Search Results (11)

The application of plant protection products (PPPs) at variable rates has gained prominence as a key strategy in precision agriculture (PA), promoting the rational use of inputs (water, fertilizers, pesticides) while improving crop yields and mitigating the environmental impacts (e.g., drift, evaporation, run-off). Despite the rapid growth of variable-rate application (VRA) systems, large-scale adoption remains fragmented, with strong emphasis on technological development and limited integration of economic, operational, and environmental assessment. To critically assess how research on VRA of PPPs has evolved and where significant knowledge gaps persist, this study conducted a bibliometric and scientometric analysis of the relevant literature aimed at mapping the scientific evolution, identifying trends and analyzing the gaps that limit the consolidation of the VRA domain. By identifying these imbalances, this study provides a critical reference framework to drive future research toward more robust, comparable, and globally relevant VRA solutions in PPP applications. Scopus and Web of Science (WoS) databases were used, encompassing English-language scientific articles published between 2005 and 2025. The search strategy combined two sets of terms related to PPP application and variable-rate systems. The VOSviewer software was utilized for quantitative analysis. The bibliometric analysis assessed the temporal and geographical distribution of publications and identified the most productive authors, while the scientometric analysis visualized keyword co-occurrence networks and citation patterns among authors and countries. The results indicated that research activity culminated in a significant peak during the 2020–2024 period, with an upward trajectory for partial data of 2025. The United States and China emerged as leading contributors to scientific output. The most frequent keywords revealed the advancement of technologies such as pulse width modulation (PWM) technology, sensors, and automation. Although this research area is rapidly expanding, its consolidation still requires greater geographical participation and deeper technical exploration across various research fronts. Full article

Efficient nitrogen (N) management is essential for sustaining crop productivity while minimizing environmental impacts associated with excessive fertilizer use. Variable-rate application (VRA) offers a precision-based approach to matching N inputs with crop demand, yet winter wheat responses to reduced N rates are still underexplored. This study evaluated winter wheat (Triticum aestivum L.) performance under variable and uniform N application strategies using canopy greenness (NDVI), grain yield, plant N content, nitrogen use efficiency (NUE), and fertilizer costs as indicators. Reduced N treatments (40% and 60% VRA rates) were compared with a uniform (100%) application. Canopy greenness increased across all treatments over time, with NDVI values ranging from 0.855 early in the season to approximately 0.94 at later growth stages, and statistically significant among N rates (p < 0.05). Grain yield was highest under the low N rate (1676.81 kg ha⁻¹), although yield differences among treatments were not statistically significant (p > 0.05). Similarly, plant N content varied slightly across treatments, ranging from 1.73% to 1.82%, with no significant differences. In contrast, NUE declined sharply with increasing N rates, decreasing from 71% under the lower rate to 28% under the uniform rate. Overall, variable-rate treatments used just over half the fertilizer input and cost of the uniform rate while supporting comparable yield and plant N status. These results prove that VRA can improve nitrogen efficiency and reduce input costs without compromising winter wheat productivity, supporting its practical value for sustainable fertilizer management. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

Implementing a variable-rate application (VRA) of fertilization based on real-time crop growth status reduces costs and enhances work efficiency. However, the technical challenges associated with obtaining accurate growth-distribution maps and applying VRA, particularly with agricultural drones, remain underexplored. In this study, we specifically focused on agricultural drone-based VRA fertilization for regulating wheat protein content. First, normalized difference vegetation index (NDVI) distribution maps were obtained using multispectral images captured using a small unmanned aerial vehicle. Subsequently, a prescription map based on the NDVI values was generated to facilitate the implementation of VRA for fertilization. Continuous monitoring of changes in related vegetation indices was conducted from post-topdressing to harvest. Experimental results indicated that selecting targeted experimental survey areas based on different growth conditions can result in accurate predictions of the final yield. However, it is sill ineffective for predicting protein content or protein yield. Additionally, VRA fertilization with less fertilizer in high-NDVI areas and more fertilizer in low-NDVI areas showed no significant difference in final protein content or protein yield compared to conventional uniform fertilization. These findings provide reference data for advancing precision agriculture by addressing field-scale variability for high-quality and uniform production while presenting further research challenges. Full article

Coffee production in mountainous regions faces significant challenges to mechanization, particularly in management and fertilization. Fertilizer application remains largely manual, reducing accuracy and failing to meet the demands of variable-rate application (VRA). This study developed a portable VRA fertilizer applicator with an embedded electronic control system. The innovation lies in its electrically driven metering mechanism integrated with an electronic control unit (ECU), enabling site-specific fertilization based on prescription maps or predefined rates while recording application coordinates. The mechanism was tested under laboratory and field conditions, evaluating its performance across four fertilizer types, varying inclination angles, and rotational speeds. Results showed a coefficient variation of 0.41% for doses above 24 g, demonstrating high consistency irrespective of fertilizer type or terrain slope. Field tests using potassium chloride (KCl) prescriptions (55, 123, and 185 g/plant; 220, 492, and 740 kg/ha) revealed minimal deviations, with the largest at 22.72 g and the smallest at 0.384 g. These findings demonstrate the applicator’s precision and efficiency, addressing the challenges of mountainous terrains. This system provides technological advancement for sustainable coffee production, enhancing resource optimization and supporting precision agriculture in challenging environments. Full article

This study investigates the effects of sensor-based, variable-rate mineral nitrogen (N) application (VRA) in winter wheat (Triticum aestivum L.) on the spatial variability of grain yield, protein content, N uptake, N balance, and N efficiency compared with uniform N application (UA). To analyze the effects of VRA and UA on yield and N balance parameters, on-farm strip trials were conducted on heterogeneous arable fields covering an area of 49 hectares. The trials were carried out over a four-year period, from 2020 to 2023, with crops under both application methods placed in strips side-by-side. The N fertilizer requirements for growth stages (GSs) 32 and 39 were determined using an online map-overlay VRA method. This method integrated the site-specific yield potential and current plant development derived from spectral reflectance measurements using a tractor-mounted sensor system. The results show that the application of N fertilizer can be reduced by up to 38 kg ha⁻¹ yr⁻¹. The N efficiency can be increased by 15% and a significant reduction in variability of N balances can be achieved. However, the effects on yield and N efficiency are highly dependent on the specific application conditions (weather conditions, disease occurrence, and crop development). Not every field trial showed advantages of VRA over UA fertilization. Overall, the VRA system demonstrated encouraging potential, functioning as intended. However, further adjustment and optimization are required to ensure that the VRA fertilization system works robustly and reliably under on-farm conditions. Full article

Precision agriculture techniques contribute to optimizing the use of agricultural inputs, as they consider the spatial and temporal variability in the production factors. Prescription maps of limestone and fertilizers at variable rates (VRA) can be generated using various soil sampling techniques, such as point grid sampling, cell sampling, and management zone sampling. However, low-density grid sampling often fails to capture the spatial variability in soil properties, leading to inaccurate fertilizer recommendations. Sampling techniques by cells or management zones can generate maps of better quality and at lower costs than the sampling system by degree of points with low sampling density. Thus, this study aimed to compare the accuracy of different sampling techniques for mapping soil attributes in precision agriculture. For this purpose, the following sampling techniques were used: high-density point grid sampling method, low-density point grid sampling method, cell sampling method, management zone sampling method, and conventional method (considering the mean). Six areas located in the Brazilian states of Bahia, Minas Gerais, Mato Grosso, Goias, Mato Grosso do Sul, and Sao Paulo were used. The Root-Mean-Square-Error (RMSE) method was determined for each method using cross-validation. It was concluded that the cell method generated the lowest error, followed by the high-density point grid sampling method. Management zone sampling showed a lower error compared to the low-density point grid sampling method. By comparing different sampling techniques, we demonstrate that management zone and cell grid sampling can reduce soil sampling while maintaining comparable or superior accuracy in soil attribute mapping. Full article

Precision agriculture (PA), management zone (MZ) strategies at the field level, soil analyses, deficit irrigation (DI), and fertilizer Variable Rate Application (VRA) are management strategies that help farmers improve crop production, fertilizer use efficiency, and irrigation water use efficiency (IWUE). In order to further investigate these management strategies, the effects of four soil MZ treatments, which were delineated using PA with fuzzy k-means clustering, two irrigation levels [IR1:FI = full drip irrigation (>90% of ${\theta }_{fc}$), IR2:VDI = variable deficit drip irrigation (60–75% of ${\theta }_{fc}$)], and four VRA fertilizations were studied on coriander yield and essential oil content in a two-year research project in Greece. A daily soil-water-crop-atmosphere (SWCA) balance model and a daily depletion model were developed using sensor measurements (climatic parameter sensors as well as soil moisture sensors). Unbalanced one-way ANOVA (p = 0.05) statistical analysis results revealed that correct delineation of MZs by PA with fuzzy k-means clustering, if applied under deficit irrigation and VRA fertilization, leads to increased essential oil content of coriander with statistically significant differences (SSD) and lower fruit yields; however, without SSD differences among management zones, when appropriate VRA fertilization is applied to leverage soil nutrient levels through the different fuzzy clustered MZs for farming sustainability. Moreover, VDI compared to full irrigation in different MZs yields 22.85% to 29.44% in water savings, thus raising IWUE (up to 64.112 kg m⁻³), nitrogen efficiency (up to 5.623), and N-P-K fertilizer productivity (up to 5.329). Full article

The soil and human health issues are closely linked. Properly managed nitrogen (N) does not endanger human health and increases crop production, nevertheless when overused and uncontrolled, can contribute to side effects. This research was intended to highlight that there is a need for carrying out monitoring studies in agricultural areas in order to expand the available knowledge on the content of N forms in agricultural lands and proper management in farming practice. The impact of two types of fertilization, concerning spatially variable (VRA) and uniform (UNI) N dose, on the distribution of N forms in soils was analyzed. The analysis was performed on the basis of soil monitoring data from agricultural fields located in three different experimental sites in Poland. The analyses performed at selected sites were supported by statistical evaluation and recognition of spatial diversification of N forms in soil. It was revealed that the movement of unused N forms to deeper parts of the soil, and therefore to the groundwater system, is more limited due to VRA fertilization. Finally, it was also concluded that the management in agricultural practice should be based on the prediction of spatial variability of soil properties that allow to ensure proper application of N fertilizers, resulting in the reduction of possible N losses. Full article

Precision Agriculture (PA) is a crop site-specific management system that aims for sustainability, adopting agricultural practices more friendly to the environment, like the variable rate application (VRA) technique. Many studies have dealt with the effectiveness of VRA to reduce nitrogen (N) fertilizer, while achieving increased profit and productivity. However, only limited attention was given to VRA’s environmental impact. In this study an International Organization for Standardization (ISO) based Life Cycle Assessment (LCA) performed to identify the environmental effects of N VRA on a small pear orchard, compared to the conventional uniform application. A Cradle to Gate system with a functional unit (FU) of 1 kg of pears was analyzed including high quality primary data of two productive years, including also the non-productive years, as well as all the emissions during pear growing and the supply chains of all inputs, projecting them to the lifespan of the orchard. A methodology was adopted, modelling individual years and averaging over the orchard’s lifetime. Results showed that Climate change, Water scarcity, Fossil fuels and Particulate formation were the most contributing impact categories to the overall environmental impact of the pear orchard lifespan, where climate change and particulates were largely determined by CO₂, N₂O, and NH₃ emissions to the air from fertilizer production and application, and as CO₂ from tractor use. Concerning fertilization practice, when VRA was combined with a high yield year, this resulted in significantly reduced environmental impact. LCA evaluating an alternative fertilizer management system in a Greek pear orchard revealed the environmental impact reduction potential of that system. Full article

For farmers, the application of Precision Agriculture (PA) technology is expected to lead to an increase in profitability. For society, PA is expected to lead to increased sustainability. The objective of this paper is to determine for a number of common PA practices how much they increase profitability and sustainability. For potato production in The Netherlands, we considered variable rate application (VRA) of soil herbicide, fungicide for late blight control, sidedress N, and haulm killing herbicide. For olive production in Greece, we considered spatially variable application of P and K fertilizer and lime. For each of the above scenarios, we quantified the value of outputs, the cost of inputs, and the environmental costs. This allowed us to calculate profit as well as social profit, where the latter is defined as revenues minus conventional costs minus the external costs of production. Social profit can be considered an overall measure of sustainability. Our calculations show that PA in potatoes increases profit by 21% (420 € ha⁻¹) and social profit by 26%. In olives, VRA application of P, K, and lime leads to a strong reduction in nutrient use and although this leads to an increase in sustainability, it has only a small effect on profit and on social profit. In conclusion, PA increases sustainability in olives and both profitability and sustainability in potatoes. Full article