ecosystem stability to over 90%, with soil fertility recovery reaching 95%. In herbicide-free scenarios, introducing natural predators such as bats and birds mitigated disturbances and promoted ecological balance. Using XGBoost (Extreme Gradient Boosting) to analyze 200-day community dynamics, pest control, resource allocation, and chemical disturbance were identified as dominant drivers. EEBS-based multi-scenario optimization revealed that organic farming achieves the highest alignment between ecological restoration and economic benefits. The model demonstrated strong predictive power ($R^2$ = 0.9619, RMSE = 0.0330), offering a quantitative basis for green agricultural transitions and sustainable agroecosystem management. Full article

Soil environmental quality in arid oases is crucial for regional ecological security but faces multi-source heavy metal (HM) contamination risks. This study aimed to (1) characterize the spatial distribution of soil HMs (As, Cd, Cr, Cu, Hg, and Zn) in the Ka Shi gar oasis, Xinjiang, (2) quantify the driving effect of irrigation water, and (3) elucidate interactions between HMs, soil properties, and land use types. Using 591 soil and 12 irrigation water samples, spatial patterns were mapped via inverse distance weighting interpolation, with drivers and interactions analyzed through correlation and land use comparisons. Results revealed significant spatial heterogeneity in HMs with no consistent regional trend: As peaked in arable land (5.27–40.20 μg/g) influenced by parent material and agriculture, Cd posed high ecological risk in gardens (max 0.29 μg/g), and Zn reached exceptional levels (412.00 μg/g) in gardens linked to industry/fertilizers. Irrigation water impacts were HM-specific: water contributed to soil As enrichment, whereas high water Cr did not elevate soil Cr (indicating industrial dominance), and Cd/Cu showed no significant link. Interactions with soil properties were regulated by land use: in arable land, As correlated positively with EC/TN and negatively with pH; in gardens, HMs generally decreased with pH, enhancing mobility risk; in forests, SOM adsorption immobilized HMs; in construction land, Hg correlated with SOM/TP, suggesting industrial-organic synergy. This study advances understanding by demonstrating that HM enrichment arises from natural and anthropogenic factors, with the spatial heterogeneity of irrigation water’s driving effect critically regulated by land use type, providing a spatially explicit basis for targeted pollution control and sustainable oasis management. Full article

In the coming decades, the agricultural system will predictably rely on organic material to produce crops and maintain food security. Currently, the use of inorganic fertilizers to grow crops and vegetables, such as Swiss chard, spinach, and lettuce, is on the rise and has been proven to be detrimental to the soil in the long run. Hence, there is a growing need to use organic waste material, such as spent coffee grounds (SCGs), to grow crops. Spent coffee grounds are made of depleted coffee beans that contain important soluble compounds. This study aimed to determine the influence of different levels (0.32 g, 0.63 g, 0.92 g, and 1.20 g) of spent coffee grounds on the metabolomic profile of Swiss chard. The 1H-nuclear magnetic resonance (NMR) results showed that Swiss chard grown with different levels of SCGs contains a total of 10 metabolites, which included growth-promoting metabolites (trehalose; betaine), defense mechanism metabolites (alanine; cartinine), energy-reserve metabolites (sucrose; 1,6 Anhydro-β-D-glucose), root metabolites (thymine), stress-related metabolites (2-deoxyadenosine), caffeine metabo-lites (1,3 Dimethylurate), and body-odor metabolites (trimethylamine). Interestingly, caprate, with the abovementioned metabolites, was detected in Swiss chard grown without the application of SCGs. The findings of the current study suggest that SCGs are an ideal organic material for growing Swiss chard for its healthy metabolites. Full article

This study presents a terrain-integrated Soil Management Unit (SMU) framework for precision agriculture in semi-arid tropical basaltic soils. Using high resolution (10-ha grid) sampling across 4627 geo-referenced locations and machine learning-enhanced integration of terrain attributes with legacy soil maps, and (3) quantitative validation of intra-SMU homogeneity, 15 SMUs were delineated based on landform, soil depth, texture, and slope. Principal Component Analysis (PCA) revealed SMU11 as the most heterogeneous (68.8%). Geo-statistical analysis revealed structured variability in soil pH (range = 1173 m) and nutrients availability with micronutrient sufficiency following Mn > Fe > Cu > Zn, (Zn deficient in SMU13). Organic carbon strongly correlated with key nutrients (AvK, r = 0.83 and Zn, r = 0.86). This represents the first systematic implementation of terrain-integrated SMU delineation in India’s basaltic landscapes, demonstrating a potential for 20–25% input savings. The spatially explicit fertility-integrated SMU framework provides a robust basis for developing decision support systems aimed at optimizing location-specific nutrient and land management strategies. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

The widespread use of pesticides in modern agriculture has significantly enhanced food production by managing pests and diseases; however, their degradation in soil can lead to unintended consequences for soil fertility and nutrient cycling. This review explores the mechanisms of pesticide degradation, both abiotic and biotic, and the soil factors influencing these processes. It critically examines how degradation products impact soil microbial communities, organic matter decomposition, and key nutrient cycles, including nitrogen, phosphorus, potassium, and micronutrients. This review highlights emerging evidence linking pesticide residues with altered enzymatic activity, disrupted microbial populations, and reduced nutrient bioavailability, potentially compromising soil structure, water retention, and long-term productivity. Additionally, it discusses the broader environmental and agricultural implications, including decreased crop yields, biodiversity loss, and groundwater contamination. Sustainable management strategies such as bioremediation, the use of biochar, eco-friendly pesticides, and integrated pest management (IPM) are evaluated for mitigating these adverse effects. Finally, this review outlines future research directions emphasizing long-term studies, biotechnology innovations, and predictive modeling to support resilient agroecosystems. Understanding the intricate relationship between pesticide degradation and soil health is crucial to ensuring sustainable agriculture and food security. Full article

The genus Trichoderma plays a pivotal role in sustainable agriculture through its multifaceted contributions to plant health and productivity. This review explores Trichoderma’s biological functions, including its roles as a biocontrol agent, plant growth promoter, and stress resilience enhancer. By producing various enzymes, secondary metabolites, and volatile organic compounds, Trichoderma effectively suppresses plant pathogens, promotes root development, and primes plant immune responses. This review details the evolutionary adaptations of Trichoderma, which has transitioned from saprotrophism to mycoparasitism and established beneficial symbiotic relationships with plants. It also highlights the ecological versatility of Trichoderma in colonizing plant roots and improving soil health, while emphasizing its role in mitigating both biotic and abiotic stressors. With increasing recognition as a biostimulant and biocontrol agent, Trichoderma has become a key player in reducing chemical inputs and advancing eco-friendly farming practices. This review addresses challenges such as strain selection, formulation stability, and regulatory hurdles and concludes by advocating for continued research to optimize Trichoderma’s applications in addressing climate change, enhancing food security, and promoting a sustainable agricultural future. Full article

This study investigates the production and economic feasibility of biochar for smallholder and family farms in Central Amazonia, with potential implications for other tropical regions. The costs of construction of a prototype mobile kiln and biochar production were evaluated, using small-sized biomass from acai (Euterpe oleracea Mart.) agro-industrial residues as feedstock. The biochar produced was characterised in terms of its liming capacity (calcium carbonate equivalence, CaCO_3eq), nutrient content via organic fertilisation methods, and ash analysis by ICP-OES. Field trials with cowpea assessed economic outcomes, as well scenarios of fractional biochar application and cost comparison between biochar production in the prototype kiln and a traditional earth-brick kiln. The prototype kiln showed production costs of USD 0.87–2.06 kg⁻¹, whereas traditional kiln significantly reduced costs (USD 0.03–0.08 kg⁻¹). Biochar application alone increased cowpea revenue by 34%, while combining biochar and lime raised cowpea revenues by up to 84.6%. Owing to high input costs and the low value of the crop, the control treatment generated greater net revenue compared to treatments using lime alone. Moreover, biochar produced in traditional kilns provided a 94% increase in net revenue compared to liming. The estimated externalities indicated that carbon credits represented the most significant potential source of income (USD 2217 ha⁻¹). Finally, fractional biochar application in ten years can retain over 97% of soil carbon content, demonstrating potential for sustainable agriculture and carbon sequestration and a potential further motivation for farmers if integrated into carbon markets. Public policies and technological adaptations are essential for facilitating biochar adoption by small-scale tropical farmers. Full article

Insect pests cause severe damage and yield losses to many agricultural crops globally. The use of chemical pesticides on agricultural crops is not recommended because of their toxic effects on the environment and consumers. In addition, pesticide toxicity reduces soil fertility, poisons ground waters, and is hazardous to soil biota. Therefore, applications of entomopathogenic nematodes (EPNs) and plant growth-promoting rhizobacteria (PGPR) are an alternative, eco-friendly solution to chemical pesticides and mineral-based fertilizers to enhance plant health and promote sustainable food security. This review focuses on the biological and ecological aspects of these organisms while also highlighting the practical application of molecular communication approaches in developing a novel plant health product. This insight will support this innovative approach that combines PGPR and EPNs for sustainable crop production. Several studies have reported positive interactions between nematodes and bacteria. Although the combined presence of both organisms has been shown to promote plant growth, the molecular interactions between them are still under investigation. Integrating molecular communication studies in the development of a new product could help in understanding their relationships and, in turn, support the combination of these organisms into a single plant health product. Full article

Peanut cultivation is widely practiced using plastic mulch film, resulting in the accumulation of microplastics/nanoplastics (MPs/NPs) in agricultural soils, potentially negatively affecting peanut growth. To investigate the effects of two polystyrene (PS) sizes (5 μm, 50 nm) and three concentrations (0, 10, and 100 mg L⁻¹) on peanut growth, photosynthetic efficiency, and physiological characteristics, a 15-day hydroponic experiment was conducted using peanut seedlings as the experimental material. The results indicated that PS-MPs/NPs inhibited peanut growth, reduced soil and plant analyzer development (SPAD) values (6.7%), and increased levels of malondialdehyde (MDA, 22.0%), superoxide anion (O₂⁻, 3.8%) superoxide dismutase (SOD, 16.1%) and catalase (CAT, 12.1%) activity, and ascorbic acid (ASA, 12.6%) and glutathione (GSH, 9.1%) contents compared to the control. Moreover, high concentrations (100 mg L⁻¹) of PS-MPs/NPs reduced the peanut shoot fresh weight (16.1%) and SPAD value (7.2%) and increased levels of MDA (17.1%), O₂⁻ (5.6%), SOD (10.6%), POD (27.2%), CAT (7.3%), ASA (12.3%), and GSH (6.8%) compared to low concentrations (10 mg L⁻¹) of PS-MPs/NPs. Notably, under the same concentration, the impact of 50 nm PS-NPs was stronger than that of 5 μm PS-MPs. The peanut shoot fresh weight of PS-NPs was lower than that of PS-MPs by an average of 7.9%. Additionally, we found that with an increasing exposure time of PS-MPs/NPs, the inhibitory effect of low concentrations of PS-MPs/NPs on the fresh weight was decreased by 2.5%/9.9% (5 d) and then increased by 7.7%/2.7% (15 d). Conversely, high concentrations of PS-MPs/NPs consistently reduced the fresh weight. Correlation analysis revealed a clear positive correlation between peanut biomass and both the SPAD values as well as Fv/Fm, and a negative correlation with MDA, SOD, CAT, ASA, and GSH. Furthermore, the presence of PS-MPs/NPs in roots, stems, and leaves was confirmed using a confocal laser scanning microscope. The internalization of PS-MPs/NPs within peanut tissues negatively impacted peanut growth by increasing the MDA and O₂⁻ levels, reducing the SPAD values, and inhibiting the photosynthetic capacity. In conclusion, the study demonstrated that the effects of PS on peanuts were correlated with the PS size, concentration, and exposure time, highlighting the potential risk of 50 nm to 5 μm PS being absorbed by peanuts. Full article