Search Results (206)

The popularity of small-scale and local fruit and vegetable production has increased in recent years due to perceived economic, environmental, and social benefits. However, these operations face contamination risks that both consumers and small-scale producers may underestimate. The present study aimed to assess the microbiological and chemical hazards on fruit, vegetables, soil, and water samples from small-scale farms in north-western Portugal during pre-harvest activities. Additionally, the study investigated farmers’ non-compliance with food safety regulations and good agricultural practices (GAPs), exploring how their behaviour might contribute to the identified hazards. A before-and-after analysis of non-compliant behaviours was conducted to determine the impact of training on improving food safety practices. The analysis identified the presence of pathogenic bacteria, pesticides, flame retardant residues, nitrates, and heavy metals. Lead (Pb) concentrations exceeded EU limits in organic carrots from one producer (0.156 ± 0.043 mg/kg) and in chard from another (0.450 ± 0.126 mg/kg). Cadmium (Cd) levels were also above regulatory thresholds in bell peppers (0.023 ± 0.009 mg/kg) and organic tomatoes (0.026 ± 0.015 mg/kg) from two different producers. Elevated levels of heavy metals were detected in irrigation water from two sites, with zinc (Zn) at 0.2503 ± 0.0075 mg/L and Pb at 0.0218 ± 0.0073 mg/L. Among food samples, the most prevalent microorganisms were Pseudomonas spp. (88.2%), Bacillus cereus (76.5%), and aerobic mesophilic bacteria (100%). Phosphorus flame retardants (PFRs), particularly tris(2-butoxyethyl) phosphate (TBEP), were detected in all food and soil samples. Some EU-banned pesticides were detected in food and soil samples, but at levels below the maximum residue limits (MRLs). Chlorpyrifos (35.3%) and p,p’-DDD (23.5%) were the most detected pesticides in food samples. After the training, GAP behaviour improved, particularly that related to hygiene. However, issues related to record-keeping and soil and water analyses persisted, indicating ongoing challenges in achieving full compliance. Full article

Accurate irrigation volume prediction is crucial for sustainable agriculture. This study enhances precision irrigation by integrating diverse datasets, including historical irrigation records, soil moisture, and climatic factors, collected from a small-scale commercial estate vineyard in southwestern Idaho, the United States of America (USA), over a period of three years (2017–2019). Focusing on long-term irrigation forecasting, addressing a critical gap in sustainable water management, we use machine learning (ML) methods to predict future irrigation needs, with improved accuracy. We designed, developed, and tested a Long Short-Term Memory (LSTM) model, which achieved a Mean Squared Error (MSE) of 0.37, and evaluated its performance against a simpler baseline linear regression (LinReg) model, which yielded a higher MSE of 1.29. We validate the results of the LSTM model using a cross-validation technique, wherein a mean MSE of 0.18 was achieved. The low value of the statistical analysis (p-value = 0.0009) of a paired t-test confirmed that the improvement is significant. This research shows the potential of Artificial Intelligence (AI) to optimize irrigation planning and advance sustainable precision agriculture (PA), by providing a practical tool for long-term forecasting and that supports data-driven decisions. Full article

This Technical Note presents the design and implementation of a low-cost modular growth chamber developed to keep mother plants under controlled environmental conditions for vegetative propagation. The system was conceived as an accessible alternative to expensive commercial equipment, offering reproducibility and adaptability for small-scale and research-based cultivation. The proposed chamber integrates thermal insulation, LED lighting, forced ventilation through the implementation of extractors, a recirculating irrigation system with double filtration, and a sensor-based environmental monitoring platform operated via an Arduino UNO microcontroller. The design features a removable tray that serves as a support for the mother plant, an observation window covered by a movable dark acrylic that prevents the passage of external light, and a vertical structure that facilitates optimal space utilization and ergonomic access. Functionality was conducted using a Stevia rebaudiana Bertoni mother plant maintained for 30 days under monitored conditions. Environmental parameters—temperature, relative humidity, and illuminance—were recorded continuously. The plant showed vegetative development through new shoot emergence and the growth in height of the plant, and despite a loss in foliage expansion, it confirmed the chamber’s capacity to support sustained growth. Although no statistical replication or control group was included in this preliminary evaluation, the system demonstrates technical feasibility and practical utility. This chamber provides a replicable platform for future experimentation and propagation studies. Complete technical specifications, schematics, and component lists are provided to enable replication and further development by other researchers. The growth chamber design aligns with the goals of open-source agricultural innovation and supports knowledge transfer in controlled-environment plant propagation technologies. Full article

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions. Full article

This study aimed to reveal the characteristics of returned water in paddy fields at different scales and the rules of its reuse in China’s Ganfu Plain Irrigation District through multiscale (field, lateral canal, main canal, small watershed) observations, thereby optimizing water resource management and improving water use efficiency. Subsequent investigations during the 2021–2022 double-cropping rice seasons revealed that the tillering stage emerged as a critical drainage period, with 49.5% and 52.2% of total drainage occurring during this phase in early and late rice, respectively. Multiscale drainage heterogeneity displayed distinct patterns, with early rice following a “decrease-increase” trend while late rice exhibited “decrease-peak-decline” dynamics. Smaller scales (field and lateral canal) produced 37.1% higher drainage than larger scales (main canal and small watershed) during the reviving stage. In contrast, post-jointing-booting stages showed 103.6% higher drainage at larger scales. Return flow utilization peaked at the field-lateral canal scales, while dynamic regulation of Fangxi Lake’s storage capacity achieved 60% reuse efficiency at the watershed scale. We propose an integrated optimization strategy combining tillering-stage irrigation/drainage control, multiscale hydraulic interception (control gates and pond weirs), and dynamic watershed storage scheduling. This framework provides theoretical and practical insights for enhancing water use efficiency and mitigating non-point source pollution in plain irrigation districts. Full article

Water plays a critical role in the growth and management of fresh produce, being a vital resource and a potential vector for pathogens. To address these concerns, guidelines for the microbiological quality of treated wastewater, recreational, irrigation, and drinking water have been established worldwide. With multiple outbreaks linked to Escherichia coli (E. coli) contamination, monitoring and improving water quality standards have become essential, especially for small-scale and limited-resource farmers. The Food Safety and Modernization Act (FSMA, 2014) in the United States was introduced to regulate microbiological safety of produce, focusing on irrigation water. Approximately 77% of farmers in Kentucky are small farmers, of which, 4.2% supply directly to consumers through various avenues, accounting for approximately USD 24 million a year. This study examined the microbial quality of irrigation water used in Kentucky, focusing on the presence and number of coliform bacteria and E. coli. The report covers findings from a year-long program providing free microbial water quality testing to producers (n = 90), analyzing groundwater and surface water samples (n = 296). Results indicate surface water showing a significantly higher risk of exceeding FSMA thresholds. The findings emphasize the need for continued outreach, education, and accessible testing resources to support compliance with evolving Produce Safety Rule regulations, especially among small-scale producers. Full article

A pilot-scale treatment system was developed to manage winery wastewater (WWW) generated by small and medium wineries. The system incorporated three stages: pre-treatment for suspended solids removal and a two-step aerobic biotreatment. The biotreatment phase utilized a bioaugmented bioreactor with encapsulated Pseudomonas putida F1, employing the Small Bioreactor Platform (SBP) technology. This innovative encapsulation method enhanced the breakdown of recalcitrant compounds and accelerated the biodegradation process. The second reactor was operated as a Sequence Batch Bioreactor (SBR) to remove the remaining organics and solids. Over the 100 days of operation, the mean WWW flow rate was 0.5 m³/d with average organic loads of 3950 mg/L COD (chemical oxygen demand) and 2220 mg/L BOD (biological oxygen demand), operating with a hydraulic retention time (HRT) of 4 days. Reductions of up to 96% in BOD and 90% in COD values were observed with stable removal rates over time. The novelty of this study is that it offers a new, effective aerobic biological treatment process, embracing bioaugmentation of encapsulated biomass followed by SBR for WWW with a relatively short HRT, high organics removal, and a stable treatment process. The effluent quality from this treatment system met the regulatory requirements for release to a municipal wastewater treatment plant and potentially also for irrigation. Full article

Agriculture is the largest consumer of freshwater resources, accounting for approximately 70% of total water withdrawals. In semi-arid regions like Zacatecas, Mexico, water scarcity and climate variability pose critical challenges to small-scale farmers. This study evaluates the effectiveness of integrating modern irrigation technologies with traditional water management practices to enhance agricultural resilience. Analysis of climatic data (1961–2020) revealed a statistically significant increase in annual precipitation of 2.01 mm year⁻¹ in the Leobardo Reynoso module (p < 0.05), while the Miguel Alemán module exhibited a decline ranging from −0.54 mm year⁻¹ to −2.22 mm year⁻¹, exacerbating water scarcity. Pressurized irrigation systems in Leobardo Reynoso improved application efficiency to 87.5%, compared to 50% in traditional furrow irrigation. Despite these advancements, conveyance efficiency remains low (60%) due to extensive open canal networks. Climate projections indicate a 6–11% increase in irrigation water demand for staple crops by 2065, driven by rising evapotranspiration rates. Findings underscore the need for policy interventions, infrastructure upgrades, and financial support to sustain agricultural productivity in water-stressed environments. Full article

In a number of developing countries, low productivity and technical inefficiency, with climate change looming in the background, remain a severe challenge for the agricultural sector, especially smallholder farmers. To enhance smallholder farmers’ livelihoods in terms of agricultural productivity while mitigating the adverse effects of climate change, improving technical efficiency in a sustainable manner is a promising option. One possible alternative is the use of solar-powered small-scale irrigation systems in areas vulnerable to climate change to ensure sufficient access to water. This study uses stochastic frontier analysis to analyze technical efficiency and its determinants among smallholder farmers who benefit from a solar-powered small-scale irrigation scheme in Gitaraga, Bugesera District, Rwanda. Similar smallholders from a neighboring village, who were not participating in the irrigation scheme, represent the control group. The results suggest that inputs such as land, water, and labor are positively correlated to agricultural productivity. Farmers participating in the irrigation scheme are 31.2 percentage points more technically efficient compared to non-participants, despite similar climatic conditions. Thus, relaxing the water constraint on arable land will increase agricultural productivity. Explanatory inefficiency determinants include years of farming experience and market access. Subsequently, policy makers should continue to support programs that improve smallholder access to sustainable irrigation schemes, other infrastructure, extension services, and upstream value chains, as well as markets. Full article

AgERA5 (ECMWF) is a relatively new climate dataset specifically designed for agricultural applications. MERRA-2 (NASA) is also used in agricultural applications; however, it was not specifically designed for this purpose. Despite the proven value of these datasets in assessing global climate patterns, their effectiveness in small-scale agricultural contexts remains unclear. This research aims to fill this gap by assessing the suitability and performance of AgERA5 and MERRA-2 in precision irrigation management, which is crucial for regions with limited ground data availability. The wine-making region of Nemea, Greece, with its complex and challenging terrain is used as a characteristic case study. The datasets are assessed for key weather variables and for irrigation planning, using detailed local meteorological station data as a reference. The results reveal that both products have serious limitations in small scale irrigation scheduling applications in contrast to what was reported in previous studies for other regions. The uneven performance of global datasets in different regions due to lack of sufficient observation data for reanalysis data calibration was also indicated. Comparing the two datasets, AgERA5 outperforms MERRA-2, especially in precipitation and reference evapotranspiration. MERRA-2 shows comparable potential in irrigation planning, as it occasionally matches or exceeds AgERA5’s performance. The study findings underscore the importance of evaluating metanalysis datasets in the application area before their use for precision agriculture, particularly in regions with complex topography. Full article

In making irrigation decisions, farmers typically rely on local observation and experience, such as observing crops and neighbors’ actions. Research has mainly focused on understanding crop water requirements to improve farming practices, but it is important to acknowledge that farmers have unique perspectives, access to diverse local “signals”, and experience. The challenge is to strike a balance between complex technical assessments of field conditions (the science) and harnessing farmers’ skills to manage their irrigation in ways that maximize yield and quality. This study established a basis for specifying minimum data requirements for pragmatic, but not necessarily perfect, irrigation decision-making for small-scale Vietnamese coffee farmers. This study focuses on three areas in Dak Lak province in the Central Highlands of Vietnam. To explore the role of monitoring in irrigation management, two contrasting monitoring systems were set up to collect soil, weather, and irrigation data. We also compared a variety of water balance models with different data requirements, with a focus on processes that used “passive data collection”, i.e., farmers do not manually collect data, rather data can be accessed readily from external sources. In Vietnam, traditional hosepipe irrigation is applied where it is impractical to know the volume of applied water. The proposed Low Data Model (LDM) is suited to more informed irrigation scheduling decisions, which have potential to improve the likelihood of coffee growers adopting measurement-based decision-making. While researchers may seek a detailed daily sub-millimeter understanding of soil water dynamics, farmers require practical decision support if there is to be any adoption of improved methods. This study offers a simple and practical approach for irrigation scheduling rather than a model using numerically perfect data that is unachievable in the field. The work demonstrates that on-site rainfall data is essential. However, other data can be collected passively to reduce the burden of data collection on users. This approach may enhance the likelihood of model-based irrigation scheduling being adopted by coffee farmers in Vietnam. Full article

In Burkina Faso, small-scale, community-managed irrigation systems play a crucial role in stabilizing agricultural production and improving food security. Over the past three decades, the state has transferred the management of these irrigation systems to local farmer organizations in the hope of improving efficiency and sustainability. This study assesses the long-term performance of six irrigation perimeters Dakiri, Gorgo, Itenga, Mogtedo, Savili, and Wedbila through an in-depth analysis of governance models, infrastructure conditions, and financial sustainability. Performance indicators such as relative water supply (RWS), gross production per unit of irrigation water (PbIr), and water charge recovery rates were used to assess the effectiveness of farmer-led irrigation management. The results reveal persistent governance and financial challenges as well as issues such as water wastage and low yield persisting, despite decades of implementation of farmer-led management. The degradation of irrigation infrastructure, coupled with declining water fee collection rates, threatens the sustainability of these systems. A comparative analysis of international cases suggests that a hybrid governance model, in which the state provides technical and financial support while strengthening accountability mechanisms, could improve the performance of these irrigation systems. This study recommends a shift towards greater state intervention, improved financial mechanisms, and the adoption of digital monitoring tools to ensure a more efficient and sustainable management framework. Full article

Aridification in the U.S. Southwest has led to tension about conservation and land management strategy. Strain on multi-generational agricultural livelihoods and nearly 150-year-old Colorado River water adjudication necessitates solutions from transdisciplinary partnerships. In this study, farmers and ranchers in a small San Juan River headwater community of southwestern Colorado engaged in a participatory, convergent research study prioritizing local objectives and policy. Acknowledging the historic and sometimes perceived role of academic institutions as representing urban interests, our goal was to highlight how research can support rural governance. This process involved creating community partnerships, analyzing data, and supporting results distribution to the surveyed population through social media. The survey was designed to support a local waterway management plan. Survey results showed lack of water availability and climate changes were selected by producers as most negatively affecting their operations, and many were extremely interested in agroforestry methods and drought-resistant crop species. Statistical analysis identified that satisfaction with community resources was positively correlated with scale of production, satisfaction with irrigation equipment, and familiarity with water rights. We hope to contribute our framework of a convergent, place-based research design for wider applications in other regions to uncover solutions to resource challenges. Full article

The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency of utilization (cuts) in grasslands. Therefore, the main goal of the review is to examine the techniques for using PA applications to monitor productivity and quality in forage and grasslands. To achieve this, the authors discuss several monitoring technologies for biomass and plant stand characteristics (including quality) that make it possible to adopt digital farming in forages and grassland management. The review provides an overview about mass flow and impact sensors, moisture sensors, remote sensing-based approaches, near-infrared (NIR) spectroscopy, and mapping field heterogeneity and promotes decision support systems (DSSs) in this field. At a small scale, advanced sensors such as optical, thermal, and radar sensors mountable on drones; LiDAR (Light Detection and Ranging); and hyperspectral imaging techniques can be used for assessing plant and soil characteristics. At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. It is known that the delineation of sward heterogeneity is more difficult in mixed grasslands due to spectral similarity among species. Thanks to Diversity-Interactions models, jointly assessing various species interactions under mixed grasslands is allowed. Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. The authors recommend a future research direction for the inclusion of costs and economic returns of digital technologies for precision grasslands and fodder production. Full article

The aim of this study is to construct and validate an expert system to predict the adaptation of irrigation technologies, water-saving strategies, and monitoring tools by small-scale farmers in Egypt. The research investigates the impact of economic, educational, environmental, and social factors on adaptation rates. To build the expert system, extensive knowledge was collected from experts, key concepts were identified, and production rules were created to generate tailored scenarios. These scenarios utilize the empirical cumulative distribution function (ECDF), selecting the scenario with the highest ECDF as the optimal irrigation technology. This approach ensures well-informed, data-driven decisions that are tailored to specific conditions. The expert system was evaluated under the conditions of ten small farms in Egypt. The results indicate that water cost and availability are significant drivers of technology adaptation. Specifically, subsurface drip irrigation (SDI) demonstrated an adaptation percentage of 75% at high water costs, with probabilities of 0.67 and 0.33, while soil mulching (SM) showed a 75% adaptation rate with a probability of 0.33 in high-cost scenarios. Conversely, when water availability was high, the adaptation percentage for all techniques was zero, but it reached 100% adaptation with a probability of 0.76 for SM and SDI and a probability of 1 for variable number of drippers (VND) and the use of sensors as monitoring tools during water shortages. Educational attainment and professional networks enhance the adaptation of advanced technologies and monitoring tools, emphasizing the role of knowledge and community engagement. Environmental conditions, including soil texture and salinity levels, directly affect the choice of irrigation methods and water-saving practices, highlighting the need for localized solutions. The source of irrigation water, whether groundwater or surface water, influences the preference for water-saving technologies. The study underscores the importance of tailored approaches to address the challenges and opportunities faced by small farmers in Egypt, promoting sustainable agriculture and efficient water management. The evaluation findings reveal that SDI is the most favored irrigation technology, with a probability of 0.55, followed by variable number of drippers (VND) at 0.38 and ultralow drip irrigation (ULDI) at 0.07 across various scenarios for small farmers. Regulated deficit irrigation (RDI) and SM are equally preferred water-saving strategies, each with a probability of 0.50. Sensors emerged as the preferred monitoring tool, boasting a high probability of 0.94. The analysis reveals the critical roles of economic pressures, educational levels, environmental conditions, and social networks in shaping the adaptation of sustainable agricultural practices. Full article