Search Results (19,055)

Olive cultivation constitutes a fundamental Mediterranean rural activity in Greece, as it primarily accounts for the country’s substantial socio-economic development. Although the olive tree is one of the best acclimated species, its overall performance may be significantly impacted by changes in the climate. Thus, by considering the lack of scientific research on the climate suitability evaluation of olive groves over the entire Greek territory, a study between the geomorphological parameter mapping of Greece (altitude, aspect, slope, and terrain roughness) and the respective required atmospheric conditions for the olive crop’s growth (temperature, precipitation, and frost days) was performed. Every parameter is reclassified to translate its value into a score, and the final suitability map is the outcome of the aggregation of all score maps. Individually, the overall suitability for olive cultivation is high in Greece, given its extensive area, resulting in a high score (8–10); geomorphological and climatic conditions (34.44% and 59.40%, respectively); and overall suitability conditions (42.00%) for olive cultivation. Over the identified olive grove areas, the model gives a high score (8–10) for 91.59% of the cases. The model may be characterized by its simplicity, usability, flexibility, and efficiency. The current modelling procedure may serve as a means for identifying suitable areas for the sustainable and productive development of olive cultivation. Full article

Climate change and socio-economic transformation increasingly challenge the stability of China’s food supply. This study aims to optimize grain crop layouts by integrating natural suitability and nutritional supply within a unified analytical framework. Using the MaxEnt model incorporating bioclimatic, topographic, and soil variables, we simulated the natural suitability of major grain crops and compared it with actual planting patterns based on the SPAM dataset. Results revealed substantial spatial discrepancies between actual and suitable distributions, with national planting diversity index increasing by 26.42% (from 0.53 to 0.67) under suitable conditions. Wheat and maize are most suited to northern China, rice and tuber crops to southern regions, while soybean performs optimally in the northeast. Nutrient supply potential also improved substantially under the suitable scenario, with energy, protein, fat, and carbohydrate increasing by 56.9 × 10⁸ KJ, 77.2 × 10⁶ g, 23.3 × 10⁶ g, and 48.6 × 10⁶ g per million people, respectively. Among alternative structures, maize-soybean and maize-based planting structures better aligned with both natural adaptability and nutritional balance (e.g., in Inner Mongolia and Heilongjiang), whereas rice-based structure showed weaker correspondence (e.g., in Shanghai). These findings demonstrate that naturally adaptive optimization can enhance both environmental compatibility and nutritional adequacy, providing scientific guidance for developing climate-resilient and nutrition-oriented crop layout strategies in China. Full article

Rice cultivation accounts for roughly 10% of worldwide anthropogenic greenhouse gas emissions, making it a significant source of methane (CH₄) Despite modest observational constraints, estimates of worldwide CH₄ emissions from rice agriculture range from 18–115 Tg CH₄ yr⁻¹. CH₄ is a potent greenhouse gas, and its oxidation produces tropospheric ozone (O₃), which is harmful to public health and crop production when combined with nitrogen oxides (NOx) and sunlight. Elevated O₃ levels reduce air quality, crop productivity, and human respiratory health. This study presents an AI-driven framework that combines ensemble learning, hyperparameter optimisation (HPs), and SHAP-based explainability to enhance CH₄ emission predictions from rice paddies in India, Bangladesh, and Vietnam. The model consists of two stages: (1) a classification stage to distinguish between zero and non-zero CH₄ emissions, and (2) a regression stage to estimate emission magnitudes for non-zero situations. The framework also incorporates O₃ and asthma incidence data to assess the downstream impacts of CH₄-driven ozone formation on air quality and health outcomes. Understanding the factors that drive optimal model performance and the relative importance of features affecting model outputs is still an ongoing field of research. To address these issues, we present an integrated approach that utilises recent improvements in model optimisation and employs SHapley Additive ExPlanations (SHAP) to find the most relevant variables affecting methane (CH₄) emission forecasts. In addition, we developed a web-based artificial intelligence platform to help policymakers and stakeholders with climate strategy and sustainable agriculture by visualising methane fluxes from 2018 to 2020, ensuring practical applicability. Our findings show that ensemble learning considerably improves the accuracy of CH₄ emission prediction, minimises uncertainty, and shows the wider benefits of methane reduction for climate stability, air quality, and public health. Full article

Sweetpotato (Ipomoea batatas) is a staple crop of strategic importance in West Africa, particularly in Côte d’Ivoire. However, its productivity is increasingly under threat due to viral diseases. Given the lack of updated epidemiological data over the past three decades, a nationwide survey was conducted in September 2023 across 94 fields in 83 locations covering seven agroecological zones of the country. A total of 221 symptomatic and asymptomatic leaf samples were analyzed using PCR for DNA viruses and RT-PCR for RNA viruses. The overall viral incidence rate calculated was 65.61%, with significant regional variations (35–97.18%, p < 0.001) and notable differences in the severity of symptoms (p = 0.0095). Agroecological zone I was the most affected, while agroecological zones IV and V were the least impacted. Four viruses were identified: cucumber mosaic virus (CMV), sweet potato leaf curl virus (SPLCV), sweet potato feathery mottle virus (SPFMV), and sweet potato chlorotic stunt virus (SPCSV). No badnaviruses were found. CMV was the most common virus found in single infections (43.44%), followed by SPLCV (5.43%). SPFMV and SPCSV were only observed in mixed infections, particularly CMV/SPLCV (14.03%) and CMV/SPFMV (1.81%). Two triple infections were also detected: SPFMV/SPCSV/CMV and SPFMV/SPLCV/CMV. In total, 34 partial coat protein sequences were obtained (28 SPLCV, 4 SPFMV, 1 CMV, 1 SPCSV). Phylogenetic analysis revealed a high similarity between SPLCV isolates characterized in Côte d’Ivoire and those from Burkina Faso, Europe (Spain, Italy), and the Americas (USA, Puerto Rico) with nucleotide identity values ranging from 98% to 100%. The Côte d’Ivoire SPCSV sequence showed 97.92% nucleotide identity with European isolates, whereas SPFMV sequences exhibited greater diversity (77–89% identity) but clustered within the West African lineage. Sweetpotato viral diseases were detected mostly in mixed-cropping fields (66.85%). This work provides the first epidemiological update on sweetpotato viral diseases since 1987 and the first molecular evidence of the nationwide presence of SPLCV and SPCSV in Côte d’Ivoire. Full article

The normal metabolism of transient starch in leaves plays a vital role in determining photosynthesis and final crop yield. However, the molecular mechanisms linking abnormal transient starch metabolism to premature leaf senescence remain unclear. Here, we isolate a rice mutant, lses1, with leaf yellowing and premature senescence, as well as excessive accumulation of starch granules in chloroplasts. Genetic analysis revealed that this trait is controlled by a single recessive nuclear gene. Through BSA-seq preliminary gene mapping, map-based cloning, and sequencing alignment, the candidate gene was pinpointed to LOC_Os02g40860 on chromosome 2, which encodes OsCKI1, a casein kinase I family member. The identity of LSES1 was confirmed functionally: genetic complementation with the native genomic sequence rescued the wild-type phenotype, while CRISPR/Cas9 knockout of the gene in wild-type plants recapitulated the premature senescence. This confirmed that LSES1/OsCKI1 is involved in the regulation of leaf senescence. Notably, one improved knockout line, KO-2, displayed significant agronomic improvements in grain length, grain width, number of productive ears, and number of filled grains per panicle, along with a significant increase in grain yield per plant, highlighting its potential breeding value. Subcellular localization and tissue-specific expression analysis showed that LSES1 is primarily nuclear-localized and constitutively expressed. Full article

Taro (Colocasia esculenta) is a socioeconomically and nutritionally important crop that is predominantly cultivated in the derived savanna and humid forest agroecosystems of Nigeria. Taro production in the country has declined since the taro leaf blight (TLB) outbreak caused by Phytophthora colocasiae Raciborski. This study conducted field surveys during the 2021–2022 production season to assess the status of taro diseases, as well as a structured questionnaire to capture farmers’ management practices and the socio-economic determinants of taro cultivation across seven major taro-producing states in Nigeria. Data was collected from 63 randomly selected farmers across 53 farms, and 449 corms were sampled from farms and markets to assess corm-borne diseases. Sixty-three percent of farmers identified biotic constraints as the major production challenge, with TLB recognized as the most significant threat. Virus-symptomatic plants were not observed in the farmers’ fields, but the occurrence of Dasheen mosaic virus (or Potyvirus dasheenis) (DsMV, genus Potyvirus) was detected among the plants regenerated from corms collected from farms and markets. The widespread occurrence of TLB and DsMV suggests that these two pathogens pose a serious threat to taro production and that there is a risk of further spread through the continuous recycling of self-sourced planting materials across seasons. Full article

The use of digital tools in agroecological crop production can help mitigate current farming challenges such as labour shortage and climate change. The aim of this study was to map digital tools used in crop production, assess their impacts across economic, environmental, and social dimensions, and determine their potential as enablers of agroecology. A systematic search and screening process, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology, identified 453 relevant studies. The results showed that most digital tools are applied for crop monitoring (83.4%), with unmanned aerial vehicles (37.7%) and camera sensors (75.2% combined) being the most frequently used technologies. Farm Management Information Systems (57.6%) and Decision Support Systems (25.2%) dominated the tool categories, while platforms for market access, social networking, and collaborative learning were rare. Most tools addressed the first tier of agroecology, which refers to input reduction, highlighting a strong focus on efficiency improvements rather than systemic redesign. Although digital tools demonstrated positive contributions to social, environmental, and economic dimensions, studies concentrated mainly on economic benefits. Future research should investigate the potential role of digital technologies in advancing higher tiers of agroecology, emphasising participatory design, agroecosystem services, and broader coverage of the agricultural value chain. Full article

Smart Agriculture (SA) combines cutting edge technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and real-time sensing systems with traditional farming practices to enhance productivity, optimize resource use, and support environmental sustainability. A key aspect of SA is the continuous monitoring of field conditions, particularly Soil Moisture (SM), which plays a crucial role in crop growth and water management. Accurate forecasting of SM allows farmers to make timely irrigation decisions, improve field management, and conserve water. To support this, recent studies have increasingly adopted soil sensors, local weather data, and AI-based data-driven models for SM forecasting. In the literature, most existing review articles lack a structured framework and often overlook recent advancements, including privacy-preserving Federated Learning (FL), Transfer Learning (TL), and the integration of Large Language Models (LLMs). To address this gap, this paper proposes a novel taxonomy for SM forecasting and presents a comprehensive review of existing approaches, including traditional machine learning, deep learning, and hybrid models. Using the PRISMA methodology, we reviewed over 189 papers and selected 68 peer-reviewed studies published between 2017 and 2025. These studies are analyzed based on sensor types, input features, AI techniques, data durations, and evaluation metrics. Six guiding research questions were developed to shape the review and inform the taxonomy. Finally, this work identifies promising research directions, such as the application of TinyML for edge deployment, explainable AI for improved transparency, and privacy-aware model training. This review aims to provide researchers and practitioners with valuable insights for building accurate, scalable, and trustworthy SM forecasting systems to advance SA. Full article

This study explores the impact of shelterbelt forest plantations on agricultural productivity in Ukraine. The purpose of this article is to investigate how forest belts and land use patterns affect crop yields and agricultural land use in Ukraine, and to compare these patterns with factors contributing to forest cover loss in EU countries in order to develop practical management recommendations. Using geoinformation modeling and correlation analysis, we examined the relationship between shelterbelt coverage and agricultural indicators, including land leasing, crop yields and the planted area under annual and biennial crops. The total area of agricultural land protected by these plantations amounted to 51.66 thousand hectares, generating an additional 206.64 thousand centners of grain annually. Given the average price of 12.23 euros per centner for cereals and legumes, the total economic effect was estimated at approximately 2.53 million euros per year. The study also presents theoretical and methodological approaches for mathematically modeling economic indicators of forestry land use, drawing on successful practices from the European Union regarding sustainable development under significant anthropogenic, economic, and climatic pressures. The results highlight that shelterbelt plantations, once established, are among the most cost-effective agronomic practices, offering long-term environmental and economic benefits for sustainable agricultural development. Full article

Glyphosate, a widely used non-selective herbicide, has been a subject of intense scientific debate due to its environmental persistence and potential health risks. This review examines glyphosate’s mechanisms of action, its effects on crop production, and its broader environmental impact, including soil degradation, water contamination, and biodiversity loss. Furthermore, it examines the expanding body of research linking glyphosate exposure to various human health concerns, including metabolic, neurological, reproductive, and oncological disorders. The review also assesses glyphosate’s role in hindering the achievement of the Sustainable Development Goals (SDGs), particularly those related to food security, health, access to clean water, and the protection of marine ecosystems. Finally, potential alternatives to glyphosate-based weed control, including organic and non-chemical methods, are discussed to promote sustainable agricultural practices that balance productivity with ecological and public health considerations. The evidence reviewed highlights glyphosate’s pervasive presence across ecosystems and its potential to disrupt both environmental and human health. The findings underscore the urgent need to regulate glyphosate use, prioritize soil and water protection, and accelerate the transition toward sustainable, low-toxicity weed management strategies that align with global sustainability objectives. Full article

Agricultural diversification represents an important strategy for promoting sustainability and resilience in rural regions. Goji berries (Lycium barbarum) have emerged as a promising alternative crop due to their high nutritional and functional potential. In this sense, the search for new crops to diversify the production in southwestern Spain is of main interest for farmers to adapt their productions to consumers claims and to climate change, having alternatives to the classical crops (olives, grapes for wine, etc.). This study evaluated several quality related parameters of three goji berry varieties cultivated in the southwest of Spain. Texture profile analysis (TPA) and puncture tests revealed varietal differences in firmness, cohesiveness, and springiness, influenced by genotype and harvesting time. Other morphological and quality parameters such as moisture, total soluble solids, titratable acidity and color were also affected. Significant differences in antioxidant capacity (ABTS and DPPH assays) were found among the varieties and harvesting times, with NQ7 exhibiting the highest values. Phenolic compounds were identified and quantified by LC–HRMS/MS, detecting 33 compounds, with most belonging to the hydroxycinnamic acids, flavonols and flavanones families. NQ7 presented the highest total phenolic content (74.787 mg/100 g DW), with rutin, coumaric acid derivatives, and naringenin as major contributors. The correlation analysis confirmed a strong relationship between total phenolic content and antioxidant capacity. Overall, the results indicated that goji berries grown in southwestern Spain exhibited favorable quality and bioactive profiles, supporting their suitability for sustainable production and commercialization, including further applications as functional food ingredients. Full article

Wheat blast, caused by Pyricularia oryzae Triticum lineage (PoTl), is one of the most destructive and significant fungal diseases affecting wheat crops. The stability of the G143A mutation in the cytB gene, which confers resistance to Quinone outside inhibitor fungicides (QoIs) in PoTl isolates, has not been extensively studied. This study was conducted to evaluate the stability of fungicide resistance, fitness, and competitive ability of the QoI-resistant (R) PoTl isolates group over nine and five consecutive transfer cycles in vitro and in vivo, respectively, without fungicide exposure. No changes in azoxystrobin sensitivity were observed in either the QoI-resistant or sensitive (S) PoTl isolate groups after the successive transfer cycles in vitro and in vivo. The mycelial growth of the QoI-R PoTl isolate group remained stable, while the conidial germination capacity increased over time. For the QoI-resistant isolates, leaf and head disease, conidial production, and the latent period on wheat leaves did not change between the first and fifth infection cycles. In each transfer cycle, the highest levels of leaf and head disease, as well as the largest quantities of conidia collected from wheat leaves, were observed in isolate mixtures. Also, the G143A mutation responsible for QoI resistance remained stable after five transfer cycles of the QoI-resistant (0S:100R) isolate on wheat leaves. Our findings indicate that the G143A mutation remains stable, and there are adaptive benefits in QoI-R PoTl isolates. We discuss the ecological implications of the wheat blast population’s adaptation and PoTl QoIs resistance stability in wheat-cropping areas in Brazil. Full article

Conventional agriculture, focusing on productivity rather than sustainability, have long abandoned hulled wheats. With them not only striking genetic diversity but valuable, health-promoting food sources became lost. Although einkorn and emmer—two of the most ancient wheat species—are generally considered good candidates of sustainable agriculture especially for pesticide-free cropping, they have remained largely unrecognized. To assess their agronomic potential in comparison with modern wheats grown under the same conditions, comprehensive research was conducted, combining multi-location participatory on-farm and small-plot trials. Our findings confirmed that most landraces of emmer and einkorn exhibited strong weed suppression ability, making them suitable for organic cultivation, and effective resistance against diseases—including Fusarium spp. and associated deoxynivalenol (DON) mycotoxin accumulation. Both species were entirely avoided by cereal leaf beetles (Oulema spp.) and had, on average, 2.6% more grain protein content than common wheat. Although they command significantly higher market prices, their (hulled) yields were comparable to modern wheat only in extreme years or at sites typically producing 3–5 t/ha of wheat. Nevertheless, the cultivation of emmer and einkorn presents a more sustainable "sow-and-harvest" alternative, free from pesticide and mycotoxin residue risks, while also enhances biodiversity from the field to the table. Full article

Vegetative propagation through stem cuttings and in vitro microcuttings enables large-scale multiplication of superior genotypes in various crop species. This approach is widely used both to propagate and select trees with desirable genetic traits as well as to preserve a significant proportion of genetic diversity. However, successful plant regeneration using this technique requires the development of an adventitious root (AR) system at the base of cuttings or microcuttings. Reduced root formation and functionality strongly limit the application of vegetative propagation, both in vivo and in vitro. The complex process of AR development is greatly influenced by the physiological state of the donor plant, as well as by genetic and environmental factors. Among the environmental factors involved, light quality and intensity have been mainly studied empirically. This review summarizes advances in understanding how light quantity and quality influence in vitro rooting of micropropagated plants, emphasizing species-specific responses. Furthermore, medium components such as sugars and growth regulators, which interact significantly with light, are also considered. Based on existing studies across different plant species, particularly in the absence of growth regulators, the most effective spectrum for root induction is a temporary enrichment of red light, either alone or combined with small amounts of blue or green light. An efficient root growth occurs when the explants are re-exposed to white light, typically at intensities of 40–50 μmol m⁻² s⁻¹. After root development, exposing the microcuttings to higher intensities could help acclimatization. Finally, considering its capacity to precisely regulate light quality and intensity, LED technology offers a valuable tool for optimizing the rooting process and reducing production costs. Full article

Soil degradation, declining fertility, and rising greenhouse gas emissions highlight the urgent need for sustainable soil management strategies. Among them, biochar has gained recognition as a multifunctional material capable of enhancing soil fertility, sequestering carbon, and valorizing biomass residues within circular economy frameworks. This review synthesizes evidence from 186 peer-reviewed studies to evaluate how feedstock diversity, pyrolysis temperature, and elemental composition shape the agronomic and environmental performance of biochar. Crop residues dominated the literature (17.6%), while wood, manures, sewage sludge, and industrial by-products provided more targeted functionalities. Pyrolysis temperature emerged as the primary performance driver: 300–400 °C biochars improved pH, cation exchange capacity (CEC), water retention, and crop yield, whereas 450–550 °C biochars favored stability, nutrient concentration, and long-term carbon sequestration. Elemental composition averaged 60.7 wt.% C, 2.1 wt.% N, and 27.5 wt.% O, underscoring trade-offs between nutrient supply and structural persistence. Greenhouse gas (GHG) outcomes were context-dependent, with consistent Nitrous Oxide (N₂O) reductions in loam and clay soils but variable CH₄ responses in paddy systems. An emerging trend, present in 10.6% of studies, is the integration of artificial intelligence (AI) to improve predictive accuracy, adsorption modeling, and life-cycle assessment. Collectively, the evidence confirms that biochar cannot be universally optimized but must be tailored to specific objectives, ranging from soil fertility enhancement to climate mitigation. Full article