Search Results (3,903)

This review explores the diverse applications and therapeutic potential of endophytic microbes, emphasizing both fungal and bacterial endophytes. These microorganisms reside within plant tissues without causing harm and play an important role in enhancing plant growth, nutrient acquisition, and resistance to pathogens. They produce phytohormones, facilitate nutrient uptake, solubilize essential nutrients, fix nitrogen, and improve stress tolerance. Furthermore, endophytes contribute to agricultural sustainability by producing plant growth regulators, providing biocontrol against pathogens through antimicrobial compounds, and competing for resources. Integrating endophytic microbes into agricultural practices can reduce reliance on chemical fertilizers and pesticides, promoting eco-friendly and sustainable farming. This review highlights the dual role of endophytic microbes in fostering sustainable agriculture and providing novel therapeutic applications. By minimizing dependence on chemical inputs, endophytes support environmental health while boosting crop yields. The synthesis underscores the importance of leveraging endophytic microbes to tackle global food security and sustainability challenges. Full article

Phosphate fertilization management under no-till farming has important implications for sustainable agriculture, especially in highly weathered soils. A field experiment examined the effects of phosphorus (P) sources and application modes on soil P levels, plant P nutrition, and grain yields of a wheat–soybean cropping system under no-till. Five cycles of a wheat–soybean crop succession were evaluated on an Oxisol in the period from 2016 to 2021 in the State of Parana, Brazil. The treatments consisted of fertilization with monoammonium phosphate (MAP) and single superphosphate (SSP), in addition to a control without P, to subplots within plots with in-furrow and broadcast P applications. The annual application of 100 kg of P₂O₅ ha⁻¹ from phosphate sources, either broadcast or in the sowing furrow, was sufficient to maintain an adequate level of P in the soil, supply P demand for the secession of wheat–soybean crops, and obtain high grain yields. In a wheat–soybean cropping system, the application of the fertilizers MAP or SSP-based phosphates in the sowing furrow or broadcast in wheat crop is a strategy that should be encouraged in highly weathered soils under no-till to minimize P fixation to soil particles, improve P-leaf concentration, and increase wheat and soybean grain yields. Full article

The efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R² of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environmental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance. Full article

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale. Full article

Increasing interest is being devoted to environmentally friendly strategies, such as the use of plant biostimulants, to enhance crop performance and concurrently ensure food security under the perspective of sustainable management. The effects of two biostimulant formulations (protein hydrolysate and spirulina) on four Italian traditional storage onion cultivars (Ramata di Montoro, Rossa di Tropea, Rocca Bruna, Dorata di Parma) were investigated in Naples province (southern Italy), in terms of yield, quality, shelf-life, bioactive compounds, and mineral composition. Ramata di Montoro showed the highest levels of yield (66.4 t ha⁻¹) and vitamin C (31.5 mg g⁻¹ d.w.) and the longest shelf-life (228 days). Significant increases in marketable yield were recorded under the applications of both protein hydrolysate (+15.5%) and spirulina (+12.4%) compared to the untreated control. The two biostimulant formulations significantly increased bulb shelf-life and the contents of polyphenols (201.4 mg gallic acid eq. 100 g⁻¹ d.w. on average vs. 158.6 of the untreated control), vitamin C (26.8 mg g⁻¹ d.w. on average vs. 22), and both lipophilic and hydrophilic antioxidant activities. These findings demonstrate the effectiveness of both protein hydrolysate and spirulina as sustainable tools for enhancing both yield and quality parameters within the frame of environmentally friendly farming management. Full article

Since the domestication of plants, pathogenic fungi have consistently threatened crop production, evolving genetically to develop increased virulence under various selection pressures. Understanding their evolutionary trends is crucial for predicting and designing control measures against future disease outbreaks. This paper reviews the evolution of fungal pathogens from natural habitats to agricultural settings, focusing on eight significant phytopathogens: Pyricularia oryzae, Botrytis cinerea, Puccinia spp., Fusarium graminearum, F. oxysporum, Blumeria graminis, Zymoseptoria tritici, and Colletotrichum spp. Also, we explore the mechanism used to understand evolutionary trends in these fungi. The studied pathogens have evolved in agroecosystems through either (1) introduction from elsewhere; or (2) local origins involving co-evolution with host plants, host shifts, or genetic variations within existing strains. Genetic variation, generated via sexual recombination and various asexual mechanisms, often drives pathogen evolution. While sexual recombination is rare and mainly occurs at the center of origin of the pathogen, asexual mechanisms such as mutations, parasexual recombination, horizontal gene or chromosome transfer, and chromosomal structural variations are predominant. Farming practices like mono-cropping resistant cultivars and prolonged use of fungicides with the same mode of action can drive the emergence of new pathotypes. Furthermore, host range does not necessarily impact pathogen adaptation and evolution. Although halting pathogen evolution is impractical, its pace can be slowed by managing selective pressures, optimizing farming practices, and enforcing quarantine regulations. The study of pathogen evolution has been transformed by advancements in molecular biology, genomics, and bioinformatics, utilizing methods like next-generation sequencing, comparative genomics, transcriptomics and population genomics. However, continuous research remains essential to monitor how pathogens evolve over time and to develop proactive strategies that mitigate their impact on agriculture. Full article

Recurrent selection for early vigour traits in wheat (Triticum aestivum L.) has provided an opportunity to generate competitive biotypes to suppress agronomically important weeds. Quantifying the potential benefits of competitive genotypes, including yield improvement and reduced frequency of herbicide application when incorporated into a long-term rotation, is vital to increase grower adoption. In this simple economic model, we evaluated a weed-suppressive early vigour genotype utilising on-farm experimental results and simulation analysis to predict gross margins for a seven-year wheat-canola rotation in southeastern Australia. The model applied a local weather sequence and predicted wheat production potential, costs and benefits over time. An early vigour wheat genotype was compared to commercial wheat cultivars for weed control, yield and actual production cost. With respect to weed control, three scenarios were evaluated in the model: standard herbicide use with a commercial cultivar (A), herbicide use reduced moderately by inclusion of an early vigour wheat genotype and elimination of the postharvest grass herbicide (B) or inclusion of an early vigour wheat genotype and withdrawal of both postharvest grass and broadleaf herbicides (C). Cost savings for the use of a competitive wheat genotype ranged from 12 AUD/ha in scenario B to 40 AUD/ha in scenario C, for a total saving of 52 AUD/ha. The model generated annual background gross margins, which varied from 300 AUD/ha to 1400 AUD/ha based on historical weather conditions, production costs and crop prices over the 30-year period from 1992 to 2021. The benefits of lower costs for each of the three scenarios are presented with rolling seven-year average wheat–canola rotation gross margins over the 30-year period. The limitations of this model for evaluation of weed suppression and cost benefits are discussed, as well as relative opportunities for adoption of early vigour traits in wheat. Full article

Mulching practices have demonstrated the potential to increase crop yields and resource utilization efficiency. However, the response of different crops with various growth stages to different mulching practices remains unclear, particularly in the rainfed agroecosystem. Therefore, a two-year field experiment (2013–2015) of different crops (wheat, maize, and potato) was conducted to evaluate the effects of three different mulching treatments: straw strip mulching (SM), plastic film mulching (PM), and conventional planting without mulching as the control (CK), on soil moisture and temperature, evapotranspiration (ET), water use efficiency (WUE), crop yield and economic benefits on the Loess Plateau. The results indicated that both mulching practices significantly increased the soil water content (SM: 4.3% and PM: 3.6%) compared to CK. However, the effects on soil temperature varied between mulching practices, PM increased soil temperature by 4.9% compared to CK, while SM decreased it by 6.3%. The improved soil hydrothermal conditions, characterized by favorable temperatures and higher soil water status would lead to a higher crop daily growth rate (5.3–49.8%), as well as greater dry matter accumulation (4.7–36.7%). Furthermore, mulching practice (SM and PM) has a significant influence on crop yield and its components of various crops, as well as WUE. The mean grain yield of SM and PM was, respectively, increased by 11.4% and 27.1% for winter wheat, compared to CK, 1.8% and 24.3% for spring maize, and 23.0% and 13.9% for potato, respectively. Compared to CK, PM yielded a higher net economic benefit and WUE for winter wheat and spring maize, while SM presented the best economic benefit and WUE for potato. In conclusion, a comprehensive analysis of crop yield, economic benefits, and resource utilization efficiency suggests that straw strip mulching for potato is a more sustainable environmentally friendly mulching practice, recommended for rainfed farming systems on the Loess Plateau and areas with similar climatic conditions. Full article

Modern potato varieties from high-input, conventional farming-focused breeding programs produce substantially (up to 45%) lower yields when grown in organic production systems, and this was shown to be primarily due to less efficient fertilization and late blight (Phytophthora infestans) control methods being used in organic farming. It has been hypothesized that the breeding of potato varieties suitable for the organic/low-input sector should (i) focus on increasing nutrient (especially N) use efficiency, (ii) introduce durable late blight resistance, and (iii) be based on selection under low-input conditions. To test this hypothesis, we used an existing long-term factorial field experiment (the NEFG trials) to assess the effect of crop management practices (rotation design, fertilization regime, and crop protection methods) used in conventional and organic farming systems on crop health, tuber yield, and mineral composition parameters in two potato varieties, Santé and Sarpo mira, that were developed in breeding programs for high and low-input farming systems, respectively. Results showed that, compared to Santé, the variety Sarpo mira was more resistant to foliar and tuber blight but more susceptible to potato scab (Streptomyces scabies) and produced higher yields and tubers with higher concentrations of nutritionally desirable mineral nutrients but lower concentrations of Cd. The study also found that, compared to the Cu-fungicides permitted for late blight control in organic production, application of synthetic chemical fungicides permitted and widely used in conventional production resulted in significantly lower late blight severity in Sante but not in Sarpo mira. Results from both ANOVA and redundancy analysis (RDA) indicate that the effects of climatic (precipitation, radiation, and temperature) and agronomic (fertilization and crop protection) explanatory variables on crop health and yield differed considerably between the two varieties. Specifically, the RDA identified crop protection as a significant driver for Santé but not Sarpo mira, while precipitation was the strongest driver for crop health and yield for Sarpo mira but not Santé. In contrast, the effect of climatic and agronomic drivers on tuber mineral and toxic metal concentrations in the two varieties was found to be similar. Our results support the hypothesis that selection of potato varieties under low agrochemical input conditions can deliver varieties that combine (i) late blight resistance/tolerance, (ii) nutrient use efficiency, and (iii) yield potential in organic farming systems. Full article

One of the most popular varieties in crop farming is wheat. In Lithuania, more than 460 winter wheat varieties are registered in the State Register of Plant Varieties. One of the most popular and time-tested varieties is ‘Skagen’, which is highly valued for its winter hardiness. The aim of the research is to determine the influence of different pre-crops on the winter survival of the wheat variety ‘Skagen’ in Albeluvisol soils. For the experiment, fields of the winter wheat (Triticum aestivum) variety ‘Skagen’ from farms in the Lazdijai district were chosen. The experiment was conducted from 2017 to 2018. Plant count, chlorophyll index, and weed count were evaluated. After evaluating the differences in plant density after winter, it was found that a significantly greater reduction in plant density, 98.06%, occurred after winter wheat and 97.62% after spring wheat pre-crops compared to perennial grass pre-crops. The highest chlorophyll index was in winter wheat crops, where the pre-crops were peas, winter rape, and perennial grasses, respectively, ranging from 17.78% to 19.57%. Properly selected pre-crops reduce the risk of overwintering and form a strong crop from the beginning of vegetation. Full article

The Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. Traditional NDVI calculation methods often rely on multispectral or hyperspectral imagery, which are costly and complex to operate, thus limiting their applicability in small-scale farms and developing countries. To address these limitations, this study proposes an NDVI estimation method based on low-cost RGB (red, green, and blue) UAV (unmanned aerial vehicle) imagery combined with deep learning techniques. This study utilizes field data from five major crops (cotton, rice, maize, rape, and wheat) throughout their whole growth periods. RGB images were used to extract conventional features, including color indices (CIs), texture features (TFs), and vegetation coverage, while convolutional features (CFs) were extracted using the deep learning network ResNet50 to optimize the model. The results indicate that the model, optimized with CFs, significantly enhanced NDVI estimation accuracy. Specifically, the R² values for maize, rape, and wheat during their whole growth periods reached 0.99, while those for rice and cotton were 0.96 and 0.93, respectively. Notably, the accuracy improvement in later growth periods was most pronounced for cotton and maize, with average R² increases of 0.15 and 0.14, respectively, whereas wheat exhibited a more modest improvement of only 0.04. This method leverages deep learning to capture structural changes in crop populations, optimizing conventional image features and improving NDVI estimation accuracy. This study presents an NDVI estimation approach applicable to the whole growth period of common crops, particularly those with significant population variations, and provides a valuable reference for estimating other vegetation indices using low-cost UAV-acquired RGB images. Full article

The integration of ecosystem services (ESs) valuation into agricultural policy frameworks is critical for fostering sustainable land management practices. This study leverages the redesigned version of the bio-economic farm model MODAM (Multi-Objective Decision Support Tool for Agro-Ecosystem Management) to estimate the shadow prices of ESs, enabling the derivation of demand and supply curves for nitrate leaching and soil erosion control, respectively. Two hypothetical farms in Brandenburg, Germany—a smaller, arable farm in Märkisch-Oderland and a larger, diversified farm with livestock in Oder-Spree—are analyzed to explore the heterogeneity in shadow prices and corresponding cropping patterns. The results reveal that larger farms exhibit greater elasticity in response to green taxes on nitrate use and lower costs for supplying erosion control compared to smaller farms. This study highlights the utility of shadow prices as proxies for setting green taxes and payments for ecosystem services (PESs), while emphasizing the need for differentiated policy designs to address disparities between farm types. This research underscores the potential of model-based ESs valuation to provide robust economic measures for policy design, fostering sustainable agricultural practices and ecosystem conservation. Full article

This study investigated soil fungal biodiversity in wheat-based crop rotation systems on Chernozem soil within the Pannonian Basin, focusing on the effects of tillage, crop rotation, and soil properties. Over three years, soil samples from ten plots were analyzed, revealing significant fungal diversity with Shannon–Wiener diversity indices ranging from 1.90 in monoculture systems to 2.38 in a fertilized two-year crop rotation. Dominant fungi, including Fusarium oxysporum, Penicillium sp., and Aspergillus sp., showed distinct preferences for soil conditions such as pH and organic matter (OM). Conservation tillage significantly enhanced fungal diversity and richness, with the highest diversity observed in a three-year crop rotation system incorporating cover crops, which achieved an average winter wheat yield of 7.0 t ha⁻¹—47% higher than unfertilized monoculture systems. Increased OM and nitrogen levels in these systems correlated with greater fungal abundance and diversity. Canonical correspondence analysis revealed strong relationships between fungal communities and soil properties, particularly pH and calcium carbonate content. These findings highlight the importance of tailored crop rotation and tillage strategies to improve soil health, enhance microbial biodiversity, and boost agricultural sustainability in temperate climates, providing valuable insights for mitigating the impacts of intensive farming and climate change. Full article

Efficient and accurate weed detection in wheat fields is critical for precision agriculture to optimize crop yield and minimize herbicide usage. The dataset for weed detection in wheat fields was created, encompassing 5967 images across eight well-balanced weed categories, and it comprehensively covers the entire growth cycle of spring wheat as well as the associated weed species observed throughout this period. Based on this dataset, PMDNet, an improved object detection model built upon the YOLOv8 architecture, was introduced and optimized for wheat field weed detection tasks. PMDNet incorporates the Poly Kernel Inception Network (PKINet) as the backbone, the self-designed Multi-Scale Feature Pyramid Network (MSFPN) for multi-scale feature fusion, and Dynamic Head (DyHead) as the detection head, resulting in significant performance improvements. Compared to the baseline YOLOv8n model, PMDNet increased [email protected] from 83.6% to 85.8% (+2.2%) and [email protected]:0.95 from 65.7% to 69.6% (+5.9%). Furthermore, PMDNet outperformed several classical single-stage and two-stage object detection models, achieving the highest precision (94.5%, 14.1% higher than Faster-RCNN) and [email protected] (85.8%, 5.4% higher than RT-DETR-L). Under the stricter [email protected]:0.95 metric, PMDNet reached 69.6%, surpassing Faster-RCNN by 16.7% and RetinaNet by 13.1%. Real-world video detection tests further validated PMDNet’s practicality, achieving 87.7 FPS and demonstrating high precision in detecting weeds in complex backgrounds and small targets. These advancements highlight PMDNet’s potential for practical applications in precision agriculture, providing a robust solution for weed management and contributing to the development of sustainable farming practices. Full article

Poultry litter waste management poses a significant global challenge, attributed to its characteristics (odorous, organic, pathogenic, attracting flies). Conventional approaches to managing poultry litter involve composting, biogas generation, or direct field application. Recently, there has been a surge of interest in a novel technology that involves the bioconversion of organic waste utilizing insects (known as entomoremediation), particularly focusing on black soldier fly larvae (BSFL), and has demonstrated successful transformation of various organic waste materials into insect meal and frass (referred to as organic frasstilizer). Black soldier flies have the capacity to consume any organic waste material (ranging from livestock litter, food scraps, fruit and vegetable residues, sewage, sludge, municipal solid waste, carcasses, and defatted seed meal) and convert it into valuable BSFL insect meal (suitable for animal feed) and frass (serving as an organic fertilizer). The bioconversion of poultry litter by black soldier flies offers numerous advantages over traditional methods, notably in terms of reduced land and water requirements, lower emissions, cost-effectiveness, swift processing, and the production of both animal feeds and organic fertilizers. This review focuses on the existing knowledge of BSFL, their potential in bioconverting poultry litter into BSFL meal and frass, and the utilization of BSFL in poultry nutrition, emphasizing the necessity for further innovation to enhance this sustainable circular economy approach. Full article