Search Results (502)

The date palm (Phoenix dactylifera L.) is fundamental to food security, livelihoods, and agro-ecosystem sustainability across the Arabian Peninsula, yet its production is increasingly constrained by water scarcity, salinity, climate change, and biotic stresses. Smart agriculture technologies offer opportunities to improve crop monitoring, resource-use efficiency, and decision-making; however, their application in date palm production remains fragmented and insufficiently validated. This study evaluates the development, geographic distribution, technological focus, research approaches, and thematic structure of smart agriculture research on date palms in the Arabian Peninsula and other major date-producing countries. A bibliometric analysis was conducted using peer-reviewed English-language articles and reviews indexed in Scopus and Web of Science from 2016 to 2026, with 2026 representing a partial year. Following screening and duplicate removal, 164 published studies were retained. Publication output increased gradually, although the trend was not statistically significant (β = 1.25 articles year⁻¹, p = 0.068; Mann–Kendall τ = 0.489, p = 0.060). The literature was concentrated in the Gulf Cooperation Council (GCC) region, with Saudi Arabia contributing 54 publications (32.9%). General agronomy/other topics accounted for 46.3% of publications, while remote sensing/UAVs (17.1%), GIS/geospatial technologies (14.0%), IoT/sensors (13.4%), and AI/machine learning (9.8%) represented the leading specific technology categories. Water and irrigation management dominated thematic applications (46.3%), whereas Smart Farming/IoT accounted for only 11.0%. Observational and monitoring studies comprised 50.0% of publications, compared with only 6.1% involving field experiments, indicating limited field-scale validation. Internationally co-authored studies received significantly more citations than domestically authored studies (16.55 vs. 8.68; p = 0.0007). Thematic analysis identified four major clusters: geospatial monitoring, water management, pest control, and sustainability, with AI/machine learning showing marked growth after 2022. Altogether, the field is expanding but remains fragmented, highlighting the need for integrated IoT and sensor networks, rigorous field trials, AI-driven decision-support systems, and stronger regional and international collaboration to enable scalable, context-specific smart agriculture for date palm production. Full article

Despite growing interest in caffeine as a potential plant biostimulant, its effects on perennial crops such as olive remain poorly understood. This study investigated the effects of low-dose caffeine application on leaf gas exchange and photosynthetic activity, fruit ripening, and fruit quality in olive trees (Olea europaea L. cv. ‘Leccino’). Five-year-old potted plants were irrigated for 52 days with either water (control) or water supplemented with ¹³C-caffeine. Caffeine-treated plants exhibited enhanced photosynthetic performance, characterized by consistently higher stomatal conductance (+44%), net photosynthetic rate (+21%), and transpiration (95%) throughout the experimental period. Fruit ripening progressed more rapidly in treated plants, as reflected by a higher pigmentation index (+8.8%) and an increase in oil accumulation (+14%), while fruit size and water content remained unchanged. Polyphenolic profiling revealed a marked enrichment in several key compounds, including oleuropein, verbascoside, hydroxytyrosol, and ligstroside, indicating a broad stimulation of the phenylpropanoid and secoiridoid pathways. Caffeine was detected exclusively in roots, confirming minimal translocation to aerial tissues. Hormonal analysis showed elevated abscisic acid levels in treated fruits (+44.2%), consistent with the observed advancement in ripening. Although these results demonstrate the potential of caffeine as a biostimulant in olive, the use of potted plants under controlled greenhouse conditions represents a limitation, and further field trials are required to confirm these effects under commercial growing conditions. Full article

Germination is highly sensitive to abiotic stresses such as salinity, which can impair seedling establishment and seed reserve mobilization. Quinoa is a promising crop due to its salinity tolerance and high nutritional value. This study evaluated the effects of NaCl-induced salinity on germination performance, metabolic responses, and nutritional quality of three quinoa seed lots representing two varieties, Regalona (Q1) and Amarilla sacaca (Q2, Q3). Salinity significantly reduced germination rate and kinetics, as well as radicle and shoot growth, with the strongest effects at 500 mM NaCl. During the germination process, starch and protein contents decreased, reflecting reserve mobilization, while vitamin E increased under moderate salinity, particularly in Q1 and Q2, but declined under severe stress. Secondary metabolites, including phenolics, flavonoids, tannins, and saponins, exhibited strong genotype- and stress-dependent variation. HPLC-DAD identified gallic, syringic, vanillic, caffeic, and protocatechuic acids, as well as rutin, quercetin, and kaempferol, with marked differences between raw seeds and seedlings. At moderate salinity (300 mM NaCl), TFC increased by 22% and 42% in Q1 and Q2, respectively, relative to RS, whereas Q3 showed a 45% reduction. Under high salinity (500 mM NaCl), TFC decreased by 83% and 15% in Q1 and Q2, respectively, while Q3 exhibited a 35% increase relative to RS. These contrasting responses highlight genotype-dependent changes in flavonoid accumulation and reflect distinct metabolic plasticity among quinoa genotypes. Overall, these findings support quinoa as a promising functional microgreen crop for producing nutrient- and bioactive-rich foods under controlled conditions. Full article

Drought is a major abiotic constraint on plant productivity, and plant growth-promoting rhizobacteria (PGPR) provide a sustainable strategy for improving crop drought tolerance. The rhizosphere of the dominant Mu Us Desert plant Artemisia ordosica may harbor drought-adapted microorganisms, but their functions remain insufficiently characterized. In this study, drought-tolerant rhizobacteria isolated from the A. ordosica rhizosphere were screened for growth-promoting effects on lettuce (Lactuca sativa L.) under controlled pot conditions. The dominant strain FSO7, identified as Pseudomonas fluorescens, exhibited the strongest effects. Under drought stress, FSO7 inoculation significantly increased plant height, stem width, fresh weight, and dry weight by 26.62%, 111.29%, 310.19%, and 44.02%, respectively, relative to uninoculated controls. Integrated transcriptomic and untargeted metabolomic analyses showed that FSO7 inoculation was associated with marked changes in lettuce carbon metabolism under drought, involving genes and metabolites related to starch and sucrose metabolism, glycolysis, and the TCA cycle, concurrently with up-regulation of genes in the flavonoid biosynthetic pathway. These findings suggest that P. fluorescens FSO7 may alleviate drought-induced growth inhibition in lettuce through coordinated modulation of carbon metabolism and flavonoid-mediated antioxidant metabolism. This strain represents a promising microbial resource for developing bioinoculants for water-limited agriculture. Full article

Greenhouse vegetable production is important for food security, but intensive long-term cultivation can threaten soil sustainability. Although previous studies have reported that cultivation duration alters microbial community structure, research on greenhouse soils with crop rotation involving cucumbers and bitter melons remains rare, especially lacking in predicted microbial functional profiles. Therefore, this study performed 16S rRNA amplicon sequencing on greenhouse soils from four different cultivation years, aiming to evaluate the changes in soil microbial community structure and function due to long-term greenhouse cultivation. Over the first three years of cultivation, both alpha diversity and enzyme activity in the soil increased. Continuous cultivation and routine management were accompanied by marked shifts in the soil microbiota. Specifically, after 9 years, bacterial communities in surface and subsurface soils became highly imbalanced, with the Chao1 index decreasing by 65.64% and 82.46% compared to 3-year soils. Soils with shorter cultivation periods (1 and 3 years) had three times as many biomarkers as those with longer periods (6 and 9 years), and surface soils had nine times more biomarkers than subsurface soils. NH₄⁺-N and urease were significantly associated with bacterial and archaeal communities. PICRUSt2-based predictions suggested that the 9-year greenhouse soils had a higher inferred abundance of genes associated with certain metabolic processes compared to other cultivation durations, indicating potential functional shifts in the microbial community across the cultivation-duration gradient. These findings provide insights into soil microbial responses to cultivation duration and support sustainable management of greenhouse soils under cucumber–bitter melon rotation. Full article

Ecuadorian agri-food supply chains are characterized by strong territorial heterogeneity, crop perishability, and unequal production concentration, creating significant socio-economic and logistical challenges. To address these territorial disparities, this study employs an AI-driven clustering approach to classify Ecuador’s 23 continental provinces by their agri-food production and supply chain integration profiles. Using official 2024 provincial tabulations from the National Institute of Statistics and Censuses of Ecuador, seven variables were analyzed: harvested banana area, banana production, cocoa production, rice production, corn production, potato production, and banana yield. The methodology combined descriptive statistics and Pearson correlations with unsupervised machine learning techniques, specifically hierarchical agglomerative clustering and k-means algorithms after z-score standardization. The descriptive results showed extreme dispersion across provinces, with coefficients of variation exceeding 100% for all variables and marked right skew. The final four-cluster solution (k = 4, BSS/TSS = 78.65%, average silhouette = 0.43) differentiated: (cluster 1) a potato-specialized Andean profile represented by Carchi; (cluster 2) a broad group of low-intensity provinces requiring inclusive logistical integration; (cluster 3) medium-scale banana provinces with high yields; and (cluster 4) the dominant tropical production core formed by Guayas and Los Rios. The typology explains 78.65% of the standardized variability and provides the territorial evidence base required for designing differentiated supply-chain planning, resilience strategies, and public policies tailored to each provincial typology. Full article

Honey bee foragers specializing in nectar, pollen, and water collection differ in their physiological demands and in how the collected resource is transported. Here, we investigated whether physiological and behavioral differences among forager types are associated with crop load, the amount of solution carried in the crop. Alternatively, we tested whether crop sugar concentration was more closely related to foraging specialization. We compared sucrose responsiveness, sucrose ingestion, crop load, and crop sugar concentration in nectar, pollen, and water foragers at the beginning and end of the foraging visit. At the beginning of the visit, pollen foragers carried the heaviest crop loads with the highest sugar concentrations and showed the lowest sucrose responsiveness and ingestion, whereas nectar foragers carried lighter crop loads with intermediate sugar concentrations and showed higher responsiveness and ingestion. Water foragers exhibited the lightest and most dilute crop contents and the highest sucrose responsiveness and ingestion. By the end of the visit, nectar foragers showed reduced responsiveness and ingestion as their crop loads increased, whereas pollen foragers showed the opposite pattern as their crop loads decreased. Notably, water foragers also showed reduced sucrose responsiveness and ingestion as their crop loads increased, despite a marked decrease in crop sugar concentration. In water foragers, crop load and crop sugar concentration changed in opposite directions, with behavioral responses consistently decreasing as crop load increased. Overall, these findings support a link between crop load and foraging specialization and suggest a role for crop distension in modulating sucrose responsiveness and feeding motivation in honey bees. Full article

Field crops change continuously across growth stages, exhibit substantial spatial heterogeneity, and must be managed within short operational windows. This study presents a structured narrative review of information sensing and intelligent monitoring from pre-sowing conditions to stand establishment, growth and yield formation, biotic stress, maturity, lodging, and harvest readiness. The literature is organized by growth stage and analyzed through a common chain of agricultural need, observable variable, sensing platform, data processing method, validation design, state interpretation, and management or equipment output. Satellite remote sensing, unmanned aerial vehicle sensing, ground and proximal sensing, field Internet of Things, machinery-mounted sensors, multisource fusion, crop models, and machine learning methods are compared according to spatial support, temporal continuity, scale matching, field robustness, transfer conditions, uncertainty, and operational applicability. The reviewed studies report crop-phenotype retrieval, field-environment characterization, and biotic-stress identification under specified conditions, whereas cross-stage state inheritance, consistent reference measurements, independent validation, and conversion of monitoring results into executable tasks remain insufficiently established. The review therefore develops a lifecycle-oriented information-processing perspective in which multisource observations are quality-marked, interpreted as stage states, linked across time and scale, and checked against management and equipment records. Future work should strengthen cross-crop and cross-region validation, mechanistic and data-driven model coordination, uncertainty reporting, interoperability, and field feedback without presuming universally autonomous decision-making. Full article

Traditional intercropping systems represent an economically feasible and sustainable strategy for the safe utilization of cadmium (Cd) contaminated farmland. To assess the safe production potential of a maize potato intercropping system in high-heological-Cd background agricultural areas, four planting patterns were set up in the Chongqing karst area, China: maize monoculture (T1), maize–potato intercropping with a 1:1 row ratio (T2), maize–potato intercropping with a 2:2 row ratio (T3), and potato monoculture (T4). We systematically compared soil pH, total Cd, and available Cd across the different planting patterns. Cd concentrations were determined in multiple organs of the two crops: roots, stems, nodes, leaves, husks, cobs, and grains for maize; and roots, stems, leaves, and tubers for potato. Furthermore, Cd bioconcentration factors (BCF), translocation factors (TF), dietary health risks posed by edible organs, and economic benefits under each cropping system were analyzed. The results revealed that: (1) Maize and potato cultivation modified the microenvironment of the soil surrounding the roots. Total and available Cd concentrations in the soil surrounding potato roots were lower than those in the soil surrounding maize roots, although the difference was not significant (p < 0.05). (2) Compared with potato monoculture, 1:1 and 2:2 row ratios of maize–potato intercropping significantly reduced Cd concentrations in fresh potato tubers by 38.81% and 38.01%, respectively; tuber Cd concentrations complied with the national food safety limit. (3) Marked interspecific differences were observed in Cd accumulation and translocation traits between maize and potato. The bioconcentration factor (BCF) of all maize organs was below 1, and Cd concentration and accumulation capacity exhibited a decreasing gradient trend following the order: roots > leaves > nodes > husks ≈ stems > cobs > grains. For potato, the BCF values of roots, stems, leaves, and tubers were all greater than 1, and their Cd accumulation capacities were 9.13-, 67.33-, 20.84-, and 37.67-fold higher than the respective organs of maize, respectively. (4) Based on health risk assessment and economic benefit evaluation, the two maize potato intercropping systems (T2 and T3) achieved substantially higher combined economic returns than maize monoculture (T1), while presenting markedly lower dietary health risks than potato monoculture (T4). This case study validates the hypothesis that maize–potato intercropping alters cadmium translocation and distribution responses within the soil–crop system, and demonstrates its potential to improve farmland economic benefits while guaranteeing agricultural product safety. Consequently, these intercropping patterns represent a promising strategy for promoting safe and green production on High-Geological-Cd background farmland. Full article

In perennial fruit trees, erratic floral initiation and alternate bearing represent major horticultural challenges, yet how vegetative shoot maturity conditions low-temperature responsiveness remains poorly understood. Here, using litchi (Litchi chinensis Sonn.) as a model, we deciphered the physiological and molecular framework governing maturity-dependent floral induction under winter chilling. Mature terminal shoots exhibited exceptional flowering competence and superior inflorescence morphology, whereas immature expanding shoots failed to initiate pure panicles. Dynamic metabolic profiling revealed that shoot maturation established a high-energy status characterized by elevated sucrose accumulation and sustained transcription of trehalose-6-phosphate synthase genes (LcTPS1/3/4). At the transcriptional level, specific members of LcSPL1, LcSPL3, and LcSPL10 were strongly upregulated in mature shoots, reaching peak expression at the “whitish millet” stage, marking morphological flower bud differentiation. Functional characterization in Arabidopsis thaliana confirmed that ectopic expression of LcSPL1/3/10 significantly accelerated flowering time and reduced rosette leaf number. Mechanistically, dual-luciferase reporter assays demonstrated that LcSPL1, LcSPL3, and LcSPL10 transcriptionally activated the promoter of the florigen gene LcFT1, with LcSPL1 exerting the strongest transactivation, whereas the immature shoot-enriched LcSPL9 repressed LcFT1 expression. Concurrently, mature leaves exhibited a dramatic surge in LcFT1 transcript levels alongside persistent suppression of the floral repressor LcTFL1. Together, our findings demonstrate that shoot maturity confers floral competence through an integrated T6P metabolism and LcSPL1/3/10–LcFT1 transcriptional cascade. This study provides crucial theoretical insights and potential targets for flowering-related regulation in perennial woody crops. Full article

Sugar transporters play a pivotal role in photoassimilate partitioning and biomass accumulation in plants. However, how whole-genome duplication (WGD) has reshaped the evolutionary trajectory of the sugar transport system in Bambusoideae remains elusive. Here, we systematically identified and characterized the SUT (Sucrose Transporters) and MST (Monosaccharide Transporters) gene families across 14 bamboo genomes representing four major lineages: herbaceous, temperate woody, neotropical woody and paleotropical woody bamboos. Our results suggest that WGD acted as the primary driving force behind the expansion of these gene families, with woody bamboos possessing significantly higher gene counts than herbaceous bamboos. Notably, extensive gene loss was observed in hexaploid lineages, aligning with the gene dosage balance hypothesis. Phylogenetic and sequence analyses demonstrated that core genes were subjected to purifying selection. Intriguingly, 21 groups of identical protein sequences across species were identified in paleotropical woody bamboos, suggesting high sequence conservation. Transcriptomic and syntenic analyses further unveiled the mechanisms of post-polyploidization expression divergence: homeologs exhibited marked expression asymmetry across subgenomes. Distinct from the broad expression pattern in herbaceous bamboos, which rely on SUT5 and a few STP (Sugar Transporter Protein) genes, woody bamboos have achieved fine-tuned expression partitioning and tissue-specific specialization during different culm developmental stages through significantly expanded SUT and STP family members. This study reveals an evolutionary pattern characterized by “WGD-driven expansion, post-polyploid fractionation, and subgenome expression divergence.” This pattern suggests a potential link to the enhanced sugar allocation efficiency required for the explosive growth of woody bamboos, offering valuable gene resources and a foundation for future functional studies in Poaceae crops. Full article

The European turtle dove (Streptopelia turtur) has undergone a marked decline across Europe, with habitat degradation considered an important driver. We examined whether different habitat-management strategies were associated with temporal changes in its relative abundance during the years 2021–2025 in Spain, coinciding with the hunting moratorium period. We analyzed 185 surveys from 47 hunting grounds in southern and eastern Spain, conducted through walked transects and point counts. Management practices included water and food provision, agricultural measures and forest management. The relative turtle dove abundance increased over the study period, with significantly higher values in 2024 and 2025 than in 2021. Agricultural measures, primarily game crops, were positively associated with turtle dove abundance (mean effect = 1.76, 95% CI: 1.18–2.62), showing a marked abundance increase after three years of implementation. By contrast, water provision and forest management showed no clear effects and food provision was negatively associated with turtle dove abundance (mean effect = 0.57, 95% CI: 0.39–0.84), although the latter association should be interpreted cautiously because food provision was widespread among the study sites. Our findings highlight the potential value of agricultural habitat interventions for conserving and restoring turtle dove populations, supporting their inclusion in adaptive, landscape-scale conservation strategies targeting this formerly declining migratory gamebird. Full article

Irrigation scheduling plays a pivotal role in bridging water resource allocation and farmland production management. For decades, scheduling in irrigation districts has predominantly relied on operators’ experience and relatively rigid water delivery plans, making it difficult to simultaneously meet the demands for timely responsiveness and precise water allocation under the combined influence of meteorological variability, changing crop water requirements, and the dynamic adjustments of water conveyance and distribution systems. The advancement of digital technologies, such as the Internet of Things, machine learning, deep reinforcement learning, and digital twins, has opened new technical pathways for optimizing irrigation scheduling. Focusing on the development of smart irrigation and scheduling optimization in irrigation districts, this paper systematically reviews the relevant literature published from January 2000 to June 2026 and delineates its evolution into three stages. Early-stage research was grounded in physical models, empirical rules, and hydraulic simulations, establishing fundamental methods for evapotranspiration estimation, crop water requirement calculation, and canal water delivery simulation. The middle stage, marked by the introduction of the Internet of Things and machine learning, enabled real-time monitoring of hydrological conditions, soil moisture, and meteorological data and promoted a data-driven transformation of water demand forecasting methods. The recent stage is characterized by the integration of deep reinforcement learning, digital twins, and knowledge graphs, which extends irrigation district scheduling from isolated single-point optimization toward multi-agent coordination and closed-loop management. Existing evidence confirms that digital technologies have yielded water-saving and yield-increasing benefits at the field scale and improved water distribution efficiency in several demonstration irrigation districts; however, their wider deployment at the district scale still faces bottlenecks such as inadequate sensing of physical execution processes, underdeveloped multi-objective trade-off mechanisms, and limited model transferability and long-term operational sustainability. To address these challenges, this paper proposes future research directions oriented toward real-time perception of water delivery and distribution status, multi-objective robust optimization, explainable artificial intelligence, and human–machine collaborative decision-making, thereby providing a reference for the theoretical development, engineering deployment, and operational management of smart irrigation district scheduling systems. Full article

Understanding the spatiotemporal evolution of waterlogging intensity across winter oilseed rape (WOSR) growth stages and its quantitative relationship with yield losses is critical for agricultural water management in the Middle-Lower Yangtze River Region (MLYRR). Here, a daily-scale agrometeorological index (SAPEI) was introduced to quantify waterlogging intensity during four WOSR growth stages from 1961 to 2020. Waterlogging intensity increased significantly over the past six decades in most areas, particularly during the overwintering stage (38 stations, 57% of all stations), with the 2000s representing the historical high levels for most provinces, followed by those in the 1990s. The northeastern and southernmost MLYRR were identified as highly waterlogging-prone regions, and the overwintering stage exhibited substantially larger daily waterlogging intensity than other stages. The yield-reducing impact of severe waterlogging (SAPEI > 1.0) was greater than general waterlogging (SAPEI > 0.5), with larger absolute regression coefficients of waterlogging indices. The flowering–maturing stage, followed by the overwintering stage, was the most waterlogging-sensitive period, and it was also selected as the sole explanatory variable in up to 14 districts in stepwise regression. A marked spatial mismatch was found between waterlogging proneness and crop sensitivity, suggesting that drainage scheduling should account for crop waterlogging sensitivity rather than waterlogging intensity alone. These findings provide guidance for targeted WOSR drainage planning in key regions of the MLYRR under climate change. Full article

Cassava is the sixth most important food crop globally, valued for its high starch accumulation in tuberous roots, which supports diverse applications in food processing and industrial production. Quality evaluation is vital for its utilization, yet rapid and efficient assessment methods remain underexplored. In this study, 67 cassava varieties were collected to assess their nutritional quality. Near-infrared spectroscopy (NIRS) in the wavenumber range of 11,000–4000 cm⁻¹ was applied to predict key quality traits of cassava flour from different varieties. Combined with partial least squares (PLS), principal component analysis (PCA), and internal cross-validation, a preliminary NIRS-based screening framework for cassava flour quality indicators was established. The protein and moisture models achieved an excellent quantitative prediction performance (${\mathrm{R}}_{\mathrm{c}\mathrm{v}}^2$ > 0.82, RPD > 4.0), qualifying them as reliable tools for routine analysis. In contrast, the models for starch, amylose, and amylopectin showed substantial overfitting with low cross-validation accuracies (${\mathrm{R}}_{\mathrm{c}\mathrm{v}}^2$ = 0.19–0.38), restricting their use to only preliminary screening. The fat model performed poorly (${\mathrm{R}}_{\mathrm{c}\mathrm{v}}^2$ = 0.16, RPD = 1.08), rendering it unsuitable for any quantitative or screening application. This study provides an exploratory screening framework for rapid multi-index evaluation of cassava flour quality. However, the marked variability in predictive performance across constituents highlights the critical need for external validation to improve model robustness. Full article