Search Results (415)

The increasing consumption of fresh organic produce has given rise to concerns regarding the microbiological safety of minimally processed foods. Organic cultivation may be associated with increased exposure to environmental microorganisms due to soil-based inputs and reduced chemical interventions, including both beneficial taxa and potential foodborne pathogens. Fresh produce is known to harbour complex microbial ecosystems, which are shaped by farming practices, plant physiology, handling, packaging and storage, particularly in raw-consumed products such as leafy greens and strawberries. In this study, bacterial (16S rRNA) and eukaryotic (18S rRNA) communities were characterized by amplicon sequencing. In parallel, an amoeba-associated bacterial microbiome was analyzed and DVC-FISH was used to assess the viability and metabolic activity of pathogenic bacteria internalized within free-living amoebae (FLA). No significant differences in alpha or beta diversity were observed between organic and conventional products, suggesting microbiome convergence at the retail stage driven by post-harvest handling and processing. Potentially pathogenic genera, including Pseudomonas, Stenotrophomonas, and Acinetobacter (bacterial), as well as Tilletiopsis, Candida, and Naegleria (eukaryotic), were identified in both organic and non-organic microbiomes. The viability of FLA-internalized Pseudomonas spp. was confirmed by DVC-FISH, demonstrating that FLA act as reservoirs, enhancing pathogen persistence in fresh produce. This integrated assessment of organic and conventional fruits and vegetables at the retail stage highlights the importance of post-harvest handling and retail conditions in shaping microbiological safety. The integration of microbiome profiling with targeted viability analyses demonstrates that downstream stages are critical control points for food safety and consumer exposure, beyond the influence of the production system alone. Full article

Climate change is a major hazard to the agricultural systems of the world, as it is changing the temperature regimes, precipitation patterns, and soil dynamics, which are weakening crop production and the stability of ecosystems. The proposed research is a hybrid modeling framework that combines Multiple Linear Regression (MLR) with a deep learning architecture (PatchTST) based on the Transformer to quantify and predict the effect of climate variability on the productivity of agriculture. Multi-source data, including global weather data, crop data, and ISRIC-WISE soil data, were harmonized through stringent preprocessing steps that included imputation, normalization, and spatial-temporal alignment. The regression analysis reveals a statistically significant negative impact of temperature on crop yield, while precipitation and soil fertility exhibit positive contributions. To capture complex non-linear dependencies and long-term temporal patterns, the PatchTST model was trained using time-series inputs enriched with satellite-derived vegetation indices. The proposed model significantly outperforms conventional deep learning approaches, achieving an R² of 0.98, RMSE of 0.0172, and MAE of 0.0134. Attention-based interpretability highlights soil moisture and NDVI as dominant predictors, reinforcing the model’s physical and agronomic relevance. The findings indicate that integrating interpretable statistical models with advanced deep learning improves predictive accuracy while addressing the transparency limitations of black-box approaches. The framework supports practical deployment across regional crop planning, climate risk policymaking, and farm-level decision support systems, demonstrating its direct applicability to real-world agricultural management. Full article

Soil degradation driven by inappropriate soil management is a serious global challenge, while climate change-induced yield declines are increasing the conversion of natural ecosystems to agricultural land. This review examines how soil structure influences soil health, focusing on organo-mineral complexes derived from microbial biomass and soil organic carbon-to-clay (SOC/Clay) ratio as an indicator of structural quality. Regenerative agriculture based on conservation farming practices helps mitigate SOC depletion and aligns with the nature-based solutions framework. In Hokkaido, Japan, 10 years of clean agricultural applications (cover crops and organic matter application) increased SOC storage in farmland affected by volcanic eruption. This was associated with improved bulk density, porosity, cation exchange capacity, and phosphate absorption capacity, indicating improved soil health. The increased SOC rose SOC/Clay ratio to levels comparable with unaffected farmland (≥1/13). When the SOC/Clay ratio exceeded 1/13 (soil carbon storage level of 30 t C/ha/15 cm), carbon sequestration rate became negative. This suggests that improved soil health and structural quality may promote carbon saturation and stimulate microbial decomposition of existing SOC. While the threshold for SOC/Clay ratio varies depending on soil type, vegetation type, climatic conditions, and land use, changes in the SOC/Clay ratio can provide insights into changes in soil health and structural quality. Full article

This study developed and evaluated a cloud-based smart irrigation platform (DSmart Farming) integrating low-cost sensors and IoT technology for automated irrigation control in community greenhouses of Puen Jai Insee, organic group in Sa Kaeo Province. The system combined soil moisture, air temperature, and relative humidity sensors, with a LoRa32-based control unit in each greenhouse and a central web-based management application linked to a MariaDB database on a cloud server. Five vegetable crops, including cherry tomato, broccoli, cabbage, Chinese kale, and kale, were grown over two distinct seasons under four irrigation strategies in a completely randomized design with three replications: three smart irrigation treatments based on soil moisture thresholds (on/off at 40/50%, 45/55%, and 50/60%) and a farmer-managed conventional irrigation control. The smart irrigation system maintained root-zone moisture within the target range (approximately 50–60%) and moderated greenhouse microclimate, preventing daytime temperatures from exceeding 40 °C, in contrast to 40–45 °C peaks in the conventional greenhouses. Across crops, smart irrigation increased yields by 20–29% while reducing water use by 41–60% compared to conventional practice, leading to income increases of 20–56%, depending on the crop. Bacterial soft rot caused by Pectobacterium carotovorum subsp. carotovorum occurred only under conventional irrigation, whereas no soft rot or other major diseases were detected in smart-irrigated greenhouses. These results demonstrate that the DSmart Farming system can enhance water use efficiency, avoid disease incidence, and improve the productivity and profitability of organic greenhouse vegetable production in water-limited smallholder systems. Full article

Precision monitoring of crops using drone or unmanned aerial vehicle (UAV) technology is rapidly growing as a climate-smart agriculture practice in rice farming systems in Sri Lanka and globally. In rice fields, the Leaf Color Chart (LCC) is traditionally used for manual comparison of a leaf to the standard LCC categories in the field to determine the fertilizer condition of the plant. However, this lacks autonomous monitoring, rapid monitoring of larger fields, scalability, and the digital transformation of the scores with sprayer drones for targeted fertilizer application. Drones with multispectral cameras could pose a greater rapid and digitalized solution for delineation of leaf color instead of LCC, in the field. Thus, this paper presents a novel attempt of digitization of conventional LCC levels 3 and 4, rice plant leaf greenness levels in the field, with classification and production of a spatial map using drone multispectral images and machine learning algorithms. The experimental setup consisted of ground sampling of LCC levels 3 and 4 from farmer fields and acquisition of drone imagery data above the field with a DJI Phantom 4 Multispectral UAV, from which fifteen vegetation indices related to crop spectra were extracted. The vegetation indices were then employed for training (70%) and testing (30%) with machine learning algorithms: Random Forest (RF), as well as SVM-linear and SVM-RBF, focusing on LCC 3–4 class classification. The results showed good classification performance, with the RF algorithm reporting a test accuracy of 98.2%, outperforming SVM-linear (82.5%) and SVM-RBF (87.5%). The RF model outputs SR, EVI, MSR, NDVI, and TCARI as feature importance indices for the classification of LCC levels 3 and 4 in the rice field. The findings of this proposed method greatly encourage the adaptation of drone technology for real-time monitoring of rice leaf fertilizer levels linked to LCC levels three and four, and spatial identification of the zones across the field. This imposes greater advancement towards climate-smart rice cultivation, targeted fertilizer application and rice field landscape pattern change analysis, underpinning the importance of field digitization. Full article

Optimizing nutrient uptake efficiency (NUE) through an understanding of system-level dynamics and crop-specific physiological thresholds is essential for the resilience of North European vegetable production under shifting climatic conditions. This study evaluated the uptake and efficiency of macro- and secondary nutrients (N, P, K, Ca, Mg) in cabbage, carrot, red beet, and onion over a five-year period (2021–2025) in Latvia, comparing organic and integrated management systems. It was hypothesized that NUE on commercial farms is currently suboptimal due to standardized bulk applications and that systems integrating sustainable practices would demonstrate higher nutrient uptake efficiency than those relying exclusively on mineral fertilization. The results revealed a notable yield–input divergence, where increased fertilization rates failed to provide proportional yield gains, as evidenced by the lack of a strong linear relationship (R² < 0.07) and variable correlation coefficients (e.g., r = 0.52 for N in cabbage and r = −0.47 for Ca in carrot). These findings suggest that abiotic stressors and technical constraints may outweigh the influence of nutrient volume alone. This divergence was less pronounced in organic farming systems compared to integrated ones and varied notably by crop. Such species-specific responses indicate a complex role for mineral nutrition in root crops that requires further physiological investigation. No consistent differences in nutrient concentrations were observed between farming systems, indicating that inter-annual climatic variability is the dominant driver of nutrient dynamics. Furthermore, the integration of green manures and supplemental irrigation triggered extreme apparent system-level N-NUE values (exceeding 500% in some cases), reflecting the successful mineralization of legacy nitrogen and enhanced mass flow to the root zone. The study concludes that current fertilization methodologies in the Baltic region may lead to over-application. To ensure climate-resilient horticulture, management strategies must transition toward balancing ionic ratios (Ca:K) and synchronizing inputs with specific crop removal rates, rather than relying on standardized bulk applications. Full article

Weed interference during early growth stages is a major constraint on sugarcane productivity, yet effective tools for species-specific detection remain limited in tropical agricultural systems. This study evaluated the spectral separability between Sugarcane (Saccharum officinarum) and a dominant weed species, Rottboellia cochinchinensis, to develop an accessible framework for early-stage weed mapping. Multispectral data acquired from an Unmanned Aerial Vehicle (UAV) and hyperspectral data obtained from a field spectrometer were utilized. Hyperspectral data were synthesized to reconstruct multispectral bands (UAV image bands) using a regularized linear synthesis model, thereby generating spectral signatures. Spectral separability between sugarcane and Rottboellia cochinchinensis was assessed visually and statistically (Jeffries–Matusita distance). Blue and Green bands provided the strongest differentiation between species, while RedEdge enhanced separability when paired with pigment-sensitive wavelengths. When using vegetation indices based on the near-infrared (NIR) band, the visual appearance of class separation was poor due to the NIR band’s sensitivity to variation in leaf internal structure, canopy architecture, water content, and spectral mixing with the soil background at the early stage of sugarcane. These results were used to differentiate weed coverage from sugarcane. Object-based image analysis (OBIA) outperformed the pixel-based method, achieving higher overall accuracy (0.9038) and a more spatially coherent weed delineation (Kappa = 0.8499). These findings suggest that synthesized spectral signatures of Rottboellia cochinchinensis and sugarcane, combined with targeted spectral indices and OBIA techniques, offer a practical and transferable approach for early detection of Rottboellia cochinchinensis at the farm level. Full article

Optimizing nutrient management in organic polytunnel production remains challenging due to the limited availability of field-based knowledge on the mineralization dynamics of organic fertilizers. At the same time, microalgae-based products such as Chlorella vulgaris have gained increasing attention in recent research, yet their interactions with nutrient supply intensity are not well understood. This study aimed to evaluate the effects of increasing nutrient supply intensities (34, 116, and 189 kg ha⁻¹ N from different organic sources), in combination with C. vulgaris foliar application, on the crop performance of kapia pepper and a subsequent leafy green crop under on-farm organic polytunnel conditions on soil with moderate organic matter content. Increasing production intensity did not result in significant improvements in pepper yield or vegetative biomass (p > 0.05), and no significant residual effects of nutrient supply were detected in the yield of the subsequent leafy green crop (p: 0.08–0.94). C. vulgaris treatment showed predominantly non-significant but positive trends in several parameters, but only in combination with high-intensity technology, while reducing the total pest damage of the thrips and stinkbug index up to 15.7% in most technology variations. These results indicate that the effects of C. vulgaris may be strongly context-dependent and confirm that increasing the intensity of nutrient supply may carry the risks of conventionalization of organic farming practices. Full article

Accurate estimation of crop development, water use and yield is essential for improving irrigation management in Mediterranean agricultural systems under increasing climate variability. However, many crop models require extensive input data and technical expertise, limiting their operational use by farmers and technicians. This study proposes an integrated agro-modelling framework that combines thermal time modelling, satellite-derived vegetation indices and simplified yield estimation approaches to assess maize phenology, crop water use and productivity under real farming conditions. A key component of the framework is the use of the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series to dynamically identify crop growth stages and derive actual basal crop coefficients (K_{cb act}), enabling the estimation of actual crop transpiration (T_{c act}). These NDVI-based estimates of actual K_cb and T_c were evaluated against simulations from the previously calibrated soil water balance model SIMDualKc. The results showed that the temporal profiles of the NDVI successfully captured the progression of the maize growth stages, although some discrepancies were observed during early stages of development due to the effects of the soil background and the satellite revisit intervals. An empirical relationship between the NDVI and K_cb was developed using multi-year observations and model simulations, improving crop transpiration estimation under field conditions. The NDVI-based approach adequately reproduced daily transpiration dynamics with good agreement with SIMDualKc simulations, yielding RMSE values of 0.11–0.69 mm d⁻¹ and errors generally below 21% of the mean transpiration rate. Seasonal transpiration estimates showed stronger agreement once canopy cover reached its maximum. The integrated AEZ–Stewart modelling framework incorporating NDVI-based transpiration estimations provided accurate yield predictions, with RMSE values of 1.7–2.3 t ha⁻¹ (representing less than 14% of the observed yields). Overall, the proposed framework demonstrates strong potential as a practical and scalable decision-support tool for irrigation management and yield assessment in Mediterranean maize systems. Its novelty lies in the operational integration of NDVI-derived crop development and transpiration estimates within a simplified yield modelling structure, offering a transferable approach applicable to other regions and cropping systems where satellite data are available. Full article

Fiji, with its many islands and mountainous terrain, has only about 11% of its total land area (2000 km²) suitable for cultivation. Therefore, it aims to meet both energy and food production simultaneously through agricultural photovoltaic (APV) systems. This study proposed an optimal agricultural management of APV system to increase farm income and solve the problem of low vegetable production. The practice is planned based on the data from farmer surveys, field study, simulation analysis, and agricultural market analysis. Firstly, a farmer survey was conducted to gather data on the agricultural activities and income of local farmers. Based on the survey results, field studies with various vegetables were conducted in an APV system. In simulation, yields of lettuce, taro, long bean, and cucumber were estimated in the APV system with different cropping management techniques (planting schedule and plant density). With the average yields of lettuce, taro, long bean, and cucumber at highest plant densities being (72.4, 71.1, 3.9, and 10.8) Mg/ha, respectively, according to economic analysis, the highest gross margin was achieved in taro in the APV system. This study shows that the APV system can increase farmers’ annual household income by 1.19 to 1.38%, which represents a meaningful absolute gain given the low average income levels identified in the farm survey. Full article

To assess food safety conditions among family farmers supplying the National School Feeding Program (PNAE) in the Federal District, Brazil. This exploratory mixed-methods study was subdivided into two main phases: (i) samples of fruits, vegetables, water, soil, and farmers’ feces were analyzed microbiologically and/or parasitologically across nine properties; (ii) sociodemographic and Good Agricultural Practices (GAP) questionnaires were administered, followed by semi-structured interviews to evaluate their perceptions of food safety. Participants were males (100%), of mixed race (88.9%), aged 41–50 years (44.4%), with secondary education (33.3%), and an income between USD 1000 and USD 2000 (33.3%). Samples from food (n = 162), water (n = 18), soil (n = 90), and feces (n = 6) were analyzed. All fruit and vegetable samples, and 83.3% of water samples exceeded acceptable limits for at least one of the microorganisms analyzed. 86.7% of the soil samples showed high levels of contamination. Parasitic contamination was detected in 50.6% of the fruit and vegetable samples, in 63.3% of the soil samples, and in none of the water samples. Most farms used deep or artesian wells (77.7%) and non-connected septic pits (77.7%). Organic fertilization predominated (88.8%), with chemical fertilizers occasionally used (11.2%). Farmers demonstrated strong environmental awareness but limited technical knowledge of food safety. Results indicate persistent vulnerability despite ethical and ecological commitment. Continuous training and stronger public policies are essential to enhance GAP adherence, ensure food microbiological safety, and sustain PNAE objectives. Full article

This study evaluated the performance of a smart farming technology (SFT) and a climate-smart agriculture (CSA) approach for improving irrigation management in pecan (Carya illinoinensis) orchards in México through soil moisture monitoring, evapotranspiration estimation, and real-time data integration. Continuous monitoring allowed irrigation to be maintained at field capacity, preventing plant stress while avoiding total soil saturation or permanent wilting point. Calibration of soil moisture sensors showed a very strong correlation (R² = 0.99) between sensor reverse voltage and volumetric soil water content in predominant sandy loam soils, confirming the reliability of the monitoring system for irrigation scheduling. Seasonal analysis of reference evapotranspiration (ETo) and crop evapotranspiration (ETc) revealed increasing atmospheric water demand during summer months, with crop coefficient (Kc) values ranging from approximately 0.3 during dormancy to 1.0–1.3 during peak vegetative growth. After five years of field implementation of the technology, results showed water savings exceeding 50% compared with traditional flood irrigation practices. The optimized irrigation schedule reduced total seasonal irrigation depth from 216 cm to 128 cm, representing a 59% reduction in applied water while maintaining adequate soil moisture conditions for crop development at field capacity (FC). These results highlight the potential of integrating sensor-based monitoring, evapotranspiration modeling, and IoT platforms to enhance water-use efficiency and support sustainable pecan production under increasing climate variability. Full article

Integrated Pest Management (IPM) is a well-established framework for agricultural crop protection. IPM has typically been represented as a pyramid of practices, beginning at the base with agronomic and cultural approaches and gravitating upwards towards chemical plant protection products. However, this representation is an insufficient description of IPM adoption at the farm level as it cannot incorporate the many challenges faced by farmers and growers. This paper presents an alternative approach to contextualize horticultural IPM based on a sample of commercial vegetable and fruit growers from the Republic of Ireland (N = 45), based on an existing model. This model incorporates the management, business, and sustainability aspects influencing IPM adoption. Utilization of the model on data collected from growers through a series of semi-structured interviews contributed to a. a comprehensive exploration of the type of IPM tools being used and b. contextualized analysis of the data with the intention of capturing grower perspectives. The findings highlight a crop and production system effect on the type of IPM tools used by the growers and suggest influence from a range of internal and external motivating and limiting factors. Excerpts from the grower interviews underline the complexity of IPM and draw attention to the lack of grower-centricity captured by IPM paradigms heretofore. Full article

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present. Full article

The common vole is one of the mammalian pests causing more agricultural damage in Europe. Since the late 1970s, this species has invaded the Duero valley in NW Spain, colonizing ca. 5 million ha of agricultural areas of the valley in about 20 years. Once settled in agricultural landscapes, the species experienced cyclic population outbreaks causing crop damages. The major vole population outbreak of 2006–2007 was managed by the Regional Government (Junta de Castilla y León, JCYL) mainly through large-scale application of anticoagulant rodenticides (ARs) and widespread destruction of field margins, natural vegetation patches, and crop stubbles by burning. These actions caused serious damage to regional agrarian biodiversity, including small game species. The coordinated action of scientific institutions and environmental NGOs, with the support of the main Spanish hunting association at a critical time, led to a progressive shift in pest management strategies during subsequent outbreaks, promoting the adoption of biological control and other management techniques causing less environmental damage. Finally, JCYL implemented an IPM program mainly based on biological control, good farming practices, and habitat management. This program has been increasingly adopted in recent years, leading to a marked reduction in chemical control and the complete elimination of burning as a tool of management. Over this period, the scientific knowledge of the species’ ecology has expanded substantially, providing key insights for the development and refinement of IPM strategies. Here, we synthesize this body of knowledge and provide additional recommendations to further improve the current IPM program, which may serve as a model for rodent pest management in other regions worldwide. Full article